system

Abstract

Provide a system. 【Solution means】 Means for acquiring an image of an area using an image collection device, Artificial intelligence analysis means for detecting inappropriate parking based on the image, Means for identifying a target when the inappropriate parking is detected and storing the identification information in a storage device, Information processing means for displaying a warning when the identified target passes through a specific route, Means for deleting the stored information when the parking is appropriately released after the warning is displayed, Position information generation means for providing an appropriate parking space, Evaluation means for awarding a reward when the appropriate parking behavior is confirmed, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, abandoned bicycles and inappropriate parking in urban areas have caused traffic obstacles and have become a problem that impairs the safety of pedestrians. In such a situation, there is a need for a means to effectively deter illegal parking without high costs or manpower, but the current system has a problem that it is difficult to respond quickly and efficiently. Therefore, it is required to detect inappropriate parking behavior in real time and give a direct warning to the parker to prevent minor crimes.

Means for Solving the Problems

[0005] This invention provides a system that uses artificial intelligence technology to detect improperly parked bicycles in real time by installing an image capture device in a bicycle parking area. Specifically, based on the video footage acquired by the image capture device, improper parking is detected by an artificial intelligence analysis means, the person who parked the bicycle is identified using facial recognition technology based on the results, and the information is stored in a database. Furthermore, when the person parked passes through the ticket gate, an information processing means displays a warning, and the image is blurred for privacy reasons to encourage the person to move their bicycle. After this warning, if the bicycle is properly released, the corresponding person's information is automatically deleted from the database. This invention provides a direct and effective deterrent effect on bicycle parkers, making it possible to solve the problem of abandoned bicycles at low cost and effectively.

[0006] An "image capture device" is a device used to acquire video footage of a bicycle parking area, and includes a camera and a shooting sensor.

[0007] "Artificial intelligence analysis means" refers to AI technology and its processing system used to analyze acquired video footage and detect improperly parked bicycles in real time.

[0008] "Inappropriate parking" refers to the state of parking a bicycle outside of a designated parking area, and includes any actions that obstruct traffic or pedestrian flow.

[0009] "Identification information" refers to information used to identify an individual, specifically data obtained using facial recognition technology.

[0010] A "database" is a collection of information used to store identification information and records of improper parking, and to utilize them for subsequent processing.

[0011] "Information processing means" refers to technology and its operating system for displaying warnings based on identification information, and includes linkage with a database.

[0012] "Displaying a warning" refers to the act of visually presenting a message to a violator to draw attention to inappropriate behavior.

[0013] "Mosaic processing" refers to the process of blurring or reducing the resolution of videos or images to prevent the identification of individuals.

[0014] A "bicycle parker" refers to a person who parks their bicycle in a designated bicycle parking area, and is the target person in this system. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system for effectively solving the problem of abandoned bicycles and improper parking in urban areas. This system is designed to monitor parking activities in real time and deter illegal activities by utilizing an image capture device, artificial intelligence analysis means, a database, and information processing means.

[0037] Specifically, the server receives video data from an image capture device installed in the bicycle parking area. Based on this video data, an artificial intelligence analysis system detects improperly parked bicycles. Here, improper parking refers to bicycles parked in no-parking zones. The server uses facial recognition technology to identify the parked cyclist and stores their identification information in a database. At this stage, for privacy reasons, the identified facial images are blurred.

[0038] When a cyclist attempts to pass through a station's ticket gate, a terminal installed at the gate sends the person's information to a server. The server checks the information in its database, and if the person is found to be illegally parked, the terminal displays a warning. This warning displays a message that reads "Parking is prohibited" along with a blurred image. This encourages the cyclist to reconsider and move their bicycle.

[0039] If the user voluntarily moves their bicycle to a proper location, the server checks the image data again to confirm that the bicycle is no longer parked. After this confirmation, the server deletes the corresponding identification information from the database. As a result, the warning will no longer be displayed the next time the user passes through the ticket gate.

[0040] This system enables direct psychological deterrence and swift response to inappropriate bicycle parking, proving effective as a means of maintaining public order.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The video is captured at regular intervals to detect improperly parked bicycles.

[0044] Step 2:

[0045] The server uses artificial intelligence analysis based on the acquired video data to detect improperly parked bicycles. This process executes an algorithm to identify bicycles parked in no-parking zones.

[0046] Step 3:

[0047] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person who parked it. Information and characteristics of the identified person are stored in a database, but facial images are blurred to protect privacy.

[0048] Step 4:

[0049] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. This allows the server to share information about people passing through the ticket gates.

[0050] Step 5:

[0051] The server compares the transmitted camera footage with the violator information in its database. If a match is found, the server sends a warning message to the ticket gate terminal.

[0052] Step 6:

[0053] The terminal displays a warning message on its screen based on instructions from the server. This message, such as "Parking is prohibited," is displayed along with a blurred image.

[0054] Step 7:

[0055] If a user moves a bicycle that was improperly parked, the server will acquire video footage again from the image capture device to confirm that the bicycle has been released from its parking position.

[0056] Step 8:

[0057] Once the server confirms that the bicycle has been released from parking, it deletes the corresponding identification information from the database. This prevents the same person from seeing a warning the next time they pass through the ticket gate.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] This invention aims to efficiently solve the problem of improper bicycle parking in urban environments and maintain public order. Since improperly parked bicycles can obstruct pedestrian traffic and reduce safety, there is a need to monitor and deter them in real time. Conventional methods have problems such as requiring manpower for monitoring and making immediate response difficult.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for receiving image data of a bicycle parking area using an image acquisition device, means for using a recognition system to recognize a parked bicycle based on the image data and determine whether it is located in a no-parking zone, and means for identifying an individual based on the determination result and storing the identification information on a recording medium. This makes it possible to detect inappropriate parking in real time and immediately issue a warning to the person who parked the bicycle.

[0063] An "image acquisition device" is a device that acquires visual information of the bicycle parking area and transmits it to a server.

[0064] "Image data" refers to the digital representation of acquired visual information and is used for recognizing parked objects.

[0065] A "recognition system" is a system equipped with algorithms to analyze image data and identify and judge specific objects or situations.

[0066] "Parked object" refers to a bicycle or similar vehicle that is parked, and is the object of judgment regarding whether its location is appropriate.

[0067] A "no-parking zone" refers to a designated area where parking bicycles is not permitted.

[0068] "Personal information" refers to information used to identify a specific individual, including facial images and related data.

[0069] A "recording medium" is a digital storage device used to store information and functions as a database.

[0070] This invention is a system that utilizes an image acquisition device, a recognition system, a recording medium, and a warning display terminal to solve the problem of bicycle parking in urban areas. Specifically, the server receives image data in real time from an image acquisition device placed in the bicycle parking area. This image acquisition device generates continuous frame data and transmits it to the server via a streaming protocol.

[0071] The server uses a recognition system to identify parked bicycles based on the received image data. This system utilizes commercially available recognition software to determine whether a bicycle is present in a no-parking zone. Based on the recognition results, the server performs facial recognition using image processing libraries such as OpenCV and stores the identified individual's information in a database. This stored information is blurred to protect the individual's privacy.

[0072] When a user passes through a station's ticket gate, the terminal accesses their information and communicates with a server. The server verifies the information stored on the recording medium, and if the person is found to have parked their bicycle improperly, the terminal displays a warning message and a blurred image. This helps users reconsider improper parking.

[0073] For example, if a user parks their bicycle in a no-parking zone, the video will be captured and recognized as improper parking. A warning will be displayed when the user passes through the ticket gate, prompting them to move their bicycle to the correct location. This action will prevent the warning from being displayed the next time the user passes through the ticket gate.

[0074] Regarding the use of generative AI models, possible prompts include questions such as, "Please explain how AI can solve the problem of bicycle parking in urban areas," or "Please explain how facial recognition technology can be used to identify parked bicycles." This will allow for improvements in system accuracy and the exploration of new applications.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server receives image data of the bicycle parking area from the image acquisition device. Streaming data from the image acquisition device is used as input. The server loads this data into memory and prepares it for sequential processing of each frame. At this point, the output is image data converted into a processable format.

[0078] Step 2:

[0079] The server uses a recognition system to detect parked bicycles based on the received image data. The input is the image data formatted in step 1. The server uses an object recognition algorithm to identify bicycles in each frame and determines whether their location is within a no-parking zone. The output of this process is information indicating the location of the parked bicycles.

[0080] Step 3:

[0081] The server identifies and pinpoints a person near a recognized parked object. The input is the object's location information obtained in step 2 and the image data of the corresponding frame. The server uses the OpenCV library to perform facial recognition and generates mosaic-processed personal information. The output is the identified personal information, which has been mosaicked for privacy protection.

[0082] Step 4:

[0083] The server stores the generated personal information on a recording medium. The input is the identification information generated in step 3. The information is written to the database, which serves as the recording medium, in preparation for matching in the next step. The output is the data stored in a specific format.

[0084] Step 5:

[0085] The terminal transmits identification information to the server when a user passes through the station's ticket gate. The input is the user's IC card information or other identification means, and the server returns the verification result as output. At this stage, communication with the server takes place to determine if the user is an improperly parked cyclist.

[0086] Step 6:

[0087] The server checks the information in the database and displays a warning on the terminal if the user has parked their bicycle improperly. The input is the identification information received in step 5 and the recorded information in the database. The output is the result of the warning message and a mosaic-processed image of the user displayed on the terminal.

[0088] Step 7:

[0089] The user moves the bicycle to the correct parking area. The input is a warning from the system, and the output is that the bicycle has been moved from a no-parking zone. The server analyzes the new image data to confirm this action and detects the change in the parking status.

[0090] Step 8:

[0091] The server deletes the relevant personal information from the database after confirming that the bicycle has been released from parking. The input is the image analysis result from step 7. The output is the updated state of the database after the deletion, and the warning will no longer be displayed the next time you pass through.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Abandoned bicycles and improperly parked bicycles in urban areas pose a threat to pedestrian safety and detract from the city's aesthetics. Furthermore, existing measures are insufficient in terms of notifying people of appropriate parking spaces and providing incentives for parking, which is a challenge in effectively deterring such behavior.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for acquiring video footage of an area using an image acquisition device; artificial intelligence analysis means for detecting improper parking based on the video footage; means for identifying the object when improper parking is detected and storing the identification information in a storage device; information processing means for displaying a warning when the identified object passes through a specific path; means for deleting the stored information when the parking is properly released after the warning is displayed; location information generation means for providing an appropriate parking space; and evaluation means for providing a reward when the appropriate parking behavior is confirmed. This enables effective monitoring and deterrence of improper parking problems in urban areas, and facilitates the rational use of parking spaces and the provision of incentives to users.

[0097] An "image acquisition device" is a mechanical device used to acquire video data from a specified area.

[0098] An "artificial intelligence analysis device" is a device that uses machine learning algorithms and inference techniques to detect specific patterns or anomalies based on acquired video data.

[0099] A "storage device" is a storage medium or database used to record identified information and allow that information to be retrieved as needed.

[0100] "Information processing means" refers to a device or software that receives specific information, processes it, and obtains a desired result.

[0101] A "location information generation means" is a system that identifies the current location and appropriate destination of an object and generates information related to that location.

[0102] An "evaluation tool" is a system equipped with the function of calculating and awarding rewards when a specific action is performed.

[0103] "Rewards" refer to points or benefits awarded in response to user actions, and are used to encourage appropriate user behavior.

[0104] The system that implements this application example operates by combining multiple hardware and software components. The server receives video data of a region from an image acquisition device (e.g., a surveillance camera). This video data is processed using artificial intelligence analysis. Machine learning libraries such as TENSORFLOW® and OpenCV are used for this analysis to accurately detect improperly parked bicycles in the images.

[0105] When improperly parked bicycles are identified, the server identifies the person using the bicycle and stores the relevant information in a storage device (e.g., a database). Facial recognition technology is used for identification, but to protect individual privacy, the identified images are processed (mosaic processing).

[0106] When a user passes through a specific route, such as a train station ticket gate, an information processing system is activated and a warning is displayed on the user's terminal. This prompts the user to voluntarily remove their improperly parked bicycle, and the server uses a location information generation system to display a map on the user's terminal, providing a suitable parking space.

[0107] If proper parking is confirmed, the server will reward the user. In this evaluation system, points are added according to user behavior, and the accumulated information is updated and deleted.

[0108] As a concrete example, there is a system where, if a user who parks their bicycle improperly on their morning commute receives a warning at the ticket gate and moves to a designated space, the system immediately confirms that the bicycle has been released and awards points. This encourages users to behave correctly. Furthermore, as an example of a prompt to be input into the generating AI model for the development of this system, the following is used: "We are thinking of a smart city application to solve the problem of abandoned bicycles. Please come up with ideas for an app that integrates a system that detects improper parking in real time, notifies the user of the parking status, guides them to a proper parking location, and awards points."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives video data of a region from the image acquisition device. This data is either still images or video data provided in real time. The server temporarily stores the received image data.

[0112] Step 2:

[0113] The server sends the received video data to an artificial intelligence analysis system. Here, machine learning models such as TensorFlow are used to analyze the image data and determine the parking situation. If a pattern indicating improper parking is detected, the result is recorded.

[0114] Step 3:

[0115] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person. The identified information is then blurred to protect the individual's privacy. After processing, the identification information is registered in a database.

[0116] Step 4:

[0117] When a user attempts to pass through a station's ticket gate, the terminal sends user information to a server. The server compares this information with a database, and if matching information is found, a warning is displayed on the terminal. Upon receiving this warning, the user is prompted to release their improperly parked bicycle.

[0118] Step 5:

[0119] The user finds a suitable parking space and moves their bicycle. The terminal displays the location on a map and provides guidance based on location information instructed by the server. The user moves the bicycle to the appropriate parking space.

[0120] Step 6:

[0121] The server retrieves image data again to verify that the bicycle was properly released. This verification data is compared with previous records, and if it is determined that appropriate action has been taken, the information is deleted from the database.

[0122] Step 7:

[0123] The server calculates a reward for users whose bicycles are parked properly, using an evaluation system. The calculated reward is credited to the user's account and can be used for future use.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] In addition to solving the problems of abandoned bicycles and improper parking, this invention provides a system that identifies the user's emotions and adaptively controls the content of warnings based on that information, so that warnings to parked bicycles are effectively conveyed. Specifically, it functions by combining an image capture device, artificial intelligence analysis means, a database, information processing means, and an emotion engine.

[0126] The server first acquires video data from an image capture device in the bicycle parking area and uses artificial intelligence analysis to detect improperly parked bicycles. Once a bicycle in a no-parking zone is identified, the server identifies the person who parked it using facial recognition technology and stores that information in a database.

[0127] Next, when passing through the ticket gate, the terminal transmits video data from the ticket gate camera to the server. The server checks the database for violators and, if a violator is found, instructs the terminal to display a warning.

[0128] In this process, the emotion engine analyzes the parked cyclist's facial expressions and tone of voice to recognize the user's emotional state. For example, if the user is surprised or showing discomfort, the server changes the content and display method of the warning message according to that emotion. This makes it possible to provide users with more appropriate and effective warnings.

[0129] When a user moves their bicycle properly, the server verifies this information, and the user's identification is removed from the database. During this process, the user's emotional data obtained by the emotion engine may be used to analyze future preventative measures.

[0130] This invention makes it possible to enhance public convenience and safety by deterring illegal parking in real time while providing appropriate responses that respond to the user's feelings.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The acquired video data is immediately processed by artificial intelligence analysis to detect the presence or absence of bicycles in no-parking zones.

[0134] Step 2:

[0135] When the server detects improper parking based on video data, it uses facial recognition technology to identify the person who parked the bicycle. The characteristic information of the identified person is stored in a database, and this information includes images that have been blurred to protect privacy.

[0136] Step 3:

[0137] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. When a person who has parked their bicycle illegally passes through the ticket gate, the server compares the footage with the violator's information in its database.

[0138] Step 4:

[0139] Based on the matching results, if the server determines that the person in question is in the database, it sends an instruction to the terminal to display a warning.

[0140] Step 5:

[0141] The device uses an emotion engine to analyze the facial expressions and tone of voice of parked cyclists in real time, identifying emotions such as surprise or anger. This emotion data is used for information processing to create individually tailored warning messages.

[0142] Step 6:

[0143] The device displays personalized warning messages on its screen based on identified emotional information. For example, if the user is surprised, it will display a gentle warning; if they are angry, it will display a calming message.

[0144] Step 7:

[0145] After the server confirms that the user received the warning and moved the bicycle to the appropriate location, the server deletes the user's identification information from the database. At the same time, user sentiment data is also acquired and used for future recurrence prevention analysis.

[0146] This series of steps encourages users to park their bicycles appropriately while improving the overall deterrent effect and flexibility of the system.

[0147] (Example 2)

[0148] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0149] Public bicycle parking areas frequently suffer from improper parking, compromising the convenience and safety of other users. Traditional warning methods are uniform and do not consider the circumstances or feelings of those who park improperly, making it difficult to promote effective corrective behavior.

[0150] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0151] In this invention, the server includes means for collecting video footage of the bicycle parking area using an image acquisition device, means for identifying inappropriate parking using an automated analysis means, and means for identifying emotions from an individual's facial expressions and voice characteristics and adjusting the content of the warning. This makes it possible to provide appropriate and effective warnings according to the emotions and circumstances of the parker, thereby reducing inappropriate parking behavior.

[0152] An "image acquisition device" is a device used to collect video footage of a bicycle parking area, and usually refers to equipment such as cameras that acquire visual information.

[0153] "Automated analysis means" refers to methods used to analyze acquired video data and identify improperly parked bicycles, utilizing artificial intelligence and machine learning technologies to analyze the video.

[0154] An "information storage device" is a device for storing information about an identified individual, and usually refers to a storage system such as a database.

[0155] "Passage devices" refer to devices such as ticket gates and other equipment used when an identified individual passes through a facility.

[0156] "Information processing means" refers to means of processing information to present attention to an identified individual, and transmits information using a user interface or display.

[0157] "Emotional analysis tools" are means of identifying emotions by analyzing an individual's facial expressions and vocal characteristics, and adjusting the content and method of attention accordingly.

[0158] "Obstruction processing" refers to the process applied to identifying images to protect personal privacy, using methods such as mosaic or blurring to prevent individuals from being identified.

[0159] A "machine learning algorithm" is a mathematical method used to learn patterns and features from data and perform analysis and predictions. It is a technique used to identify inappropriate bicycle parking patterns.

[0160] This invention's system combines multiple technologies to solve the problems of abandoned bicycles and improper parking. The hardware used includes an image acquisition device (camera) for monitoring the parking area, which acquires video data in real time.

[0161] The server analyzes video data from the image acquisition device using automated analysis methods. This analysis utilizes artificial intelligence technology and machine learning algorithms. Specifically, it uses libraries such as TensorFlow and OpenCV to identify parking patterns in the video and detect improperly parked bicycles.

[0162] Once the detected bicycle parking information is confirmed, the server uses facial recognition technology to store the individual's identification information in an information storage device (database). A general facial recognition API is used for facial authentication.

[0163] Subsequently, when the identified individual passes through a passage device (such as a ticket gate or exit), the terminal displays a warning. In this process, the server uses emotion analysis tools to analyze the individual's facial expressions and tone of voice, and evaluates their emotional state. This allows the terminal to present a polite and appropriate warning, with its content and display method adjusted based on the user's emotional state.

[0164] For example, if a user expresses surprise, the server will instruct the device to display a mild-toned warning such as, "This vehicle is parked in a no-parking zone. Please be careful." Sentiment analysis uses a voice analysis API and a facial expression analysis model.

[0165] Furthermore, if a user moves their bicycle properly, the server verifies this information and removes personal data from the database. In this process, the server may anonymize and store sentiment data for future analysis. This makes it possible to more effectively deter illegal parking and improve public safety.

[0166] An example of a prompt to a generative AI model is, "Generate a detailed description of the abandoned bicycle detection and user sentiment-based warning system." This prompt is suitable for providing detailed descriptions of each function of the system and supporting an effective user experience.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server acquires video data in real time from an image acquisition device installed in the bicycle parking area. The input is the raw video transmitted from the camera, and the output is the storage of this as digital data on the server. Specifically, the camera captures images at regular intervals and transmits the video to the server.

[0170] Step 2:

[0171] The server analyzes the acquired video data. The input is the video data saved in step 1, and the output is the results of identifying improperly parked bicycles and their location information. Image recognition technology using TensorFlow and OpenCV is used to process the data in order to identify bicycles and parking spaces. Specifically, if an improperly parked bicycle is found, a label indicating it and its location coordinates are generated.

[0172] Step 3:

[0173] The server uses facial recognition technology to identify individuals parked on bicycles and stores the information in a database. The input is the facial image of the individual identified in step 2, and the output is the individual's identification information. A common facial recognition API is used to analyze the image and compare it with an existing database to identify the individual. Specifically, if identification is successful, the individual's ID and parking information are stored in the database.

[0174] Step 4:

[0175] The terminal acquires video data of users passing through the gate from the ticket gate camera and transmits it to the server. The input is real-time video data, and the output is the result of verification on the server side. Specifically, the camera continuously monitors users passing through and transmits the video.

[0176] Step 5:

[0177] The server compares the identification information of passing users with the database and, if a violation is found, instructs the terminal to issue a warning. The input is the video data transmitted in step 4 and the information from the database, and the output is an instruction to display a warning message. Specifically, when a violator is identified, the server immediately instructs the terminal to issue a corresponding warning.

[0178] Step 6:

[0179] The server uses emotion analysis tools to analyze the user's emotions from their facial expressions and voice, and adjusts the warning content accordingly. The input is the user's facial expressions and voice data from step 5, and the output is the adjusted warning message. The server processes the emotion data using a voice analysis API and facial recognition model. Specifically, if the user is showing unpleasant emotions, the warning tone is softened.

[0180] Step 7:

[0181] When a user moves their bicycle to the appropriate location, information is sent from the terminal to the server. The input is sensor and operation information confirming the user's movement, and the output is the deletion of the identification information from the database. Specifically, the system completes the procedure to confirm that the user has taken the correct action and then deletes the data.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In urban areas, improper parking of bicycles on sidewalks and in public bicycle parking areas has become a social problem. This situation not only threatens the safety of pedestrians but also detracts from the landscape and the convenience of public facilities. Conventional systems rely on manual monitoring of bicycle parking, making efficient management difficult. Furthermore, warnings can only be issued with standardized messages, lacking emotional consideration for individual cyclists, thus limiting their effectiveness. Therefore, there is a need for a system that can efficiently monitor bicycle parking and respond flexibly to individual feelings.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for acquiring video footage of the bicycle parking area using an image acquisition device, artificial intelligence analysis means for detecting inappropriate parking based on the video footage, and information processing means for displaying a warning when an identified person passes through an entrance or exit. This makes it possible to accurately monitor bicycle parking behavior in real time and enhance public safety and convenience through flexible warning displays that respond to the emotions of the parkers.

[0187] An "image acquisition device" is equipment used to capture video footage of the bicycle parking area and acquire it as data.

[0188] "Artificial intelligence analysis means" refers to technology that analyzes acquired video data to detect improperly parked bicycles.

[0189] "Information storage means" refers to a database or similar storage system that has the role of storing information about an identified person.

[0190] "Information processing means" refers to information management technology that issues instructions to display appropriate warnings to identified individuals.

[0191] An "emotion analysis engine" is a technology that recognizes and analyzes a person's emotions based on their facial expressions and tone of voice.

[0192] This invention is a system that improves the convenience and safety of public areas by detecting inappropriate parking in designated bicycle parking areas and displaying warnings tailored to the feelings of the parked cyclist. The details of the system are explained below.

[0193] The server first captures video of the bicycle parking area using an image acquisition device. This video data is processed by artificial intelligence analysis to detect improperly parked bicycles in real time. At this time, facial recognition technology is used to identify the parked person and record it in an information storage means. The information storage means stores and manages the information of the identified person using database technology.

[0194] Subsequently, when the identified person passes through the entrance or exit, the information processing system displays a warning to that person. The information processing system uses an emotion analysis engine to analyze the emotions of the parked person from their facial expressions and tone of voice, and adjusts the content and tone of the warning message according to their emotional state. This process enables a more appropriate and flexible response to the target user, ensuring that the warning is given in a way that does not cause discomfort to the parked person.

[0195] For example, if an improperly parked bicycle is detected in a park's bicycle parking area, the server can analyze whether the user identified by facial recognition technology appears surprised and display a gentle warning such as, "We apologize for startling you, but this is a no-parking zone. We appreciate your cooperation."

[0196] The following prompts can be used to leverage the AI ​​model:

[0197] "Analyze the facial expressions of users who park their bicycles improperly in specific parking areas and determine whether they are surprised or displeased. If they appear surprised, create a gentle message to convey this. For example, if the parked cyclist appears surprised, generate a message such as, 'I'm sorry to have startled you, but this is a no-parking zone. Thank you for your cooperation.'"

[0198] This system operates using image acquisition devices, servers, and terminals as hardware, and AI analysis models (TensorFlow and PyTorch) and sentiment analysis engines (OpenCV and other sentiment analysis technologies) as software. Data is managed using cloud and local database technologies.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The server uses an image acquisition device to capture video footage of the bicycle parking area. This video data becomes the input. The server converts this data into an appropriate format and prepares it for detecting improperly parked bicycles.

[0202] Step 2:

[0203] The server inputs the acquired video data into an artificial intelligence analysis system to detect improperly parked bicycles. Specifically, it uses a generative AI model to analyze the placement of bicycles in the video, identify bicycles in no-parking zones, and outputs the results. These results become the input for the next step.

[0204] Step 3:

[0205] The server identifies the parked cyclist using facial recognition technology based on identified parking violations. This identification information is stored in an information storage device. This process outputs the parked cyclist's identification information, which is then used for subsequent warning processing.

[0206] Step 4:

[0207] When a user leaves the parking area, the server acquires video footage from the entrance / exit camera and compares it with pre-stored identification information. If the identification information matches, the server prepares to display a warning message to the user. This information matching is the input for step 5.

[0208] Step 5:

[0209] The server uses an emotion analysis engine to analyze the user's emotions from their facial expressions and tone of voice. Based on this analysis, it determines, for example, whether the user is surprised and generates a warning message accordingly. A warning message is output according to the user's emotional state, and the specific display method is determined.

[0210] Step 6:

[0211] The server displays the generated warning message on the terminal. The terminal notifies the user of the warning in the appropriate format, thereby guiding the user to release their bicycle.

[0212] Step 7:

[0213] Once the server confirms that the user has properly released their bicycle from parking, the user's identification information is deleted from the information storage device. As a result, the database is updated to the latest state.

[0214] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0230] This invention is a system for effectively solving the problem of abandoned bicycles and improper parking in urban areas. This system is designed to monitor parking activities in real time and deter illegal activities by utilizing an image capture device, artificial intelligence analysis means, a database, and information processing means.

[0231] Specifically, the server receives video data from an image capture device installed in the bicycle parking area. Based on this video data, an artificial intelligence analysis system detects improperly parked bicycles. Here, improper parking refers to bicycles parked in no-parking zones. The server uses facial recognition technology to identify the parked cyclist and stores their identification information in a database. At this stage, for privacy reasons, the identified facial images are blurred.

[0232] When a cyclist attempts to pass through a station's ticket gate, a terminal installed at the gate sends the person's information to a server. The server checks the information in its database, and if the person is found to be illegally parked, the terminal displays a warning. This warning displays a message that reads "Parking is prohibited" along with a blurred image. This encourages the cyclist to reconsider and move their bicycle.

[0233] If the user voluntarily moves their bicycle to a proper location, the server checks the image data again to confirm that the bicycle is no longer parked. After this confirmation, the server deletes the corresponding identification information from the database. As a result, the warning will no longer be displayed the next time the user passes through the ticket gate.

[0234] This system enables direct psychological deterrence and swift response to inappropriate bicycle parking, proving effective as a means of maintaining public order.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The video is captured at regular intervals to detect improperly parked bicycles.

[0238] Step 2:

[0239] The server uses artificial intelligence analysis based on the acquired video data to detect improperly parked bicycles. This process executes an algorithm to identify bicycles parked in no-parking zones.

[0240] Step 3:

[0241] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person who parked it. Information and characteristics of the identified person are stored in a database, but facial images are blurred to protect privacy.

[0242] Step 4:

[0243] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. This allows the server to share information about people passing through the ticket gates.

[0244] Step 5:

[0245] The server compares the transmitted camera footage with the violator information in its database. If a match is found, the server sends a warning message to the ticket gate terminal.

[0246] Step 6:

[0247] The terminal displays a warning message on its screen based on instructions from the server. This message, such as "Parking is prohibited," is displayed along with a blurred image.

[0248] Step 7:

[0249] If a user moves a bicycle that was improperly parked, the server will acquire video footage again from the image capture device to confirm that the bicycle has been released from its parking position.

[0250] Step 8:

[0251] Once the server confirms that the bicycle has been released from parking, it deletes the corresponding identification information from the database. This prevents the same person from seeing a warning the next time they pass through the ticket gate.

[0252] (Example 1)

[0253] Next, we will describe Example 1. 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."

[0254] This invention aims to efficiently solve the problem of improper bicycle parking in urban environments and maintain public order. Since improperly parked bicycles can obstruct pedestrian traffic and reduce safety, there is a need to monitor and deter them in real time. Conventional methods have problems such as requiring manpower for monitoring and making immediate response difficult.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for receiving image data of a bicycle parking area using an image acquisition device, means for using a recognition system to recognize a parked bicycle based on the image data and determine whether it is located in a no-parking zone, and means for identifying an individual based on the determination result and storing the identification information on a recording medium. This makes it possible to detect inappropriate parking in real time and immediately issue a warning to the person who parked the bicycle.

[0257] An "image acquisition device" is a device that acquires visual information of the bicycle parking area and transmits it to a server.

[0258] "Image data" refers to the digital representation of acquired visual information and is used for recognizing parked objects.

[0259] A "recognition system" is a system equipped with algorithms to analyze image data and identify and judge specific objects or situations.

[0260] "Parked object" refers to a bicycle or similar vehicle that is parked, and is the object of judgment regarding whether its location is appropriate.

[0261] A "no-parking zone" refers to a designated area where parking bicycles is not permitted.

[0262] "Personal information" refers to information used to identify a specific individual, including facial images and related data.

[0263] A "recording medium" is a digital storage device used to store information and functions as a database.

[0264] This invention is a system that utilizes an image acquisition device, a recognition system, a recording medium, and a warning display terminal to solve the problem of bicycle parking in urban areas. Specifically, the server receives image data in real time from an image acquisition device placed in the bicycle parking area. This image acquisition device generates continuous frame data and transmits it to the server via a streaming protocol.

[0265] The server uses a recognition system to identify parked bicycles based on the received image data. This system utilizes commercially available recognition software to determine whether a bicycle is present in a no-parking zone. Based on the recognition results, the server performs facial recognition using image processing libraries such as OpenCV and stores the identified individual's information in a database. This stored information is blurred to protect the individual's privacy.

[0266] When a user passes through a station's ticket gate, the terminal accesses their information and communicates with a server. The server verifies the information stored on the recording medium, and if the person is found to have parked their bicycle improperly, the terminal displays a warning message and a blurred image. This helps users reconsider improper parking.

[0267] For example, if a user parks their bicycle in a no-parking zone, the video will be captured and recognized as improper parking. A warning will be displayed when the user passes through the ticket gate, prompting them to move their bicycle to the correct location. This action will prevent the warning from being displayed the next time the user passes through the ticket gate.

[0268] Regarding the use of generative AI models, possible prompts include questions such as, "Please explain how AI can solve the problem of bicycle parking in urban areas," or "Please explain how facial recognition technology can be used to identify parked bicycles." This will allow for improvements in system accuracy and the exploration of new applications.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server receives image data of the bicycle parking area from the image acquisition device. Streaming data from the image acquisition device is used as input. The server loads this data into memory and prepares it for sequential processing of each frame. At this point, the output is image data converted into a processable format.

[0272] Step 2:

[0273] The server uses a recognition system to detect parked bicycles based on the received image data. The input is the image data formatted in step 1. The server uses an object recognition algorithm to identify bicycles in each frame and determines whether their location is within a no-parking zone. The output of this process is information indicating the location of the parked bicycles.

[0274] Step 3:

[0275] The server identifies and pinpoints a person near a recognized parked object. The input is the object's location information obtained in step 2 and the image data of the corresponding frame. The server uses the OpenCV library to perform facial recognition and generates mosaic-processed personal information. The output is the identified personal information, which has been mosaicked for privacy protection.

[0276] Step 4:

[0277] The server stores the generated personal information on a recording medium. The input is the identification information generated in step 3. The information is written to the database, which serves as the recording medium, in preparation for matching in the next step. The output is the data stored in a specific format.

[0278] Step 5:

[0279] The terminal transmits identification information to the server when a user passes through the station's ticket gate. The input is the user's IC card information or other identification means, and the server returns the verification result as output. At this stage, communication with the server takes place to determine if the user is an improperly parked cyclist.

[0280] Step 6:

[0281] The server collates the information in the database and displays a warning on the terminal if the corresponding user is parking their bicycle inappropriately. The input is the identification information received in step 5 and the recorded information in the database. The output is the result that a warning message and a mosaic-processed user image are displayed on the terminal.

[0282] Step 7:

[0283] The user moves the bicycle to the correct parking area. The input is the warning from the system, and the output is that the bicycle has been moved from the no-parking area. The server newly analyzes the image data to confirm this operation and detects the change in the parking state.

[0284] Step 8:

[0285] After the server confirms that the parking has been released, it deletes the corresponding personal information from the database. The input is the image analysis result in step 7. The output is the state of the database updated by the deletion, and no warning will be displayed during the next passage.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Abandoned bicycles and inappropriate parking in urban areas pose problems that threaten the safety of pedestrians and damage the beauty of the city. Also, existing countermeasures are not sufficient in notifying appropriate parking spaces and providing incentives for parking behavior, and the issue is that they have not led to effective deterrence.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for acquiring video footage of an area using an image acquisition device; artificial intelligence analysis means for detecting improper parking based on the video footage; means for identifying the object when improper parking is detected and storing the identification information in a storage device; information processing means for displaying a warning when the identified object passes through a specific path; means for deleting the stored information when the parking is properly released after the warning is displayed; location information generation means for providing an appropriate parking space; and evaluation means for providing a reward when the appropriate parking behavior is confirmed. This enables effective monitoring and deterrence of improper parking problems in urban areas, and facilitates the rational use of parking spaces and the provision of incentives to users.

[0291] An "image acquisition device" is a mechanical device used to acquire video data from a specified area.

[0292] An "artificial intelligence analysis device" is a device that uses machine learning algorithms and inference techniques to detect specific patterns or anomalies based on acquired video data.

[0293] A "storage device" is a storage medium or database used to record identified information and allow that information to be retrieved as needed.

[0294] "Information processing means" refers to a device or software that receives specific information, processes it, and obtains a desired result.

[0295] A "location information generation means" is a system that identifies the current location and appropriate destination of an object and generates information related to that location.

[0296] An "evaluation tool" is a system equipped with the function of calculating and awarding rewards when a specific action is performed.

[0297] "Rewards" refer to points or benefits awarded in response to user actions, and are used to encourage appropriate user behavior.

[0298] The system that implements this application example operates by combining multiple hardware and software components. The server receives video data of a region from an image acquisition device (e.g., a surveillance camera). This video data is processed using artificial intelligence analysis. Machine learning libraries such as TensorFlow and OpenCV are used for this analysis to accurately detect improperly parked bicycles in the images.

[0299] When improperly parked bicycles are identified, the server identifies the person using the bicycle and stores the relevant information in a storage device (e.g., a database). Facial recognition technology is used for identification, but to protect individual privacy, the identified images are processed (mosaic processing).

[0300] When a user passes through a specific route, such as a train station ticket gate, an information processing system is activated and a warning is displayed on the user's terminal. This prompts the user to voluntarily remove their improperly parked bicycle, and the server uses a location information generation system to display a map on the user's terminal, providing a suitable parking space.

[0301] If proper parking is confirmed, the server will reward the user. In this evaluation system, points are added according to user behavior, and the accumulated information is updated and deleted.

[0302] As a concrete example, there is a system where, if a user who parks their bicycle improperly on their morning commute receives a warning at the ticket gate and moves to a designated space, the system immediately confirms that the bicycle has been released and awards points. This encourages users to behave correctly. Furthermore, as an example of a prompt to be input into the generating AI model for the development of this system, the following is used: "We are thinking of a smart city application to solve the problem of abandoned bicycles. Please come up with ideas for an app that integrates a system that detects improper parking in real time, notifies the user of the parking status, guides them to a proper parking location, and awards points."

[0303] The process of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0304] Step 1:

[0305] The server receives video data of the area from the image collection device. This data is still image or video data provided in real time. The server temporarily stores the received image data.

[0306] Step 2:

[0307] The server transmits the received video data to the artificial intelligence analysis means. Here, a machine learning model such as TensorFlow is used to analyze the image data and determine the parking situation. When a pattern indicating inappropriate parking is detected, the result is recorded.

[0308] Step 3:

[0309] When the server detects inappropriate parking, it identifies the target using face recognition technology. The identified information is mosaicked considering the privacy of the individual. After processing, the identification information is registered in the database.

[0310] Step 4:

[0311] When the user attempts to pass through the ticket gate of the station, the terminal transmits the user information to the server. The server checks with the database and, if there is corresponding information, displays a warning on the terminal. Upon receiving this warning, the user is urged to release the inappropriate parking.

[0312] Step 5:

[0313] The user searches for an appropriate parking space and moves the bicycle. The terminal displays and guides the position on the map based on the position information instructed by the server. The user moves the bicycle to an appropriate parking space.

[0314] Step 6:

[0315] The server retrieves image data again to verify that the bicycle was properly released. This verification data is compared with previous records, and if it is determined that appropriate action has been taken, the information is deleted from the database.

[0316] Step 7:

[0317] The server calculates a reward for users whose bicycles are parked properly, using an evaluation system. The calculated reward is credited to the user's account and can be used for future use.

[0318] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0319] In addition to solving the problems of abandoned bicycles and improper parking, this invention provides a system that identifies the user's emotions and adaptively controls the content of warnings based on that information, so that warnings to parked bicycles are effectively conveyed. Specifically, it functions by combining an image capture device, artificial intelligence analysis means, a database, information processing means, and an emotion engine.

[0320] The server first acquires video data from an image capture device in the bicycle parking area and uses artificial intelligence analysis to detect improperly parked bicycles. Once a bicycle in a no-parking zone is identified, the server identifies the person who parked it using facial recognition technology and stores that information in a database.

[0321] Next, when passing through the ticket gate, the terminal transmits video data from the ticket gate camera to the server. The server checks the database for violators and, if a violator is found, instructs the terminal to display a warning.

[0322] In this process, the emotion engine analyzes the parked cyclist's facial expressions and tone of voice to recognize the user's emotional state. For example, if the user is surprised or showing discomfort, the server changes the content and display method of the warning message according to that emotion. This makes it possible to provide users with more appropriate and effective warnings.

[0323] When a user moves their bicycle properly, the server verifies this information, and the user's identification is removed from the database. During this process, the user's emotional data obtained by the emotion engine may be used to analyze future preventative measures.

[0324] This invention makes it possible to enhance public convenience and safety by deterring illegal parking in real time while providing appropriate responses that respond to the user's feelings.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The acquired video data is immediately processed by artificial intelligence analysis to detect the presence or absence of bicycles in no-parking zones.

[0328] Step 2:

[0329] When the server detects improper parking based on video data, it uses facial recognition technology to identify the person who parked the bicycle. The characteristic information of the identified person is stored in a database, and this information includes images that have been blurred to protect privacy.

[0330] Step 3:

[0331] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. When a person who has parked their bicycle illegally passes through the ticket gate, the server compares the footage with the violator's information in its database.

[0332] Step 4:

[0333] Based on the matching results, if the server determines that the person in question is in the database, it sends an instruction to the terminal to display a warning.

[0334] Step 5:

[0335] The device uses an emotion engine to analyze the facial expressions and tone of voice of parked cyclists in real time, identifying emotions such as surprise or anger. This emotion data is used for information processing to create individually tailored warning messages.

[0336] Step 6:

[0337] The device displays personalized warning messages on its screen based on identified emotional information. For example, if the user is surprised, it will display a gentle warning; if they are angry, it will display a calming message.

[0338] Step 7:

[0339] After the server confirms that the user received the warning and moved the bicycle to the appropriate location, the server deletes the user's identification information from the database. At the same time, user sentiment data is also acquired and used for future recurrence prevention analysis.

[0340] This series of steps encourages users to park their bicycles appropriately while improving the overall deterrent effect and flexibility of the system.

[0341] (Example 2)

[0342] Next, we will describe Example 2. 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".

[0343] Public bicycle parking areas frequently suffer from improper parking, compromising the convenience and safety of other users. Traditional warning methods are uniform and do not consider the circumstances or feelings of those who park improperly, making it difficult to promote effective corrective behavior.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for collecting video footage of the bicycle parking area using an image acquisition device, means for identifying inappropriate parking using an automated analysis means, and means for identifying emotions from an individual's facial expressions and voice characteristics and adjusting the content of the warning. This makes it possible to provide appropriate and effective warnings according to the emotions and circumstances of the parker, thereby reducing inappropriate parking behavior.

[0346] An "image acquisition device" is a device used to collect video footage of a bicycle parking area, and usually refers to equipment such as cameras that acquire visual information.

[0347] "Automated analysis means" refers to methods used to analyze acquired video data and identify improperly parked bicycles, utilizing artificial intelligence and machine learning technologies to analyze the video.

[0348] An "information storage device" is a device for storing information about an identified individual, and usually refers to a storage system such as a database.

[0349] "Passage devices" refer to devices such as ticket gates and other equipment used when an identified individual passes through a facility.

[0350] "Information processing means" refers to means of processing information to present attention to an identified individual, and transmits information using a user interface or display.

[0351] "Emotional analysis tools" are means of identifying emotions by analyzing an individual's facial expressions and vocal characteristics, and adjusting the content and method of attention accordingly.

[0352] "Obstruction processing" refers to the process applied to identifying images to protect personal privacy, using methods such as mosaic or blurring to prevent individuals from being identified.

[0353] A "machine learning algorithm" is a mathematical method used to learn patterns and features from data and perform analysis and predictions. It is a technique used to identify inappropriate bicycle parking patterns.

[0354] This invention's system combines multiple technologies to solve the problems of abandoned bicycles and improper parking. The hardware used includes an image acquisition device (camera) for monitoring the parking area, which acquires video data in real time.

[0355] The server analyzes video data from the image acquisition device using automated analysis methods. This analysis utilizes artificial intelligence technology and machine learning algorithms. Specifically, it uses libraries such as TensorFlow and OpenCV to identify parking patterns in the video and detect improperly parked bicycles.

[0356] Once the detected bicycle parking information is confirmed, the server uses facial recognition technology to store the individual's identification information in an information storage device (database). A general facial recognition API is used for facial authentication.

[0357] Subsequently, when the identified individual passes through a passage device (such as a ticket gate or exit), the terminal displays a warning. In this process, the server uses emotion analysis tools to analyze the individual's facial expressions and tone of voice, and evaluates their emotional state. This allows the terminal to present a polite and appropriate warning, with its content and display method adjusted based on the user's emotional state.

[0358] For example, if a user expresses surprise, the server will instruct the device to display a mild-toned warning such as, "This vehicle is parked in a no-parking zone. Please be careful." Sentiment analysis uses a voice analysis API and a facial expression analysis model.

[0359] Furthermore, if a user moves their bicycle properly, the server verifies this information and removes personal data from the database. In this process, the server may anonymize and store sentiment data for future analysis. This makes it possible to more effectively deter illegal parking and improve public safety.

[0360] An example of a prompt to a generative AI model is, "Generate a detailed description of the abandoned bicycle detection and user sentiment-based warning system." This prompt is suitable for providing detailed descriptions of each function of the system and supporting an effective user experience.

[0361] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0362] Step 1:

[0363] The server acquires video data in real time from an image acquisition device installed in the bicycle parking area. The input is the raw video transmitted from the camera, and the output is the storage of this as digital data on the server. Specifically, the camera captures images at regular intervals and transmits the video to the server.

[0364] Step 2:

[0365] The server analyzes the acquired video data. The input is the video data saved in step 1, and the output is the results of identifying improperly parked bicycles and their location information. Image recognition technology using TensorFlow and OpenCV is used to process the data in order to identify bicycles and parking spaces. Specifically, if an improperly parked bicycle is found, a label indicating it and its location coordinates are generated.

[0366] Step 3:

[0367] The server uses facial recognition technology to identify individuals parked on bicycles and stores the information in a database. The input is the facial image of the individual identified in step 2, and the output is the individual's identification information. A common facial recognition API is used to analyze the image and compare it with an existing database to identify the individual. Specifically, if identification is successful, the individual's ID and parking information are stored in the database.

[0368] Step 4:

[0369] The terminal acquires video data of users passing through the gate from the ticket gate camera and transmits it to the server. The input is real-time video data, and the output is the result of verification on the server side. Specifically, the camera continuously monitors users passing through and transmits the video.

[0370] Step 5:

[0371] The server compares the identification information of passing users with the database and, if a violation is found, instructs the terminal to issue a warning. The input is the video data transmitted in step 4 and the information from the database, and the output is an instruction to display a warning message. Specifically, when a violator is identified, the server immediately instructs the terminal to issue a corresponding warning.

[0372] Step 6:

[0373] The server uses emotion analysis tools to analyze the user's emotions from their facial expressions and voice, and adjusts the warning content accordingly. The input is the user's facial expressions and voice data from step 5, and the output is the adjusted warning message. The server processes the emotion data using a voice analysis API and facial recognition model. Specifically, if the user is showing unpleasant emotions, the warning tone is softened.

[0374] Step 7:

[0375] When a user moves their bicycle to the appropriate location, information is sent from the terminal to the server. The input is sensor and operation information confirming the user's movement, and the output is the deletion of the identification information from the database. Specifically, the system completes the procedure to confirm that the user has taken the correct action and then deletes the data.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] In urban areas, improper parking of bicycles on sidewalks and in public bicycle parking areas has become a social problem. This situation not only threatens the safety of pedestrians but also detracts from the landscape and the convenience of public facilities. Conventional systems rely on manual monitoring of bicycle parking, making efficient management difficult. Furthermore, warnings can only be issued with standardized messages, lacking emotional consideration for individual cyclists, thus limiting their effectiveness. Therefore, there is a need for a system that can efficiently monitor bicycle parking and respond flexibly to individual feelings.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for acquiring video footage of the bicycle parking area using an image acquisition device, artificial intelligence analysis means for detecting inappropriate parking based on the video footage, and information processing means for displaying a warning when an identified person passes through an entrance or exit. This makes it possible to accurately monitor bicycle parking behavior in real time and enhance public safety and convenience through flexible warning displays that respond to the emotions of the parkers.

[0381] An "image acquisition device" is equipment used to capture video footage of the bicycle parking area and acquire it as data.

[0382] "Artificial intelligence analysis means" refers to technology that analyzes acquired video data to detect improperly parked bicycles.

[0383] "Information storage means" refers to a database or similar storage system that has the role of storing information about an identified person.

[0384] "Information processing means" refers to information management technology that issues instructions to display appropriate warnings to identified individuals.

[0385] An "emotion analysis engine" is a technology that recognizes and analyzes a person's emotions based on their facial expressions and tone of voice.

[0386] This invention is a system that improves the convenience and safety of public areas by detecting inappropriate parking in designated bicycle parking areas and displaying warnings tailored to the feelings of the parked cyclist. The details of the system are explained below.

[0387] The server first captures video of the bicycle parking area using an image acquisition device. This video data is processed by artificial intelligence analysis to detect improperly parked bicycles in real time. At this time, facial recognition technology is used to identify the parked person and record it in an information storage means. The information storage means stores and manages the information of the identified person using database technology.

[0388] Subsequently, when the identified person passes through the entrance or exit, the information processing system displays a warning to that person. The information processing system uses an emotion analysis engine to analyze the emotions of the parked person from their facial expressions and tone of voice, and adjusts the content and tone of the warning message according to their emotional state. This process enables a more appropriate and flexible response to the target user, ensuring that the warning is given in a way that does not cause discomfort to the parked person.

[0389] For example, if an improperly parked bicycle is detected in a park's bicycle parking area, the server can analyze whether the user identified by facial recognition technology appears surprised and display a gentle warning such as, "We apologize for startling you, but this is a no-parking zone. We appreciate your cooperation."

[0390] The following prompts can be used to leverage the AI ​​model:

[0391] "Analyze the facial expressions of users who park their bicycles improperly in specific parking areas and determine whether they are surprised or displeased. If they appear surprised, create a gentle message to convey this. For example, if the parked cyclist appears surprised, generate a message such as, 'I'm sorry to have startled you, but this is a no-parking zone. Thank you for your cooperation.'"

[0392] This system operates using image acquisition devices, servers, and terminals as hardware, and AI analysis models (TensorFlow and PyTorch) and sentiment analysis engines (OpenCV and other sentiment analysis technologies) as software. Data is managed using cloud and local database technologies.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The server uses an image acquisition device to capture video footage of the bicycle parking area. This video data becomes the input. The server converts this data into an appropriate format and prepares it for detecting improperly parked bicycles.

[0396] Step 2:

[0397] The server inputs the acquired video data into an artificial intelligence analysis system to detect improperly parked bicycles. Specifically, it uses a generative AI model to analyze the placement of bicycles in the video, identify bicycles in no-parking zones, and outputs the results. These results become the input for the next step.

[0398] Step 3:

[0399] The server identifies the parked cyclist using facial recognition technology based on identified parking violations. This identification information is stored in an information storage device. This process outputs the parked cyclist's identification information, which is then used for subsequent warning processing.

[0400] Step 4:

[0401] When a user leaves the parking area, the server acquires video footage from the entrance / exit camera and compares it with pre-stored identification information. If the identification information matches, the server prepares to display a warning message to the user. This information matching is the input for step 5.

[0402] Step 5:

[0403] The server uses an emotion analysis engine to analyze the user's emotions from their facial expressions and tone of voice. Based on this analysis, it determines, for example, whether the user is surprised and generates a warning message accordingly. A warning message is output according to the user's emotional state, and the specific display method is determined.

[0404] Step 6:

[0405] The server displays the generated warning message on the terminal. The terminal notifies the user of the warning in the appropriate format, thereby guiding the user to release their bicycle.

[0406] Step 7:

[0407] Once the server confirms that the user has properly released their bicycle from parking, the user's identification information is deleted from the information storage device. As a result, the database is updated to the latest state.

[0408] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0414] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0416] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0418] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0419] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0420] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0421] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0423] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0424] This invention is a system for effectively solving the problem of abandoned bicycles and improper parking in urban areas. This system is designed to monitor parking activities in real time and deter illegal activities by utilizing an image capture device, artificial intelligence analysis means, a database, and information processing means.

[0425] Specifically, the server receives video data from an image capture device installed in the bicycle parking area. Based on this video data, an artificial intelligence analysis system detects improperly parked bicycles. Here, improper parking refers to bicycles parked in no-parking zones. The server uses facial recognition technology to identify the parked cyclist and stores their identification information in a database. At this stage, for privacy reasons, the identified facial images are blurred.

[0426] When a cyclist attempts to pass through a station's ticket gate, a terminal installed at the gate sends the person's information to a server. The server checks the information in its database, and if the person is found to be illegally parked, the terminal displays a warning. This warning displays a message that reads "Parking is prohibited" along with a blurred image. This encourages the cyclist to reconsider and move their bicycle.

[0427] If the user voluntarily moves their bicycle to a proper location, the server checks the image data again to confirm that the bicycle is no longer parked. After this confirmation, the server deletes the corresponding identification information from the database. As a result, the warning will no longer be displayed the next time the user passes through the ticket gate.

[0428] This system enables direct psychological deterrence and swift response to inappropriate bicycle parking, proving effective as a means of maintaining public order.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The video is captured at regular intervals to detect improperly parked bicycles.

[0432] Step 2:

[0433] The server uses artificial intelligence analysis based on the acquired video data to detect improperly parked bicycles. This process executes an algorithm to identify bicycles parked in no-parking zones.

[0434] Step 3:

[0435] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person who parked it. Information and characteristics of the identified person are stored in a database, but facial images are blurred to protect privacy.

[0436] Step 4:

[0437] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. This allows the server to share information about people passing through the ticket gates.

[0438] Step 5:

[0439] The server compares the transmitted camera footage with the violator information in its database. If a match is found, the server sends a warning message to the ticket gate terminal.

[0440] Step 6:

[0441] The terminal displays a warning message on its screen based on instructions from the server. This message, such as "Parking is prohibited," is displayed along with a blurred image.

[0442] Step 7:

[0443] If a user moves a bicycle that was improperly parked, the server will acquire video footage again from the image capture device to confirm that the bicycle has been released from its parking position.

[0444] Step 8:

[0445] Once the server confirms that the bicycle has been released from parking, it deletes the corresponding identification information from the database. This prevents the same person from seeing a warning the next time they pass through the ticket gate.

[0446] (Example 1)

[0447] Next, we will describe Example 1. 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."

[0448] This invention aims to efficiently solve the problem of improper bicycle parking in urban environments and maintain public order. Since improperly parked bicycles can obstruct pedestrian traffic and reduce safety, there is a need to monitor and deter them in real time. Conventional methods have problems such as requiring manpower for monitoring and making immediate response difficult.

[0449] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0450] In this invention, the server includes means for receiving image data of a bicycle parking area using an image acquisition device, means for using a recognition system to recognize a parked bicycle based on the image data and determine whether it is located in a no-parking zone, and means for identifying an individual based on the determination result and storing the identification information on a recording medium. This makes it possible to detect inappropriate parking in real time and immediately issue a warning to the person who parked the bicycle.

[0451] An "image acquisition device" is a device that acquires visual information of the bicycle parking area and transmits it to a server.

[0452] "Image data" refers to the digital representation of acquired visual information and is used for recognizing parked objects.

[0453] A "recognition system" is a system equipped with algorithms to analyze image data and identify and judge specific objects or situations.

[0454] "Parked object" refers to a bicycle or similar vehicle that is parked, and is the object of judgment regarding whether its location is appropriate.

[0455] A "no-parking zone" refers to a designated area where parking bicycles is not permitted.

[0456] "Personal information" refers to information used to identify a specific individual, including facial images and related data.

[0457] A "recording medium" is a digital storage device used to store information and functions as a database.

[0458] This invention is a system that utilizes an image acquisition device, a recognition system, a recording medium, and a warning display terminal to solve the problem of bicycle parking in urban areas. Specifically, the server receives image data in real time from an image acquisition device placed in the bicycle parking area. This image acquisition device generates continuous frame data and transmits it to the server via a streaming protocol.

[0459] The server uses a recognition system to identify parked bicycles based on the received image data. This system utilizes commercially available recognition software to determine whether a bicycle is present in a no-parking zone. Based on the recognition results, the server performs facial recognition using image processing libraries such as OpenCV and stores the identified individual's information in a database. This stored information is blurred to protect the individual's privacy.

[0460] When a user passes through a station's ticket gate, the terminal accesses their information and communicates with a server. The server verifies the information stored on the recording medium, and if the person is found to have parked their bicycle improperly, the terminal displays a warning message and a blurred image. This helps users reconsider improper parking.

[0461] For example, if a user parks their bicycle in a no-parking zone, the video will be captured and recognized as improper parking. A warning will be displayed when the user passes through the ticket gate, prompting them to move their bicycle to the correct location. This action will prevent the warning from being displayed the next time the user passes through the ticket gate.

[0462] Regarding the use of generative AI models, possible prompts include questions such as, "Please explain how AI can solve the problem of bicycle parking in urban areas," or "Please explain how facial recognition technology can be used to identify parked bicycles." This will allow for improvements in system accuracy and the exploration of new applications.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The server receives image data of the bicycle parking area from the image acquisition device. Streaming data from the image acquisition device is used as input. The server loads this data into memory and prepares it for sequential processing of each frame. At this point, the output is image data converted into a processable format.

[0466] Step 2:

[0467] The server uses a recognition system to detect parked bicycles based on the received image data. The input is the image data formatted in step 1. The server uses an object recognition algorithm to identify bicycles in each frame and determines whether their location is within a no-parking zone. The output of this process is information indicating the location of the parked bicycles.

[0468] Step 3:

[0469] The server identifies and pinpoints a person near a recognized parked object. The input is the object's location information obtained in step 2 and the image data of the corresponding frame. The server uses the OpenCV library to perform facial recognition and generates mosaic-processed personal information. The output is the identified personal information, which has been mosaicked for privacy protection.

[0470] Step 4:

[0471] The server stores the generated personal information on a recording medium. The input is the identification information generated in step 3. The information is written to the database, which serves as the recording medium, in preparation for matching in the next step. The output is the data stored in a specific format.

[0472] Step 5:

[0473] The terminal transmits identification information to the server when a user passes through the station's ticket gate. The input is the user's IC card information or other identification means, and the server returns the verification result as output. At this stage, communication with the server takes place to determine if the user is an improperly parked cyclist.

[0474] Step 6:

[0475] The server checks the information in the database and displays a warning on the terminal if the user has parked their bicycle improperly. The input is the identification information received in step 5 and the recorded information in the database. The output is the result of the warning message and a mosaic-processed image of the user displayed on the terminal.

[0476] Step 7:

[0477] The user moves the bicycle to the correct parking area. The input is a warning from the system, and the output is that the bicycle has been moved from a no-parking zone. The server analyzes the new image data to confirm this action and detects the change in the parking status.

[0478] Step 8:

[0479] The server deletes the relevant personal information from the database after confirming that the bicycle has been released from parking. The input is the image analysis result from step 7. The output is the updated state of the database after the deletion, and the warning will no longer be displayed the next time you pass through.

[0480] (Application Example 1)

[0481] Next, we will explain Application Example 1. In the following explanation, 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."

[0482] Abandoned bicycles and improperly parked bicycles in urban areas pose a threat to pedestrian safety and detract from the city's aesthetics. Furthermore, existing measures are insufficient in terms of notifying people of appropriate parking spaces and providing incentives for parking, which is a challenge in effectively deterring such behavior.

[0483] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0484] In this invention, the server includes means for acquiring video footage of an area using an image acquisition device; artificial intelligence analysis means for detecting improper parking based on the video footage; means for identifying the object when improper parking is detected and storing the identification information in a storage device; information processing means for displaying a warning when the identified object passes through a specific path; means for deleting the stored information when the parking is properly released after the warning is displayed; location information generation means for providing an appropriate parking space; and evaluation means for providing a reward when the appropriate parking behavior is confirmed. This enables effective monitoring and deterrence of improper parking problems in urban areas, and facilitates the rational use of parking spaces and the provision of incentives to users.

[0485] An "image acquisition device" is a mechanical device used to acquire video data from a specified area.

[0486] An "artificial intelligence analysis device" is a device that uses machine learning algorithms and inference techniques to detect specific patterns or anomalies based on acquired video data.

[0487] A "storage device" is a storage medium or database used to record identified information and allow that information to be retrieved as needed.

[0488] "Information processing means" refers to a device or software that receives specific information, processes it, and obtains a desired result.

[0489] A "location information generation means" is a system that identifies the current location and appropriate destination of an object and generates information related to that location.

[0490] An "evaluation tool" is a system equipped with the function of calculating and awarding rewards when a specific action is performed.

[0491] "Rewards" refer to points or benefits awarded in response to user actions, and are used to encourage appropriate user behavior.

[0492] The system that implements this application example operates by combining multiple hardware and software components. The server receives video data of a region from an image acquisition device (e.g., a surveillance camera). This video data is processed using artificial intelligence analysis. Machine learning libraries such as TensorFlow and OpenCV are used for this analysis to accurately detect improperly parked bicycles in the images.

[0493] When improperly parked bicycles are identified, the server identifies the person using the bicycle and stores the relevant information in a storage device (e.g., a database). Facial recognition technology is used for identification, but to protect individual privacy, the identified images are processed (mosaic processing).

[0494] When a user passes through a specific route, such as a train station ticket gate, an information processing system is activated and a warning is displayed on the user's terminal. This prompts the user to voluntarily remove their improperly parked bicycle, and the server uses a location information generation system to display a map on the user's terminal, providing a suitable parking space.

[0495] If proper parking is confirmed, the server will reward the user. In this evaluation system, points are added according to user behavior, and the accumulated information is updated and deleted.

[0496] As a concrete example, there is a system where, if a user who parks their bicycle improperly on their morning commute receives a warning at the ticket gate and moves to a designated space, the system immediately confirms that the bicycle has been released and awards points. This encourages users to behave correctly. Furthermore, as an example of a prompt to be input into the generating AI model for the development of this system, the following is used: "We are thinking of a smart city application to solve the problem of abandoned bicycles. Please come up with ideas for an app that integrates a system that detects improper parking in real time, notifies the user of the parking status, guides them to a proper parking location, and awards points."

[0497] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0498] Step 1:

[0499] The server receives video data of a region from the image acquisition device. This data is either still images or video data provided in real time. The server temporarily stores the received image data.

[0500] Step 2:

[0501] The server sends the received video data to an artificial intelligence analysis system. Here, machine learning models such as TensorFlow are used to analyze the image data and determine the parking situation. If a pattern indicating improper parking is detected, the result is recorded.

[0502] Step 3:

[0503] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person. The identified information is then blurred to protect the individual's privacy. After processing, the identification information is registered in a database.

[0504] Step 4:

[0505] When a user attempts to pass through a station's ticket gate, the terminal sends user information to a server. The server compares this information with a database, and if matching information is found, a warning is displayed on the terminal. Upon receiving this warning, the user is prompted to release their improperly parked bicycle.

[0506] Step 5:

[0507] The user finds a suitable parking space and moves their bicycle. The terminal displays the location on a map and provides guidance based on location information instructed by the server. The user moves the bicycle to the appropriate parking space.

[0508] Step 6:

[0509] The server retrieves image data again to verify that the bicycle was properly released. This verification data is compared with previous records, and if it is determined that appropriate action has been taken, the information is deleted from the database.

[0510] Step 7:

[0511] The server calculates a reward for users whose bicycles are parked properly, using an evaluation system. The calculated reward is credited to the user's account and can be used for future use.

[0512] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0513] In addition to solving the problems of abandoned bicycles and improper parking, this invention provides a system that identifies the user's emotions and adaptively controls the content of warnings based on that information, so that warnings to parked bicycles are effectively conveyed. Specifically, it functions by combining an image capture device, artificial intelligence analysis means, a database, information processing means, and an emotion engine.

[0514] The server first acquires video data from an image capture device in the bicycle parking area and uses artificial intelligence analysis to detect improperly parked bicycles. Once a bicycle in a no-parking zone is identified, the server identifies the person who parked it using facial recognition technology and stores that information in a database.

[0515] Next, when passing through the ticket gate, the terminal transmits video data from the ticket gate camera to the server. The server checks the database for violators and, if a violator is found, instructs the terminal to display a warning.

[0516] In this process, the emotion engine analyzes the parked cyclist's facial expressions and tone of voice to recognize the user's emotional state. For example, if the user is surprised or showing discomfort, the server changes the content and display method of the warning message according to that emotion. This makes it possible to provide users with more appropriate and effective warnings.

[0517] When a user moves their bicycle properly, the server verifies this information, and the user's identification is removed from the database. During this process, the user's emotional data obtained by the emotion engine may be used to analyze future preventative measures.

[0518] This invention makes it possible to enhance public convenience and safety by deterring illegal parking in real time while providing appropriate responses that respond to the user's feelings.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The acquired video data is immediately processed by artificial intelligence analysis to detect the presence or absence of bicycles in no-parking zones.

[0522] Step 2:

[0523] When the server detects improper parking based on video data, it uses facial recognition technology to identify the person who parked the bicycle. The characteristic information of the identified person is stored in a database, and this information includes images that have been blurred to protect privacy.

[0524] Step 3:

[0525] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. When a person who has parked their bicycle illegally passes through the ticket gate, the server compares the footage with the violator's information in its database.

[0526] Step 4:

[0527] Based on the matching results, if the server determines that the person in question is in the database, it sends an instruction to the terminal to display a warning.

[0528] Step 5:

[0529] The device uses an emotion engine to analyze the facial expressions and tone of voice of parked cyclists in real time, identifying emotions such as surprise or anger. This emotion data is used for information processing to create individually tailored warning messages.

[0530] Step 6:

[0531] The device displays personalized warning messages on its screen based on identified emotional information. For example, if the user is surprised, it will display a gentle warning; if they are angry, it will display a calming message.

[0532] Step 7:

[0533] After the server confirms that the user received the warning and moved the bicycle to the appropriate location, the server deletes the user's identification information from the database. At the same time, user sentiment data is also acquired and used for future recurrence prevention analysis.

[0534] This series of steps encourages users to park their bicycles appropriately while improving the overall deterrent effect and flexibility of the system.

[0535] (Example 2)

[0536] Next, we will describe Example 2. 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."

[0537] Public bicycle parking areas frequently suffer from improper parking, compromising the convenience and safety of other users. Traditional warning methods are uniform and do not consider the circumstances or feelings of those who park improperly, making it difficult to promote effective corrective behavior.

[0538] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0539] In this invention, the server includes means for collecting video footage of the bicycle parking area using an image acquisition device, means for identifying inappropriate parking using an automated analysis means, and means for identifying emotions from an individual's facial expressions and voice characteristics and adjusting the content of the warning. This makes it possible to provide appropriate and effective warnings according to the emotions and circumstances of the parker, thereby reducing inappropriate parking behavior.

[0540] An "image acquisition device" is a device used to collect video footage of a bicycle parking area, and usually refers to equipment such as cameras that acquire visual information.

[0541] "Automated analysis means" refers to methods used to analyze acquired video data and identify improperly parked bicycles, utilizing artificial intelligence and machine learning technologies to analyze the video.

[0542] An "information storage device" is a device for storing information about an identified individual, and usually refers to a storage system such as a database.

[0543] "Passage devices" refer to devices such as ticket gates and other equipment used when an identified individual passes through a facility.

[0544] "Information processing means" refers to means of processing information to present attention to an identified individual, and transmits information using a user interface or display.

[0545] "Emotional analysis tools" are means of identifying emotions by analyzing an individual's facial expressions and vocal characteristics, and adjusting the content and method of attention accordingly.

[0546] "Obstruction processing" refers to the process applied to identifying images to protect personal privacy, using methods such as mosaic or blurring to prevent individuals from being identified.

[0547] A "machine learning algorithm" is a mathematical method used to learn patterns and features from data and perform analysis and predictions. It is a technique used to identify inappropriate bicycle parking patterns.

[0548] This invention's system combines multiple technologies to solve the problems of abandoned bicycles and improper parking. The hardware used includes an image acquisition device (camera) for monitoring the parking area, which acquires video data in real time.

[0549] The server analyzes video data from the image acquisition device using automated analysis methods. This analysis utilizes artificial intelligence technology and machine learning algorithms. Specifically, it uses libraries such as TensorFlow and OpenCV to identify parking patterns in the video and detect improperly parked bicycles.

[0550] Once the detected bicycle parking information is confirmed, the server uses facial recognition technology to store the individual's identification information in an information storage device (database). A general facial recognition API is used for facial authentication.

[0551] Subsequently, when the identified individual passes through a passage device (such as a ticket gate or exit), the terminal displays a warning. In this process, the server uses emotion analysis tools to analyze the individual's facial expressions and tone of voice, and evaluates their emotional state. This allows the terminal to present a polite and appropriate warning, with its content and display method adjusted based on the user's emotional state.

[0552] For example, if a user expresses surprise, the server will instruct the device to display a mild-toned warning such as, "This vehicle is parked in a no-parking zone. Please be careful." Sentiment analysis uses a voice analysis API and a facial expression analysis model.

[0553] Furthermore, if a user moves their bicycle properly, the server verifies this information and removes personal data from the database. In this process, the server may anonymize and store sentiment data for future analysis. This makes it possible to more effectively deter illegal parking and improve public safety.

[0554] An example of a prompt to a generative AI model is, "Generate a detailed description of the abandoned bicycle detection and user sentiment-based warning system." This prompt is suitable for providing detailed descriptions of each function of the system and supporting an effective user experience.

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] The server acquires video data in real time from an image acquisition device installed in the bicycle parking area. The input is the raw video transmitted from the camera, and the output is the storage of this as digital data on the server. Specifically, the camera captures images at regular intervals and transmits the video to the server.

[0558] Step 2:

[0559] The server analyzes the acquired video data. The input is the video data saved in step 1, and the output is the results of identifying improperly parked bicycles and their location information. Image recognition technology using TensorFlow and OpenCV is used to process the data in order to identify bicycles and parking spaces. Specifically, if an improperly parked bicycle is found, a label indicating it and its location coordinates are generated.

[0560] Step 3:

[0561] The server uses facial recognition technology to identify individuals parked on bicycles and stores the information in a database. The input is the facial image of the individual identified in step 2, and the output is the individual's identification information. A common facial recognition API is used to analyze the image and compare it with an existing database to identify the individual. Specifically, if identification is successful, the individual's ID and parking information are stored in the database.

[0562] Step 4:

[0563] The terminal acquires video data of users passing through the gate from the ticket gate camera and transmits it to the server. The input is real-time video data, and the output is the result of verification on the server side. Specifically, the camera continuously monitors users passing through and transmits the video.

[0564] Step 5:

[0565] The server compares the identification information of passing users with the database and, if a violation is found, instructs the terminal to issue a warning. The input is the video data transmitted in step 4 and the information from the database, and the output is an instruction to display a warning message. Specifically, when a violator is identified, the server immediately instructs the terminal to issue a corresponding warning.

[0566] Step 6:

[0567] The server uses emotion analysis tools to analyze the user's emotions from their facial expressions and voice, and adjusts the warning content accordingly. The input is the user's facial expressions and voice data from step 5, and the output is the adjusted warning message. The server processes the emotion data using a voice analysis API and facial recognition model. Specifically, if the user is showing unpleasant emotions, the warning tone is softened.

[0568] Step 7:

[0569] When a user moves their bicycle to the appropriate location, information is sent from the terminal to the server. The input is sensor and operation information confirming the user's movement, and the output is the deletion of the identification information from the database. Specifically, the system completes the procedure to confirm that the user has taken the correct action and then deletes the data.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, 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."

[0572] In urban areas, improper parking of bicycles on sidewalks and in public bicycle parking areas has become a social problem. This situation not only threatens the safety of pedestrians but also detracts from the landscape and the convenience of public facilities. Conventional systems rely on manual monitoring of bicycle parking, making efficient management difficult. Furthermore, warnings can only be issued with standardized messages, lacking emotional consideration for individual cyclists, thus limiting their effectiveness. Therefore, there is a need for a system that can efficiently monitor bicycle parking and respond flexibly to individual feelings.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes means for acquiring video footage of the bicycle parking area using an image acquisition device, artificial intelligence analysis means for detecting inappropriate parking based on the video footage, and information processing means for displaying a warning when an identified person passes through an entrance or exit. This makes it possible to accurately monitor bicycle parking behavior in real time and enhance public safety and convenience through flexible warning displays that respond to the emotions of the parkers.

[0575] An "image acquisition device" is equipment used to capture video footage of the bicycle parking area and acquire it as data.

[0576] "Artificial intelligence analysis means" refers to technology that analyzes acquired video data to detect improperly parked bicycles.

[0577] "Information storage means" refers to a database or similar storage system that has the role of storing information about an identified person.

[0578] "Information processing means" refers to information management technology that issues instructions to display appropriate warnings to identified individuals.

[0579] An "emotion analysis engine" is a technology that recognizes and analyzes a person's emotions based on their facial expressions and tone of voice.

[0580] This invention is a system that improves the convenience and safety of public areas by detecting inappropriate parking in designated bicycle parking areas and displaying warnings tailored to the feelings of the parked cyclist. The details of the system are explained below.

[0581] The server first captures video of the bicycle parking area using an image acquisition device. This video data is processed by artificial intelligence analysis to detect improperly parked bicycles in real time. At this time, facial recognition technology is used to identify the parked person and record it in an information storage means. The information storage means stores and manages the information of the identified person using database technology.

[0582] Subsequently, when the identified person passes through the entrance or exit, the information processing system displays a warning to that person. The information processing system uses an emotion analysis engine to analyze the emotions of the parked person from their facial expressions and tone of voice, and adjusts the content and tone of the warning message according to their emotional state. This process enables a more appropriate and flexible response to the target user, ensuring that the warning is given in a way that does not cause discomfort to the parked person.

[0583] For example, if an improperly parked bicycle is detected in a park's bicycle parking area, the server can analyze whether the user identified by facial recognition technology appears surprised and display a gentle warning such as, "We apologize for startling you, but this is a no-parking zone. We appreciate your cooperation."

[0584] The following prompts can be used to leverage the AI ​​model:

[0585] "Analyze the facial expressions of users who park their bicycles improperly in specific parking areas and determine whether they are surprised or displeased. If they appear surprised, create a gentle message to convey this. For example, if the parked cyclist appears surprised, generate a message such as, 'I'm sorry to have startled you, but this is a no-parking zone. Thank you for your cooperation.'"

[0586] This system operates using image acquisition devices, servers, and terminals as hardware, and AI analysis models (TensorFlow and PyTorch) and sentiment analysis engines (OpenCV and other sentiment analysis technologies) as software. Data is managed using cloud and local database technologies.

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The server uses an image acquisition device to capture video footage of the bicycle parking area. This video data becomes the input. The server converts this data into an appropriate format and prepares it for detecting improperly parked bicycles.

[0590] Step 2:

[0591] The server inputs the acquired video data into an artificial intelligence analysis system to detect improperly parked bicycles. Specifically, it uses a generative AI model to analyze the placement of bicycles in the video, identify bicycles in no-parking zones, and outputs the results. These results become the input for the next step.

[0592] Step 3:

[0593] The server identifies the parked cyclist using facial recognition technology based on identified parking violations. This identification information is stored in an information storage device. This process outputs the parked cyclist's identification information, which is then used for subsequent warning processing.

[0594] Step 4:

[0595] When a user leaves the parking area, the server acquires video footage from the entrance / exit camera and compares it with pre-stored identification information. If the identification information matches, the server prepares to display a warning message to the user. This information matching is the input for step 5.

[0596] Step 5:

[0597] The server uses an emotion analysis engine to analyze the user's emotions from their facial expressions and tone of voice. Based on this analysis, it determines, for example, whether the user is surprised and generates a warning message accordingly. A warning message is output according to the user's emotional state, and the specific display method is determined.

[0598] Step 6:

[0599] The server displays the generated warning message on the terminal. The terminal notifies the user of the warning in the appropriate format, thereby guiding the user to release their bicycle.

[0600] Step 7:

[0601] Once the server confirms that the user has properly released their bicycle from parking, the user's identification information is deleted from the information storage device. As a result, the database is updated to the latest state.

[0602] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0603] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0604] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0608] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0609] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0610] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0611] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0612] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0613] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0614] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0615] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0616] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0617] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0618] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] This invention is a system for effectively solving the problem of abandoned bicycles and improper parking in urban areas. This system is designed to monitor parking activities in real time and deter illegal activities by utilizing an image capture device, artificial intelligence analysis means, a database, and information processing means.

[0620] Specifically, the server receives video data from an image capture device installed in the bicycle parking area. Based on this video data, an artificial intelligence analysis system detects improperly parked bicycles. Here, improper parking refers to bicycles parked in no-parking zones. The server uses facial recognition technology to identify the parked cyclist and stores their identification information in a database. At this stage, for privacy reasons, the identified facial images are blurred.

[0621] When a cyclist attempts to pass through a station's ticket gate, a terminal installed at the gate sends the person's information to a server. The server checks the information in its database, and if the person is found to be illegally parked, the terminal displays a warning. This warning displays a message that reads "Parking is prohibited" along with a blurred image. This encourages the cyclist to reconsider and move their bicycle.

[0622] If the user voluntarily moves their bicycle to a proper location, the server checks the image data again to confirm that the bicycle is no longer parked. After this confirmation, the server deletes the corresponding identification information from the database. As a result, the warning will no longer be displayed the next time the user passes through the ticket gate.

[0623] This system enables direct psychological deterrence and swift response to inappropriate bicycle parking, proving effective as a means of maintaining public order.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The video is captured at regular intervals to detect improperly parked bicycles.

[0627] Step 2:

[0628] The server uses artificial intelligence analysis based on the acquired video data to detect improperly parked bicycles. This process executes an algorithm to identify bicycles parked in no-parking zones.

[0629] Step 3:

[0630] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person who parked it. Information and characteristics of the identified person are stored in a database, but facial images are blurred to protect privacy.

[0631] Step 4:

[0632] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. This allows the server to share information about people passing through the ticket gates.

[0633] Step 5:

[0634] The server compares the transmitted camera footage with the violator information in its database. If a match is found, the server sends a warning message to the ticket gate terminal.

[0635] Step 6:

[0636] The terminal displays a warning message on its screen based on instructions from the server. This message, such as "Parking is prohibited," is displayed along with a blurred image.

[0637] Step 7:

[0638] If a user moves a bicycle that was improperly parked, the server will acquire video footage again from the image capture device to confirm that the bicycle has been released from its parking position.

[0639] Step 8:

[0640] Once the server confirms that the bicycle has been released from parking, it deletes the corresponding identification information from the database. This prevents the same person from seeing a warning the next time they pass through the ticket gate.

[0641] (Example 1)

[0642] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] This invention aims to efficiently solve the problem of improper bicycle parking in urban environments and maintain public order. Since improperly parked bicycles can obstruct pedestrian traffic and reduce safety, there is a need to monitor and deter them in real time. Conventional methods have problems such as requiring manpower for monitoring and making immediate response difficult.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0645] In this invention, the server includes means for receiving image data of a bicycle parking area using an image acquisition device, means for using a recognition system to recognize a parked bicycle based on the image data and determine whether it is located in a no-parking zone, and means for identifying an individual based on the determination result and storing the identification information on a recording medium. This makes it possible to detect inappropriate parking in real time and immediately issue a warning to the person who parked the bicycle.

[0646] An "image acquisition device" is a device that acquires visual information of the bicycle parking area and transmits it to a server.

[0647] "Image data" refers to the digital representation of acquired visual information and is used for recognizing parked objects.

[0648] A "recognition system" is a system equipped with algorithms to analyze image data and identify and judge specific objects or situations.

[0649] "Parked object" refers to a bicycle or similar vehicle that is parked, and is the object of judgment regarding whether its location is appropriate.

[0650] A "no-parking zone" refers to a designated area where parking bicycles is not permitted.

[0651] "Personal information" refers to information used to identify a specific individual, including facial images and related data.

[0652] A "recording medium" is a digital storage device used to store information and functions as a database.

[0653] This invention is a system that utilizes an image acquisition device, a recognition system, a recording medium, and a warning display terminal to solve the problem of bicycle parking in urban areas. Specifically, the server receives image data in real time from an image acquisition device placed in the bicycle parking area. This image acquisition device generates continuous frame data and transmits it to the server via a streaming protocol.

[0654] The server uses a recognition system to identify parked bicycles based on the received image data. This system utilizes commercially available recognition software to determine whether a bicycle is present in a no-parking zone. Based on the recognition results, the server performs facial recognition using image processing libraries such as OpenCV and stores the identified individual's information in a database. This stored information is blurred to protect the individual's privacy.

[0655] When a user passes through a station's ticket gate, the terminal accesses their information and communicates with a server. The server verifies the information stored on the recording medium, and if the person is found to have parked their bicycle improperly, the terminal displays a warning message and a blurred image. This helps users reconsider improper parking.

[0656] For example, if a user parks their bicycle in a no-parking zone, the video will be captured and recognized as improper parking. A warning will be displayed when the user passes through the ticket gate, prompting them to move their bicycle to the correct location. This action will prevent the warning from being displayed the next time the user passes through the ticket gate.

[0657] Regarding the use of generative AI models, possible prompts include questions such as, "Please explain how AI can solve the problem of bicycle parking in urban areas," or "Please explain how facial recognition technology can be used to identify parked bicycles." This will allow for improvements in system accuracy and the exploration of new applications.

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] The server receives image data of the bicycle parking area from the image acquisition device. Streaming data from the image acquisition device is used as input. The server loads this data into memory and prepares it for sequential processing of each frame. At this point, the output is image data converted into a processable format.

[0661] Step 2:

[0662] The server uses a recognition system to detect parked bicycles based on the received image data. The input is the image data formatted in step 1. The server uses an object recognition algorithm to identify bicycles in each frame and determines whether their location is within a no-parking zone. The output of this process is information indicating the location of the parked bicycles.

[0663] Step 3:

[0664] The server identifies and pinpoints a person near a recognized parked object. The input is the object's location information obtained in step 2 and the image data of the corresponding frame. The server uses the OpenCV library to perform facial recognition and generates mosaic-processed personal information. The output is the identified personal information, which has been mosaicked for privacy protection.

[0665] Step 4:

[0666] The server stores the generated personal information on a recording medium. The input is the identification information generated in step 3. The information is written to the database, which serves as the recording medium, in preparation for matching in the next step. The output is the data stored in a specific format.

[0667] Step 5:

[0668] The terminal transmits identification information to the server when a user passes through the station's ticket gate. The input is the user's IC card information or other identification means, and the server returns the verification result as output. At this stage, communication with the server takes place to determine if the user is an improperly parked cyclist.

[0669] Step 6:

[0670] The server checks the information in the database and displays a warning on the terminal if the user has parked their bicycle improperly. The input is the identification information received in step 5 and the recorded information in the database. The output is the result of the warning message and a mosaic-processed image of the user displayed on the terminal.

[0671] Step 7:

[0672] The user moves the bicycle to the correct parking area. The input is a warning from the system, and the output is that the bicycle has been moved from a no-parking zone. The server analyzes the new image data to confirm this action and detects the change in the parking status.

[0673] Step 8:

[0674] The server deletes the relevant personal information from the database after confirming that the bicycle has been released from parking. The input is the image analysis result from step 7. The output is the updated state of the database after the deletion, and the warning will no longer be displayed the next time you pass through.

[0675] (Application Example 1)

[0676] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] Abandoned bicycles and improperly parked bicycles in urban areas pose a threat to pedestrian safety and detract from the city's aesthetics. Furthermore, existing measures are insufficient in terms of notifying people of appropriate parking spaces and providing incentives for parking, which is a challenge in effectively deterring such behavior.

[0678] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0679] In this invention, the server includes means for acquiring video footage of an area using an image acquisition device; artificial intelligence analysis means for detecting improper parking based on the video footage; means for identifying the object when improper parking is detected and storing the identification information in a storage device; information processing means for displaying a warning when the identified object passes through a specific path; means for deleting the stored information when the parking is properly released after the warning is displayed; location information generation means for providing an appropriate parking space; and evaluation means for providing a reward when the appropriate parking behavior is confirmed. This enables effective monitoring and deterrence of improper parking problems in urban areas, and facilitates the rational use of parking spaces and the provision of incentives to users.

[0680] An "image acquisition device" is a mechanical device used to acquire video data from a specified area.

[0681] An "artificial intelligence analysis device" is a device that uses machine learning algorithms and inference techniques to detect specific patterns or anomalies based on acquired video data.

[0682] A "storage device" is a storage medium or database used to record identified information and allow that information to be retrieved as needed.

[0683] "Information processing means" refers to a device or software that receives specific information, processes it, and obtains a desired result.

[0684] A "location information generation means" is a system that identifies the current location and appropriate destination of an object and generates information related to that location.

[0685] An "evaluation tool" is a system equipped with the function of calculating and awarding rewards when a specific action is performed.

[0686] "Rewards" refer to points or benefits awarded in response to user actions, and are used to encourage appropriate user behavior.

[0687] The system that implements this application example operates by combining multiple hardware and software components. The server receives video data of a region from an image acquisition device (e.g., a surveillance camera). This video data is processed using artificial intelligence analysis. Machine learning libraries such as TensorFlow and OpenCV are used for this analysis to accurately detect improperly parked bicycles in the images.

[0688] When improperly parked bicycles are identified, the server identifies the person using the bicycle and stores the relevant information in a storage device (e.g., a database). Facial recognition technology is used for identification, but to protect individual privacy, the identified images are processed (mosaic processing).

[0689] When a user passes through a specific route, such as a train station ticket gate, an information processing system is activated and a warning is displayed on the user's terminal. This prompts the user to voluntarily remove their improperly parked bicycle, and the server uses a location information generation system to display a map on the user's terminal, providing a suitable parking space.

[0690] If proper parking is confirmed, the server will reward the user. In this evaluation system, points are added according to user behavior, and the accumulated information is updated and deleted.

[0691] As a concrete example, there is a system where, if a user who parks their bicycle improperly on their morning commute receives a warning at the ticket gate and moves to a designated space, the system immediately confirms that the bicycle has been released and awards points. This encourages users to behave correctly. Furthermore, as an example of a prompt to be input into the generating AI model for the development of this system, the following is used: "We are thinking of a smart city application to solve the problem of abandoned bicycles. Please come up with ideas for an app that integrates a system that detects improper parking in real time, notifies the user of the parking status, guides them to a proper parking location, and awards points."

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The server receives video data of a region from the image acquisition device. This data is either still images or video data provided in real time. The server temporarily stores the received image data.

[0695] Step 2:

[0696] The server sends the received video data to an artificial intelligence analysis system. Here, machine learning models such as TensorFlow are used to analyze the image data and determine the parking situation. If a pattern indicating improper parking is detected, the result is recorded.

[0697] Step 3:

[0698] If an improperly parked bicycle is detected, the server uses facial recognition technology to identify the person. The identified information is then blurred to protect the individual's privacy. After processing, the identification information is registered in a database.

[0699] Step 4:

[0700] When a user attempts to pass through a station's ticket gate, the terminal sends user information to a server. The server compares this information with a database, and if matching information is found, a warning is displayed on the terminal. Upon receiving this warning, the user is prompted to release their improperly parked bicycle.

[0701] Step 5:

[0702] The user finds a suitable parking space and moves their bicycle. The terminal displays the location on a map and provides guidance based on location information instructed by the server. The user moves the bicycle to the appropriate parking space.

[0703] Step 6:

[0704] The server retrieves image data again to verify that the bicycle was properly released. This verification data is compared with previous records, and if it is determined that appropriate action has been taken, the information is deleted from the database.

[0705] Step 7:

[0706] The server calculates a reward for users whose bicycles are parked properly, using an evaluation system. The calculated reward is credited to the user's account and can be used for future use.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] In addition to solving the problems of abandoned bicycles and improper parking, this invention provides a system that identifies the user's emotions and adaptively controls the content of warnings based on that information, so that warnings to parked bicycles are effectively conveyed. Specifically, it functions by combining an image capture device, artificial intelligence analysis means, a database, information processing means, and an emotion engine.

[0709] The server first acquires video data from an image capture device in the bicycle parking area and uses artificial intelligence analysis to detect improperly parked bicycles. Once a bicycle in a no-parking zone is identified, the server identifies the person who parked it using facial recognition technology and stores that information in a database.

[0710] Next, when passing through the ticket gate, the terminal transmits video data from the ticket gate camera to the server. The server checks the database for violators and, if a violator is found, instructs the terminal to display a warning.

[0711] In this process, the emotion engine analyzes the parked cyclist's facial expressions and tone of voice to recognize the user's emotional state. For example, if the user is surprised or showing discomfort, the server changes the content and display method of the warning message according to that emotion. This makes it possible to provide users with more appropriate and effective warnings.

[0712] When a user moves their bicycle properly, the server verifies this information, and the user's identification is removed from the database. During this process, the user's emotional data obtained by the emotion engine may be used to analyze future preventative measures.

[0713] This invention makes it possible to enhance public convenience and safety by deterring illegal parking in real time while providing appropriate responses that respond to the user's feelings.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The server acquires video data in real time from image capture devices installed in the bicycle parking area. The acquired video data is immediately processed by artificial intelligence analysis to detect the presence or absence of bicycles in no-parking zones.

[0717] Step 2:

[0718] When the server detects improper parking based on video data, it uses facial recognition technology to identify the person who parked the bicycle. The characteristic information of the identified person is stored in a database, and this information includes images that have been blurred to protect privacy.

[0719] Step 3:

[0720] The terminal transmits video footage from cameras installed at the station's ticket gates to a server. When a person who has parked their bicycle illegally passes through the ticket gate, the server compares the footage with the violator's information in its database.

[0721] Step 4:

[0722] Based on the matching results, if the server determines that the person in question is in the database, it sends an instruction to the terminal to display a warning.

[0723] Step 5:

[0724] The device uses an emotion engine to analyze the facial expressions and tone of voice of parked cyclists in real time, identifying emotions such as surprise or anger. This emotion data is used for information processing to create individually tailored warning messages.

[0725] Step 6:

[0726] The device displays personalized warning messages on its screen based on identified emotional information. For example, if the user is surprised, it will display a gentle warning; if they are angry, it will display a calming message.

[0727] Step 7:

[0728] After the server confirms that the user received the warning and moved the bicycle to the appropriate location, the server deletes the user's identification information from the database. At the same time, user sentiment data is also acquired and used for future recurrence prevention analysis.

[0729] This series of steps encourages users to park their bicycles appropriately while improving the overall deterrent effect and flexibility of the system.

[0730] (Example 2)

[0731] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] Public bicycle parking areas frequently suffer from improper parking, compromising the convenience and safety of other users. Traditional warning methods are uniform and do not consider the circumstances or feelings of those who park improperly, making it difficult to promote effective corrective behavior.

[0733] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0734] In this invention, the server includes means for collecting video footage of the bicycle parking area using an image acquisition device, means for identifying inappropriate parking using an automated analysis means, and means for identifying emotions from an individual's facial expressions and voice characteristics and adjusting the content of the warning. This makes it possible to provide appropriate and effective warnings according to the emotions and circumstances of the parker, thereby reducing inappropriate parking behavior.

[0735] An "image acquisition device" is a device used to collect video footage of a bicycle parking area, and usually refers to equipment such as cameras that acquire visual information.

[0736] "Automated analysis means" refers to methods used to analyze acquired video data and identify improperly parked bicycles, utilizing artificial intelligence and machine learning technologies to analyze the video.

[0737] An "information storage device" is a device for storing information about an identified individual, and usually refers to a storage system such as a database.

[0738] "Passage devices" refer to devices such as ticket gates and other equipment used when an identified individual passes through a facility.

[0739] "Information processing means" refers to means of processing information to present attention to an identified individual, and transmits information using a user interface or display.

[0740] "Emotional analysis tools" are means of identifying emotions by analyzing an individual's facial expressions and vocal characteristics, and adjusting the content and method of attention accordingly.

[0741] "Obstruction processing" refers to the process applied to identifying images to protect personal privacy, using methods such as mosaic or blurring to prevent individuals from being identified.

[0742] A "machine learning algorithm" is a mathematical method used to learn patterns and features from data and perform analysis and predictions. It is a technique used to identify inappropriate bicycle parking patterns.

[0743] This invention's system combines multiple technologies to solve the problems of abandoned bicycles and improper parking. The hardware used includes an image acquisition device (camera) for monitoring the parking area, which acquires video data in real time.

[0744] The server analyzes video data from the image acquisition device using automated analysis methods. This analysis utilizes artificial intelligence technology and machine learning algorithms. Specifically, it uses libraries such as TensorFlow and OpenCV to identify parking patterns in the video and detect improperly parked bicycles.

[0745] Once the detected bicycle parking information is confirmed, the server uses facial recognition technology to store the individual's identification information in an information storage device (database). A general facial recognition API is used for facial authentication.

[0746] Subsequently, when the identified individual passes through a passage device (such as a ticket gate or exit), the terminal displays a warning. In this process, the server uses emotion analysis tools to analyze the individual's facial expressions and tone of voice, and evaluates their emotional state. This allows the terminal to present a polite and appropriate warning, with its content and display method adjusted based on the user's emotional state.

[0747] For example, if a user expresses surprise, the server will instruct the device to display a mild-toned warning such as, "This vehicle is parked in a no-parking zone. Please be careful." Sentiment analysis uses a voice analysis API and a facial expression analysis model.

[0748] Furthermore, if a user moves their bicycle properly, the server verifies this information and removes personal data from the database. In this process, the server may anonymize and store sentiment data for future analysis. This makes it possible to more effectively deter illegal parking and improve public safety.

[0749] An example of a prompt to a generative AI model is, "Generate a detailed description of the abandoned bicycle detection and user sentiment-based warning system." This prompt is suitable for providing detailed descriptions of each function of the system and supporting an effective user experience.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] The server acquires video data in real time from an image acquisition device installed in the bicycle parking area. The input is the raw video transmitted from the camera, and the output is the storage of this as digital data on the server. Specifically, the camera captures images at regular intervals and transmits the video to the server.

[0753] Step 2:

[0754] The server analyzes the acquired video data. The input is the video data saved in step 1, and the output is the results of identifying improperly parked bicycles and their location information. Image recognition technology using TensorFlow and OpenCV is used to process the data in order to identify bicycles and parking spaces. Specifically, if an improperly parked bicycle is found, a label indicating it and its location coordinates are generated.

[0755] Step 3:

[0756] The server uses facial recognition technology to identify individuals parked on bicycles and stores the information in a database. The input is the facial image of the individual identified in step 2, and the output is the individual's identification information. A common facial recognition API is used to analyze the image and compare it with an existing database to identify the individual. Specifically, if identification is successful, the individual's ID and parking information are stored in the database.

[0757] Step 4:

[0758] The terminal acquires video data of users passing through the gate from the ticket gate camera and transmits it to the server. The input is real-time video data, and the output is the result of verification on the server side. Specifically, the camera continuously monitors users passing through and transmits the video.

[0759] Step 5:

[0760] The server compares the identification information of passing users with the database and, if a violation is found, instructs the terminal to issue a warning. The input is the video data transmitted in step 4 and the information from the database, and the output is an instruction to display a warning message. Specifically, when a violator is identified, the server immediately instructs the terminal to issue a corresponding warning.

[0761] Step 6:

[0762] The server uses emotion analysis tools to analyze the user's emotions from their facial expressions and voice, and adjusts the warning content accordingly. The input is the user's facial expressions and voice data from step 5, and the output is the adjusted warning message. The server processes the emotion data using a voice analysis API and facial recognition model. Specifically, if the user is showing unpleasant emotions, the warning tone is softened.

[0763] Step 7:

[0764] When a user moves their bicycle to the appropriate location, information is sent from the terminal to the server. The input is sensor and operation information confirming the user's movement, and the output is the deletion of the identification information from the database. Specifically, the system completes the procedure to confirm that the user has taken the correct action and then deletes the data.

[0765] (Application Example 2)

[0766] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0767] In urban areas, improper parking of bicycles on sidewalks and in public bicycle parking areas has become a social problem. This situation not only threatens the safety of pedestrians but also detracts from the landscape and the convenience of public facilities. Conventional systems rely on manual monitoring of bicycle parking, making efficient management difficult. Furthermore, warnings can only be issued with standardized messages, lacking emotional consideration for individual cyclists, thus limiting their effectiveness. Therefore, there is a need for a system that can efficiently monitor bicycle parking and respond flexibly to individual feelings.

[0768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0769] In this invention, the server includes means for acquiring video footage of the bicycle parking area using an image acquisition device, artificial intelligence analysis means for detecting inappropriate parking based on the video footage, and information processing means for displaying a warning when an identified person passes through an entrance or exit. This makes it possible to accurately monitor bicycle parking behavior in real time and enhance public safety and convenience through flexible warning displays that respond to the emotions of the parkers.

[0770] An "image acquisition device" is equipment used to capture video footage of the bicycle parking area and acquire it as data.

[0771] "Artificial intelligence analysis means" refers to technology that analyzes acquired video data to detect improperly parked bicycles.

[0772] "Information storage means" refers to a database or similar storage system that has the role of storing information about an identified person.

[0773] "Information processing means" refers to information management technology that issues instructions to display appropriate warnings to identified individuals.

[0774] An "emotion analysis engine" is a technology that recognizes and analyzes a person's emotions based on their facial expressions and tone of voice.

[0775] This invention is a system that improves the convenience and safety of public areas by detecting inappropriate parking in designated bicycle parking areas and displaying warnings tailored to the feelings of the parked cyclist. The details of the system are explained below.

[0776] The server first captures video of the bicycle parking area using an image acquisition device. This video data is processed by artificial intelligence analysis to detect improperly parked bicycles in real time. At this time, facial recognition technology is used to identify the parked person and record it in an information storage means. The information storage means stores and manages the information of the identified person using database technology.

[0777] Subsequently, when the identified person passes through the entrance or exit, the information processing system displays a warning to that person. The information processing system uses an emotion analysis engine to analyze the emotions of the parked person from their facial expressions and tone of voice, and adjusts the content and tone of the warning message according to their emotional state. This process enables a more appropriate and flexible response to the target user, ensuring that the warning is given in a way that does not cause discomfort to the parked person.

[0778] For example, if an improperly parked bicycle is detected in a park's bicycle parking area, the server can analyze whether the user identified by facial recognition technology appears surprised and display a gentle warning such as, "We apologize for startling you, but this is a no-parking zone. We appreciate your cooperation."

[0779] The following prompts can be used to leverage the AI ​​model:

[0780] "Analyze the facial expressions of users who park their bicycles improperly in specific parking areas and determine whether they are surprised or displeased. If they appear surprised, create a gentle message to convey this. For example, if the parked cyclist appears surprised, generate a message such as, 'I'm sorry to have startled you, but this is a no-parking zone. Thank you for your cooperation.'"

[0781] This system operates using image acquisition devices, servers, and terminals as hardware, and AI analysis models (TensorFlow and PyTorch) and sentiment analysis engines (OpenCV and other sentiment analysis technologies) as software. Data is managed using cloud and local database technologies.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The server uses an image acquisition device to capture video footage of the bicycle parking area. This video data becomes the input. The server converts this data into an appropriate format and prepares it for detecting improperly parked bicycles.

[0785] Step 2:

[0786] The server inputs the acquired video data into an artificial intelligence analysis system to detect improperly parked bicycles. Specifically, it uses a generative AI model to analyze the placement of bicycles in the video, identify bicycles in no-parking zones, and outputs the results. These results become the input for the next step.

[0787] Step 3:

[0788] The server identifies the parked cyclist using facial recognition technology based on identified parking violations. This identification information is stored in an information storage device. This process outputs the parked cyclist's identification information, which is then used for subsequent warning processing.

[0789] Step 4:

[0790] When a user leaves the parking area, the server acquires video footage from the entrance / exit camera and compares it with pre-stored identification information. If the identification information matches, the server prepares to display a warning message to the user. This information matching is the input for step 5.

[0791] Step 5:

[0792] The server uses an emotion analysis engine to analyze the user's emotions from their facial expressions and tone of voice. Based on this analysis, it determines, for example, whether the user is surprised and generates a warning message accordingly. A warning message is output according to the user's emotional state, and the specific display method is determined.

[0793] Step 6:

[0794] The server displays the generated warning message on the terminal. The terminal notifies the user of the warning in the appropriate format, thereby guiding the user to release their bicycle.

[0795] Step 7:

[0796] Once the server confirms that the user has properly released their bicycle from parking, the user's identification information is deleted from the information storage device. As a result, the database is updated to the latest state.

[0797] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0798] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0799] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0800] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0801] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0802] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0803] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0804] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0805] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0806] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0807] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0808] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0809] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0810] 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.

[0811] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0812] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0813] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0814] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0815] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0816] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0817] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0818] The following is further disclosed regarding the embodiments described above.

[0819] (Claim 1)

[0820] A means of acquiring video footage of the bicycle parking area using an image capture device,

[0821] An artificial intelligence analysis means for detecting improperly parked bicycles based on the aforementioned video,

[0822] A means for identifying a person when the aforementioned improper parking is detected and storing the identification information in a database,

[0823] Information processing means for displaying a warning when an identified person passes through the ticket gate,

[0824] A means for deleting person information from the database when the bicycle is properly released after the aforementioned warning is displayed,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, further comprising means for applying a mosaic effect to an identification image in order to protect the privacy of a person when the aforementioned warning is displayed.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the artificial intelligence analysis means includes means for using a machine learning algorithm to identify a bicycle parking pattern.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A means for receiving image data of a bicycle parking area using an image acquisition device,

[0833] A means of using a recognition system to recognize parked objects based on the aforementioned image data and to determine whether they are located in a no-parking zone,

[0834] A means for identifying an individual based on the aforementioned judgment result and storing the identified information on a recording medium,

[0835] Control means for displaying a warning when a detected individual passes through a gate,

[0836] After confirming that the bicycle has been moved to an permitted parking location following the display of the aforementioned warning, a means of deleting personal information from the recording medium,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, comprising means for processing specific images in order to ensure the privacy of individuals.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for using a learning algorithm to identify the parking arrangement in the recognition system.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A means for acquiring images of a region using an image acquisition device,

[0845] An artificial intelligence analysis means for detecting improperly parked bicycles based on the aforementioned video,

[0846] A means for identifying the object when the aforementioned improper parking is detected and storing the identification information in a storage device,

[0847] Information processing means for displaying a warning when an identified object passes through a specific path,

[0848] A means for erasing accumulated information when the bicycle is properly released after the aforementioned warning is displayed,

[0849] A means for generating location information to provide an appropriate bicycle parking space,

[0850] An evaluation means for awarding a reward when the aforementioned appropriate parking behavior is confirmed,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising means for applying image processing to an identifying image in order to protect the privacy of an individual when the aforementioned warning is displayed.

[0854] (Claim 3)

[0855] The system according to claim 1, wherein the artificial intelligence analysis means includes means for using machine learning techniques to identify behavioral patterns.

[0856] "Example 2 of combining an emotion engine"

[0857] (Claim 1)

[0858] A means for collecting video footage of the bicycle parking area using an image acquisition device,

[0859] An automated analysis means for identifying improperly parked bicycles based on the aforementioned video,

[0860] A means for identifying an individual when the aforementioned improperly parked bicycle is identified and registering the identification data in an information storage device,

[0861] Information processing means for displaying a warning when the identified individual passes through the passage device,

[0862] A means for deleting personal information from an information storage device when the parking situation is appropriately improved after the aforementioned warning is issued,

[0863] An emotion analysis tool that identifies emotions from an individual's facial expressions and vocal characteristics, and adjusts the content and method of attention based on that information,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, comprising means for obscuring the identification image in order to protect the privacy of an individual when the aforementioned warning is presented.

[0867] (Claim 3)

[0868] The system according to claim 1, wherein the automated analysis means includes means for using a machine learning algorithm to identify a bicycle parking pattern.

[0869] "Application example 2 when combining with an emotional engine"

[0870] (Claim 1)

[0871] A means for acquiring video footage of a bicycle parking area using an image acquisition device,

[0872] An artificial intelligence analysis means for detecting improperly parked bicycles based on the aforementioned video,

[0873] A means for identifying a person when the aforementioned improper parking is detected and storing the identification information in an information storage means,

[0874] Information processing means for displaying a warning when an identified person passes through an entrance / exit,

[0875] The means for analyzing a person's emotions when the aforementioned warning is displayed, and for adjusting the content and display method of the warning message according to those emotions,

[0876] A means for deleting person information from the information storage means when the bicycle is properly released after the aforementioned warning is displayed,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for applying image processing to an identification image in order to protect the personal information of a person when the aforementioned warning is displayed.

[0880] (Claim 3)

[0881] The system according to claim 1, wherein the artificial intelligence analysis means includes means for using a machine learning method to identify bicycle parking behavior. [Explanation of Symbols]

[0882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring images of a region using an image acquisition device, An artificial intelligence analysis means for detecting improperly parked bicycles based on the aforementioned video, A means for identifying the object when the aforementioned improper parking is detected and storing the identification information in a storage device, Information processing means for displaying a warning when an identified object passes through a specific path, A means for erasing accumulated information when the bicycle is properly released after the aforementioned warning is displayed, A means for generating location information to provide an appropriate bicycle parking space, An evaluation means for awarding a reward when the aforementioned appropriate parking behavior is confirmed, A system that includes this.

2. The system according to claim 1, further comprising means for applying image processing to an identification image in order to protect the privacy of an individual when the aforementioned warning is displayed.

3. The system according to claim 1, wherein the artificial intelligence analysis means includes means for using machine learning techniques to identify behavioral patterns.

Images