System

Abstract

The system according to the embodiment aims to effectively monitor illegal dumping and quickly take measures.SOLUTION: A system includes an identification unit, a collation unit, a generation unit, a transmission unit, and a warning unit. The identification unit identifies a license plate. The collation unit collates information on the number plate identified by the identification unit with a vehicle registration database. The generation unit generates a warning message for the vehicle owner identified by the collation unit. The transmission unit transmits the warning message generated by the generation unit. The warning part performs siren or voice announcement when the action of the illegal dumping is detected by the identification part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately monitor and combat illegal dumping, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively monitor illegal dumping and take prompt measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a matching unit, a generation unit, a sending unit, and a warning unit. The identification unit identifies license plates. The matching unit matches the information of the license plates identified by the identification unit with a vehicle registration database. The generation unit generates a warning message to the vehicle owner identified by the matching unit. The sending unit sends the warning message generated by the generation unit. The warning unit sounds a siren or makes a voice announcement when the identification unit detects an act of illegal dumping. [Effects of the Invention]

[0007] The system according to the embodiment can effectively monitor illegal dumping and take measures promptly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An embodiment of the illegal dumping prevention system of the present invention prevents illegal dumping in areas where dumping is common. This system installs AI cameras in areas where dumping is common, identifies vehicle license plates, and compares the information with a vehicle registration database. If the vehicle owner is identified as a result of the comparison, the generation AI selects effective wording and sends a warning message. It can also activate a siren or play a voice announcement urging the driver not to dump. For example, the AI ​​camera can capture high-resolution images even at night and accurately read license plates. The license plate information identified by the AI ​​camera is then compared with a vehicle registration database in real time to quickly identify the vehicle owner. Once the vehicle owner is identified as a result of the comparison, the generation AI selects effective wording based on past data and generates a warning message. For example, this message may include, "Illegal dumping is prohibited by law. Please dispose of the waste appropriately to prevent recurrence." Furthermore, if the AI ​​camera detects illegal dumping, a siren sounds and a voice announcement is played, such as, "Please refrain from illegal dumping." This is expected to have a deterrent effect on illegal dumping. As a result, the illegal dumping prevention system can effectively prevent illegal dumping in areas with a high level of dumping. Combining AI cameras with generative AI enables quick and effective response, contributing to the protection of the local environment.

[0029] An illegal dumping prevention system according to an embodiment includes an identification unit, a matching unit, a generation unit, a sending unit, and a warning unit. The identification unit identifies vehicle license plates. For example, the identification unit can capture high-resolution images even at night and accurately read license plates. The identification unit can also use AI to utilize image recognition technology to improve the accuracy of license plate identification. The matching unit compares the license plate information identified by the identification unit with a vehicle registration database. For example, the matching unit compares the information with the database in real time to quickly identify the vehicle owner. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. The generation unit generates a warning message for the vehicle owner identified by the matching unit. For example, the generation unit uses a generation AI to select effective wording based on past data and generate the warning message. The generation AI can optimize the wording of the warning message using a text generation AI (e.g., LLM). The sending unit sends the warning message generated by the generation unit. For example, the sending unit sends a warning message to the identified vehicle owner by means of email, mail, or the like. The sending unit can also optimize the sending means and timing using AI. The warning unit sounds a siren or makes a voice announcement when the identification unit detects an act of illegal dumping. For example, the warning unit sounds a siren and makes a voice announcement such as, "Please stop illegal dumping." The warning unit can also optimize the content and timing of the voice announcement using AI. As a result, the illegal dumping prevention system according to the embodiment can effectively prevent illegal dumping.

[0030] The recognition unit can capture high-resolution images even at night. The recognition unit can capture high-resolution images, such as 1080p or 4K. The recognition unit can use an infrared camera or a highly sensitive sensor to capture high-resolution images even at night. For example, the recognition unit uses an infrared camera to capture clear images even at night. The recognition unit can also capture high-resolution images even in low-light environments using a highly sensitive sensor. Furthermore, the recognition unit can use image processing technology to reduce noise in images captured at night and improve recognition accuracy. This allows license plates to be accurately identified even at night.

[0031] The generation unit can select effective wording based on past data. For example, the generation unit analyzes data on past warning messages and selects effective wording. The generation unit can use the generation AI to extract the most effective wording from past data and generate a warning message. For example, the generation unit references a database of past warning messages and selects wording that is effective in preventing recurrence. The generation unit can also use the generation AI to automatically optimize the wording of the warning message. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate an effective warning message to prevent recurrence," and the generation AI generates the optimal wording. This makes it possible to generate an effective warning message.

[0032] The warning unit can sound a siren and make a voice announcement to prevent dumping. For example, when the warning unit detects illegal dumping, it sounds a siren. The warning unit can adjust the volume and tone of the siren to provide an effective warning. The warning unit also makes a voice announcement to convey a message to prevent dumping. For example, the warning unit may make a voice announcement such as, "Please refrain from illegal dumping." The warning unit can also use AI to optimize the content and timing of the voice announcement. For example, the warning unit may input a prompt to the AI, such as, "Generate an effective voice announcement to prevent illegal dumping," and the AI ​​will generate the optimal voice announcement. This is expected to have a deterrent effect on illegal dumping.

[0033] The matching unit can perform real-time matching with a vehicle registration database. For example, the matching unit matches license plate information identified by the identification unit with the vehicle registration database in real time. The matching unit can use high-speed data processing technology to perform real-time matching with the database. For example, the matching unit can use high-speed database query technology to quickly match license plate information. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. For example, the matching unit can input a prompt to the AI ​​saying, "Please generate the optimal algorithm for matching with the vehicle registration database in real time," and the AI ​​will generate the optimal matching algorithm. This allows the vehicle owner to be identified quickly.

[0034] The sending unit can send a warning message to the identified vehicle owner. For example, the sending unit sends the warning message generated by the generation unit to the identified vehicle owner. The sending unit can send the warning message using means such as email or mail. For example, the sending unit can quickly send the warning message using email. The sending unit can also send the warning message as a formal document using mail. Furthermore, the sending unit can use AI to optimize the sending means and timing. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending means and timing for the identified vehicle owner," and the AI ​​selects the optimal sending means and timing. This allows the warning message to be sent effectively to the vehicle owner.

[0035] The identification unit can link multiple cameras to identify license plates from different angles. For example, the identification unit installs multiple cameras and simultaneously photographs license plates from different angles. The identification unit can integrate images from the multiple cameras to improve the accuracy of license plate identification. For example, the identification unit analyzes images from the multiple cameras and compares the license plate identification results to improve accuracy. The identification unit can also use AI to optimize the linkage of the multiple cameras to improve identification accuracy. For example, the identification unit inputs a prompt to the AI, such as "Generate the optimal method for linking multiple cameras to improve license plate identification accuracy," and the AI ​​generates the optimal method. This improves identification accuracy.

[0036] The identification unit can automatically correct dirt and damage on license plates for identification. For example, the identification unit corrects dirt on license plates using image processing technology to improve identification accuracy. The identification unit can also improve identification accuracy by using an algorithm to complement dirt and damaged areas. For example, the identification unit uses image processing technology to remove dirt from license plates and make the characters clearer. The identification unit can also use AI to estimate and complement missing areas to complement damaged areas. For example, the identification unit inputs a prompt to the AI, saying, "Please generate the optimal method to correct dirt and damage on license plates to improve identification accuracy," and the AI ​​generates the optimal method. This allows license plates to be accurately identified even if they are dirty or damaged.

[0037] The identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. For example, the identification unit simultaneously identifies the color of a vehicle when identifying a license plate. The identification unit can identify the color of a vehicle using color recognition technology. For example, the identification unit identifies the color of a vehicle using image analysis technology. The identification unit can also simultaneously identify the shape of a vehicle when identifying a license plate. The identification unit can identify the shape of a vehicle using a shape recognition algorithm. For example, the identification unit identifies the shape of a vehicle using image analysis technology. Furthermore, the identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. This makes it possible to simultaneously identify the color and shape of a vehicle.

[0038] The identification unit can improve the identification accuracy based on the speed and movement of the vehicle when identifying the license plate. For example, the identification unit detects the speed of the vehicle and adjusts the identification accuracy. The identification unit can detect the speed of the vehicle using speed measurement technology. For example, the identification unit measures the speed of the vehicle using radar or LIDAR. The identification unit can also analyze the movement of the vehicle and improve the identification accuracy. The identification unit can analyze the movement of the vehicle using a movement analysis algorithm. For example, the identification unit identifies the movement of the vehicle using image analysis technology. Furthermore, the identification unit can improve the identification accuracy by simultaneously considering the speed and movement of the vehicle. This makes it possible to improve the identification accuracy by considering the speed and movement of the vehicle.

[0039] When identifying a license plate, the identification unit can adjust the identification accuracy based on surrounding environmental information. For example, the identification unit adjusts the identification accuracy taking weather information into consideration. The identification unit can acquire weather information and adjust the identification accuracy. For example, the identification unit acquires weather information using a weather sensor. The identification unit can also adjust the identification accuracy taking lighting conditions into consideration. The identification unit can detect lighting conditions and adjust the identification accuracy. For example, the identification unit detects lighting conditions using an illuminance sensor. Furthermore, the identification unit can adjust the identification accuracy taking weather information and lighting conditions into consideration simultaneously. This makes it possible to adjust the identification accuracy taking surrounding environmental information into consideration.

[0040] The matching unit can improve matching accuracy by referring to past matching history during matching. For example, the matching unit improves matching accuracy by referring to past matching history. The matching unit can analyze past matching history and improve matching accuracy. For example, the matching unit improves matching accuracy by referring to a past matching history database. The matching unit can also use AI to optimize the matching algorithm based on past matching history. For example, the matching unit inputs a prompt to the AI ​​saying, "Please generate an optimal method for improving matching accuracy by referring to past matching history," and the AI ​​generates an optimal method. In this way, matching accuracy can be improved by referring to past matching history.

[0041] The matching unit can integrate multiple databases during matching to improve matching accuracy. For example, the matching unit integrates multiple vehicle registration databases to improve matching accuracy. The matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. For example, the matching unit integrates a vehicle registration database and an insurance database to improve matching accuracy. The matching unit can also integrate a vehicle registration database and a maintenance history database to improve matching accuracy. For example, the matching unit integrates a vehicle registration database and a maintenance history database to improve matching accuracy. Furthermore, the matching unit can use AI to optimize the integration of multiple databases to improve matching accuracy. For example, the matching unit inputs a prompt to the AI ​​saying, "Generate an optimal method for integrating multiple databases to improve matching accuracy," and the AI ​​generates an optimal method. This makes it possible to integrate multiple databases and improve matching accuracy.

[0042] When generating a warning message, the generation unit can adjust the wording by taking into account the vehicle owner's attribute information. For example, the generation unit selects appropriate wording according to the vehicle owner's age. The generation unit can select appropriate wording according to the vehicle owner's gender. For example, the generation unit selects the most effective wording based on the vehicle owner's age and gender. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the vehicle owner's attribute information. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message by taking into account the vehicle owner's attribute information," and the generation AI generates the optimal wording. This makes it possible to adjust the wording by taking into account the vehicle owner's attribute information.

[0043] When generating a warning message, the generation unit can customize the wording according to the type and frequency of the violation. For example, the generation unit selects appropriate wording according to the type of violation. The generation unit can select appropriate wording according to the frequency of the violation. For example, the generation unit selects the most effective wording based on the type and frequency of the violation. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the type and frequency of the violation. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message taking into consideration the type and frequency of the violation," and the generation AI generates the optimal wording. This makes it possible to customize the wording according to the type and frequency of the violation.

[0044] When sending a warning message, the sending unit can record the sending history and adjust the timing of resending. For example, the sending unit records the sending history of the warning message and adjusts the timing of resending. The sending unit can analyze the sending history and determine the optimal timing of resending. For example, the sending unit refers to a sending history database and adjusts the timing of resending. The sending unit can also use AI to optimize the timing of resending based on the sending history. For example, the sending unit inputs a prompt to the AI ​​saying, "Please record the sending history and generate the optimal method for adjusting the timing of resending," and the AI ​​generates the optimal method. This makes it possible to record the sending history and adjust the timing of resending.

[0045] When sending a warning message, the sending unit can adjust the sending method taking into account the recipient's reception environment. For example, the sending unit selects the optimal sending method depending on the recipient's device. The sending unit can adjust the sending method taking into account the recipient's network conditions. For example, the sending unit detects the recipient's device type and network conditions and selects the optimal sending method. The sending unit can also use AI to optimize the sending method based on the recipient's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending method taking into account the recipient's reception environment," and the AI ​​selects the optimal sending method. This makes it possible to select the optimal sending method depending on the recipient's reception environment.

[0046] The warning unit may adjust the volume of the siren taking into account the surrounding environmental sound when issuing a warning. For example, if the surrounding environmental sound is loud, the warning unit may increase the volume of the siren. If the surrounding environmental sound is quiet, the warning unit may decrease the volume of the siren. For example, the warning unit may detect the surrounding environmental sound using an environmental sound sensor and adjust the volume of the siren. The warning unit may also use AI to optimize the volume of the siren based on the surrounding environmental sound. For example, the warning unit may input a prompt to the AI ​​saying, "Generate an optimal method for adjusting the siren volume taking into account the surrounding environmental sound," and the AI ​​may generate the optimal method. This allows the volume of the siren to be adjusted according to the surrounding environmental sound.

[0047] When issuing a warning, the warning unit can make different voice announcements depending on the type of dumping behavior. For example, in the case of dumping garbage, the warning unit can make a voice announcement saying, "Please stop dumping garbage." In the case of illegal parking, the warning unit can make a voice announcement saying, "Please stop illegal parking." For example, the warning unit makes an appropriate voice announcement depending on the type of dumping behavior. The warning unit can also use AI to optimize the content of the voice announcement based on the type of dumping behavior. For example, the warning unit inputs a prompt to the AI ​​saying, "Generate the optimal voice announcement depending on the type of dumping behavior," and the AI ​​generates the optimal voice announcement. This makes it possible to make an appropriate voice announcement depending on the type of dumping behavior.

[0048] The warning unit may flash surrounding lights to provide a visual warning when issuing a warning. For example, the warning unit may flash surrounding lights to provide a visual warning such as "Please refrain from illegal dumping." The warning unit may flash surrounding lights to provide a visual warning such as "This area is under surveillance." For example, the warning unit may flash surrounding lights to provide a visual warning such as "Illegal dumping is prohibited by law." The warning unit may also use AI to optimize the flashing pattern of surrounding lights. For example, the warning unit may input a prompt to the AI ​​such as "Generate the optimal method for flashing surrounding lights to provide a visual warning," and the AI ​​may generate the optimal method. This improves the warning effect by providing a visual warning.

[0049] The warning unit can display a warning message on the display when a warning is issued. For example, the warning unit can display a warning message on the display saying, "Please refrain from illegal dumping." The warning unit can display a warning message on the display saying, "This area is being monitored." For example, the warning unit can display a warning message on the display saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the content and display method of the warning message. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal way to display the warning message on the display," and the AI ​​will generate the optimal method. This improves the warning effect by visually displaying the warning message.

[0050] The warning unit can provide warning sounds and announcements in multiple languages. For example, the warning unit can provide warning sounds and announcements in English and Japanese. The warning unit can provide warning sounds and announcements in Chinese and Korean. For example, the warning unit can provide warning sounds and announcements in multiple languages ​​to accommodate foreigners. The warning unit can also use AI to translate the warning sounds and announcements into multiple languages. For example, the warning unit can input a prompt to the AI ​​saying, "Please generate the optimal way to provide the warning sounds and announcements in multiple languages," and the AI ​​can generate the optimal way. This allows warnings to be provided in multiple languages ​​to accommodate foreigners.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The identification unit can not only identify the vehicle's license plate, but also analyze the vehicle's driving pattern. For example, the identification unit can analyze the vehicle's driving speed and direction in real time to detect abnormal driving patterns. The identification unit can also determine whether a particular vehicle frequently enters and exits an illegal dumping area based on past driving data. Furthermore, the identification unit provides the analysis results of the driving pattern to the matching unit, which can use this as reference information when identifying the vehicle's owner. In this way, the identification unit can improve the effectiveness of preventing illegal dumping by analyzing the vehicle's driving pattern.

[0053] The recognition unit not only captures high-resolution images even at night, but can also automatically switch shooting modes according to weather conditions. For example, the recognition unit automatically deploys a waterproof cover in rainy weather to prevent the lens from fogging up. When it is snowing, the recognition unit activates a lens heater to melt the snow and ensure clear visibility. Furthermore, in strong winds, the recognition unit activates a stabilizer to suppress camera vibration, allowing for blur-free images to be captured. This allows the recognition unit to maintain high recognition accuracy under a variety of weather conditions.

[0054] The generator not only selects effective wording based on past data, but can also customize the tone and style of the warning message. For example, the generator may generate a warning message in a casual tone for younger users and a more polite tone for older users. The generator can also adjust the style of the warning message according to the local culture and language. Furthermore, the generator can generate a warning message in a stern tone for certain violations and a gentle tone for minor violations. This allows the generator to provide the most effective warning message to the recipient.

[0055] The warning unit can sound a siren and make audio announcements to prevent dumping, as well as flash a warning light. For example, when the warning unit detects illegal dumping, it can flash a red warning light. The warning unit can also automatically adjust the brightness of the warning light at night to ensure visibility according to the surrounding environment. Furthermore, the warning unit can visually convey different types of warnings by changing the flashing pattern of the warning light. In this way, the warning unit can enhance the deterrent effect of illegal dumping by adding a visual warning means.

[0056] The matching unit not only checks against the vehicle registration database in real time, but can also integrate multiple databases to improve matching accuracy. For example, the matching unit may integrate the vehicle registration database with the insurance database to more accurately identify vehicle owner information. The matching unit may also check against a criminal history database to identify vehicles that have illegally dumped in the past. Furthermore, the matching unit may link with a local surveillance camera network and use image data captured by other cameras for matching. This allows the matching unit to utilize multiple data sources to improve matching accuracy.

[0057] The sending unit not only sends a warning message to the identified vehicle owner, but also adjusts the delivery method taking into account the reception environment of the destination. For example, the sending unit selects the optimal delivery method depending on the destination's device. The sending unit can adjust the delivery method taking into account the destination's network conditions. For example, the sending unit detects the destination's device type and network conditions and selects the optimal delivery method. The sending unit can also use AI to optimize the delivery method based on the destination's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal delivery method taking into account the destination's reception environment," and the AI ​​selects the optimal delivery method. This makes it possible to select the optimal delivery method depending on the destination's reception environment.

[0058] The identification unit can link multiple cameras to identify license plates from different angles. For example, the identification unit installs multiple cameras and simultaneously photographs license plates from different angles. The identification unit can integrate images from the multiple cameras to improve the accuracy of license plate identification. For example, the identification unit analyzes images from the multiple cameras and compares the license plate identification results to improve accuracy. The identification unit can also use AI to optimize the linkage of multiple cameras to improve identification accuracy. For example, the identification unit inputs a prompt to the AI, such as "Generate the optimal method for linking multiple cameras to improve license plate identification accuracy," and the AI ​​generates the optimal method. This improves identification accuracy.

[0059] The identification unit can automatically correct dirt and damage on license plates for identification. For example, the identification unit can correct dirt on license plates using image processing technology to improve identification accuracy. The identification unit can also improve identification accuracy by using an algorithm to complement dirt and damaged areas. For example, the identification unit can use image processing technology to remove dirt from license plates and make the characters clearer. The identification unit can also use AI to estimate and complement missing areas to complement damaged areas. For example, the identification unit can input a prompt to the AI, saying, "Please generate the optimal method to correct dirt and damage on the license plate to improve identification accuracy," and the AI ​​will generate the optimal method. This allows license plates to be accurately identified even if they are dirty or damaged.

[0060] The identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. For example, the identification unit simultaneously identifies the color of a vehicle when identifying a license plate. The identification unit can identify the color of a vehicle using color recognition technology. For example, the identification unit can identify the color of a vehicle using image analysis technology. The identification unit can also simultaneously identify the shape of a vehicle when identifying a license plate. The identification unit can identify the shape of a vehicle using a shape recognition algorithm. For example, the identification unit can identify the shape of a vehicle using image analysis technology. Furthermore, the identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. This makes it possible to simultaneously identify the color and shape of a vehicle.

[0061] The identification unit can improve the identification accuracy based on the speed and movement of the vehicle when identifying the license plate. For example, the identification unit detects the speed of the vehicle and adjusts the identification accuracy. The identification unit can detect the speed of the vehicle using speed measurement technology. For example, the identification unit can measure the speed of the vehicle using radar or LIDAR. The identification unit can also analyze the movement of the vehicle and improve the identification accuracy. The identification unit can analyze the movement of the vehicle using a movement analysis algorithm. For example, the identification unit can identify the movement of the vehicle using image analysis technology. Furthermore, the identification unit can improve the identification accuracy by simultaneously considering the speed and movement of the vehicle. This makes it possible to improve the identification accuracy by taking the speed and movement of the vehicle into consideration.

[0062] When identifying a license plate, the identification unit can adjust the identification accuracy based on surrounding environmental information. For example, the identification unit adjusts the identification accuracy taking weather information into consideration. The identification unit can acquire weather information and adjust the identification accuracy. For example, the identification unit acquires weather information using a weather sensor. The identification unit can also adjust the identification accuracy taking lighting conditions into consideration. The identification unit can detect lighting conditions and adjust the identification accuracy. For example, the identification unit detects lighting conditions using an illuminance sensor. Furthermore, the identification unit can adjust the identification accuracy taking weather information and lighting conditions into consideration simultaneously. This makes it possible to adjust the identification accuracy taking surrounding environmental information into consideration.

[0063] During matching, the matching unit can improve matching accuracy by referring to past matching history. For example, the matching unit improves matching accuracy by referring to past matching history. The matching unit can analyze past matching history and improve matching accuracy. For example, the matching unit improves matching accuracy by referring to a past matching history database. The matching unit can also use AI to optimize the matching algorithm based on past matching history. For example, the matching unit inputs a prompt to the AI ​​saying, "Please generate an optimal method for improving matching accuracy by referring to past matching history," and the AI ​​generates an optimal method. In this way, matching accuracy can be improved by referring to past matching history.

[0064] The matching unit can integrate multiple databases during matching to improve matching accuracy. For example, the matching unit can integrate multiple vehicle registration databases to improve matching accuracy. The matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. For example, the matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. The matching unit can also integrate a vehicle registration database and a maintenance history database to improve matching accuracy. For example, the matching unit can integrate a vehicle registration database and a maintenance history database to improve matching accuracy. Furthermore, the matching unit can use AI to optimize the integration of multiple databases to improve matching accuracy. For example, the matching unit inputs a prompt to the AI ​​saying, "Generate an optimal method for integrating multiple databases to improve matching accuracy," and the AI ​​generates an optimal method. This makes it possible to integrate multiple databases and improve matching accuracy.

[0065] When generating a warning message, the generation unit can adjust the wording by taking into account the vehicle owner's attribute information. For example, the generation unit selects appropriate wording according to the vehicle owner's age. The generation unit can select appropriate wording according to the vehicle owner's gender. For example, the generation unit selects the most effective wording based on the vehicle owner's age and gender. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the vehicle owner's attribute information. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message by taking into account the vehicle owner's attribute information," and the generation AI generates the optimal wording. This makes it possible to adjust the wording by taking into account the vehicle owner's attribute information.

[0066] When generating a warning message, the generation unit can customize the wording depending on the type and frequency of the violation. For example, the generation unit selects appropriate wording depending on the type of violation. The generation unit can select appropriate wording depending on the frequency of the violation. For example, the generation unit selects the most effective wording based on the type and frequency of the violation. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the type and frequency of the violation. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message taking into consideration the type and frequency of the violation," and the generation AI generates the optimal wording. This makes it possible to customize the wording depending on the type and frequency of the violation.

[0067] When sending a warning message, the sending unit can record the sending history and adjust the timing of resending. For example, the sending unit records the sending history of warning messages and adjusts the timing of resending. The sending unit can analyze the sending history and determine the optimal timing of resending. For example, the sending unit refers to a sending history database and adjusts the timing of resending. The sending unit can also use AI to optimize the timing of resending based on the sending history. For example, the sending unit inputs a prompt to the AI ​​saying, "Please record the sending history and generate the optimal method for adjusting the timing of resending," and the AI ​​generates the optimal method. This makes it possible to record the sending history and adjust the timing of resending.

[0068] When sending a warning message, the sending unit can adjust the sending method taking into account the recipient's reception environment. For example, the sending unit selects the optimal sending method depending on the recipient's device. The sending unit can adjust the sending method taking into account the recipient's network conditions. For example, the sending unit detects the recipient's device type and network conditions and selects the optimal sending method. The sending unit can also use AI to optimize the sending method based on the recipient's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending method taking into account the recipient's reception environment," and the AI ​​selects the optimal sending method. This makes it possible to select the optimal sending method depending on the recipient's reception environment.

[0069] The warning unit may adjust the volume of the siren taking into account the surrounding environmental sounds when issuing a warning. For example, if the surrounding environmental sounds are loud, the warning unit may increase the volume of the siren. If the surrounding environmental sounds are quiet, the warning unit may decrease the volume of the siren. For example, the warning unit may detect the surrounding environmental sounds using an environmental sound sensor and adjust the volume of the siren. The warning unit may also use AI to optimize the volume of the siren based on the surrounding environmental sounds. For example, the warning unit may input a prompt to the AI ​​saying, "Generate an optimal method for adjusting the siren volume taking into account the surrounding environmental sounds," and the AI ​​may generate the optimal method. This allows the volume of the siren to be adjusted according to the surrounding environmental sounds.

[0070] When issuing a warning, the warning unit can make different voice announcements depending on the type of dumping behavior. For example, in the case of dumping garbage, the warning unit can make a voice announcement saying, "Please stop dumping garbage." In the case of illegal parking, the warning unit can make a voice announcement saying, "Please stop illegal parking." For example, the warning unit can make an appropriate voice announcement depending on the type of dumping behavior. The warning unit can also use AI to optimize the content of the voice announcement based on the type of dumping behavior. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal voice announcement depending on the type of dumping behavior," and the AI ​​can generate the optimal voice announcement. This makes it possible to make an appropriate voice announcement depending on the type of dumping behavior.

[0071] The warning unit can flash surrounding lights to provide a visual warning when issuing a warning. For example, the warning unit can flash surrounding lights to provide a visual warning saying, "Please refrain from illegal dumping." The warning unit can flash surrounding lights to provide a visual warning saying, "This area is under surveillance." For example, the warning unit can flash surrounding lights to provide a visual warning saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the flashing pattern of surrounding lights. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal method for flashing surrounding lights to provide a visual warning," and the AI ​​can generate the optimal method. This improves the warning effect by providing a visual warning.

[0072] The warning unit can display a warning message on the display when issuing a warning. For example, the warning unit can display a warning message on the display saying, "Please refrain from illegal dumping." The warning unit can display a warning message on the display saying, "This area is being monitored." For example, the warning unit can display a warning message on the display saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the content and display method of the warning message. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal way to display the warning message on the display," and the AI ​​will generate the optimal method. This improves the warning effect by visually displaying the warning message.

[0073] The warning unit can provide warning sounds and announcements in multiple languages. For example, the warning unit can provide warning sounds and announcements in English and Japanese. The warning unit can provide warning sounds and announcements in Chinese and Korean. For example, the warning unit can provide warning sounds and announcements in multiple languages ​​to accommodate foreigners. The warning unit can also use AI to translate the warning sounds and announcements into multiple languages. For example, the warning unit can input a prompt to the AI ​​saying, "Please generate the optimal way to provide the warning sounds and announcements in multiple languages," and the AI ​​will generate the optimal way. This allows warnings to be provided in multiple languages ​​to accommodate foreigners.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The recognition unit identifies the vehicle's license plate. For example, the recognition unit can take high-resolution images and accurately read license plates, even at night. The recognition unit can also use AI to utilize image recognition technology to improve the accuracy of license plate recognition. Step 2: The matching unit compares the license plate information identified by the identification unit with the vehicle registration database. For example, the matching unit can compare the information with the database in real time to quickly identify the vehicle owner. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. Step 3: The generator generates a warning message for the vehicle owner identified by the collation unit. For example, the generator uses a generation AI to select effective wording based on past data and generate the warning message. The generation AI can use a text generation AI (e.g., LLM) to optimize the wording of the warning message. Step 4: The sending unit sends the warning message generated by the generation unit. For example, the sending unit sends the warning message to the identified vehicle owner by means of email, mail, or other means. The sending unit can also use AI to optimize the means and timing of delivery. Step 5: If the recognition unit detects illegal dumping, the warning unit sounds a siren and makes a voice announcement. For example, the warning unit sounds a siren and makes a voice announcement such as, "Please stop illegal dumping." The warning unit can also use AI to optimize the content and timing of the voice announcement.

[0076] (Example 2) An embodiment of the illegal dumping prevention system of the present invention prevents illegal dumping in areas where dumping is common. This system installs AI cameras in areas where dumping is common, identifies vehicle license plates, and compares the information with a vehicle registration database. If the vehicle owner is identified as a result of the comparison, the generation AI selects effective wording and sends a warning message. It can also activate a siren or play a voice announcement urging the driver not to dump. For example, the AI ​​camera can capture high-resolution images even at night and accurately read license plates. The license plate information identified by the AI ​​camera is then compared with a vehicle registration database in real time to quickly identify the vehicle owner. Once the vehicle owner is identified as a result of the comparison, the generation AI selects effective wording based on past data and generates a warning message. For example, this message may include, "Illegal dumping is prohibited by law. Please dispose of the waste appropriately to prevent recurrence." Furthermore, if the AI ​​camera detects illegal dumping, a siren sounds and a voice announcement is played, such as, "Please refrain from illegal dumping." This is expected to have a deterrent effect on illegal dumping. As a result, the illegal dumping prevention system can effectively prevent illegal dumping in areas with a high level of dumping. Combining AI cameras with generative AI enables quick and effective response, contributing to the protection of the local environment.

[0077] An illegal dumping prevention system according to an embodiment includes an identification unit, a matching unit, a generation unit, a sending unit, and a warning unit. The identification unit identifies vehicle license plates. For example, the identification unit can capture high-resolution images even at night and accurately read license plates. The identification unit can also use AI to utilize image recognition technology to improve the accuracy of license plate identification. The matching unit compares the license plate information identified by the identification unit with a vehicle registration database. For example, the matching unit compares the information with the database in real time to quickly identify the vehicle owner. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. The generation unit generates a warning message for the vehicle owner identified by the matching unit. For example, the generation unit uses a generation AI to select effective wording based on past data and generate the warning message. The generation AI can optimize the wording of the warning message using a text generation AI (e.g., LLM). The sending unit sends the warning message generated by the generation unit. For example, the sending unit sends a warning message to the identified vehicle owner by means of email, mail, or the like. The sending unit can also optimize the sending means and timing using AI. The warning unit sounds a siren or makes a voice announcement when the identification unit detects an act of illegal dumping. For example, the warning unit sounds a siren and makes a voice announcement such as, "Please stop illegal dumping." The warning unit can also optimize the content and timing of the voice announcement using AI. As a result, the illegal dumping prevention system according to the embodiment can effectively prevent illegal dumping.

[0078] The recognition unit can capture high-resolution images even at night. The recognition unit can capture high-resolution images, such as 1080p or 4K. The recognition unit can use an infrared camera or a highly sensitive sensor to capture high-resolution images even at night. For example, the recognition unit uses an infrared camera to capture clear images even at night. The recognition unit can also capture high-resolution images even in low-light environments using a highly sensitive sensor. Furthermore, the recognition unit can use image processing technology to reduce noise in images captured at night and improve recognition accuracy. This allows license plates to be accurately identified even at night.

[0079] The generation unit can select effective wording based on past data. For example, the generation unit analyzes data on past warning messages and selects effective wording. The generation unit can use the generation AI to extract the most effective wording from past data and generate a warning message. For example, the generation unit references a database of past warning messages and selects wording that is effective in preventing recurrence. The generation unit can also use the generation AI to automatically optimize the wording of the warning message. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate an effective warning message to prevent recurrence," and the generation AI generates the optimal wording. This makes it possible to generate an effective warning message.

[0080] The warning unit can sound a siren and make a voice announcement to prevent dumping. For example, when the warning unit detects illegal dumping, it sounds a siren. The warning unit can adjust the volume and tone of the siren to provide an effective warning. The warning unit also makes a voice announcement to convey a message to prevent dumping. For example, the warning unit may make a voice announcement such as, "Please refrain from illegal dumping." The warning unit can also use AI to optimize the content and timing of the voice announcement. For example, the warning unit may input a prompt to the AI, such as, "Generate an effective voice announcement to prevent illegal dumping," and the AI ​​will generate the optimal voice announcement. This is expected to have a deterrent effect on illegal dumping.

[0081] The matching unit can perform real-time matching with a vehicle registration database. For example, the matching unit matches license plate information identified by the identification unit with the vehicle registration database in real time. The matching unit can use high-speed data processing technology to perform real-time matching with the database. For example, the matching unit can use high-speed database query technology to quickly match license plate information. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. For example, the matching unit can input a prompt to the AI ​​saying, "Please generate the optimal algorithm for matching with the vehicle registration database in real time," and the AI ​​will generate the optimal matching algorithm. This allows the vehicle owner to be identified quickly.

[0082] The sending unit can send a warning message to the identified vehicle owner. For example, the sending unit sends the warning message generated by the generation unit to the identified vehicle owner. The sending unit can send the warning message using means such as email or mail. For example, the sending unit can quickly send the warning message using email. The sending unit can also send the warning message as a formal document using mail. Furthermore, the sending unit can use AI to optimize the sending means and timing. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending means and timing for the identified vehicle owner," and the AI ​​selects the optimal sending means and timing. This allows the warning message to be sent effectively to the vehicle owner.

[0083] The identification unit can estimate the user's emotions and adjust the identification accuracy of the license plate based on the estimated user's emotions. The identification unit, for example, uses emotion recognition technology to estimate the user's emotions. The identification unit can analyze data such as the user's facial expressions and voice to estimate the emotions. For example, the identification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion recognition algorithm. The identification unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the identification unit can adjust the identification accuracy of the license plate based on the estimated user's emotions. For example, if the user is stressed, the identification accuracy can be increased to reduce false recognition. Alternatively, if the user is relaxed, the identification accuracy can be kept normal and processing speed can be prioritized. This allows the identification accuracy to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The identification unit can link multiple cameras to identify license plates from different angles. For example, the identification unit installs multiple cameras and simultaneously photographs license plates from different angles. The identification unit can integrate images from the multiple cameras to improve the accuracy of license plate identification. For example, the identification unit analyzes images from the multiple cameras and compares the license plate identification results to improve accuracy. The identification unit can also use AI to optimize the linkage of the multiple cameras to improve identification accuracy. For example, the identification unit inputs a prompt to the AI, such as "Generate the optimal method for linking multiple cameras to improve license plate identification accuracy," and the AI ​​generates the optimal method. This improves identification accuracy.

[0085] The identification unit can automatically correct dirt and damage on license plates for identification. For example, the identification unit corrects dirt on license plates using image processing technology to improve identification accuracy. The identification unit can also improve identification accuracy by using an algorithm to complement dirt and damaged areas. For example, the identification unit uses image processing technology to remove dirt from license plates and make the characters clearer. The identification unit can also use AI to estimate and complement missing areas to complement damaged areas. For example, the identification unit inputs a prompt to the AI, saying, "Please generate the optimal method to correct dirt and damage on license plates to improve identification accuracy," and the AI ​​generates the optimal method. This allows license plates to be accurately identified even if they are dirty or damaged.

[0086] The identification unit can estimate the user's emotions and determine the priority of license plates to be identified based on the estimated user emotions. The identification unit, for example, uses emotion recognition technology to estimate the user's emotions. The identification unit can analyze data such as the user's facial expressions and voice to estimate emotions. For example, the identification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion recognition algorithm. The identification unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the identification unit determines the priority of license plates to be identified based on the estimated user emotions. For example, if the user is feeling stressed, license plates with high importance can be prioritized for identification. On the other hand, if the user is relaxed, license plates can be identified with a normal priority. This allows the priority to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. For example, the identification unit simultaneously identifies the color of a vehicle when identifying a license plate. The identification unit can identify the color of a vehicle using color recognition technology. For example, the identification unit identifies the color of a vehicle using image analysis technology. The identification unit can also simultaneously identify the shape of a vehicle when identifying a license plate. The identification unit can identify the shape of a vehicle using a shape recognition algorithm. For example, the identification unit identifies the shape of a vehicle using image analysis technology. Furthermore, the identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. This makes it possible to simultaneously identify the color and shape of a vehicle.

[0088] The identification unit can improve the identification accuracy based on the speed and movement of the vehicle when identifying the license plate. For example, the identification unit detects the speed of the vehicle and adjusts the identification accuracy. The identification unit can detect the speed of the vehicle using speed measurement technology. For example, the identification unit measures the speed of the vehicle using radar or LIDAR. The identification unit can also analyze the movement of the vehicle and improve the identification accuracy. The identification unit can analyze the movement of the vehicle using a movement analysis algorithm. For example, the identification unit identifies the movement of the vehicle using image analysis technology. Furthermore, the identification unit can improve the identification accuracy by simultaneously considering the speed and movement of the vehicle. This makes it possible to improve the identification accuracy by considering the speed and movement of the vehicle.

[0089] When identifying a license plate, the identification unit can adjust the identification accuracy based on surrounding environmental information. For example, the identification unit adjusts the identification accuracy taking weather information into consideration. The identification unit can acquire weather information and adjust the identification accuracy. For example, the identification unit acquires weather information using a weather sensor. The identification unit can also adjust the identification accuracy taking lighting conditions into consideration. The identification unit can detect lighting conditions and adjust the identification accuracy. For example, the identification unit detects lighting conditions using an illuminance sensor. Furthermore, the identification unit can adjust the identification accuracy taking weather information and lighting conditions into consideration simultaneously. This makes it possible to adjust the identification accuracy taking surrounding environmental information into consideration.

[0090] The matching unit can estimate the user's emotions and adjust the matching priority based on the estimated user emotions. The matching unit, for example, uses emotion recognition technology to estimate the user's emotions. The matching unit can analyze data such as the user's facial expressions and voice to estimate emotions. For example, the matching unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion recognition algorithm. The matching unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the matching unit adjusts the matching priority based on the estimated user emotions. For example, if the user is stressed, it can prioritize matching with a higher importance. On the other hand, if the user is relaxed, it can perform matching with a normal priority. This allows the matching priority to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The matching unit can improve matching accuracy by referring to past matching history during matching. For example, the matching unit improves matching accuracy by referring to past matching history. The matching unit can analyze past matching history and improve matching accuracy. For example, the matching unit improves matching accuracy by referring to a past matching history database. The matching unit can also use AI to optimize the matching algorithm based on past matching history. For example, the matching unit inputs a prompt to the AI ​​saying, "Please generate an optimal method for improving matching accuracy by referring to past matching history," and the AI ​​generates an optimal method. In this way, matching accuracy can be improved by referring to past matching history.

[0092] The matching unit can integrate multiple databases during matching to improve matching accuracy. For example, the matching unit integrates multiple vehicle registration databases to improve matching accuracy. The matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. For example, the matching unit integrates a vehicle registration database and an insurance database to improve matching accuracy. The matching unit can also integrate a vehicle registration database and a maintenance history database to improve matching accuracy. For example, the matching unit integrates a vehicle registration database and a maintenance history database to improve matching accuracy. Furthermore, the matching unit can use AI to optimize the integration of multiple databases to improve matching accuracy. For example, the matching unit inputs a prompt to the AI ​​saying, "Generate an optimal method for integrating multiple databases to improve matching accuracy," and the AI ​​generates an optimal method. This makes it possible to integrate multiple databases and improve matching accuracy.

[0093] The generation unit can estimate the user's emotion and adjust the way the warning message is expressed based on the estimated user's emotion. The generation unit, for example, uses emotion recognition technology to estimate the user's emotion. The generation unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. The generation unit can also adjust the way the warning message is expressed based on the estimated user's emotion. For example, if the user is stressed, a calm expression can be used. On the other hand, if the user is relaxed, a normal expression can be used. This allows the way the warning message is expressed to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] When generating a warning message, the generation unit can adjust the wording by taking into account the vehicle owner's attribute information. For example, the generation unit selects appropriate wording according to the vehicle owner's age. The generation unit can select appropriate wording according to the vehicle owner's gender. For example, the generation unit selects the most effective wording based on the vehicle owner's age and gender. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the vehicle owner's attribute information. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message by taking into account the vehicle owner's attribute information," and the generation AI generates the optimal wording. This makes it possible to adjust the wording by taking into account the vehicle owner's attribute information.

[0095] When generating a warning message, the generation unit can customize the wording according to the type and frequency of the violation. For example, the generation unit selects appropriate wording according to the type of violation. The generation unit can select appropriate wording according to the frequency of the violation. For example, the generation unit selects the most effective wording based on the type and frequency of the violation. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the type and frequency of the violation. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message taking into consideration the type and frequency of the violation," and the generation AI generates the optimal wording. This makes it possible to customize the wording according to the type and frequency of the violation.

[0096] The sending unit can estimate the user's emotion and adjust the method of sending the warning message based on the estimated user's emotion. The sending unit, for example, uses emotion recognition technology to estimate the user's emotion. The sending unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the sending unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The sending unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the sending unit can adjust the method of sending the warning message based on the estimated user's emotion. For example, if the user is feeling stressed, the warning message can be sent by email. Alternatively, if the user is relaxed, the warning message can be sent by mail. This allows the method of sending the warning message to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] When sending a warning message, the sending unit can record the sending history and adjust the timing of resending. For example, the sending unit records the sending history of the warning message and adjusts the timing of resending. The sending unit can analyze the sending history and determine the optimal timing of resending. For example, the sending unit refers to a sending history database and adjusts the timing of resending. The sending unit can also use AI to optimize the timing of resending based on the sending history. For example, the sending unit inputs a prompt to the AI ​​saying, "Please record the sending history and generate the optimal method for adjusting the timing of resending," and the AI ​​generates the optimal method. This makes it possible to record the sending history and adjust the timing of resending.

[0098] When sending a warning message, the sending unit can adjust the sending method taking into account the recipient's reception environment. For example, the sending unit selects the optimal sending method depending on the recipient's device. The sending unit can adjust the sending method taking into account the recipient's network conditions. For example, the sending unit detects the recipient's device type and network conditions and selects the optimal sending method. The sending unit can also use AI to optimize the sending method based on the recipient's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending method taking into account the recipient's reception environment," and the AI ​​selects the optimal sending method. This makes it possible to select the optimal sending method depending on the recipient's reception environment.

[0099] The warning unit can estimate the user's emotion and adjust the content of the siren or voice announcement based on the estimated user's emotion. The warning unit, for example, uses emotion recognition technology to estimate the user's emotion. The warning unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the warning unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The warning unit can also record the user's voice and estimate the emotion using voice analysis technology. The warning unit can also adjust the content of the siren or voice announcement based on the estimated user's emotion. For example, if the user is stressed, a gentle voice announcement can be made. Alternatively, if the user is relaxed, a normal voice announcement can be made. This allows the content of the siren or voice announcement to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The warning unit may adjust the volume of the siren taking into account the surrounding environmental sound when issuing a warning. For example, if the surrounding environmental sound is loud, the warning unit may increase the volume of the siren. If the surrounding environmental sound is quiet, the warning unit may decrease the volume of the siren. For example, the warning unit may detect the surrounding environmental sound using an environmental sound sensor and adjust the volume of the siren. The warning unit may also use AI to optimize the volume of the siren based on the surrounding environmental sound. For example, the warning unit may input a prompt to the AI ​​saying, "Generate an optimal method for adjusting the siren volume taking into account the surrounding environmental sound," and the AI ​​may generate the optimal method. This allows the volume of the siren to be adjusted according to the surrounding environmental sound.

[0101] When issuing a warning, the warning unit can make different voice announcements depending on the type of dumping behavior. For example, in the case of dumping garbage, the warning unit can make a voice announcement saying, "Please stop dumping garbage." In the case of illegal parking, the warning unit can make a voice announcement saying, "Please stop illegal parking." For example, the warning unit makes an appropriate voice announcement depending on the type of dumping behavior. The warning unit can also use AI to optimize the content of the voice announcement based on the type of dumping behavior. For example, the warning unit inputs a prompt to the AI ​​saying, "Generate the optimal voice announcement depending on the type of dumping behavior," and the AI ​​generates the optimal voice announcement. This makes it possible to make an appropriate voice announcement depending on the type of dumping behavior.

[0102] The warning unit may flash surrounding lights to provide a visual warning when issuing a warning. For example, the warning unit may flash surrounding lights to provide a visual warning such as "Please refrain from illegal dumping." The warning unit may flash surrounding lights to provide a visual warning such as "This area is under surveillance." For example, the warning unit may flash surrounding lights to provide a visual warning such as "Illegal dumping is prohibited by law." The warning unit may also use AI to optimize the flashing pattern of surrounding lights. For example, the warning unit may input a prompt to the AI ​​such as "Generate the optimal method for flashing surrounding lights to provide a visual warning," and the AI ​​may generate the optimal method. This improves the warning effect by providing a visual warning.

[0103] The warning unit can display a warning message on the display when a warning is issued. For example, the warning unit can display a warning message on the display saying, "Please refrain from illegal dumping." The warning unit can display a warning message on the display saying, "This area is being monitored." For example, the warning unit can display a warning message on the display saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the content and display method of the warning message. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal way to display the warning message on the display," and the AI ​​will generate the optimal method. This improves the warning effect by visually displaying the warning message.

[0104] The warning unit can provide warning sounds and announcements in multiple languages. For example, the warning unit can provide warning sounds and announcements in English and Japanese. The warning unit can provide warning sounds and announcements in Chinese and Korean. For example, the warning unit can provide warning sounds and announcements in multiple languages ​​to accommodate foreigners. The warning unit can also use AI to translate the warning sounds and announcements into multiple languages. For example, the warning unit can input a prompt to the AI ​​saying, "Please generate the optimal way to provide the warning sounds and announcements in multiple languages," and the AI ​​can generate the optimal way. This allows warnings to be provided in multiple languages ​​to accommodate foreigners. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned identification unit, matching unit, generation unit, sending unit, and warning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identification unit identifies the vehicle's license plate using the camera 42 of the smart device 14, and the control unit 46A improves the identification accuracy. The matching unit matches the license plate information with a vehicle registration database using the identification processing unit 290 of the data processing device 12. The generation unit generates a warning message using the identification processing unit 290 of the data processing device 12, and the sending unit sends the warning message using the identification processing unit 290 of the data processing device 12. The warning unit makes an audio announcement using the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned identification unit, matching unit, generation unit, sending unit, and warning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identification unit identifies the vehicle's license plate using the camera 42 of the smart glasses 214, and the control unit 46A improves the identification accuracy. The matching unit matches the license plate information with a vehicle registration database using the identification processing unit 290 of the data processing device 12. The generation unit generates a warning message using the identification processing unit 290 of the data processing device 12, and the sending unit sends the warning message using the identification processing unit 290 of the data processing device 12. The warning unit makes an audio announcement using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned identification unit, matching unit, generation unit, sending unit, and warning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the identification unit identifies the license plate of a vehicle using the camera 42 of the headset type terminal 314, and the control unit 46A improves the identification accuracy. The matching unit matches the license plate information with a vehicle registration database using the identification processing unit 290 of the data processing device 12. The generation unit generates a warning message using the identification processing unit 290 of the data processing device 12, and the sending unit sends the warning message using the identification processing unit 290 of the data processing device 12. The warning unit makes an audio announcement using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned identification unit, matching unit, generation unit, sending unit, and warning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identification unit identifies the license plate of a vehicle using the camera 42 of the robot 414, and the control unit 46A improves the identification accuracy. The matching unit matches the license plate information with a vehicle registration database using the identification processing unit 290 of the data processing device 12. The generation unit generates a warning message using the identification processing unit 290 of the data processing device 12, and the sending unit sends the warning message using the identification processing unit 290 of the data processing device 12. The warning unit makes an audio announcement using the speaker 240 of the robot 414.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The identification unit can not only identify the vehicle's license plate, but also analyze the vehicle's driving pattern. For example, the identification unit can analyze the vehicle's driving speed and direction in real time to detect abnormal driving patterns. The identification unit can also determine whether a particular vehicle frequently enters and exits an illegal dumping area based on past driving data. Furthermore, the identification unit provides the analysis results of the driving pattern to the matching unit, which can use this as reference information when identifying the vehicle's owner. In this way, the identification unit can improve the effectiveness of preventing illegal dumping by analyzing the vehicle's driving pattern.

[0107] The recognition unit not only captures high-resolution images even at night, but can also automatically switch shooting modes according to weather conditions. For example, the recognition unit automatically deploys a waterproof cover in rainy weather to prevent the lens from fogging up. When it is snowing, the recognition unit activates a lens heater to melt the snow and ensure clear visibility. Furthermore, in strong winds, the recognition unit activates a stabilizer to suppress camera vibration, allowing for blur-free images to be captured. This allows the recognition unit to maintain high recognition accuracy under a variety of weather conditions.

[0108] The generator not only selects effective wording based on past data, but can also customize the tone and style of the warning message. For example, the generator may generate a warning message in a casual tone for younger users and a more polite tone for older users. The generator can also adjust the style of the warning message according to the local culture and language. Furthermore, the generator can generate a warning message in a stern tone for certain violations and a gentle tone for minor violations. This allows the generator to provide the most effective warning message to the recipient.

[0109] The warning unit can sound a siren and make audio announcements to prevent dumping, as well as flash a warning light. For example, when the warning unit detects illegal dumping, it can flash a red warning light. The warning unit can also automatically adjust the brightness of the warning light at night to ensure visibility according to the surrounding environment. Furthermore, the warning unit can visually convey different types of warnings by changing the flashing pattern of the warning light. In this way, the warning unit can enhance the deterrent effect of illegal dumping by adding a visual warning means.

[0110] The matching unit not only checks against the vehicle registration database in real time, but can also integrate multiple databases to improve matching accuracy. For example, the matching unit may integrate the vehicle registration database with the insurance database to more accurately identify vehicle owner information. The matching unit may also check against a criminal history database to identify vehicles that have illegally dumped in the past. Furthermore, the matching unit may link with a local surveillance camera network and use image data captured by other cameras for matching. This allows the matching unit to utilize multiple data sources to improve matching accuracy.

[0111] The sending unit not only sends a warning message to the identified vehicle owner, but also adjusts the delivery method taking into account the reception environment of the destination. For example, the sending unit selects the optimal delivery method depending on the destination's device. The sending unit can adjust the delivery method taking into account the destination's network conditions. For example, the sending unit detects the destination's device type and network conditions and selects the optimal delivery method. The sending unit can also use AI to optimize the delivery method based on the destination's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal delivery method taking into account the destination's reception environment," and the AI ​​selects the optimal delivery method. This makes it possible to select the optimal delivery method depending on the destination's reception environment.

[0112] The identification unit can estimate the user's emotions and adjust the identification accuracy of the license plate based on the estimated user's emotions. For example, the identification unit uses emotion recognition technology to estimate the user's emotions. The identification unit can analyze data such as the user's facial expressions and voice to estimate the emotions. For example, the identification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion recognition algorithm. The identification unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the identification unit can adjust the identification accuracy of the license plate based on the estimated user's emotions. For example, if the user is stressed, the identification accuracy can be increased to reduce false recognition. Alternatively, if the user is relaxed, the identification accuracy can be kept normal and processing speed can be prioritized. This allows the identification accuracy to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The identification unit can link multiple cameras to identify license plates from different angles. For example, the identification unit installs multiple cameras and simultaneously photographs license plates from different angles. The identification unit can integrate images from the multiple cameras to improve the accuracy of license plate identification. For example, the identification unit analyzes images from the multiple cameras and compares the license plate identification results to improve accuracy. The identification unit can also use AI to optimize the linkage of multiple cameras to improve identification accuracy. For example, the identification unit inputs a prompt to the AI, such as "Generate the optimal method for linking multiple cameras to improve license plate identification accuracy," and the AI ​​generates the optimal method. This improves identification accuracy.

[0114] The identification unit can automatically correct dirt and damage on license plates for identification. For example, the identification unit can correct dirt on license plates using image processing technology to improve identification accuracy. The identification unit can also improve identification accuracy by using an algorithm to complement dirt and damaged areas. For example, the identification unit can use image processing technology to remove dirt from license plates and make the characters clearer. The identification unit can also use AI to estimate and complement missing areas to complement damaged areas. For example, the identification unit can input a prompt to the AI, saying, "Please generate the optimal method to correct dirt and damage on the license plate to improve identification accuracy," and the AI ​​will generate the optimal method. This allows license plates to be accurately identified even if they are dirty or damaged.

[0115] The identification unit can estimate the user's emotions and determine the priority of license plates to be identified based on the estimated user emotions. For example, the identification unit uses emotion recognition technology to estimate the user's emotions. The identification unit can analyze data such as the user's facial expressions and voice to estimate emotions. For example, the identification unit captures the user's facial expressions with a camera and estimates the emotion using an emotion recognition algorithm. The identification unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the identification unit determines the priority of license plates to be identified based on the estimated user emotions. For example, if the user is feeling stressed, license plates with high importance can be prioritized for identification. On the other hand, if the user is relaxed, license plates can be identified with a normal priority. This allows the priority to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. For example, the identification unit simultaneously identifies the color of a vehicle when identifying a license plate. The identification unit can identify the color of a vehicle using color recognition technology. For example, the identification unit can identify the color of a vehicle using image analysis technology. The identification unit can also simultaneously identify the shape of a vehicle when identifying a license plate. The identification unit can identify the shape of a vehicle using a shape recognition algorithm. For example, the identification unit can identify the shape of a vehicle using image analysis technology. Furthermore, the identification unit can simultaneously identify the color and shape of a vehicle when identifying a license plate. This makes it possible to simultaneously identify the color and shape of a vehicle.

[0117] The identification unit can improve the identification accuracy based on the speed and movement of the vehicle when identifying the license plate. For example, the identification unit detects the speed of the vehicle and adjusts the identification accuracy. The identification unit can detect the speed of the vehicle using speed measurement technology. For example, the identification unit can measure the speed of the vehicle using radar or LIDAR. The identification unit can also analyze the movement of the vehicle and improve the identification accuracy. The identification unit can analyze the movement of the vehicle using a movement analysis algorithm. For example, the identification unit can identify the movement of the vehicle using image analysis technology. Furthermore, the identification unit can improve the identification accuracy by simultaneously considering the speed and movement of the vehicle. This makes it possible to improve the identification accuracy by taking the speed and movement of the vehicle into consideration.

[0118] When identifying a license plate, the identification unit can adjust the identification accuracy based on surrounding environmental information. For example, the identification unit adjusts the identification accuracy taking weather information into consideration. The identification unit can acquire weather information and adjust the identification accuracy. For example, the identification unit acquires weather information using a weather sensor. The identification unit can also adjust the identification accuracy taking lighting conditions into consideration. The identification unit can detect lighting conditions and adjust the identification accuracy. For example, the identification unit detects lighting conditions using an illuminance sensor. Furthermore, the identification unit can adjust the identification accuracy taking weather information and lighting conditions into consideration simultaneously. This makes it possible to adjust the identification accuracy taking surrounding environmental information into consideration.

[0119] The matching unit can estimate the user's emotions and adjust the matching priority based on the estimated user emotions. For example, the matching unit uses emotion recognition technology to estimate the user's emotions. The matching unit can analyze data such as the user's facial expressions and voice to estimate emotions. For example, the matching unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion recognition algorithm. The matching unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the matching unit adjusts the matching priority based on the estimated user emotions. For example, if the user is stressed, it can prioritize matching with a high level of importance. On the other hand, if the user is relaxed, it can perform matching with a normal priority. This allows the matching priority to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] During matching, the matching unit can improve matching accuracy by referring to past matching history. For example, the matching unit improves matching accuracy by referring to past matching history. The matching unit can analyze past matching history and improve matching accuracy. For example, the matching unit improves matching accuracy by referring to a past matching history database. The matching unit can also use AI to optimize the matching algorithm based on past matching history. For example, the matching unit inputs a prompt to the AI ​​saying, "Please generate an optimal method for improving matching accuracy by referring to past matching history," and the AI ​​generates an optimal method. In this way, matching accuracy can be improved by referring to past matching history.

[0121] The matching unit can integrate multiple databases during matching to improve matching accuracy. For example, the matching unit can integrate multiple vehicle registration databases to improve matching accuracy. The matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. For example, the matching unit can integrate a vehicle registration database and an insurance database to improve matching accuracy. The matching unit can also integrate a vehicle registration database and a maintenance history database to improve matching accuracy. For example, the matching unit can integrate a vehicle registration database and a maintenance history database to improve matching accuracy. Furthermore, the matching unit can use AI to optimize the integration of multiple databases to improve matching accuracy. For example, the matching unit inputs a prompt to the AI ​​saying, "Generate an optimal method for integrating multiple databases to improve matching accuracy," and the AI ​​generates an optimal method. This makes it possible to integrate multiple databases and improve matching accuracy.

[0122] The generation unit can estimate the user's emotion and adjust the way the warning message is expressed based on the estimated user's emotion. For example, the generation unit uses emotion recognition technology to estimate the user's emotion. The generation unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit adjusts the way the warning message is expressed based on the estimated user's emotion. For example, if the user is feeling stressed, a calm expression can be used. On the other hand, if the user is relaxed, a normal expression can be used. This allows the way the warning message is expressed to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] When generating a warning message, the generation unit can adjust the wording by taking into account the vehicle owner's attribute information. For example, the generation unit selects appropriate wording according to the vehicle owner's age. The generation unit can select appropriate wording according to the vehicle owner's gender. For example, the generation unit selects the most effective wording based on the vehicle owner's age and gender. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the vehicle owner's attribute information. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message by taking into account the vehicle owner's attribute information," and the generation AI generates the optimal wording. This makes it possible to adjust the wording by taking into account the vehicle owner's attribute information.

[0124] When generating a warning message, the generation unit can customize the wording depending on the type and frequency of the violation. For example, the generation unit selects appropriate wording depending on the type of violation. The generation unit can select appropriate wording depending on the frequency of the violation. For example, the generation unit selects the most effective wording based on the type and frequency of the violation. The generation unit can also use a generation AI to automatically optimize the wording of the warning message based on the type and frequency of the violation. For example, the generation unit inputs a prompt to the generation AI saying, "Please generate the optimal warning message taking into consideration the type and frequency of the violation," and the generation AI generates the optimal wording. This makes it possible to customize the wording depending on the type and frequency of the violation.

[0125] The sending unit can estimate the user's emotion and adjust the method of sending the warning message based on the estimated user's emotion. For example, the sending unit uses emotion recognition technology to estimate the user's emotion. The sending unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the sending unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The sending unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the sending unit can adjust the method of sending the warning message based on the estimated user's emotion. For example, if the user is feeling stressed, the warning message can be sent by email. Alternatively, if the user is relaxed, the warning message can be sent by mail. This allows the method of sending the warning message to be adjusted depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0126] When sending a warning message, the sending unit can record the sending history and adjust the timing of resending. For example, the sending unit records the sending history of warning messages and adjusts the timing of resending. The sending unit can analyze the sending history and determine the optimal timing of resending. For example, the sending unit refers to a sending history database and adjusts the timing of resending. The sending unit can also use AI to optimize the timing of resending based on the sending history. For example, the sending unit inputs a prompt to the AI ​​saying, "Please record the sending history and generate the optimal method for adjusting the timing of resending," and the AI ​​generates the optimal method. This makes it possible to record the sending history and adjust the timing of resending.

[0127] When sending a warning message, the sending unit can adjust the sending method taking into account the recipient's reception environment. For example, the sending unit selects the optimal sending method depending on the recipient's device. The sending unit can adjust the sending method taking into account the recipient's network conditions. For example, the sending unit detects the recipient's device type and network conditions and selects the optimal sending method. The sending unit can also use AI to optimize the sending method based on the recipient's reception environment. For example, the sending unit inputs a prompt to the AI ​​saying, "Please select the optimal sending method taking into account the recipient's reception environment," and the AI ​​selects the optimal sending method. This makes it possible to select the optimal sending method depending on the recipient's reception environment.

[0128] The warning unit can estimate the user's emotion and adjust the content of the siren or voice announcement based on the estimated user's emotion. For example, the warning unit uses emotion recognition technology to estimate the user's emotion. The warning unit can analyze data such as the user's facial expression and voice to estimate the emotion. For example, the warning unit can capture the user's facial expression with a camera and estimate the emotion using an emotion recognition algorithm. The warning unit can also record the user's voice and estimate the emotion using voice analysis technology. The warning unit can also adjust the content of the siren or voice announcement based on the estimated user's emotion. For example, if the user is feeling stressed, a gentle voice announcement can be made. Alternatively, if the user is relaxed, a normal voice announcement can be made. This allows the content of the siren or voice announcement to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The warning unit may adjust the volume of the siren taking into account the surrounding environmental sounds when issuing a warning. For example, if the surrounding environmental sounds are loud, the warning unit may increase the volume of the siren. If the surrounding environmental sounds are quiet, the warning unit may decrease the volume of the siren. For example, the warning unit may detect the surrounding environmental sounds using an environmental sound sensor and adjust the volume of the siren. The warning unit may also use AI to optimize the volume of the siren based on the surrounding environmental sounds. For example, the warning unit may input a prompt to the AI ​​saying, "Generate an optimal method for adjusting the siren volume taking into account the surrounding environmental sounds," and the AI ​​may generate the optimal method. This allows the volume of the siren to be adjusted according to the surrounding environmental sounds.

[0130] When issuing a warning, the warning unit can make different voice announcements depending on the type of dumping behavior. For example, in the case of dumping garbage, the warning unit can make a voice announcement saying, "Please stop dumping garbage." In the case of illegal parking, the warning unit can make a voice announcement saying, "Please stop illegal parking." For example, the warning unit can make an appropriate voice announcement depending on the type of dumping behavior. The warning unit can also use AI to optimize the content of the voice announcement based on the type of dumping behavior. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal voice announcement depending on the type of dumping behavior," and the AI ​​can generate the optimal voice announcement. This makes it possible to make an appropriate voice announcement depending on the type of dumping behavior.

[0131] The warning unit can flash surrounding lights to provide a visual warning when issuing a warning. For example, the warning unit can flash surrounding lights to provide a visual warning saying, "Please refrain from illegal dumping." The warning unit can flash surrounding lights to provide a visual warning saying, "This area is under surveillance." For example, the warning unit can flash surrounding lights to provide a visual warning saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the flashing pattern of surrounding lights. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal method for flashing surrounding lights to provide a visual warning," and the AI ​​can generate the optimal method. This improves the warning effect by providing a visual warning.

[0132] The warning unit can display a warning message on the display when issuing a warning. For example, the warning unit can display a warning message on the display saying, "Please refrain from illegal dumping." The warning unit can display a warning message on the display saying, "This area is being monitored." For example, the warning unit can display a warning message on the display saying, "Illegal dumping is prohibited by law." The warning unit can also use AI to optimize the content and display method of the warning message. For example, the warning unit can input a prompt to the AI ​​saying, "Generate the optimal way to display the warning message on the display," and the AI ​​will generate the optimal method. This improves the warning effect by visually displaying the warning message.

[0133] The warning unit can provide warning sounds and announcements in multiple languages. For example, the warning unit can provide warning sounds and announcements in English and Japanese. The warning unit can provide warning sounds and announcements in Chinese and Korean. For example, the warning unit can provide warning sounds and announcements in multiple languages ​​to accommodate foreigners. The warning unit can also use AI to translate the warning sounds and announcements into multiple languages. For example, the warning unit can input a prompt to the AI ​​saying, "Please generate the optimal way to provide the warning sounds and announcements in multiple languages," and the AI ​​will generate the optimal way. This allows warnings to be provided in multiple languages ​​to accommodate foreigners.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The recognition unit identifies the vehicle's license plate. For example, the recognition unit can take high-resolution images and accurately read license plates, even at night. The recognition unit can also use AI to utilize image recognition technology to improve the accuracy of license plate recognition. Step 2: The matching unit compares the license plate information identified by the identification unit with the vehicle registration database. For example, the matching unit can compare the information with the database in real time to quickly identify the vehicle owner. The matching unit can also use AI to optimize the matching algorithm and improve matching accuracy. Step 3: The generator generates a warning message for the vehicle owner identified by the collation unit. For example, the generator uses a generation AI to select effective wording based on past data and generate the warning message. The generation AI can use a text generation AI (e.g., LLM) to optimize the wording of the warning message. Step 4: The sending unit sends the warning message generated by the generation unit. For example, the sending unit sends the warning message to the identified vehicle owner by means of email, mail, or other means. The sending unit can also use AI to optimize the means and timing of delivery. Step 5: If the recognition unit detects illegal dumping, the warning unit sounds a siren and makes a voice announcement. For example, the warning unit sounds a siren and makes a voice announcement such as, "Please stop illegal dumping." The warning unit can also use AI to optimize the content and timing of the voice announcement.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0176] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0179] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0180] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0181] 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.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0185] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0192] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0197] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0198] 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.

[0199] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0200] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0201] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0202] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0204] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0206] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0207] [Explanation of symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an identification unit for identifying a license plate; a verification unit that verifies the information on the license plate identified by the identification unit against a vehicle registration database; a generation unit that generates a warning message for the vehicle owner identified by the matching unit; a sending unit that sends the warning message generated by the generating unit; and a warning unit that issues a siren or a voice announcement when the identification unit detects an act of illegal dumping. A system characterized by:

2. The identification unit Capture images with specific resolution values ​​even at night 2. The system of claim 1.

3. The generation unit Select effective wording based on past data 2. The system of claim 1.

4. The warning unit Sounding sirens and making audio announcements to discourage dumping 2. The system of claim 1.

5. The collation unit Real-time matching against vehicle registration database 2. The system of claim 1.

6. The sending unit Send a warning letter to the identified vehicle owner 2. The system of claim 1.

7. The identification unit Estimating user emotions and adjusting license plate recognition accuracy based on the estimated user emotions 2. The system of claim 1.

8. The identification unit Linking multiple cameras to identify license plates from different angles 2. The system of claim 1.

Images