System

Abstract

A system is provided.SOLUTION: This system is provided with a means for analyzing input AI from a user by using generated date, a means for registering them in a database based on the analyzed result and a means for reporting the completion of registering to the user.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] In conventional police work, procedures such as accident registration, license-related matters, and crime response are performed analogically, which makes the procedures complicated and time-consuming, resulting in low public satisfaction with the service. Furthermore, data management is primarily paper-based, which makes it difficult to efficiently search and manage data. This invention aims to digitize these analog processes, improving work efficiency and public satisfaction. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for analyzing data input from users using generative AI, a means for registering the analysis results in a database, and a means for notifying the user that registration is complete. The system also includes a means for accepting detailed accident information from users via an online form, a means for converting the accepted accident information into JSON format and sending it to a server, and a means for analyzing and registering the received accident information using a generative AI model. Furthermore, the system also includes a means for accepting license-related renewal applications online, a means for analyzing and verifying uploaded documents such as identification and photographs using a generative AI model, and a means for updating the analyzed data in the license database. These features enable police work to be more efficient and public satisfaction to be improved.

[0006] A "generative AI model" refers to artificial intelligence technology that analyzes data input by users and performs appropriate processing or complements the information.

[0007] "User" refers to an individual or corporation that uses the police's online system to input information or carry out procedures.

[0008] "Database" refers to an information management system that permanently stores analyzed data, accident information, and license information, and allows searches and updates as needed.

[0009] "Data analysis" refers to the process of processing received data with a generative AI model to fill in incomplete information or detect anomalies.

[0010] "Accident information" refers to the type of accident, date and time, location, and information about the people involved that a user enters into an online form.

[0011] "License Renewal Application" refers to the application information and uploading required documents that a User enters into the online form to extend or change a license.

[0012] "Online Form" refers to a web-based input form that allows a user to enter data such as accident information or license renewal applications.

[0013] "Server" refers to a computer system that receives data sent by users and uses generative AI models to analyze the data and update the database. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] 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.

[0019] 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.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system that uses generative AI models to digitize police work and automate procedures such as accident registration, license-related procedures, and crime response. Specific embodiments of the present invention are described below.

[0036] The system involves a series of processes for collecting input data from users, analyzing it using a generative AI model, and registering the results in a database.

[0037] Accident registration function embodiment

[0038] 1. The user enters the accident information

[0039] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[0040] 2. The device sends the entered data to the server

[0041] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0042] 3. The server analyzes the received data

[0043] The server stores the received JSON data in a temporary data store and then analyzes it using a generative AI model, filling in incomplete information and identifying suspicious data.

[0044] 4. The server saves the data to the database

[0045] Based on the analysis results, the final data is stored in the accident registration database, and a notification is sent to the relevant police station based on the location of the accident.

[0046] 5. The server notifies the user that registration is complete

[0047] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[0048] Implementation of license-related procedures

[0049] 1. The user applies for license renewal online

[0050] The user uses the device to access the license renewal application form on the police website, enters the necessary license information (such as name, address, and current license number), and uploads the necessary documents (such as identification and photographs).

[0051] 2. The device sends the entered data and documents to the server

[0052] The device sends the entered data and documents (PDF or image format) to the server, which ensures data integrity and security.

[0053] 3. The server analyzes the received data

[0054] The server analyzes the received data and documents, verifies the document content using a generative AI model, and checks for validity and suitability.

[0055] 4. The server requests that the data be corrected (if necessary)

[0056] If the analysis reveals any defects, the server will send a correction request to the user, who will then correct the data and submit it again.

[0057] 5. The server updates the license database

[0058] If the corrected data is deemed acceptable, the server updates the license database, which prepares the new license for issuance.

[0059] 6. The server issues a new license and sends the user instructions on how to mail it.

[0060] The server will issue a new driver's license and send instructions for mailing the new license to the user via email. The user will then receive the instructions and the new license will be mailed to the registered address.

[0061] Specific examples

[0062] Specific examples of accident registration

[0063] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[0064] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[0065] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[0066] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[0067] 5. Send an email notification to the user stating that the accident registration has been completed.

[0068] Specific examples of license renewal

[0069] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[0070] 2. The device converts the data into PDF and image format and sends it to the server.

[0071] 3. The server analyzes the received data using the generated AI model and verifies the documents.

[0072] 4. If there is a problem, the server sends a correction request to the user and prompts them to re-enter the information.

[0073] 5. Once the data is verified, the server updates the license database.

[0074] 6. Your new license will be issued and instructions on how to mail it will be sent to you.

[0075] The above is a specific embodiment of the present invention, which will improve the efficiency of police work and increase public satisfaction.

[0076] The processing flow will be explained below.

[0077] Processing steps for the Incident Registration function

[0078] Step 1:

[0079] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[0080] Step 2:

[0081] The user completes the input and presses the "Submit" button.

[0082] Step 3:

[0083] The terminal converts the input data into JSON format.

[0084] Step 4:

[0085] The device sends the generated JSON data to the server via the API.

[0086] Step 5:

[0087] The server parses the received JSON data and converts it into the appropriate format.

[0088] Step 6:

[0089] The server saves the transformed data to a temporary data store.

[0090] Step 7:

[0091] The server sends the data to the generative AI model and begins analysis.

[0092] Step 8:

[0093] A generative AI model analyzes incoming data and detects incomplete information or anomalous data.

[0094] Step 9:

[0095] Generative AI models generate the additional information needed to complement the data.

[0096] Step 10:

[0097] Based on the analysis results, the server stores the final data in the accident registration database.

[0098] Step 11:

[0099] The server sends a notification to the relevant police station.

[0100] Step 12:

[0101] The server sends a notification to the user that the accident registration has been completed.

[0102] License-related procedure processing steps

[0103] Step 1:

[0104] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[0105] Step 2:

[0106] The user uploads the required documents (ID, photo, etc.).

[0107] Step 3:

[0108] The user completes the input and upload and presses the "Submit" button.

[0109] Step 4:

[0110] The device converts entered data and uploaded documents into PDF and image formats.

[0111] Step 5:

[0112] The terminal sends the generated data and documents to the server.

[0113] Step 6:

[0114] The server prepares to parse the received data and documents.

[0115] Step 7:

[0116] The server sends the data and documents to the generative AI model and begins analysis.

[0117] Step 8:

[0118] A generative AI model analyzes the content of incoming documents and checks their validity and suitability.

[0119] Step 9:

[0120] Based on the analysis results, the server sends a correction request to the user as necessary.

[0121] Step 10:

[0122] The user makes corrections and submits the data and documents again.

[0123] Step 11:

[0124] The server re-analyzes and verifies the corrected data and documents.

[0125] Step 12:

[0126] The server checks the integrity of the data and then updates the license database.

[0127] Step 13:

[0128] The server processes the issuance of a new license.

[0129] Step 14:

[0130] The server will send the user instructions on how to mail in a new license.

[0131] Example 1

[0132] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] In conventional police work, procedures related to accident registration, license renewal, etc. are complicated and require a lot of manpower and time. This often leads to delays in procedures, information omissions, and inaccurate data registration. In addition, users have to physically visit the police station, which requires time and effort. It is necessary to solve these issues and improve the efficiency of police work and user convenience.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0135] In this invention, the server includes means for accepting input data from a user via an online form, means for converting the accepted data into JSON format and sending it to the server, means for saving the accepted data in a temporary data store, means for analyzing the accepted data using a generative AI model and completing incomplete information, means for registering the analysis results in a database, and means for notifying the user of completion of registration, thereby enabling the digitalization and automation of police work.

[0136] 1. "User"

[0137] "User" refers to an individual or organization that uses this system, specifically, a person who provides information related to accident registration and license renewal.

[0138] 2. "Online Form"

[0139] "Online Form" means an electronic input screen for entering and submitting data over the Internet.

[0140] 3. “JSON format”

[0141] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format that describes data in key-value pair format.

[0142] 4. "Server"

[0143] "Server" refers to a centralized computer system that performs a series of processes such as receiving, analyzing, storing, and notifying data.

[0144] 5. "Data Store"

[0145] "Data store" refers to a storage area for temporarily or permanently storing data.

[0146] 6. "Generative AI Models"

[0147] "Generative AI model" refers to an algorithm and its implementation that uses artificial intelligence technology to analyze, complement, and generate data.

[0148] 7. "Database"

[0149] "Database" refers to an electronic information management system for efficiently storing, retrieving, and managing structured information.

[0150] 8. “Notification”

[0151] "Notification" refers to the act of transmitting processing results or necessary information to users or relevant institutions via email or messaging systems.

[0152] 9. "Accident Information"

[0153] "Accident information" refers to detailed data about traffic accidents, etc., including the type of accident, date and time, location, and information about the people involved.

[0154] 10. "License Renewal Application"

[0155] "License Renewal Application" refers to the application process for completing the driver's license renewal procedure online.

[0156] 11. "Identification Card"

[0157] "Identification" refers to an official document used to verify a person's identity, such as a passport or driver's license.

[0158] 12. "Documents"

[0159] "Documents" refers to documents and images required for license-related procedures, including identification documents and photographs.

[0160] 13. “Analysis”

[0161] "Analysis" refers to the process of analyzing received data to extract necessary information or fill in any incomplete parts.

[0162] 14. "Complement"

[0163] "Completion" refers to the act of adding incomplete information or missing data to make the data complete.

[0164] 15. "Defects"

[0165] "Defect" refers to a state in which there are omissions or errors in data or documents and the required conditions are not met.

[0166] This invention is a system that supports the digitization and automation of police operations such as accident registration, license renewal, etc. This system collects input data from users, analyzes it using a generative AI model, and provides a series of processes for registering the data in a database and notifying users.

[0167] The system uses a user's device (such as a PC or smartphone), a central server, a data store, and a generative AI model. Specific embodiments are described below.

[0168] Accident registration function embodiment

[0169] 1. The user enters the accident information

[0170] The user uses the device to access the police's online form. For example, the user logs in to the police's official website through a web browser and enters detailed information about the accident (such as the type of accident, date and time, location, and information about the people involved) into the form. For example, this includes input such as "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[0171] 2. The device sends the entered data to the server

[0172] The terminal converts the data entered by the user into JSON format and sends it to the server via API, which ensures efficient and secure data transfer, specifically using the HTTPS protocol.

[0173] 3. The server analyzes the received data

[0174] The server stores the received JSON data in a temporary data store, for example, using cloud storage such as Amazon S3 or Google Cloud Storage. The server then analyzes the data using a generative AI model to fill in any incomplete information. This process fills in any data detected as unknown or missing.

[0175] 4. The server saves the data to the database

[0176] Based on the analysis results, the server formats the final data and stores it in the accident registration database, using a relational database such as MySQL or PostgreSQL, and also uses SMS, email, and webhooks to send automatic notifications to the relevant police stations based on the location of the accident.

[0177] 5. The server notifies the user that registration is complete

[0178] The server generates a message indicating the successful registration and sends a notification to the user's email address or messaging platform. An example notification might include "Accident registration completed. Registration number is 12345." This process uses the SMTP protocol and a Push notification API.

[0179] Implementation of license-related procedures

[0180] 1. The user applies for license renewal online

[0181] The user accesses the police website using a device and opens the license renewal application form. The form prompts the user to enter or upload the necessary license information (such as name, address, and current license number) and necessary documents (such as identification and photographs). For example, the user may enter information such as "Name: Taro Yamada," "Address: Chiyoda-ku, Tokyo," and "License number: 1234567890."

[0182] 2. The device sends the entered data and documents to the server

[0183] The terminal converts the entered data and documents into a digital format (PDF or image) and sends them to the server via API, which ensures data integrity and security, also using the HTTPS protocol.

[0184] 3. The server analyzes the received data

[0185] The server analyzes the received data and documents and uses a generative AI model to check for validity and compatibility, for example, ensuring the ID photo is clear and the name and license number match.

[0186] 4. The server requests that the data be corrected (if necessary)

[0187] If the analysis reveals any errors, the server notifies the user of the specific details and requests corrections. For example, requests for corrections may include "the photo is unclear" or "the name and license number do not match." The user then resubmits the corrected data.

[0188] 5. The server updates the license database

[0189] After correction, if the data is deemed to be correct, the server updates the license database. The new license information is confirmed and the next step is taken.

[0190] 6. The server issues a new license and sends the user instructions on how to mail it.

[0191] The server initiates the issuance process for the new driver's license and sends the user a notification to mail it to the registered address. For example, the notification may include something like, "Your new driver's license has been issued. It will be mailed to your registered address within a few days." This process also uses the SMTP protocol and the Push notification API.

[0192] Example prompt

[0193] Specific examples of accident registration

[0194] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[0195] Specific examples of license renewal

[0196] The user accesses the license renewal form on the police website on their PC and uploads the required information (e.g., "Name: Yamada Taro," "Address: Chiyoda-ku, Tokyo," "License number: 1234567890") and documents.

[0197] In this way, the present invention realizes improved efficiency in police work and increased public satisfaction.

[0198] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0199] Processing steps for the Incident Registration function

[0200] Step 1: User enters incident information

[0201] Input: The user accesses the police's online form using a smartphone or PC and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) For example, the user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[0202] Output: The information you enter becomes the data sent from the device.

[0203] Step 2: The device sends the entered data to the server

[0204] Input: The incident information entered by the user into the form.

[0205] Data processing and data calculation: The terminal converts the input accident information into JSON format and sends it to the server via API using the HTTPS protocol.

[0206] Specific operation: For example, the device generates JSON data such as {"accident type":"car collision", "date and time":"October 12, 2023", "location":"Shinjuku-ku, Tokyo"} and sends it to the server.

[0207] Output: The JSON data sent to the server.

[0208] Step 3: Parse the data received by the server

[0209] Input: JSON data received from the terminal.

[0210] Data processing and data calculation: The server stores the received JSON data in a temporary data store and then runs the generative AI model to analyze the data. The generative AI model completes incomplete information and detects abnormal data. For example, if the "type of accident" is missing, it completes the information as "car collision."

[0211] How it works: The server stores the data in storage such as Amazon S3 or Google Cloud Storage, then runs the generative AI model to analyze it.

[0212] Output: Imputed data as the analysis result.

[0213] Step 4: The server saves the data to the database

[0214] Input: Data parsed and imputed by the generative AI model.

[0215] Data Processing and Data Calculation: The server formats the final data and stores it in the accident registration database, and also sends notifications to the relevant police stations based on the accident location information.

[0216] What it does: The server stores the data in a relational database such as MySQL or PostgreSQL and sends notifications via SMS or email to relevant organizations.

[0217] Output: Accident information stored in the database and notifications sent.

[0218] Step 5: The server notifies the user that registration is complete

[0219] Input: Data stored in the accident registration database.

[0220] Data processing and data calculation: The server generates a registration completion notification message and sends it to the user's email address or messaging system.

[0221] Specific operation: The server sends an email using the SMTP protocol or sends a message using the Push notification API.

[0222] Output: Notification message "Accident registration completed."

[0223] License-related procedure processing steps

[0224] Step 1: User applies for license renewal online

[0225] Input: The user accesses the license renewal application form on the police website using a PC or smartphone, and enters and uploads the required information (name, address, current license number, etc.) and required documents (e.g., ID, photo, etc.).

[0226] Specific operation: For example, enter "Name: Yamada Taro", "Address: Chiyoda-ku, Tokyo", "License number: 1234567890", and upload an image file of the required identification.

[0227] Output: Input and uploaded data.

[0228] Step 2: The device sends the entered data and documents to the server

[0229] Input: Information entered and uploaded by users.

[0230] Data processing and data calculation: The terminal converts the entered data and documents into a digital format (PDF or image) and sends it to the server through an API using the HTTPS protocol.

[0231] Specific operation: The terminal converts the input data into PDF or image format and sends it to the server.

[0232] Output: Data and documents sent to the server.

[0233] Step 3: The server parses the received data

[0234] Input: Data and documents received from the terminal.

[0235] Data processing and data calculation: The server analyzes the incoming data and documents, and uses a generative AI model to verify the document contents and check for validity and compatibility, for example, whether the photo on the ID card is clear and whether the name and license number match.

[0236] What it does: The server runs the generative AI model to analyze data and documents.

[0237] Output: Analysis results (check results of validity and conformity of data and documents).

[0238] Step 4: Server requests data correction (if necessary)

[0239] Input: If there are any errors in the analysis results.

[0240] Data Processing and Data Arithmetic: The server identifies specific deficiencies and generates and sends a message to the user requesting corrections, such as "The photo is blurry" or "The name and license number do not match."

[0241] Specific operation: The server notifies the user by email using the SMTP protocol.

[0242] Output: A notification message of the correction request containing the details of the deficiency.

[0243] Step 5: The server updates the license database

[0244] Input: Data and documents submitted again after correction.

[0245] Data processing and data calculation: If the data is determined to be problem-free, the server registers and updates the new license information in the license database.

[0246] Specific operation: The server registers and updates data in a database such as MySQL or PostgreSQL.

[0247] Output: Updated license database.

[0248] Step 6: The server issues a new license and sends the user instructions on how to mail it.

[0249] Input: Updated license data.

[0250] Data processing and data calculation: The server initiates the new license issuance procedure, generates instructions for mailing the license, and notifies the user.

[0251] Specific operation: The server sends an invitation message using the SMTP protocol or the Push notification API.

[0252] Output: Notification of new driver's license issuance procedure.

[0253] In this way, this system will improve the efficiency of police work and increase public satisfaction.

[0254] (Application example 1)

[0255] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] In traditional police and security services, incident reports and accident information from users are often collected and analyzed manually, which not only lacks efficiency but also makes them prone to human error. Furthermore, speeding up incident response and providing feedback to users is also an issue. To solve these problems, there is a need for data analysis and automatic notification systems using generative AI models.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0258] In this invention, the server includes means for analyzing input data from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for collecting detailed information about the security incident from the user, means for converting the collected incident information into JSON format and sending it to the server, means for analyzing the received incident information using a generative AI model and completing the detailed information, and means for sending notifications to relevant personnel based on the analysis results. This automates the collection and analysis of security incident and accident information, enabling fast and efficient responses.

[0259] "Generative AI" is an artificial intelligence model that analyzes input data from users and, if necessary, fills in missing information or generates new data.

[0260] A "database" is a system for systematically storing and managing collected and analyzed data.

[0261] A "notification" is a message or alert that informs a user or relevant personnel of some information.

[0262] "Online Form" means a web-based interface for entering and submitting data over the Internet.

[0263] The "JSON format" is a format for expressing data in a structured text-based format, making it easier to exchange data.

[0264] A "server" is a computer system that provides various services such as data storage, analysis, and notification via a network.

[0265] A "security incident" is a safety-related occurrence such as unauthorized access, vandalism, or noise problems.

[0266] "Person in Charge" refers to the person or professional responsible for responding to and managing incidents and accidents.

[0267] "Incident Information" means detailed data about a security incident, including the date, time, location, people involved, and a detailed description.

[0268] The present invention is a system that automates reporting, analysis, and notification of security incidents using a generative AI model. Specific embodiments of the present invention are described below.

[0269] Security Incident Reporting Practices

[0270] 1. The user enters the security incident information

[0271] Users access the security service's application on their smartphones, enter details about the incident (type of incident, date and time, location, relevant information), and then press the "send" button.

[0272] 2. The device sends the entered data to the server

[0273] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0274] 3. The server analyzes the received data

[0275] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model, completing incomplete information and identifying suspicious data.

[0276] 4. The server saves the data to the database

[0277] Based on the analysis results, the final data is saved in the incident registration database, and notifications are sent to relevant personnel based on the incident location information.

[0278] 5. The server notifies the user that registration is complete

[0279] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[0280] Generate complete details

[0281] The server uses the generative AI model to generate appropriate detailed information if the incident information provided by the user is insufficient. For example, if the "detailed incident description" is missing, the server generates additional information based on each data element.

[0282] Hardware and software used

[0283] Hardware:

[0284] Smartphone: A device for users to enter incident information.

[0285] Server: A computer system for receiving, analyzing, storing, and notifying data.

[0286] software:

[0287] Generative AI model (e.g. GPT-3): Analyzes the incoming data and generates the necessary details.

[0288] JSON format: A format used to structure and transfer data.

[0289] API: An interface for exchanging data between a device and a server.

[0290] Database (e.g. MySQL): A system for storing analysis results.

[0291] Specific examples

[0292] Please use the following examples to further understand the functionality of this system.

[0293] Example 1: Security Incident Reporting

[0294] 1. The user enters "Type of incident: Vandalism," "Date and time: October 11, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[0295] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[0296] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[0297] 4. Store the final data in the incident registration database and send notifications to relevant personnel.

[0298] 5. Send an email notification to the user stating that the incident report has been completed.

[0299] Example prompt sentence:

[0300] Please complete any missing or incomplete information based on the following security incident data:

[0301] {

[0302] "incident_type": "vandalism",

[0303] "date_time": "2023-10-11 14:00",

[0304] "location": "Chuo-ku, Osaka",

[0305] "details": "The window of the convenience store was broken",

[0306] "reporter_contact": "example@example.com"

[0307] }

[0308] The above is a specific embodiment for carrying out the present invention, which automates the collection and analysis of security incident and accident information, enabling a quick and efficient response.

[0309] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0310] Step 1:

[0311] The user enters information about a security incident. The user accesses the application using a smartphone, enters the type of incident, date and time, location, and related information into an online form, and then presses the "Submit" button. The input here is specific details of the incident in text format.

[0312] Step 2:

[0313] The device sends the entered data to the server. The device (smartphone) converts the entered data into JSON format and sends it to the server via the API. At this time, the data is sent to the server in an appropriate format via the API, so data transfer is efficient and accurate.

[0314] Step 3:

[0315] The server saves the received data in a temporary data store. The server saves the received JSON data in a temporary data store for subsequent analysis. The input here is JSON format data, and the output is saved to the temporary data store.

[0316] Step 4:

[0317] The server analyzes the data using a generative AI model. The server inputs the saved JSON data into a generative AI model (e.g., GPT-3) and analyzes the data. During the analysis process, incomplete information is completed and suspicious data is identified. The input is JSON data, and the output is the completed detailed information and analysis results.

[0318] Step 5:

[0319] The server stores the analysis results in a database. The server then organizes the analysis results obtained by the generative AI model as final data and stores them in the incident registration database. The input here is the analysis results, and the output is the registered data stored in the database.

[0320] Step 6:

[0321] The server sends notifications to relevant personnel based on the analysis results. The server automatically sends notifications to relevant security personnel based on the data stored in the incident registration database. The input here is the registered data, and the output is a notification message to the personnel. Specific operations include automatically sending email notifications to the personnel's email addresses and push notifications to dedicated applications.

[0322] Step 7:

[0323] The server notifies the user that registration is complete. The server generates a message indicating that the incident report has been successfully completed and sends it to the user's email address or to a notification system via an application. The input here is the registration completion information, and the output is a notification message to the user.

[0324] This will enable the realization of a system that efficiently and quickly collects, analyzes, registers, and notifies security incidents through all processing steps.

[0325] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0326] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[0327] Accident registration function embodiment

[0328] 1. The user enters the accident information

[0329] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[0330] 2. The device sends the entered data to the server

[0331] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0332] 3. The server analyzes the received data

[0333] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model and emotion engine. The generative AI model analyzes the content of the data, and the emotion engine analyzes the user's emotions when they input. For example, if the user is feeling very stressed or anxious, the system will select an appropriate response.

[0334] 4. The server saves the data to the database

[0335] Based on the analysis results, the final data is stored in the accident registration database. The results of the sentiment analysis are also recorded and notifications are sent to the relevant police stations if necessary.

[0336] 5. The server notifies the user that registration is complete

[0337] A message indicating the completion of registration is generated and sent to the user's email address or online messaging system. The emotion engine selects the message taking into account the user's feelings.

[0338] Implementation of license-related procedures

[0339] 1. The user applies for license renewal online

[0340] The user uses the device to access the license renewal application form on the police website, enters the required license information (name, address, current license number, etc.), and uploads the required documents (ID, photo, etc.).

[0341] 2. The device sends the entered data and documents to the server

[0342] The terminal sends the entered data and uploaded documents (PDF or image format) to the server.

[0343] 3. The server analyzes the received data

[0344] The server analyzes the received data and documents using a generative AI model and an emotion engine. The generative AI model checks the contents of the document, and the emotion engine analyzes the user's emotions. For example, if the user is feeling anxious, the server returns a reassuring message.

[0345] 4. The server requests that the data be corrected (if necessary)

[0346] If the analysis reveals any deficiencies, the server will send a correction request to the user. The emotion engine selects correction requests that take into account the user's emotions. The user then makes the corrections and submits the data and documents again.

[0347] 5. The server updates the database

[0348] If the data is verified as consistent, the server updates the license database and prepares to issue a new license.

[0349] 6. The server issues a new license and notifies you.

[0350] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also ensures that the notification message is sensitive to the user's emotions.

[0351] Specific examples

[0352] Specific examples of accident registration

[0353] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[0354] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[0355] 3. The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[0356] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[0357] 5. Send the user an emotionally sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[0358] Specific examples of license renewal

[0359] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[0360] 2. The device converts the data into PDF and image format and sends it to the server.

[0361] 3. The server analyzes the received data using the generated AI model and emotion engine, and checks the document and the user's emotions.

[0362] 4. If there is a problem, the server will send a friendly request to the user to correct the problem and prompt them to re-enter the information.

[0363] 5. Once the data is verified, the server updates the license database.

[0364] 6. A new license is issued and a "new license has been sent" notification is sent to the user in an emotionally sensitive manner.

[0365] The above is a concrete example of how the present invention can be implemented. This system will improve the efficiency of police work and increase public satisfaction.

[0366] The processing flow will be explained below.

[0367] Processing steps of the accident registration function combined with the emotion engine

[0368] Step 1:

[0369] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[0370] Step 2:

[0371] The user completes the input and presses the "Submit" button.

[0372] Step 3:

[0373] The terminal converts the input data into JSON format.

[0374] Step 4:

[0375] The device sends the generated JSON data to the server via the API.

[0376] Step 5:

[0377] The server saves the received JSON data in a temporary data store.

[0378] Step 6:

[0379] The server sends the data to the generative AI model and begins analysis.

[0380] Step 7:

[0381] Generative AI models analyze, classify, and complete data.

[0382] Step 8:

[0383] The server sends the data to the emotion engine, which analyzes the emotion of the user's input.

[0384] Step 9:

[0385] The emotion engine analyzes the user's emotions and detects, for example, impatience or stress.

[0386] Step 10:

[0387] The generative AI model complements the data by combining the analysis results of the emotion engine.

[0388] Step 11:

[0389] The server stores the data in the accident registration database based on the analysis results.

[0390] Step 12:

[0391] The server sends a notification of the accident information to the relevant police station.

[0392] Step 13:

[0393] The server creates a message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[0394] Step 14:

[0395] The server sends a notification to the user that the accident registration has been completed.

[0396] License-related procedure processing steps combined with an emotion engine

[0397] Step 1:

[0398] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[0399] Step 2:

[0400] The user uploads the required documents (ID, photo, etc.).

[0401] Step 3:

[0402] The user completes the input and upload and presses the "Submit" button.

[0403] Step 4:

[0404] The device converts entered data and uploaded documents into PDF and image formats.

[0405] Step 5:

[0406] The terminal sends the generated data and documents to the server.

[0407] Step 6:

[0408] The server stores the received data and documents in a temporary data store.

[0409] Step 7:

[0410] The server sends the data and documents to the generative AI model, which begins the analysis.

[0411] Step 8:

[0412] A generative AI model analyzes the document content and checks for compatibility.

[0413] Step 9:

[0414] The server sends the data to the emotion engine to analyze the user's emotions.

[0415] Step 10:

[0416] The emotion engine analyzes the user's emotions and detects, for example, anxiety or doubt.

[0417] Step 11:

[0418] The generative AI model complements the data by combining the analysis results of the emotion engine.

[0419] Step 12:

[0420] The server updates the data in the license database based on the analysis results.

[0421] Step 13:

[0422] If correction is necessary, the server creates a correction request message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[0423] Step 14:

[0424] The user receives a message requesting correction, and re-enters and re-uploads the data.

[0425] Step 15:

[0426] The terminal sends the corrected data and documents back to the server.

[0427] Step 16:

[0428] The server regenerates the corrected data and documents and analyzes them using an AI model and emotion engine.

[0429] Step 17:

[0430] After the data integrity is verified, the server finally updates the license database.

[0431] Step 18:

[0432] The server initiates the process of issuing a new license.

[0433] Step 19:

[0434] The server creates a notification message for the user that takes into consideration emotions based on the analysis results of the emotion engine.

[0435] Step 20:

[0436] The server will send the user instructions on how to mail in a new license.

[0437] Example 2

[0438] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0439] In modern police work, accident registration and license-related procedures are typically performed manually, requiring a great deal of time and effort. Furthermore, the lack of appropriate responses based on user emotions can lead to a poor user experience. These issues significantly hinder work efficiency and public satisfaction. The present invention aims to solve these issues, digitize and automate police work, and improve the user experience.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0441] In this invention, the server includes means for analyzing data input from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for analyzing the data input by the user using an emotion engine, means for adjusting the notification content based on the analyzed emotions, and storage means for temporarily saving the data and the analysis results. This automates accident registration and license-related procedures, enabling responses based on user emotions, improving the efficiency of police work and increasing public satisfaction.

[0442] "Generative AI" is a technology that uses artificial intelligence technology to generate new data or sentences based on given data or prompts.

[0443] An "emotion engine" is a technology that analyzes emotions from user input data and evaluates the user's psychological state.

[0444] A "database" is a system for efficiently storing, managing, and retrieving data.

[0445] An "online form" is a web-based input screen that allows a user to enter information over the Internet.

[0446] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that describes data in a text-based format, achieving both readability and efficiency in data exchange.

[0447] "API" is an abbreviation for Application Programming Interface, an interface for exchanging functions and data between different software systems.

[0448] A "temporary data store" is a storage medium or system for temporarily storing data.

[0449] A "storage means" is a system consisting of hardware and software for storing data and information.

[0450] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0451] "Notifying" is the act of conveying specific information to a user.

[0452] "Registering" is the act of storing data in an official database or system.

[0453] "User" means an individual or organization that uses the system.

[0454] "Accident details" refers to detailed data about a specific accident, such as the type of accident, date and time, location, and information about the people involved.

[0455] A "license-related renewal application" is the act of completing the online renewal procedure for a license that is approaching its expiration date.

[0456] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[0457] Accident registration function embodiment

[0458] The user

[0459] The user accesses the police's online form using a device (PC or smartphone). This form has fields for entering detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.). After the user enters all the information, they press the "Submit" button to proceed to the next processing step.

[0460] The terminal is

[0461] The device converts the input data into JSON format, which structures the data and facilitates subsequent processing. The device then transmits this JSON data to the police server via an API, a communication method that ensures accurate data transfer.

[0462] The server is

[0463] The server stores the received JSON data in a temporary data store, which acts as a preliminary stage for data analysis. The server then analyzes the data using a generative AI model, completing each field as needed. It also analyzes the user's emotions using an emotion engine. For example, if the user is experiencing high stress, it can flag the issue as needing special attention.

[0464] Once the analysis is complete, the server stores the final data in an incident registration database, which is structured for long-term storage and rapid retrieval. The results of the sentiment analysis are also recorded and notifications are sent to the ledger and relevant police stations. Notifications are sent through the appropriate internal systems.

[0465] The server is

[0466] Finally, the server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. The emotion engine selects a message that takes the user's feelings into consideration. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[0467] Implementation of license-related procedures

[0468] The user

[0469] Using the device, users access a license renewal application form on the police website, which includes fields for entering required information such as name, address, and current license number, and uploading required documents such as ID and photographs.

[0470] The terminal is

[0471] The terminal converts input data and uploaded documents into PDF or image format, then transmits the data to the server via API, with the communication encrypted to ensure safety and efficiency.

[0472] The server is

[0473] The server stores the received data and documents in a temporary data store. The data is then analyzed again using the generative AI model and emotion engine. The generative AI model examines the document in detail to identify inconsistencies and omissions. The emotion engine analyzes the user's emotions and considers measures to provide a sense of security.

[0474] If the analysis reveals any deficiencies, the server sends a correction request to the user. This correction request is also expressed in a gentle way using the emotion engine. For example, it might say, "Sorry for the inconvenience, but could you please check it again?" The user then makes the corrections and sends the data and document again.

[0475] The server is

[0476] Once the data is verified as accurate, the server updates the license database, which contains the new license information and prepares to issue a new license.

[0477] Finally, the server issues a new driver's license and sends the user instructions on how to mail it. This instruction is also sensitive, and includes a notification such as "Your new driver's license has been sent."

[0478] Specific examples

[0479] Specific examples of accident registration

[0480] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[0481] The terminal converts the input data into JSON format and sends it to the server via the API.

[0482] The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[0483] The final data is stored in the accident registration database and a notification is sent to the relevant police station.

[0484] Send the user a sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[0485] Specific examples of license renewal

[0486] The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[0487] The device converts the data into PDF and image format and sends it to the server.

[0488] The server analyzes the received data using a generated AI model and emotion engine to verify the document and determine the user's emotions.

[0489] If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to re-enter the information.

[0490] Once the data is verified, the server updates the license database.

[0491] A new driver's license is issued and a notification that "your new driver's license has been sent" is sent to the user in an emotionally sensitive manner.

[0492] This system will improve the efficiency of police work and increase public satisfaction.

[0493] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0494] Step 1:

[0495] The user enters the accident information

[0496] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) by entering specific information in the input fields and pressing the "Send" button to send the input data to the device.

[0497] Input: Type of accident, date, time, location, and information of people involved

[0498] Output: Input data to the terminal

[0499] Step 2:

[0500] The device sends the entered data to the server.

[0501] The device converts the input data into JSON format and sends it to the server via API, where it is securely transferred to the server over the network.

[0502] Input: Accident information entered by the user

[0503] Output: Send JSON format data to the server

[0504] Step 3:

[0505] The server analyzes the received data

[0506] The server stores the received JSON data in a temporary data store. It then uses a generative AI model to analyze the data and fill in any incomplete parts. It then uses an emotion engine to analyze the user's emotions. The analysis results include detailed information about the accident and user emotion analysis data.

[0507] Input: Accident information data in JSON format

[0508] Output: Analyzed accident information data and user emotion data

[0509] Step 4:

[0510] The server stores it in the database

[0511] Based on the analysis results, the server stores the final data in the accident registration database, which is stored securely and can be accessed whenever necessary. It also records the results of the sentiment analysis and sends notifications to the relevant police stations if necessary. A notification system is used to prompt the relevant authorities to take appropriate action.

[0512] Input: Analyzed accident information data and user emotion data

[0513] Output: Save data in accident registration database, notify police station

[0514] Step 5:

[0515] The server notifies the user that registration is complete

[0516] The server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. Using an emotion engine, the message is selected with consideration for the user's emotions. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[0517] Input: Analysis results, emotion analysis results

[0518] Output: A message to inform the user

[0519] Step 6:

[0520] The user applies for license renewal online

[0521] Using their device, users access the license renewal application form on the police website, enter the required license information (name, address, current license number), and upload required documents such as ID and photos.

[0522] Input: License information, required documents

[0523] Output: Input data to the terminal

[0524] Step 7:

[0525] The device sends the entered data and documents to the server.

[0526] The device converts the entered data and uploaded documents into PDF or image format and sends them to the server via API, where the data is securely transferred over the network.

[0527] Input: License information and documents entered by the user

[0528] Output: Send PDF or image data to the server

[0529] Step 8:

[0530] The server parses the received data

[0531] The server stores the received data and documents in a temporary data store. It then analyzes the data using a generative AI model and emotion engine. The generative AI model checks the contents of the uploaded documents, and the emotion engine analyzes the user's emotions. The analysis results in license information verification data and user emotion data.

[0532] Input: License information data in PDF or image format

[0533] Output: Analyzed license information data and user emotion data

[0534] Step 9:

[0535] The server requests that the data be corrected (if necessary)

[0536] If the analysis reveals any deficiencies, the server will send a correction request to the user. Using an emotion engine, the correction request will be selected with consideration for the user's emotions. The user will then make the corrections and resubmit the data and document.

[0537] Input: Analyzed license information data, deficiency information

[0538] Output: Request for correction to user, corrected data

[0539] Step 10:

[0540] The server updates the database

[0541] If the data is found to be consistent, the server updates the license database, and the new license information is registered in the database.

[0542] Input: License information data that has been verified for integrity

[0543] Output: License database update

[0544] Step 11:

[0545] The server issues a new license and notifies you.

[0546] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also considers the user's emotions when sending the notification message. A reassuring message is sent saying, "Your new driver's license has been sent."

[0547] Input: License information data

[0548] Output: Issuance of new license, notification message to user

[0549] (Application example 2)

[0550] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0551] In traditional police work, registering accident information and license-related procedures are often manual and inefficient. It is also difficult to respond in a way that takes user feelings into consideration, often resulting in low user satisfaction. This situation calls for improvement in today's digital age. In particular, in emergencies and when trouble occurs, a swift and appropriate response is required, making it important to increase users' sense of security.

[0552] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0553] In this invention, the server includes means for analyzing input data from a user using a generation AI, means for registering the analysis results in a database, means for notifying the user of completion of registration, means for analyzing the received data with an emotion analysis engine and generating a message that takes the user's emotions into consideration, and means for instructing the user on appropriate procedures depending on the situation. This not only enables efficient analysis and registration of input data from a user, but also enables responses that take the user's emotions into consideration, making it possible to improve the user's sense of security through quick and appropriate responses.

[0554] "Generative AI" is an artificial intelligence model for performing tasks such as natural language processing and image analysis, and is a technology that analyzes input data from users and generates appropriate results.

[0555] An "emotion analysis engine" is a system for identifying emotions from user input data and generating responses based on those emotions.

[0556] A "database" is an information system for efficiently storing, managing, and searching analytical results and other data.

[0557] "Online Form" means a web-based form that allows users to enter and submit information via the Internet.

[0558] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data.

[0559] An "API" is an interface for exchanging data between applications and servers, providing a way for programs to communicate with each other.

[0560] "Notification means" refers to the function or method for sending messages or warnings from the system to the user.

[0561] "License-related procedures" refers to the process of carrying out official procedures such as applying for, renewing, or reissuing a license.

[0562] "Accident registration" is the process of recording details of a road accident or other incident on the system.

[0563] "Messages that take the user's emotions into consideration" means using an emotion analysis engine to evaluate the user's emotional state and generating appropriate and reassuring messages based on the results.

[0564] "Means for instructing appropriate procedures" refers to a function that indicates to the user the specific next action to be taken based on the analysis results and emotion analysis results.

[0565] Accident registration function embodiment

[0566] The accident registration function of the present invention is a system that uses a generative AI model and an emotion analysis engine to efficiently analyze and register accident information from users.

[0567] System configuration

[0568] 1. User's device (PC or smartphone)

[0569] Users access the police's online form using their own device and enter details about the accident, such as the type of accident, date and time, location, and information about the people involved.

[0570] 2. Data transmission and analysis

[0571] The device converts the input accident information into JSON format and sends it to the server via API. The server stores the received data in a temporary data store and analyzes the accident information using the generative AI model.

[0572] 3. Emotion analysis

[0573] The server uses an emotion analysis engine to analyze the user's emotions. For example, if the user is feeling strong stress or anxiety when entering the incident, the server will take appropriate measures to respond.

[0574] 4. Registering in the database

[0575] The analyzed accident information is ultimately stored in an accident registration database. The analyzed emotional information is also recorded and notified to relevant organizations as necessary.

[0576] 5. Notification

[0577] Once the data is registered, the server generates an emotionally sensitive message and sends a notification to the user's email address or online messaging system.

[0578] Hardware and software used

[0579] Device: PC or smartphone

[0580] Generative AI models: e.g., GPT-4

[0581] Sentiment analysis engine: e.g., Google AutoML Natural Language

[0582] Database: Accident registration database

[0583] API: RESTful API

[0584] Specific examples

[0585] 1. The user enters details such as "Type of accident: Collision between two vehicles," "Date and time: November 1, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[0586] 2. The data entered by the terminal is converted to JSON format and sent to the server.

[0587] 3. The server analyzes the accident information using a generative AI model and evaluates the user's emotions using an emotion analysis engine.

[0588] 4. Register accident information and emotional information in the database.

[0589] 5. A sensitive message is sent to the user saying, "Accident registration has been completed. Thank you for your cooperation."

[0590] Example prompt sentence:

[0591] "You idiot. Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[0592] Implementation of license-related procedures

[0593] The license-related procedure function of the present invention is a system that allows users to efficiently renew or reissue licenses online.

[0594] System configuration

[0595] 1. User's device (PC or smartphone)

[0596] Users access the online license renewal form and enter the required information (name, address, license number, etc.) and upload the required documents (ID, photo, etc.).

[0597] 2. Data transmission and analysis

[0598] The device converts the entered data and uploaded documents into JSON and image formats and sends them to the server, which uses a generative AI model to review the document content and an emotion analysis engine to evaluate the user's emotions.

[0599] 3. Pointing out and correcting deficiencies

[0600] If there are any errors in the data, the server will send a friendly message to the user requesting corrections, based on the sentiment analysis engine.

[0601] 4. Updating the database

[0602] After the data has been successfully verified, the server updates the license database and prepares to issue a new license.

[0603] 5. Notification

[0604] When a new license is issued, the server generates an emotionally sensitive message and sends a notification to the user.

[0605] Hardware and software used

[0606] Device: PC or smartphone

[0607] Generative AI models: e.g., GPT-4

[0608] Sentiment analysis engine: e.g., Google AutoML Natural Language

[0609] Database: License Database

[0610] API: RESTful API

[0611] Specific examples

[0612] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[0613] 2. The device converts the data into JSON and image format and sends it to the server.

[0614] 3. The server analyzes the received data using the generated AI model and emotion analysis engine to verify the document and determine the user's emotions.

[0615] 4. If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to try again.

[0616] 5. Once the data is verified, the server updates the license database and a new license is prepared.

[0617] 6. A new license is issued and a notification that "your new license has been sent" is sent to the user in an emotionally sensitive manner.

[0618] Example prompt sentence:

[0619] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[0620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0621] Processing steps for the Incident Registration function

[0622] Step 1:

[0623] A user accesses the online form from a smartphone or PC, enters details about the accident (type of accident, date and time, location, information about the people involved, etc.), and presses the "Submit" button. The entered data is captured as form fields.

[0624] Input: Accident details (text form)

[0625] Output: Detailed information about the entered accident (text data)

[0626] Step 2:

[0627] The device converts the detailed information of the accident entered by the user into JSON format, and the converted JSON data is sent to the server via the API.

[0628] Input: Detailed information about the accident entered (text data)

[0629] Output: Accident data in JSON format

[0630] Step 3:

[0631] The server stores the received JSON data in a temporary data store, and then uses the generative AI model to analyze the accident information. Specifically, the details of the accident and related information are analyzed, and incomplete parts are filled in.

[0632] Input: Accident data in JSON format

[0633] Output: Analysis results (text data)

[0634] Step 4:

[0635] The server uses an emotion analysis engine to analyze the input data and evaluate the user's emotions, for example, whether the user is feeling stressed or anxious, and selects an appropriate response.

[0636] Input: Detailed information about the accident entered (JSON data)

[0637] Output: Emotion evaluation results (text data)

[0638] Step 5:

[0639] The server integrates the analyzed accident information with the emotion evaluation results and stores them in the accident registration database. Based on the analysis results, notifications are sent to relevant authorities (e.g., police stations).

[0640] Input: Analysis results and emotion evaluation results

[0641] Output: Accident information stored in the database

[0642] Step 6:

[0643] Based on the results of the emotion analysis, the server sends a notification to the user that registration has been completed. The notification message is sensitive to the user's emotions and is sent via email or an online messaging system.

[0644] Input: Registration completion message

[0645] Output: Notification sent to the user

[0646] License-related procedure processing steps

[0647] Step 1:

[0648] The user accesses the police's online form on a PC or smartphone, enters the necessary information for license renewal (name, address, license number, etc.), and uploads the necessary documents (identification, photo, etc.).

[0649] Input: Required license information and documents (text and image format)

[0650] Output: Input and uploaded data

[0651] Step 2:

[0652] The device converts input data and uploaded documents into JSON and image formats, which are then sent to the server via API.

[0653] Input: License information and documents (text and image formats)

[0654] Output: JSON and image data

[0655] Step 3:

[0656] The server stores the received data in a temporary data store and analyzes it using a generative AI model and a sentiment analysis engine. The generative AI model checks the document content, and the sentiment analysis evaluates the user's sentiment.

[0657] Input: JSON and image data

[0658] Output: Analysis results and emotion evaluation results (text data)

[0659] Step 4:

[0660] The server determines whether the data needs to be corrected based on the analysis results. If so, the server sends a correction request to the user in friendly language based on the emotion analysis results.

[0661] Input: Analysis results and emotion evaluation results

[0662] Output: Correction request message

[0663] Step 5:

[0664] The user corrects the input according to the correction request and submits the data again. The server reanalyzes the data and, if it is consistent, updates the license database.

[0665] Input: Corrected data (text and image format)

[0666] Output: Updated license database

[0667] Step 6:

[0668] The server processes the new license and generates a sensitive message to notify the user, which can be sent via email or an online messaging system.

[0669] Input: Result of issuing procedure and notification message

[0670] Output: Notification sent to the user

[0671] Examples of prompt statements

[0672] "Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[0673] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[0674] 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.

[0675] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0676] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0677] [Second embodiment]

[0678] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0679] 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.

[0680] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0681] 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.

[0682] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0683] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0684] 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.

[0685] 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.

[0686] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0687] 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.

[0688] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0689] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0690] The present invention is a system that uses generative AI models to digitize police work and automate procedures such as accident registration, license-related procedures, and crime response. Specific embodiments of the present invention are described below.

[0691] The system involves a series of processes for collecting input data from users, analyzing it using a generative AI model, and registering the results in a database.

[0692] Accident registration function embodiment

[0693] 1. The user enters the accident information

[0694] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[0695] 2. The device sends the entered data to the server

[0696] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0697] 3. The server analyzes the received data

[0698] The server stores the received JSON data in a temporary data store and then analyzes it using a generative AI model, filling in incomplete information and identifying suspicious data.

[0699] 4. The server saves the data to the database

[0700] Based on the analysis results, the final data is stored in the accident registration database, and a notification is sent to the relevant police station based on the location of the accident.

[0701] 5. The server notifies the user that registration is complete

[0702] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[0703] Implementation of license-related procedures

[0704] 1. The user applies for license renewal online

[0705] The user uses the device to access the license renewal application form on the police website, enters the necessary license information (such as name, address, and current license number), and uploads the necessary documents (such as identification and photographs).

[0706] 2. The device sends the entered data and documents to the server

[0707] The device sends the entered data and documents (PDF or image format) to the server, which ensures data integrity and security.

[0708] 3. The server analyzes the received data

[0709] The server analyzes the received data and documents, verifies the document content using a generative AI model, and checks for validity and suitability.

[0710] 4. The server requests that the data be corrected (if necessary)

[0711] If the analysis reveals any defects, the server will send a correction request to the user, who will then correct the data and submit it again.

[0712] 5. The server updates the license database

[0713] If the corrected data is deemed acceptable, the server updates the license database, which prepares the new license for issuance.

[0714] 6. The server issues a new license and sends the user instructions on how to mail it.

[0715] The server will issue a new driver's license and send instructions for mailing the new license to the user via email. The user will then receive the instructions and the new license will be mailed to the registered address.

[0716] Specific examples

[0717] Specific examples of accident registration

[0718] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[0719] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[0720] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[0721] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[0722] 5. Send an email notification to the user stating that the accident registration has been completed.

[0723] Specific examples of license renewal

[0724] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[0725] 2. The device converts the data into PDF and image format and sends it to the server.

[0726] 3. The server analyzes the received data using the generated AI model and verifies the documents.

[0727] 4. If there is a problem, the server sends a correction request to the user and prompts them to re-enter the information.

[0728] 5. Once the data is verified, the server updates the license database.

[0729] 6. Your new license will be issued and instructions on how to mail it will be sent to you.

[0730] The above is a specific embodiment of the present invention, which will improve the efficiency of police work and increase public satisfaction.

[0731] The processing flow will be explained below.

[0732] Processing steps for the Incident Registration function

[0733] Step 1:

[0734] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[0735] Step 2:

[0736] The user completes the input and presses the "Submit" button.

[0737] Step 3:

[0738] The terminal converts the input data into JSON format.

[0739] Step 4:

[0740] The device sends the generated JSON data to the server via the API.

[0741] Step 5:

[0742] The server parses the received JSON data and converts it into the appropriate format.

[0743] Step 6:

[0744] The server saves the transformed data to a temporary data store.

[0745] Step 7:

[0746] The server sends the data to the generative AI model and begins analysis.

[0747] Step 8:

[0748] A generative AI model analyzes incoming data and detects incomplete information or anomalous data.

[0749] Step 9:

[0750] Generative AI models generate the additional information needed to complement the data.

[0751] Step 10:

[0752] Based on the analysis results, the server stores the final data in the accident registration database.

[0753] Step 11:

[0754] The server sends a notification to the relevant police station.

[0755] Step 12:

[0756] The server sends a notification to the user that the accident registration has been completed.

[0757] License-related procedure processing steps

[0758] Step 1:

[0759] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[0760] Step 2:

[0761] The user uploads the required documents (ID, photo, etc.).

[0762] Step 3:

[0763] The user completes the input and upload and presses the "Submit" button.

[0764] Step 4:

[0765] The device converts entered data and uploaded documents into PDF and image formats.

[0766] Step 5:

[0767] The terminal sends the generated data and documents to the server.

[0768] Step 6:

[0769] The server prepares to parse the received data and documents.

[0770] Step 7:

[0771] The server sends the data and documents to the generative AI model and begins analysis.

[0772] Step 8:

[0773] A generative AI model analyzes the content of incoming documents and checks their validity and suitability.

[0774] Step 9:

[0775] Based on the analysis results, the server sends a correction request to the user as necessary.

[0776] Step 10:

[0777] The user makes corrections and submits the data and documents again.

[0778] Step 11:

[0779] The server re-analyzes and verifies the corrected data and documents.

[0780] Step 12:

[0781] The server checks the integrity of the data and then updates the license database.

[0782] Step 13:

[0783] The server processes the issuance of a new license.

[0784] Step 14:

[0785] The server will send the user instructions on how to mail in a new license.

[0786] Example 1

[0787] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] In conventional police work, procedures related to accident registration, license renewal, etc. are complicated and require a lot of manpower and time. This often leads to delays in procedures, information omissions, and inaccurate data registration. In addition, users have to physically visit the police station, which requires time and effort. It is necessary to solve these issues and improve the efficiency of police work and user convenience.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0790] In this invention, the server includes means for accepting input data from a user via an online form, means for converting the accepted data into JSON format and sending it to the server, means for saving the accepted data in a temporary data store, means for analyzing the accepted data using a generative AI model and completing incomplete information, means for registering the analysis results in a database, and means for notifying the user of completion of registration, thereby enabling the digitalization and automation of police work.

[0791] 1. "User"

[0792] "User" refers to an individual or organization that uses this system, specifically, a person who provides information related to accident registration and license renewal.

[0793] 2. "Online Form"

[0794] "Online Form" means an electronic input screen for entering and submitting data over the Internet.

[0795] 3. “JSON format”

[0796] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format that describes data in key-value pair format.

[0797] 4. "Server"

[0798] "Server" refers to a centralized computer system that performs a series of processes such as receiving, analyzing, storing, and notifying data.

[0799] 5. "Data Store"

[0800] "Data store" refers to a storage area for temporarily or permanently storing data.

[0801] 6. "Generative AI Models"

[0802] "Generative AI model" refers to an algorithm and its implementation that uses artificial intelligence technology to analyze, complement, and generate data.

[0803] 7. "Database"

[0804] "Database" refers to an electronic information management system for efficiently storing, retrieving, and managing structured information.

[0805] 8. “Notification”

[0806] "Notification" refers to the act of transmitting processing results or necessary information to users or relevant institutions via email or messaging systems.

[0807] 9. "Accident Information"

[0808] "Accident information" refers to detailed data about traffic accidents, etc., including the type of accident, date and time, location, and information about the people involved.

[0809] 10. "License Renewal Application"

[0810] "License Renewal Application" refers to the application process for completing the driver's license renewal procedure online.

[0811] 11. "Identification Card"

[0812] "Identification" refers to an official document used to verify a person's identity, such as a passport or driver's license.

[0813] 12. "Documents"

[0814] "Documents" refers to documents and images required for license-related procedures, including identification documents and photographs.

[0815] 13. “Analysis”

[0816] "Analysis" refers to the process of analyzing received data to extract necessary information or fill in any incomplete parts.

[0817] 14. "Complement"

[0818] "Completion" refers to the act of adding incomplete information or missing data to make the data complete.

[0819] 15. "Defects"

[0820] "Defect" refers to a state in which there are omissions or errors in data or documents and the required conditions are not met.

[0821] This invention is a system that supports the digitization and automation of police operations such as accident registration, license renewal, etc. This system collects input data from users, analyzes it using a generative AI model, and provides a series of processes for registering the data in a database and notifying users.

[0822] The system uses a user's device (such as a PC or smartphone), a central server, a data store, and a generative AI model. Specific embodiments are described below.

[0823] Accident registration function embodiment

[0824] 1. The user enters the accident information

[0825] The user uses the device to access the police's online form. For example, the user logs in to the police's official website through a web browser and enters detailed information about the accident (such as the type of accident, date and time, location, and information about the people involved) into the form. For example, this includes input such as "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[0826] 2. The device sends the entered data to the server

[0827] The terminal converts the data entered by the user into JSON format and sends it to the server via API, which ensures efficient and secure data transfer, specifically using the HTTPS protocol.

[0828] 3. The server analyzes the received data

[0829] The server stores the received JSON data in a temporary data store, for example, using cloud storage such as Amazon S3 or Google Cloud Storage. The server then analyzes the data using a generative AI model to fill in any incomplete information. This process fills in any data detected as unknown or missing.

[0830] 4. The server saves the data to the database

[0831] Based on the analysis results, the server formats the final data and stores it in the accident registration database, using a relational database such as MySQL or PostgreSQL, and also uses SMS, email, and webhooks to send automatic notifications to the relevant police stations based on the location of the accident.

[0832] 5. The server notifies the user that registration is complete

[0833] The server generates a message indicating the successful registration and sends a notification to the user's email address or messaging platform. An example notification might include "Accident registration completed. Registration number is 12345." This process uses the SMTP protocol and a Push notification API.

[0834] Implementation of license-related procedures

[0835] 1. The user applies for license renewal online

[0836] The user accesses the police website using a device and opens the license renewal application form. The form prompts the user to enter or upload the necessary license information (such as name, address, and current license number) and necessary documents (such as identification and photographs). For example, the user may enter information such as "Name: Taro Yamada," "Address: Chiyoda-ku, Tokyo," and "License number: 1234567890."

[0837] 2. The device sends the entered data and documents to the server

[0838] The terminal converts the entered data and documents into a digital format (PDF or image) and sends them to the server via API, which ensures data integrity and security, also using the HTTPS protocol.

[0839] 3. The server analyzes the received data

[0840] The server analyzes the received data and documents and uses a generative AI model to check for validity and compatibility, for example, ensuring the ID photo is clear and the name and license number match.

[0841] 4. The server requests that the data be corrected (if necessary)

[0842] If the analysis reveals any errors, the server notifies the user of the specific details and requests corrections. For example, requests for corrections may include "the photo is unclear" or "the name and license number do not match." The user then resubmits the corrected data.

[0843] 5. The server updates the license database

[0844] After correction, if the data is deemed to be correct, the server updates the license database. The new license information is confirmed and the next step is taken.

[0845] 6. The server issues a new license and sends the user instructions on how to mail it.

[0846] The server initiates the issuance process for the new driver's license and sends the user a notification to mail it to the registered address. For example, the notification may include something like, "Your new driver's license has been issued. It will be mailed to your registered address within a few days." This process also uses the SMTP protocol and the Push notification API.

[0847] Example prompt

[0848] Specific examples of accident registration

[0849] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[0850] Specific examples of license renewal

[0851] The user accesses the license renewal form on the police website on their PC and uploads the required information (e.g., "Name: Yamada Taro," "Address: Chiyoda-ku, Tokyo," "License number: 1234567890") and documents.

[0852] In this way, the present invention realizes improved efficiency in police work and increased public satisfaction.

[0853] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0854] Processing steps for the Incident Registration function

[0855] Step 1: User enters incident information

[0856] Input: The user accesses the police's online form using a smartphone or PC and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) For example, the user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[0857] Output: The information you enter becomes the data sent from the device.

[0858] Step 2: The device sends the entered data to the server

[0859] Input: The incident information entered by the user into the form.

[0860] Data processing and data calculation: The terminal converts the input accident information into JSON format and sends it to the server via API using the HTTPS protocol.

[0861] Specific operation: For example, the device generates JSON data such as {"accident type":"car collision", "date and time":"October 12, 2023", "location":"Shinjuku-ku, Tokyo"} and sends it to the server.

[0862] Output: The JSON data sent to the server.

[0863] Step 3: Parse the data received by the server

[0864] Input: JSON data received from the terminal.

[0865] Data processing and data calculation: The server stores the received JSON data in a temporary data store and then runs the generative AI model to analyze the data. The generative AI model completes incomplete information and detects abnormal data. For example, if the "type of accident" is missing, it completes the information as "car collision."

[0866] How it works: The server stores the data in storage such as Amazon S3 or Google Cloud Storage, then runs the generative AI model to analyze it.

[0867] Output: Imputed data as the analysis result.

[0868] Step 4: The server saves the data to the database

[0869] Input: Data parsed and imputed by the generative AI model.

[0870] Data Processing and Data Calculation: The server formats the final data and stores it in the accident registration database, and also sends notifications to the relevant police stations based on the accident location information.

[0871] What it does: The server stores the data in a relational database such as MySQL or PostgreSQL and sends notifications via SMS or email to relevant organizations.

[0872] Output: Accident information stored in the database and notifications sent.

[0873] Step 5: The server notifies the user that registration is complete

[0874] Input: Data stored in the accident registration database.

[0875] Data processing and data calculation: The server generates a registration completion notification message and sends it to the user's email address or messaging system.

[0876] Specific operation: The server sends an email using the SMTP protocol or sends a message using the Push notification API.

[0877] Output: Notification message "Accident registration completed."

[0878] License-related procedure processing steps

[0879] Step 1: User applies for license renewal online

[0880] Input: The user accesses the license renewal application form on the police website using a PC or smartphone, and enters and uploads the required information (name, address, current license number, etc.) and required documents (e.g., ID, photo, etc.).

[0881] Specific operation: For example, enter "Name: Yamada Taro", "Address: Chiyoda-ku, Tokyo", "License number: 1234567890", and upload an image file of the required identification.

[0882] Output: Input and uploaded data.

[0883] Step 2: The device sends the entered data and documents to the server

[0884] Input: Information entered and uploaded by users.

[0885] Data processing and data calculation: The terminal converts the entered data and documents into a digital format (PDF or image) and sends it to the server through an API using the HTTPS protocol.

[0886] Specific operation: The terminal converts the input data into PDF or image format and sends it to the server.

[0887] Output: Data and documents sent to the server.

[0888] Step 3: The server parses the received data

[0889] Input: Data and documents received from the terminal.

[0890] Data processing and data calculation: The server analyzes the incoming data and documents, and uses a generative AI model to verify the document contents and check for validity and compatibility, for example, whether the photo on the ID card is clear and whether the name and license number match.

[0891] What it does: The server runs the generative AI model to analyze data and documents.

[0892] Output: Analysis results (check results of validity and conformity of data and documents).

[0893] Step 4: Server requests data correction (if necessary)

[0894] Input: If there are any errors in the analysis results.

[0895] Data Processing and Data Arithmetic: The server identifies specific deficiencies and generates and sends a message to the user requesting corrections, such as "The photo is blurry" or "The name and license number do not match."

[0896] Specific operation: The server notifies the user by email using the SMTP protocol.

[0897] Output: A notification message of the correction request containing the details of the deficiency.

[0898] Step 5: The server updates the license database

[0899] Input: Data and documents submitted again after correction.

[0900] Data processing and data calculation: If the data is determined to be problem-free, the server registers and updates the new license information in the license database.

[0901] Specific operation: The server registers and updates data in a database such as MySQL or PostgreSQL.

[0902] Output: Updated license database.

[0903] Step 6: The server issues a new license and sends the user instructions on how to mail it.

[0904] Input: Updated license data.

[0905] Data processing and data calculation: The server initiates the new license issuance procedure, generates instructions for mailing the license, and notifies the user.

[0906] Specific operation: The server sends an invitation message using the SMTP protocol or the Push notification API.

[0907] Output: Notification of new driver's license issuance procedure.

[0908] In this way, this system will improve the efficiency of police work and increase public satisfaction.

[0909] (Application example 1)

[0910] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0911] In traditional police and security services, incident reports and accident information from users are often collected and analyzed manually, which not only lacks efficiency but also makes them prone to human error. Furthermore, speeding up incident response and providing feedback to users is also an issue. To solve these problems, there is a need for data analysis and automatic notification systems using generative AI models.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0913] In this invention, the server includes means for analyzing input data from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for collecting detailed information about the security incident from the user, means for converting the collected incident information into JSON format and sending it to the server, means for analyzing the received incident information using a generative AI model and completing the detailed information, and means for sending notifications to relevant personnel based on the analysis results. This automates the collection and analysis of security incident and accident information, enabling fast and efficient responses.

[0914] "Generative AI" is an artificial intelligence model that analyzes input data from users and, if necessary, fills in missing information or generates new data.

[0915] A "database" is a system for systematically storing and managing collected and analyzed data.

[0916] A "notification" is a message or alert that informs a user or relevant personnel of some information.

[0917] "Online Form" means a web-based interface for entering and submitting data over the Internet.

[0918] The "JSON format" is a format for expressing data in a structured text-based format, making it easier to exchange data.

[0919] A "server" is a computer system that provides various services such as data storage, analysis, and notification via a network.

[0920] A "security incident" is a safety-related occurrence such as unauthorized access, vandalism, or noise problems.

[0921] "Person in Charge" refers to the person or professional responsible for responding to and managing incidents and accidents.

[0922] "Incident Information" means detailed data about a security incident, including the date, time, location, people involved, and a detailed description.

[0923] The present invention is a system that automates reporting, analysis, and notification of security incidents using a generative AI model. Specific embodiments of the present invention are described below.

[0924] Security Incident Reporting Practices

[0925] 1. The user enters the security incident information

[0926] Users access the security service's application on their smartphones, enter details about the incident (type of incident, date and time, location, relevant information), and then press the "send" button.

[0927] 2. The device sends the entered data to the server

[0928] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0929] 3. The server analyzes the received data

[0930] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model, completing incomplete information and identifying suspicious data.

[0931] 4. The server saves the data to the database

[0932] Based on the analysis results, the final data is saved in the incident registration database, and notifications are sent to relevant personnel based on the incident location information.

[0933] 5. The server notifies the user that registration is complete

[0934] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[0935] Generate complete details

[0936] The server uses the generative AI model to generate appropriate detailed information if the incident information provided by the user is insufficient. For example, if the "detailed incident description" is missing, the server generates additional information based on each data element.

[0937] Hardware and software used

[0938] Hardware:

[0939] Smartphone: A device for users to enter incident information.

[0940] Server: A computer system for receiving, analyzing, storing, and notifying data.

[0941] software:

[0942] Generative AI model (e.g. GPT-3): Analyzes the incoming data and generates the necessary details.

[0943] JSON format: A format used to structure and transfer data.

[0944] API: An interface for exchanging data between a device and a server.

[0945] Database (e.g. MySQL): A system for storing analysis results.

[0946] Specific examples

[0947] Please use the following examples to further understand the functionality of this system.

[0948] Example 1: Security Incident Reporting

[0949] 1. The user enters "Type of incident: Vandalism," "Date and time: October 11, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[0950] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[0951] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[0952] 4. Store the final data in the incident registration database and send notifications to relevant personnel.

[0953] 5. Send an email notification to the user stating that the incident report has been completed.

[0954] Example prompt sentence:

[0955] Please complete any missing or incomplete information based on the following security incident data:

[0956] {

[0957] "incident_type": "vandalism",

[0958] "date_time": "2023-10-11 14:00",

[0959] "location": "Chuo-ku, Osaka",

[0960] "details": "The window of the convenience store was broken",

[0961] "reporter_contact": "example@example.com"

[0962] }

[0963] The above is a specific embodiment for carrying out the present invention, which automates the collection and analysis of security incident and accident information, enabling a quick and efficient response.

[0964] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0965] Step 1:

[0966] The user enters information about a security incident. The user accesses the application using a smartphone, enters the type of incident, date and time, location, and related information into an online form, and then presses the "Submit" button. The input here is specific details of the incident in text format.

[0967] Step 2:

[0968] The device sends the entered data to the server. The device (smartphone) converts the entered data into JSON format and sends it to the server via the API. At this time, the data is sent to the server in an appropriate format via the API, so data transfer is efficient and accurate.

[0969] Step 3:

[0970] The server saves the received data in a temporary data store. The server saves the received JSON data in a temporary data store for subsequent analysis. The input here is JSON format data, and the output is saved to the temporary data store.

[0971] Step 4:

[0972] The server analyzes the data using a generative AI model. The server inputs the saved JSON data into a generative AI model (e.g., GPT-3) and analyzes the data. During the analysis process, incomplete information is completed and suspicious data is identified. The input is JSON data, and the output is the completed detailed information and analysis results.

[0973] Step 5:

[0974] The server stores the analysis results in a database. The server then organizes the analysis results obtained by the generative AI model as final data and stores them in the incident registration database. The input here is the analysis results, and the output is the registered data stored in the database.

[0975] Step 6:

[0976] The server sends notifications to relevant personnel based on the analysis results. The server automatically sends notifications to relevant security personnel based on the data stored in the incident registration database. The input here is the registered data, and the output is a notification message to the personnel. Specific operations include automatically sending email notifications to the personnel's email addresses and push notifications to dedicated applications.

[0977] Step 7:

[0978] The server notifies the user that registration is complete. The server generates a message indicating that the incident report has been successfully completed and sends it to the user's email address or to a notification system via an application. The input here is the registration completion information, and the output is a notification message to the user.

[0979] This will enable the realization of a system that efficiently and quickly collects, analyzes, registers, and notifies security incidents through all processing steps.

[0980] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0981] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[0982] Accident registration function embodiment

[0983] 1. The user enters the accident information

[0984] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[0985] 2. The device sends the entered data to the server

[0986] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[0987] 3. The server analyzes the received data

[0988] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model and emotion engine. The generative AI model analyzes the content of the data, and the emotion engine analyzes the user's emotions when they input. For example, if the user is feeling very stressed or anxious, the system will select an appropriate response.

[0989] 4. The server saves the data to the database

[0990] Based on the analysis results, the final data is stored in the accident registration database. The results of the sentiment analysis are also recorded and notifications are sent to the relevant police stations if necessary.

[0991] 5. The server notifies the user that registration is complete

[0992] A message indicating the completion of registration is generated and sent to the user's email address or online messaging system. The emotion engine selects the message taking into account the user's feelings.

[0993] Implementation of license-related procedures

[0994] 1. The user applies for license renewal online

[0995] The user uses the device to access the license renewal application form on the police website, enters the required license information (name, address, current license number, etc.), and uploads the required documents (ID, photo, etc.).

[0996] 2. The device sends the entered data and documents to the server

[0997] The terminal sends the entered data and uploaded documents (PDF or image format) to the server.

[0998] 3. The server analyzes the received data

[0999] The server analyzes the received data and documents using a generative AI model and an emotion engine. The generative AI model checks the contents of the document, and the emotion engine analyzes the user's emotions. For example, if the user is feeling anxious, the server returns a reassuring message.

[1000] 4. The server requests that the data be corrected (if necessary)

[1001] If the analysis reveals any deficiencies, the server will send a correction request to the user. The emotion engine selects correction requests that take into account the user's emotions. The user then makes the corrections and submits the data and documents again.

[1002] 5. The server updates the database

[1003] If the data is verified as consistent, the server updates the license database and prepares to issue a new license.

[1004] 6. The server issues a new license and notifies you.

[1005] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also ensures that the notification message is sensitive to the user's emotions.

[1006] Specific examples

[1007] Specific examples of accident registration

[1008] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[1009] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[1010] 3. The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[1011] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[1012] 5. Send the user an emotionally sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[1013] Specific examples of license renewal

[1014] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1015] 2. The device converts the data into PDF and image format and sends it to the server.

[1016] 3. The server analyzes the received data using the generated AI model and emotion engine, and checks the document and the user's emotions.

[1017] 4. If there is a problem, the server will send a friendly request to the user to correct the problem and prompt them to re-enter the information.

[1018] 5. Once the data is verified, the server updates the license database.

[1019] 6. A new license is issued and a "new license has been sent" notification is sent to the user in an emotionally sensitive manner.

[1020] The above is a concrete example of how the present invention can be implemented. This system will improve the efficiency of police work and increase public satisfaction.

[1021] The processing flow will be explained below.

[1022] Processing steps of the accident registration function combined with the emotion engine

[1023] Step 1:

[1024] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[1025] Step 2:

[1026] The user completes the input and presses the "Submit" button.

[1027] Step 3:

[1028] The terminal converts the input data into JSON format.

[1029] Step 4:

[1030] The device sends the generated JSON data to the server via the API.

[1031] Step 5:

[1032] The server saves the received JSON data in a temporary data store.

[1033] Step 6:

[1034] The server sends the data to the generative AI model and begins analysis.

[1035] Step 7:

[1036] Generative AI models analyze, classify, and complete data.

[1037] Step 8:

[1038] The server sends the data to the emotion engine, which analyzes the emotion of the user's input.

[1039] Step 9:

[1040] The emotion engine analyzes the user's emotions and detects, for example, impatience or stress.

[1041] Step 10:

[1042] The generative AI model complements the data by combining the analysis results of the emotion engine.

[1043] Step 11:

[1044] The server stores the data in the accident registration database based on the analysis results.

[1045] Step 12:

[1046] The server sends a notification of the accident information to the relevant police station.

[1047] Step 13:

[1048] The server creates a message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[1049] Step 14:

[1050] The server sends a notification to the user that the accident registration has been completed.

[1051] License-related procedure processing steps combined with an emotion engine

[1052] Step 1:

[1053] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[1054] Step 2:

[1055] The user uploads the required documents (ID, photo, etc.).

[1056] Step 3:

[1057] The user completes the input and upload and presses the "Submit" button.

[1058] Step 4:

[1059] The device converts entered data and uploaded documents into PDF and image formats.

[1060] Step 5:

[1061] The terminal sends the generated data and documents to the server.

[1062] Step 6:

[1063] The server stores the received data and documents in a temporary data store.

[1064] Step 7:

[1065] The server sends the data and documents to the generative AI model, which begins the analysis.

[1066] Step 8:

[1067] A generative AI model analyzes the document content and checks for compatibility.

[1068] Step 9:

[1069] The server sends the data to the emotion engine to analyze the user's emotions.

[1070] Step 10:

[1071] The emotion engine analyzes the user's emotions and detects, for example, anxiety or doubt.

[1072] Step 11:

[1073] The generative AI model complements the data by combining the analysis results of the emotion engine.

[1074] Step 12:

[1075] The server updates the data in the license database based on the analysis results.

[1076] Step 13:

[1077] If correction is necessary, the server creates a correction request message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[1078] Step 14:

[1079] The user receives a message requesting correction, and re-enters and re-uploads the data.

[1080] Step 15:

[1081] The terminal sends the corrected data and documents back to the server.

[1082] Step 16:

[1083] The server regenerates the corrected data and documents and analyzes them using an AI model and emotion engine.

[1084] Step 17:

[1085] After the data integrity is verified, the server finally updates the license database.

[1086] Step 18:

[1087] The server initiates the process of issuing a new license.

[1088] Step 19:

[1089] The server creates a notification message for the user that takes into consideration emotions based on the analysis results of the emotion engine.

[1090] Step 20:

[1091] The server will send the user instructions on how to mail in a new license.

[1092] Example 2

[1093] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1094] In modern police work, accident registration and license-related procedures are typically performed manually, requiring a great deal of time and effort. Furthermore, the lack of appropriate responses based on user emotions can lead to a poor user experience. These issues significantly hinder work efficiency and public satisfaction. The present invention aims to solve these issues, digitize and automate police work, and improve the user experience.

[1095] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1096] In this invention, the server includes means for analyzing data input from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for analyzing the data input by the user using an emotion engine, means for adjusting the notification content based on the analyzed emotions, and storage means for temporarily saving the data and the analysis results. This automates accident registration and license-related procedures, enabling responses based on user emotions, improving the efficiency of police work and increasing public satisfaction.

[1097] "Generative AI" is a technology that uses artificial intelligence technology to generate new data or sentences based on given data or prompts.

[1098] An "emotion engine" is a technology that analyzes emotions from user input data and evaluates the user's psychological state.

[1099] A "database" is a system for efficiently storing, managing, and retrieving data.

[1100] An "online form" is a web-based input screen that allows a user to enter information over the Internet.

[1101] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that describes data in a text-based format, achieving both readability and efficiency in data exchange.

[1102] "API" is an abbreviation for Application Programming Interface, an interface for exchanging functions and data between different software systems.

[1103] A "temporary data store" is a storage medium or system for temporarily storing data.

[1104] A "storage means" is a system consisting of hardware and software for storing data and information.

[1105] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1106] "Notifying" is the act of conveying specific information to a user.

[1107] "Registering" is the act of storing data in an official database or system.

[1108] "User" means an individual or organization that uses the system.

[1109] "Accident details" refers to detailed data about a specific accident, such as the type of accident, date and time, location, and information about the people involved.

[1110] A "license-related renewal application" is the act of completing the online renewal procedure for a license that is approaching its expiration date.

[1111] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[1112] Accident registration function embodiment

[1113] The user

[1114] The user accesses the police's online form using a device (PC or smartphone). This form has fields for entering detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.). After the user enters all the information, they press the "Submit" button to proceed to the next processing step.

[1115] The terminal is

[1116] The device converts the input data into JSON format, which structures the data and facilitates subsequent processing. The device then transmits this JSON data to the police server via an API, a communication method that ensures accurate data transfer.

[1117] The server is

[1118] The server stores the received JSON data in a temporary data store, which acts as a preliminary stage for data analysis. The server then analyzes the data using a generative AI model, completing each field as needed. It also analyzes the user's emotions using an emotion engine. For example, if the user is experiencing high stress, it can flag the issue as needing special attention.

[1119] Once the analysis is complete, the server stores the final data in an incident registration database, which is structured for long-term storage and rapid retrieval. The results of the sentiment analysis are also recorded and notifications are sent to the ledger and relevant police stations. Notifications are sent through the appropriate internal systems.

[1120] The server is

[1121] Finally, the server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. The emotion engine selects a message that takes the user's feelings into consideration. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[1122] Implementation of license-related procedures

[1123] The user

[1124] Using the device, users access a license renewal application form on the police website, which includes fields for entering required information such as name, address, and current license number, and uploading required documents such as ID and photographs.

[1125] The terminal is

[1126] The terminal converts input data and uploaded documents into PDF or image format, then transmits the data to the server via API, with the communication encrypted to ensure safety and efficiency.

[1127] The server is

[1128] The server stores the received data and documents in a temporary data store. The data is then analyzed again using the generative AI model and emotion engine. The generative AI model examines the document in detail to identify inconsistencies and omissions. The emotion engine analyzes the user's emotions and considers measures to provide a sense of security.

[1129] If the analysis reveals any deficiencies, the server sends a correction request to the user. This correction request is also expressed in a gentle way using the emotion engine. For example, it might say, "Sorry for the inconvenience, but could you please check it again?" The user then makes the corrections and sends the data and document again.

[1130] The server is

[1131] Once the data is verified as accurate, the server updates the license database, which contains the new license information and prepares to issue a new license.

[1132] Finally, the server issues a new driver's license and sends the user instructions on how to mail it. This instruction is also sensitive, and includes a notification such as "Your new driver's license has been sent."

[1133] Specific examples

[1134] Specific examples of accident registration

[1135] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[1136] The terminal converts the input data into JSON format and sends it to the server via the API.

[1137] The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[1138] The final data is stored in the accident registration database and a notification is sent to the relevant police station.

[1139] Send the user a sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[1140] Specific examples of license renewal

[1141] The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1142] The device converts the data into PDF and image format and sends it to the server.

[1143] The server analyzes the received data using a generated AI model and emotion engine to verify the document and determine the user's emotions.

[1144] If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to re-enter the information.

[1145] Once the data is verified, the server updates the license database.

[1146] A new driver's license is issued and a notification that "your new driver's license has been sent" is sent to the user in an emotionally sensitive manner.

[1147] This system will improve the efficiency of police work and increase public satisfaction.

[1148] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1149] Step 1:

[1150] The user enters the accident information

[1151] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) by entering specific information in the input fields and pressing the "Send" button to send the input data to the device.

[1152] Input: Type of accident, date, time, location, and information of people involved

[1153] Output: Input data to the terminal

[1154] Step 2:

[1155] The device sends the entered data to the server.

[1156] The device converts the input data into JSON format and sends it to the server via API, where it is securely transferred to the server over the network.

[1157] Input: Accident information entered by the user

[1158] Output: Send JSON format data to the server

[1159] Step 3:

[1160] The server analyzes the received data

[1161] The server stores the received JSON data in a temporary data store. It then uses a generative AI model to analyze the data and fill in any incomplete parts. It then uses an emotion engine to analyze the user's emotions. The analysis results include detailed information about the accident and user emotion analysis data.

[1162] Input: Accident information data in JSON format

[1163] Output: Analyzed accident information data and user emotion data

[1164] Step 4:

[1165] The server stores it in the database

[1166] Based on the analysis results, the server stores the final data in the accident registration database, which is stored securely and can be accessed whenever necessary. It also records the results of the sentiment analysis and sends notifications to the relevant police stations if necessary. A notification system is used to prompt the relevant authorities to take appropriate action.

[1167] Input: Analyzed accident information data and user emotion data

[1168] Output: Save data in accident registration database, notify police station

[1169] Step 5:

[1170] The server notifies the user that registration is complete

[1171] The server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. Using an emotion engine, the message is selected with consideration for the user's emotions. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[1172] Input: Analysis results, emotion analysis results

[1173] Output: A message to inform the user

[1174] Step 6:

[1175] The user applies for license renewal online

[1176] Using their device, users access the license renewal application form on the police website, enter the required license information (name, address, current license number), and upload required documents such as ID and photos.

[1177] Input: License information, required documents

[1178] Output: Input data to the terminal

[1179] Step 7:

[1180] The device sends the entered data and documents to the server.

[1181] The device converts the entered data and uploaded documents into PDF or image format and sends them to the server via API, where the data is securely transferred over the network.

[1182] Input: License information and documents entered by the user

[1183] Output: Send PDF or image data to the server

[1184] Step 8:

[1185] The server parses the received data

[1186] The server stores the received data and documents in a temporary data store. It then analyzes the data using a generative AI model and emotion engine. The generative AI model checks the contents of the uploaded documents, and the emotion engine analyzes the user's emotions. The analysis results in license information verification data and user emotion data.

[1187] Input: License information data in PDF or image format

[1188] Output: Analyzed license information data and user emotion data

[1189] Step 9:

[1190] The server requests that the data be corrected (if necessary)

[1191] If the analysis reveals any deficiencies, the server will send a correction request to the user. Using an emotion engine, the correction request will be selected with consideration for the user's emotions. The user will then make the corrections and resubmit the data and document.

[1192] Input: Analyzed license information data, deficiency information

[1193] Output: Request for correction to user, corrected data

[1194] Step 10:

[1195] The server updates the database

[1196] If the data is found to be consistent, the server updates the license database, and the new license information is registered in the database.

[1197] Input: License information data that has been verified for integrity

[1198] Output: License database update

[1199] Step 11:

[1200] The server issues a new license and notifies you.

[1201] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also considers the user's emotions when sending the notification message. A reassuring message is sent saying, "Your new driver's license has been sent."

[1202] Input: License information data

[1203] Output: Issuance of new license, notification message to user

[1204] (Application example 2)

[1205] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1206] In traditional police work, registering accident information and license-related procedures are often manual and inefficient. It is also difficult to respond in a way that takes user feelings into consideration, often resulting in low user satisfaction. This situation calls for improvement in today's digital age. In particular, in emergencies and when trouble occurs, a swift and appropriate response is required, making it important to increase users' sense of security.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1208] In this invention, the server includes means for analyzing input data from a user using a generation AI, means for registering the analysis results in a database, means for notifying the user of completion of registration, means for analyzing the received data with an emotion analysis engine and generating a message that takes the user's emotions into consideration, and means for instructing the user on appropriate procedures depending on the situation. This not only enables efficient analysis and registration of input data from a user, but also enables responses that take the user's emotions into consideration, making it possible to improve the user's sense of security through quick and appropriate responses.

[1209] "Generative AI" is an artificial intelligence model for performing tasks such as natural language processing and image analysis, and is a technology that analyzes input data from users and generates appropriate results.

[1210] An "emotion analysis engine" is a system for identifying emotions from user input data and generating responses based on those emotions.

[1211] A "database" is an information system for efficiently storing, managing, and searching analytical results and other data.

[1212] "Online Form" means a web-based form that allows users to enter and submit information via the Internet.

[1213] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data.

[1214] An "API" is an interface for exchanging data between applications and servers, providing a way for programs to communicate with each other.

[1215] "Notification means" refers to the function or method for sending messages or warnings from the system to the user.

[1216] "License-related procedures" refers to the process of carrying out official procedures such as applying for, renewing, or reissuing a license.

[1217] "Accident registration" is the process of recording details of a road accident or other incident on the system.

[1218] "Messages that take the user's emotions into consideration" means using an emotion analysis engine to evaluate the user's emotional state and generating appropriate and reassuring messages based on the results.

[1219] "Means for instructing appropriate procedures" refers to a function that indicates to the user the specific next action to be taken based on the analysis results and emotion analysis results.

[1220] Accident registration function embodiment

[1221] The accident registration function of the present invention is a system that uses a generative AI model and an emotion analysis engine to efficiently analyze and register accident information from users.

[1222] System configuration

[1223] 1. User's device (PC or smartphone)

[1224] Users access the police's online form using their own device and enter details about the accident, such as the type of accident, date and time, location, and information about the people involved.

[1225] 2. Data transmission and analysis

[1226] The device converts the input accident information into JSON format and sends it to the server via API. The server stores the received data in a temporary data store and analyzes the accident information using the generative AI model.

[1227] 3. Emotion analysis

[1228] The server uses an emotion analysis engine to analyze the user's emotions. For example, if the user is feeling strong stress or anxiety when entering the incident, the server will take appropriate measures to respond.

[1229] 4. Registering in the database

[1230] The analyzed accident information is ultimately stored in an accident registration database. The analyzed emotional information is also recorded and notified to relevant organizations as necessary.

[1231] 5. Notification

[1232] Once the data is registered, the server generates an emotionally sensitive message and sends a notification to the user's email address or online messaging system.

[1233] Hardware and software used

[1234] Device: PC or smartphone

[1235] Generative AI models: e.g., GPT-4

[1236] Sentiment analysis engine: e.g., Google AutoML Natural Language

[1237] Database: Accident registration database

[1238] API: RESTful API

[1239] Specific examples

[1240] 1. The user enters details such as "Type of accident: Collision between two vehicles," "Date and time: November 1, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[1241] 2. The data entered by the terminal is converted to JSON format and sent to the server.

[1242] 3. The server analyzes the accident information using a generative AI model and evaluates the user's emotions using an emotion analysis engine.

[1243] 4. Register accident information and emotional information in the database.

[1244] 5. A sensitive message is sent to the user saying, "Accident registration has been completed. Thank you for your cooperation."

[1245] Example prompt sentence:

[1246] "You idiot. Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[1247] Implementation of license-related procedures

[1248] The license-related procedure function of the present invention is a system that allows users to efficiently renew or reissue licenses online.

[1249] System configuration

[1250] 1. User's device (PC or smartphone)

[1251] Users access the online license renewal form and enter the required information (name, address, license number, etc.) and upload the required documents (ID, photo, etc.).

[1252] 2. Data transmission and analysis

[1253] The device converts the entered data and uploaded documents into JSON and image formats and sends them to the server, which uses a generative AI model to review the document content and an emotion analysis engine to evaluate the user's emotions.

[1254] 3. Pointing out and correcting deficiencies

[1255] If there are any errors in the data, the server will send a friendly message to the user requesting corrections, based on the sentiment analysis engine.

[1256] 4. Updating the database

[1257] After the data has been successfully verified, the server updates the license database and prepares to issue a new license.

[1258] 5. Notification

[1259] When a new license is issued, the server generates an emotionally sensitive message and sends a notification to the user.

[1260] Hardware and software used

[1261] Device: PC or smartphone

[1262] Generative AI models: e.g., GPT-4

[1263] Sentiment analysis engine: e.g., Google AutoML Natural Language

[1264] Database: License Database

[1265] API: RESTful API

[1266] Specific examples

[1267] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1268] 2. The device converts the data into JSON and image format and sends it to the server.

[1269] 3. The server analyzes the received data using the generated AI model and emotion analysis engine to verify the document and determine the user's emotions.

[1270] 4. If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to try again.

[1271] 5. Once the data is verified, the server updates the license database and a new license is prepared.

[1272] 6. A new license is issued and a notification that "your new license has been sent" is sent to the user in an emotionally sensitive manner.

[1273] Example prompt sentence:

[1274] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[1275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1276] Processing steps for the Incident Registration function

[1277] Step 1:

[1278] A user accesses the online form from a smartphone or PC, enters details about the accident (type of accident, date and time, location, information about the people involved, etc.), and presses the "Submit" button. The entered data is captured as form fields.

[1279] Input: Accident details (text form)

[1280] Output: Detailed information about the entered accident (text data)

[1281] Step 2:

[1282] The device converts the detailed information of the accident entered by the user into JSON format, and the converted JSON data is sent to the server via the API.

[1283] Input: Detailed information about the accident entered (text data)

[1284] Output: Accident data in JSON format

[1285] Step 3:

[1286] The server stores the received JSON data in a temporary data store, and then uses the generative AI model to analyze the accident information. Specifically, the details of the accident and related information are analyzed, and incomplete parts are filled in.

[1287] Input: Accident data in JSON format

[1288] Output: Analysis results (text data)

[1289] Step 4:

[1290] The server uses an emotion analysis engine to analyze the input data and evaluate the user's emotions, for example, whether the user is feeling stressed or anxious, and selects an appropriate response.

[1291] Input: Detailed information about the accident entered (JSON data)

[1292] Output: Emotion evaluation results (text data)

[1293] Step 5:

[1294] The server integrates the analyzed accident information with the emotion evaluation results and stores them in the accident registration database. Based on the analysis results, notifications are sent to relevant authorities (e.g., police stations).

[1295] Input: Analysis results and emotion evaluation results

[1296] Output: Accident information stored in the database

[1297] Step 6:

[1298] Based on the results of the emotion analysis, the server sends a notification to the user that registration has been completed. The notification message is sensitive to the user's emotions and is sent via email or an online messaging system.

[1299] Input: Registration completion message

[1300] Output: Notification sent to the user

[1301] License-related procedure processing steps

[1302] Step 1:

[1303] The user accesses the police's online form on a PC or smartphone, enters the necessary information for license renewal (name, address, license number, etc.), and uploads the necessary documents (identification, photo, etc.).

[1304] Input: Required license information and documents (text and image format)

[1305] Output: Input and uploaded data

[1306] Step 2:

[1307] The device converts input data and uploaded documents into JSON and image formats, which are then sent to the server via API.

[1308] Input: License information and documents (text and image formats)

[1309] Output: JSON and image data

[1310] Step 3:

[1311] The server stores the received data in a temporary data store and analyzes it using a generative AI model and a sentiment analysis engine. The generative AI model checks the document content, and the sentiment analysis evaluates the user's sentiment.

[1312] Input: JSON and image data

[1313] Output: Analysis results and emotion evaluation results (text data)

[1314] Step 4:

[1315] The server determines whether the data needs to be corrected based on the analysis results. If so, the server sends a correction request to the user in friendly language based on the emotion analysis results.

[1316] Input: Analysis results and emotion evaluation results

[1317] Output: Correction request message

[1318] Step 5:

[1319] The user corrects the input according to the correction request and submits the data again. The server reanalyzes the data and, if it is consistent, updates the license database.

[1320] Input: Corrected data (text and image format)

[1321] Output: Updated license database

[1322] Step 6:

[1323] The server processes the new license and generates a sensitive message to notify the user, which can be sent via email or an online messaging system.

[1324] Input: Result of issuing procedure and notification message

[1325] Output: Notification sent to the user

[1326] Examples of prompt statements

[1327] "Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[1328] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[1329] 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.

[1330] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1331] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1332] [Third embodiment]

[1333] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1334] 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.

[1335] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1336] 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.

[1337] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1338] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1339] 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.

[1340] 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.

[1341] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1342] 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.

[1343] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1344] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1345] The present invention is a system that uses generative AI models to digitize police work and automate procedures such as accident registration, license-related procedures, and crime response. Specific embodiments of the present invention are described below.

[1346] The system involves a series of processes for collecting input data from users, analyzing it using a generative AI model, and registering the results in a database.

[1347] Accident registration function embodiment

[1348] 1. The user enters the accident information

[1349] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[1350] 2. The device sends the entered data to the server

[1351] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[1352] 3. The server analyzes the received data

[1353] The server stores the received JSON data in a temporary data store and then analyzes it using a generative AI model, filling in incomplete information and identifying suspicious data.

[1354] 4. The server saves the data to the database

[1355] Based on the analysis results, the final data is stored in the accident registration database, and a notification is sent to the relevant police station based on the location of the accident.

[1356] 5. The server notifies the user that registration is complete

[1357] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[1358] Implementation of license-related procedures

[1359] 1. The user applies for license renewal online

[1360] The user uses the device to access the license renewal application form on the police website, enters the necessary license information (such as name, address, and current license number), and uploads the necessary documents (such as identification and photographs).

[1361] 2. The device sends the entered data and documents to the server

[1362] The device sends the entered data and documents (PDF or image format) to the server, which ensures data integrity and security.

[1363] 3. The server analyzes the received data

[1364] The server analyzes the received data and documents, verifies the document content using a generative AI model, and checks for validity and suitability.

[1365] 4. The server requests that the data be corrected (if necessary)

[1366] If the analysis reveals any defects, the server will send a correction request to the user, who will then correct the data and submit it again.

[1367] 5. The server updates the license database

[1368] If the corrected data is deemed acceptable, the server updates the license database, which prepares the new license for issuance.

[1369] 6. The server issues a new license and sends the user instructions on how to mail it.

[1370] The server will issue a new driver's license and send instructions for mailing the new license to the user via email. The user will then receive the instructions and the new license will be mailed to the registered address.

[1371] Specific examples

[1372] Specific examples of accident registration

[1373] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[1374] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[1375] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[1376] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[1377] 5. Send an email notification to the user stating that the accident registration has been completed.

[1378] Specific examples of license renewal

[1379] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1380] 2. The device converts the data into PDF and image format and sends it to the server.

[1381] 3. The server analyzes the received data using the generated AI model and verifies the documents.

[1382] 4. If there is a problem, the server sends a correction request to the user and prompts them to re-enter the information.

[1383] 5. Once the data is verified, the server updates the license database.

[1384] 6. Your new license will be issued and instructions on how to mail it will be sent to you.

[1385] The above is a specific embodiment of the present invention, which will improve the efficiency of police work and increase public satisfaction.

[1386] The processing flow will be explained below.

[1387] Processing steps for the Incident Registration function

[1388] Step 1:

[1389] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[1390] Step 2:

[1391] The user completes the input and presses the "Submit" button.

[1392] Step 3:

[1393] The terminal converts the input data into JSON format.

[1394] Step 4:

[1395] The device sends the generated JSON data to the server via the API.

[1396] Step 5:

[1397] The server parses the received JSON data and converts it into the appropriate format.

[1398] Step 6:

[1399] The server saves the transformed data to a temporary data store.

[1400] Step 7:

[1401] The server sends the data to the generative AI model and begins analysis.

[1402] Step 8:

[1403] A generative AI model analyzes incoming data and detects incomplete information or anomalous data.

[1404] Step 9:

[1405] Generative AI models generate the additional information needed to complement the data.

[1406] Step 10:

[1407] Based on the analysis results, the server stores the final data in the accident registration database.

[1408] Step 11:

[1409] The server sends a notification to the relevant police station.

[1410] Step 12:

[1411] The server sends a notification to the user that the accident registration has been completed.

[1412] License-related procedure processing steps

[1413] Step 1:

[1414] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[1415] Step 2:

[1416] The user uploads the required documents (ID, photo, etc.).

[1417] Step 3:

[1418] The user completes the input and upload and presses the "Submit" button.

[1419] Step 4:

[1420] The device converts entered data and uploaded documents into PDF and image formats.

[1421] Step 5:

[1422] The terminal sends the generated data and documents to the server.

[1423] Step 6:

[1424] The server prepares to parse the received data and documents.

[1425] Step 7:

[1426] The server sends the data and documents to the generative AI model and begins analysis.

[1427] Step 8:

[1428] A generative AI model analyzes the content of incoming documents and checks their validity and suitability.

[1429] Step 9:

[1430] Based on the analysis results, the server sends a correction request to the user as necessary.

[1431] Step 10:

[1432] The user makes corrections and submits the data and documents again.

[1433] Step 11:

[1434] The server re-analyzes and verifies the corrected data and documents.

[1435] Step 12:

[1436] The server checks the integrity of the data and then updates the license database.

[1437] Step 13:

[1438] The server processes the issuance of a new license.

[1439] Step 14:

[1440] The server will send the user instructions on how to mail in a new license.

[1441] Example 1

[1442] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1443] In conventional police work, procedures related to accident registration, license renewal, etc. are complicated and require a lot of manpower and time. This often leads to delays in procedures, information omissions, and inaccurate data registration. In addition, users have to physically visit the police station, which requires time and effort. It is necessary to solve these issues and improve the efficiency of police work and user convenience.

[1444] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1445] In this invention, the server includes means for accepting input data from a user via an online form, means for converting the accepted data into JSON format and sending it to the server, means for saving the accepted data in a temporary data store, means for analyzing the accepted data using a generative AI model and completing incomplete information, means for registering the analysis results in a database, and means for notifying the user of completion of registration, thereby enabling the digitalization and automation of police work.

[1446] 1. "User"

[1447] "User" refers to an individual or organization that uses this system, specifically, a person who provides information related to accident registration and license renewal.

[1448] 2. "Online Form"

[1449] "Online Form" means an electronic input screen for entering and submitting data over the Internet.

[1450] 3. “JSON format”

[1451] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format that describes data in key-value pair format.

[1452] 4. "Server"

[1453] "Server" refers to a centralized computer system that performs a series of processes such as receiving, analyzing, storing, and notifying data.

[1454] 5. "Data Store"

[1455] "Data store" refers to a storage area for temporarily or permanently storing data.

[1456] 6. "Generative AI Models"

[1457] "Generative AI model" refers to an algorithm and its implementation that uses artificial intelligence technology to analyze, complement, and generate data.

[1458] 7. "Database"

[1459] "Database" refers to an electronic information management system for efficiently storing, retrieving, and managing structured information.

[1460] 8. “Notification”

[1461] "Notification" refers to the act of transmitting processing results or necessary information to users or relevant institutions via email or messaging systems.

[1462] 9. "Accident Information"

[1463] "Accident information" refers to detailed data about traffic accidents, etc., including the type of accident, date and time, location, and information about the people involved.

[1464] 10. "License Renewal Application"

[1465] "License Renewal Application" refers to the application process for completing the driver's license renewal procedure online.

[1466] 11. "Identification Card"

[1467] "Identification" refers to an official document used to verify a person's identity, such as a passport or driver's license.

[1468] 12. "Documents"

[1469] "Documents" refers to documents and images required for license-related procedures, including identification documents and photographs.

[1470] 13. “Analysis”

[1471] "Analysis" refers to the process of analyzing received data to extract necessary information or fill in any incomplete parts.

[1472] 14. "Complement"

[1473] "Completion" refers to the act of adding incomplete information or missing data to make the data complete.

[1474] 15. "Defects"

[1475] "Defect" refers to a state in which there are omissions or errors in data or documents and the required conditions are not met.

[1476] This invention is a system that supports the digitization and automation of police operations such as accident registration, license renewal, etc. This system collects input data from users, analyzes it using a generative AI model, and provides a series of processes for registering the data in a database and notifying users.

[1477] The system uses a user's device (such as a PC or smartphone), a central server, a data store, and a generative AI model. Specific embodiments are described below.

[1478] Accident registration function embodiment

[1479] 1. The user enters the accident information

[1480] The user uses the device to access the police's online form. For example, the user logs in to the police's official website through a web browser and enters detailed information about the accident (such as the type of accident, date and time, location, and information about the people involved) into the form. For example, this includes input such as "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[1481] 2. The device sends the entered data to the server

[1482] The terminal converts the data entered by the user into JSON format and sends it to the server via API, which ensures efficient and secure data transfer, specifically using the HTTPS protocol.

[1483] 3. The server analyzes the received data

[1484] The server stores the received JSON data in a temporary data store, for example, using cloud storage such as Amazon S3 or Google Cloud Storage. The server then analyzes the data using a generative AI model to fill in any incomplete information. This process fills in any data detected as unknown or missing.

[1485] 4. The server saves the data to the database

[1486] Based on the analysis results, the server formats the final data and stores it in the accident registration database, using a relational database such as MySQL or PostgreSQL, and also uses SMS, email, and webhooks to send automatic notifications to the relevant police stations based on the location of the accident.

[1487] 5. The server notifies the user that registration is complete

[1488] The server generates a message indicating the successful registration and sends a notification to the user's email address or messaging platform. An example notification might include "Accident registration completed. Registration number is 12345." This process uses the SMTP protocol and a Push notification API.

[1489] Implementation of license-related procedures

[1490] 1. The user applies for license renewal online

[1491] The user accesses the police website using a device and opens the license renewal application form. The form prompts the user to enter or upload the necessary license information (such as name, address, and current license number) and necessary documents (such as identification and photographs). For example, the user may enter information such as "Name: Taro Yamada," "Address: Chiyoda-ku, Tokyo," and "License number: 1234567890."

[1492] 2. The device sends the entered data and documents to the server

[1493] The terminal converts the entered data and documents into a digital format (PDF or image) and sends them to the server via API, which ensures data integrity and security, also using the HTTPS protocol.

[1494] 3. The server analyzes the received data

[1495] The server analyzes the received data and documents and uses a generative AI model to check for validity and compatibility, for example, ensuring the ID photo is clear and the name and license number match.

[1496] 4. The server requests that the data be corrected (if necessary)

[1497] If the analysis reveals any errors, the server notifies the user of the specific details and requests corrections. For example, requests for corrections may include "the photo is unclear" or "the name and license number do not match." The user then resubmits the corrected data.

[1498] 5. The server updates the license database

[1499] After correction, if the data is deemed to be correct, the server updates the license database. The new license information is confirmed and the next step is taken.

[1500] 6. The server issues a new license and sends the user instructions on how to mail it.

[1501] The server initiates the issuance process for the new driver's license and sends the user a notification to mail it to the registered address. For example, the notification may include something like, "Your new driver's license has been issued. It will be mailed to your registered address within a few days." This process also uses the SMTP protocol and the Push notification API.

[1502] Example prompt

[1503] Specific examples of accident registration

[1504] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[1505] Specific examples of license renewal

[1506] The user accesses the license renewal form on the police website on their PC and uploads the required information (e.g., "Name: Yamada Taro," "Address: Chiyoda-ku, Tokyo," "License number: 1234567890") and documents.

[1507] In this way, the present invention realizes improved efficiency in police work and increased public satisfaction.

[1508] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1509] Processing steps for the Incident Registration function

[1510] Step 1: User enters incident information

[1511] Input: The user accesses the police's online form using a smartphone or PC and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) For example, the user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[1512] Output: The information you enter becomes the data sent from the device.

[1513] Step 2: The device sends the entered data to the server

[1514] Input: The incident information entered by the user into the form.

[1515] Data processing and data calculation: The terminal converts the input accident information into JSON format and sends it to the server via API using the HTTPS protocol.

[1516] Specific operation: For example, the device generates JSON data such as {"accident type":"car collision", "date and time":"October 12, 2023", "location":"Shinjuku-ku, Tokyo"} and sends it to the server.

[1517] Output: The JSON data sent to the server.

[1518] Step 3: Parse the data received by the server

[1519] Input: JSON data received from the terminal.

[1520] Data processing and data calculation: The server stores the received JSON data in a temporary data store and then runs the generative AI model to analyze the data. The generative AI model completes incomplete information and detects abnormal data. For example, if the "type of accident" is missing, it completes the information as "car collision."

[1521] How it works: The server stores the data in storage such as Amazon S3 or Google Cloud Storage, then runs the generative AI model to analyze it.

[1522] Output: Imputed data as the analysis result.

[1523] Step 4: The server saves the data to the database

[1524] Input: Data parsed and imputed by the generative AI model.

[1525] Data Processing and Data Calculation: The server formats the final data and stores it in the accident registration database, and also sends notifications to the relevant police stations based on the accident location information.

[1526] What it does: The server stores the data in a relational database such as MySQL or PostgreSQL and sends notifications via SMS or email to relevant organizations.

[1527] Output: Accident information stored in the database and notifications sent.

[1528] Step 5: The server notifies the user that registration is complete

[1529] Input: Data stored in the accident registration database.

[1530] Data processing and data calculation: The server generates a registration completion notification message and sends it to the user's email address or messaging system.

[1531] Specific operation: The server sends an email using the SMTP protocol or sends a message using the Push notification API.

[1532] Output: Notification message "Accident registration completed."

[1533] License-related procedure processing steps

[1534] Step 1: User applies for license renewal online

[1535] Input: The user accesses the license renewal application form on the police website using a PC or smartphone, and enters and uploads the required information (name, address, current license number, etc.) and required documents (e.g., ID, photo, etc.).

[1536] Specific operation: For example, enter "Name: Yamada Taro", "Address: Chiyoda-ku, Tokyo", "License number: 1234567890", and upload an image file of the required identification.

[1537] Output: Input and uploaded data.

[1538] Step 2: The device sends the entered data and documents to the server

[1539] Input: Information entered and uploaded by users.

[1540] Data processing and data calculation: The terminal converts the entered data and documents into a digital format (PDF or image) and sends it to the server through an API using the HTTPS protocol.

[1541] Specific operation: The terminal converts the input data into PDF or image format and sends it to the server.

[1542] Output: Data and documents sent to the server.

[1543] Step 3: The server parses the received data

[1544] Input: Data and documents received from the terminal.

[1545] Data processing and data calculation: The server analyzes the incoming data and documents, and uses a generative AI model to verify the document contents and check for validity and compatibility, for example, whether the photo on the ID card is clear and whether the name and license number match.

[1546] What it does: The server runs the generative AI model to analyze data and documents.

[1547] Output: Analysis results (check results of validity and conformity of data and documents).

[1548] Step 4: Server requests data correction (if necessary)

[1549] Input: If there are any errors in the analysis results.

[1550] Data Processing and Data Arithmetic: The server identifies specific deficiencies and generates and sends a message to the user requesting corrections, such as "The photo is blurry" or "The name and license number do not match."

[1551] Specific operation: The server notifies the user by email using the SMTP protocol.

[1552] Output: A notification message of the correction request containing the details of the deficiency.

[1553] Step 5: The server updates the license database

[1554] Input: Data and documents submitted again after correction.

[1555] Data processing and data calculation: If the data is determined to be problem-free, the server registers and updates the new license information in the license database.

[1556] Specific operation: The server registers and updates data in a database such as MySQL or PostgreSQL.

[1557] Output: Updated license database.

[1558] Step 6: The server issues a new license and sends the user instructions on how to mail it.

[1559] Input: Updated license data.

[1560] Data processing and data calculation: The server initiates the new license issuance procedure, generates instructions for mailing the license, and notifies the user.

[1561] Specific operation: The server sends an invitation message using the SMTP protocol or the Push notification API.

[1562] Output: Notification of new driver's license issuance procedure.

[1563] In this way, this system will improve the efficiency of police work and increase public satisfaction.

[1564] (Application example 1)

[1565] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1566] In traditional police and security services, incident reports and accident information from users are often collected and analyzed manually, which not only lacks efficiency but also makes them prone to human error. Furthermore, speeding up incident response and providing feedback to users is also an issue. To solve these problems, there is a need for data analysis and automatic notification systems using generative AI models.

[1567] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1568] In this invention, the server includes means for analyzing input data from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for collecting detailed information about the security incident from the user, means for converting the collected incident information into JSON format and sending it to the server, means for analyzing the received incident information using a generative AI model and completing the detailed information, and means for sending notifications to relevant personnel based on the analysis results. This automates the collection and analysis of security incident and accident information, enabling fast and efficient responses.

[1569] "Generative AI" is an artificial intelligence model that analyzes input data from users and, if necessary, fills in missing information or generates new data.

[1570] A "database" is a system for systematically storing and managing collected and analyzed data.

[1571] A "notification" is a message or alert that informs a user or relevant personnel of some information.

[1572] "Online Form" means a web-based interface for entering and submitting data over the Internet.

[1573] The "JSON format" is a format for expressing data in a structured text-based format, making it easier to exchange data.

[1574] A "server" is a computer system that provides various services such as data storage, analysis, and notification via a network.

[1575] A "security incident" is a safety-related occurrence such as unauthorized access, vandalism, or noise problems.

[1576] "Person in Charge" refers to the person or professional responsible for responding to and managing incidents and accidents.

[1577] "Incident Information" means detailed data about a security incident, including the date, time, location, people involved, and a detailed description.

[1578] The present invention is a system that automates reporting, analysis, and notification of security incidents using a generative AI model. Specific embodiments of the present invention are described below.

[1579] Security Incident Reporting Practices

[1580] 1. The user enters the security incident information

[1581] Users access the security service's application on their smartphones, enter details about the incident (type of incident, date and time, location, relevant information), and then press the "send" button.

[1582] 2. The device sends the entered data to the server

[1583] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[1584] 3. The server analyzes the received data

[1585] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model, completing incomplete information and identifying suspicious data.

[1586] 4. The server saves the data to the database

[1587] Based on the analysis results, the final data is saved in the incident registration database, and notifications are sent to relevant personnel based on the incident location information.

[1588] 5. The server notifies the user that registration is complete

[1589] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[1590] Generate complete details

[1591] The server uses the generative AI model to generate appropriate detailed information if the incident information provided by the user is insufficient. For example, if the "detailed incident description" is missing, the server generates additional information based on each data element.

[1592] Hardware and software used

[1593] Hardware:

[1594] Smartphone: A device for users to enter incident information.

[1595] Server: A computer system for receiving, analyzing, storing, and notifying data.

[1596] software:

[1597] Generative AI model (e.g. GPT-3): Analyzes the incoming data and generates the necessary details.

[1598] JSON format: A format used to structure and transfer data.

[1599] API: An interface for exchanging data between a device and a server.

[1600] Database (e.g. MySQL): A system for storing analysis results.

[1601] Specific examples

[1602] Please use the following examples to further understand the functionality of this system.

[1603] Example 1: Security Incident Reporting

[1604] 1. The user enters "Type of incident: Vandalism," "Date and time: October 11, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[1605] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[1606] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[1607] 4. Store the final data in the incident registration database and send notifications to relevant personnel.

[1608] 5. Send an email notification to the user stating that the incident report has been completed.

[1609] Example prompt sentence:

[1610] Please complete any missing or incomplete information based on the following security incident data:

[1611] {

[1612] "incident_type": "vandalism",

[1613] "date_time": "2023-10-11 14:00",

[1614] "location": "Chuo-ku, Osaka",

[1615] "details": "The window of the convenience store was broken",

[1616] "reporter_contact": "example@example.com"

[1617] }

[1618] The above is a specific embodiment for carrying out the present invention, which automates the collection and analysis of security incident and accident information, enabling a quick and efficient response.

[1619] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1620] Step 1:

[1621] The user enters information about a security incident. The user accesses the application using a smartphone, enters the type of incident, date and time, location, and related information into an online form, and then presses the "Submit" button. The input here is specific details of the incident in text format.

[1622] Step 2:

[1623] The device sends the entered data to the server. The device (smartphone) converts the entered data into JSON format and sends it to the server via the API. At this time, the data is sent to the server in an appropriate format via the API, so data transfer is efficient and accurate.

[1624] Step 3:

[1625] The server saves the received data in a temporary data store. The server saves the received JSON data in a temporary data store for subsequent analysis. The input here is JSON format data, and the output is saved to the temporary data store.

[1626] Step 4:

[1627] The server analyzes the data using a generative AI model. The server inputs the saved JSON data into a generative AI model (e.g., GPT-3) and analyzes the data. During the analysis process, incomplete information is completed and suspicious data is identified. The input is JSON data, and the output is the completed detailed information and analysis results.

[1628] Step 5:

[1629] The server stores the analysis results in a database. The server then organizes the analysis results obtained by the generative AI model as final data and stores them in the incident registration database. The input here is the analysis results, and the output is the registered data stored in the database.

[1630] Step 6:

[1631] The server sends notifications to relevant personnel based on the analysis results. The server automatically sends notifications to relevant security personnel based on the data stored in the incident registration database. The input here is the registered data, and the output is a notification message to the personnel. Specific operations include automatically sending email notifications to the personnel's email addresses and push notifications to dedicated applications.

[1632] Step 7:

[1633] The server notifies the user that registration is complete. The server generates a message indicating that the incident report has been successfully completed and sends it to the user's email address or to a notification system via an application. The input here is the registration completion information, and the output is a notification message to the user.

[1634] This will enable the realization of a system that efficiently and quickly collects, analyzes, registers, and notifies security incidents through all processing steps.

[1635] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1636] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[1637] Accident registration function embodiment

[1638] 1. The user enters the accident information

[1639] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[1640] 2. The device sends the entered data to the server

[1641] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[1642] 3. The server analyzes the received data

[1643] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model and emotion engine. The generative AI model analyzes the content of the data, and the emotion engine analyzes the user's emotions when they input. For example, if the user is feeling very stressed or anxious, the system will select an appropriate response.

[1644] 4. The server saves the data to the database

[1645] Based on the analysis results, the final data is stored in the accident registration database. The results of the sentiment analysis are also recorded and notifications are sent to the relevant police stations if necessary.

[1646] 5. The server notifies the user that registration is complete

[1647] A message indicating the completion of registration is generated and sent to the user's email address or online messaging system. The emotion engine selects the message taking into account the user's feelings.

[1648] Implementation of license-related procedures

[1649] 1. The user applies for license renewal online

[1650] The user uses the device to access the license renewal application form on the police website, enters the required license information (name, address, current license number, etc.), and uploads the required documents (ID, photo, etc.).

[1651] 2. The device sends the entered data and documents to the server

[1652] The terminal sends the entered data and uploaded documents (PDF or image format) to the server.

[1653] 3. The server analyzes the received data

[1654] The server analyzes the received data and documents using a generative AI model and an emotion engine. The generative AI model checks the contents of the document, and the emotion engine analyzes the user's emotions. For example, if the user is feeling anxious, the server returns a reassuring message.

[1655] 4. The server requests that the data be corrected (if necessary)

[1656] If the analysis reveals any deficiencies, the server will send a correction request to the user. The emotion engine selects correction requests that take into account the user's emotions. The user then makes the corrections and submits the data and documents again.

[1657] 5. The server updates the database

[1658] If the data is verified as consistent, the server updates the license database and prepares to issue a new license.

[1659] 6. The server issues a new license and notifies you.

[1660] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also ensures that the notification message is sensitive to the user's emotions.

[1661] Specific examples

[1662] Specific examples of accident registration

[1663] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[1664] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[1665] 3. The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[1666] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[1667] 5. Send the user an emotionally sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[1668] Specific examples of license renewal

[1669] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1670] 2. The device converts the data into PDF and image format and sends it to the server.

[1671] 3. The server analyzes the received data using the generated AI model and emotion engine, and checks the document and the user's emotions.

[1672] 4. If there is a problem, the server will send a friendly request to the user to correct the problem and prompt them to re-enter the information.

[1673] 5. Once the data is verified, the server updates the license database.

[1674] 6. A new license is issued and a "new license has been sent" notification is sent to the user in an emotionally sensitive manner.

[1675] The above is a concrete example of how the present invention can be implemented. This system will improve the efficiency of police work and increase public satisfaction.

[1676] The processing flow will be explained below.

[1677] Processing steps of the accident registration function combined with the emotion engine

[1678] Step 1:

[1679] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[1680] Step 2:

[1681] The user completes the input and presses the "Submit" button.

[1682] Step 3:

[1683] The terminal converts the input data into JSON format.

[1684] Step 4:

[1685] The device sends the generated JSON data to the server via the API.

[1686] Step 5:

[1687] The server saves the received JSON data in a temporary data store.

[1688] Step 6:

[1689] The server sends the data to the generative AI model and begins analysis.

[1690] Step 7:

[1691] Generative AI models analyze, classify, and complete data.

[1692] Step 8:

[1693] The server sends the data to the emotion engine, which analyzes the emotion of the user's input.

[1694] Step 9:

[1695] The emotion engine analyzes the user's emotions and detects, for example, impatience or stress.

[1696] Step 10:

[1697] The generative AI model complements the data by combining the analysis results of the emotion engine.

[1698] Step 11:

[1699] The server stores the data in the accident registration database based on the analysis results.

[1700] Step 12:

[1701] The server sends a notification of the accident information to the relevant police station.

[1702] Step 13:

[1703] The server creates a message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[1704] Step 14:

[1705] The server sends a notification to the user that the accident registration has been completed.

[1706] License-related procedure processing steps combined with an emotion engine

[1707] Step 1:

[1708] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[1709] Step 2:

[1710] The user uploads the required documents (ID, photo, etc.).

[1711] Step 3:

[1712] The user completes the input and upload and presses the "Submit" button.

[1713] Step 4:

[1714] The device converts entered data and uploaded documents into PDF and image formats.

[1715] Step 5:

[1716] The terminal sends the generated data and documents to the server.

[1717] Step 6:

[1718] The server stores the received data and documents in a temporary data store.

[1719] Step 7:

[1720] The server sends the data and documents to the generative AI model, which begins the analysis.

[1721] Step 8:

[1722] A generative AI model analyzes the document content and checks for compatibility.

[1723] Step 9:

[1724] The server sends the data to the emotion engine to analyze the user's emotions.

[1725] Step 10:

[1726] The emotion engine analyzes the user's emotions and detects, for example, anxiety or doubt.

[1727] Step 11:

[1728] The generative AI model complements the data by combining the analysis results of the emotion engine.

[1729] Step 12:

[1730] The server updates the data in the license database based on the analysis results.

[1731] Step 13:

[1732] If correction is necessary, the server creates a correction request message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[1733] Step 14:

[1734] The user receives a message requesting correction, and re-enters and re-uploads the data.

[1735] Step 15:

[1736] The terminal sends the corrected data and documents back to the server.

[1737] Step 16:

[1738] The server regenerates the corrected data and documents and analyzes them using an AI model and emotion engine.

[1739] Step 17:

[1740] After the data integrity is verified, the server finally updates the license database.

[1741] Step 18:

[1742] The server initiates the process of issuing a new license.

[1743] Step 19:

[1744] The server creates a notification message for the user that takes into consideration emotions based on the analysis results of the emotion engine.

[1745] Step 20:

[1746] The server will send the user instructions on how to mail in a new license.

[1747] Example 2

[1748] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1749] In modern police work, accident registration and license-related procedures are typically performed manually, requiring a great deal of time and effort. Furthermore, the lack of appropriate responses based on user emotions can lead to a poor user experience. These issues significantly hinder work efficiency and public satisfaction. The present invention aims to solve these issues, digitize and automate police work, and improve the user experience.

[1750] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1751] In this invention, the server includes means for analyzing data input from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for analyzing the data input by the user using an emotion engine, means for adjusting the notification content based on the analyzed emotions, and storage means for temporarily saving the data and the analysis results. This automates accident registration and license-related procedures, enabling responses based on user emotions, improving the efficiency of police work and increasing public satisfaction.

[1752] "Generative AI" is a technology that uses artificial intelligence technology to generate new data or sentences based on given data or prompts.

[1753] An "emotion engine" is a technology that analyzes emotions from user input data and evaluates the user's psychological state.

[1754] A "database" is a system for efficiently storing, managing, and retrieving data.

[1755] An "online form" is a web-based input screen that allows a user to enter information over the Internet.

[1756] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that describes data in a text-based format, achieving both readability and efficiency in data exchange.

[1757] "API" is an abbreviation for Application Programming Interface, an interface for exchanging functions and data between different software systems.

[1758] A "temporary data store" is a storage medium or system for temporarily storing data.

[1759] A "storage means" is a system consisting of hardware and software for storing data and information.

[1760] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1761] "Notifying" is the act of conveying specific information to a user.

[1762] "Registering" is the act of storing data in an official database or system.

[1763] "User" means an individual or organization that uses the system.

[1764] "Accident details" refers to detailed data about a specific accident, such as the type of accident, date and time, location, and information about the people involved.

[1765] A "license-related renewal application" is the act of completing the online renewal procedure for a license that is approaching its expiration date.

[1766] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[1767] Accident registration function embodiment

[1768] The user

[1769] The user accesses the police's online form using a device (PC or smartphone). This form has fields for entering detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.). After the user enters all the information, they press the "Submit" button to proceed to the next processing step.

[1770] The terminal is

[1771] The device converts the input data into JSON format, which structures the data and facilitates subsequent processing. The device then transmits this JSON data to the police server via an API, a communication method that ensures accurate data transfer.

[1772] The server is

[1773] The server stores the received JSON data in a temporary data store, which acts as a preliminary stage for data analysis. The server then analyzes the data using a generative AI model, completing each field as needed. It also analyzes the user's emotions using an emotion engine. For example, if the user is experiencing high stress, it can flag the issue as needing special attention.

[1774] Once the analysis is complete, the server stores the final data in an incident registration database, which is structured for long-term storage and rapid retrieval. The results of the sentiment analysis are also recorded and notifications are sent to the ledger and relevant police stations. Notifications are sent through the appropriate internal systems.

[1775] The server is

[1776] Finally, the server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. The emotion engine selects a message that takes the user's feelings into consideration. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[1777] Implementation of license-related procedures

[1778] The user

[1779] Using the device, users access a license renewal application form on the police website, which includes fields for entering required information such as name, address, and current license number, and uploading required documents such as ID and photographs.

[1780] The terminal is

[1781] The terminal converts input data and uploaded documents into PDF or image format, then transmits the data to the server via API, with the communication encrypted to ensure safety and efficiency.

[1782] The server is

[1783] The server stores the received data and documents in a temporary data store. The data is then analyzed again using the generative AI model and emotion engine. The generative AI model examines the document in detail to identify inconsistencies and omissions. The emotion engine analyzes the user's emotions and considers measures to provide a sense of security.

[1784] If the analysis reveals any deficiencies, the server sends a correction request to the user. This correction request is also expressed in a gentle way using the emotion engine. For example, it might say, "Sorry for the inconvenience, but could you please check it again?" The user then makes the corrections and sends the data and document again.

[1785] The server is

[1786] Once the data is verified as accurate, the server updates the license database, which contains the new license information and prepares to issue a new license.

[1787] Finally, the server issues a new driver's license and sends the user instructions on how to mail it. This instruction is also sensitive, and includes a notification such as "Your new driver's license has been sent."

[1788] Specific examples

[1789] Specific examples of accident registration

[1790] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[1791] The terminal converts the input data into JSON format and sends it to the server via the API.

[1792] The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[1793] The final data is stored in the accident registration database and a notification is sent to the relevant police station.

[1794] Send the user a sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[1795] Specific examples of license renewal

[1796] The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1797] The device converts the data into PDF and image format and sends it to the server.

[1798] The server analyzes the received data using a generated AI model and emotion engine to verify the document and determine the user's emotions.

[1799] If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to re-enter the information.

[1800] Once the data is verified, the server updates the license database.

[1801] A new driver's license is issued and a notification that "your new driver's license has been sent" is sent to the user in an emotionally sensitive manner.

[1802] This system will improve the efficiency of police work and increase public satisfaction.

[1803] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1804] Step 1:

[1805] The user enters the accident information

[1806] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) by entering specific information in the input fields and pressing the "Send" button to send the input data to the device.

[1807] Input: Type of accident, date, time, location, and information of people involved

[1808] Output: Input data to the terminal

[1809] Step 2:

[1810] The device sends the entered data to the server.

[1811] The device converts the input data into JSON format and sends it to the server via API, where it is securely transferred to the server over the network.

[1812] Input: Accident information entered by the user

[1813] Output: Send JSON format data to the server

[1814] Step 3:

[1815] The server analyzes the received data

[1816] The server stores the received JSON data in a temporary data store. It then uses a generative AI model to analyze the data and fill in any incomplete parts. It then uses an emotion engine to analyze the user's emotions. The analysis results include detailed information about the accident and user emotion analysis data.

[1817] Input: Accident information data in JSON format

[1818] Output: Analyzed accident information data and user emotion data

[1819] Step 4:

[1820] The server stores it in the database

[1821] Based on the analysis results, the server stores the final data in the accident registration database, which is stored securely and can be accessed whenever necessary. It also records the results of the sentiment analysis and sends notifications to the relevant police stations if necessary. A notification system is used to prompt the relevant authorities to take appropriate action.

[1822] Input: Analyzed accident information data and user emotion data

[1823] Output: Save data in accident registration database, notify police station

[1824] Step 5:

[1825] The server notifies the user that registration is complete

[1826] The server generates a message indicating that registration has been completed and sends it to the user's email address or online messaging system. Using an emotion engine, the message is selected with consideration for the user's emotions. For example, a warm message such as "Accident registration has been completed. Thank you for your cooperation" may be sent.

[1827] Input: Analysis results, emotion analysis results

[1828] Output: A message to inform the user

[1829] Step 6:

[1830] The user applies for license renewal online

[1831] Using their device, users access the license renewal application form on the police website, enter the required license information (name, address, current license number), and upload required documents such as ID and photos.

[1832] Input: License information, required documents

[1833] Output: Input data to the terminal

[1834] Step 7:

[1835] The device sends the entered data and documents to the server.

[1836] The device converts the entered data and uploaded documents into PDF or image format and sends them to the server via API, where the data is securely transferred over the network.

[1837] Input: License information and documents entered by the user

[1838] Output: Send PDF or image data to the server

[1839] Step 8:

[1840] The server parses the received data

[1841] The server stores the received data and documents in a temporary data store. It then analyzes the data using a generative AI model and emotion engine. The generative AI model checks the contents of the uploaded documents, and the emotion engine analyzes the user's emotions. The analysis results in license information verification data and user emotion data.

[1842] Input: License information data in PDF or image format

[1843] Output: Analyzed license information data and user emotion data

[1844] Step 9:

[1845] The server requests that the data be corrected (if necessary)

[1846] If the analysis reveals any deficiencies, the server will send a correction request to the user. Using an emotion engine, the correction request will be selected with consideration for the user's emotions. The user will then make the corrections and resubmit the data and document.

[1847] Input: Analyzed license information data, deficiency information

[1848] Output: Request for correction to user, corrected data

[1849] Step 10:

[1850] The server updates the database

[1851] If the data is found to be consistent, the server updates the license database, and the new license information is registered in the database.

[1852] Input: License information data that has been verified for integrity

[1853] Output: License database update

[1854] Step 11:

[1855] The server issues a new license and notifies you.

[1856] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also considers the user's emotions when sending the notification message. A reassuring message is sent saying, "Your new driver's license has been sent."

[1857] Input: License information data

[1858] Output: Issuance of new license, notification message to user

[1859] (Application example 2)

[1860] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1861] In traditional police work, registering accident information and license-related procedures are often manual and inefficient. It is also difficult to respond in a way that takes user feelings into consideration, often resulting in low user satisfaction. This situation calls for improvement in today's digital age. In particular, in emergencies and when trouble occurs, a swift and appropriate response is required, making it important to increase users' sense of security.

[1862] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1863] In this invention, the server includes means for analyzing input data from a user using a generation AI, means for registering the analysis results in a database, means for notifying the user of completion of registration, means for analyzing the received data with an emotion analysis engine and generating a message that takes the user's emotions into consideration, and means for instructing the user on appropriate procedures depending on the situation. This not only enables efficient analysis and registration of input data from a user, but also enables responses that take the user's emotions into consideration, making it possible to improve the user's sense of security through quick and appropriate responses.

[1864] "Generative AI" is an artificial intelligence model for performing tasks such as natural language processing and image analysis, and is a technology that analyzes input data from users and generates appropriate results.

[1865] An "emotion analysis engine" is a system for identifying emotions from user input data and generating responses based on those emotions.

[1866] A "database" is an information system for efficiently storing, managing, and searching analytical results and other data.

[1867] "Online Form" means a web-based form that allows users to enter and submit information via the Internet.

[1868] The "JSON format" is a lightweight data exchange format for structuring, storing, and transferring data.

[1869] An "API" is an interface for exchanging data between applications and servers, providing a way for programs to communicate with each other.

[1870] "Notification means" refers to the function or method for sending messages or warnings from the system to the user.

[1871] "License-related procedures" refers to the process of carrying out official procedures such as applying for, renewing, or reissuing a license.

[1872] "Accident registration" is the process of recording details of a road accident or other incident on the system.

[1873] "Messages that take the user's emotions into consideration" means using an emotion analysis engine to evaluate the user's emotional state and generating appropriate and reassuring messages based on the results.

[1874] "Means for instructing appropriate procedures" refers to a function that indicates to the user the specific next action to be taken based on the analysis results and emotion analysis results.

[1875] Accident registration function embodiment

[1876] The accident registration function of the present invention is a system that uses a generative AI model and an emotion analysis engine to efficiently analyze and register accident information from users.

[1877] System configuration

[1878] 1. User's device (PC or smartphone)

[1879] Users access the police's online form using their own device and enter details about the accident, such as the type of accident, date and time, location, and information about the people involved.

[1880] 2. Data transmission and analysis

[1881] The device converts the input accident information into JSON format and sends it to the server via API. The server stores the received data in a temporary data store and analyzes the accident information using the generative AI model.

[1882] 3. Emotion analysis

[1883] The server uses an emotion analysis engine to analyze the user's emotions. For example, if the user is feeling strong stress or anxiety when entering the incident, the server will take appropriate measures to respond.

[1884] 4. Registering in the database

[1885] The analyzed accident information is ultimately stored in an accident registration database. The analyzed emotional information is also recorded and notified to relevant organizations as necessary.

[1886] 5. Notification

[1887] Once the data is registered, the server generates an emotionally sensitive message and sends a notification to the user's email address or online messaging system.

[1888] Hardware and software used

[1889] Device: PC or smartphone

[1890] Generative AI models: e.g., GPT-4

[1891] Sentiment analysis engine: e.g., Google AutoML Natural Language

[1892] Database: Accident registration database

[1893] API: RESTful API

[1894] Specific examples

[1895] 1. The user enters details such as "Type of accident: Collision between two vehicles," "Date and time: November 1, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[1896] 2. The data entered by the terminal is converted to JSON format and sent to the server.

[1897] 3. The server analyzes the accident information using a generative AI model and evaluates the user's emotions using an emotion analysis engine.

[1898] 4. Register accident information and emotional information in the database.

[1899] 5. A sensitive message is sent to the user saying, "Accident registration has been completed. Thank you for your cooperation."

[1900] Example prompt sentence:

[1901] "You idiot. Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[1902] Implementation of license-related procedures

[1903] The license-related procedure function of the present invention is a system that allows users to efficiently renew or reissue licenses online.

[1904] System configuration

[1905] 1. User's device (PC or smartphone)

[1906] Users access the online license renewal form and enter the required information (name, address, license number, etc.) and upload the required documents (ID, photo, etc.).

[1907] 2. Data transmission and analysis

[1908] The device converts the entered data and uploaded documents into JSON and image formats and sends them to the server, which uses a generative AI model to review the document content and an emotion analysis engine to evaluate the user's emotions.

[1909] 3. Pointing out and correcting deficiencies

[1910] If there are any errors in the data, the server will send a friendly message to the user requesting corrections, based on the sentiment analysis engine.

[1911] 4. Updating the database

[1912] After the data has been successfully verified, the server updates the license database and prepares to issue a new license.

[1913] 5. Notification

[1914] When a new license is issued, the server generates an emotionally sensitive message and sends a notification to the user.

[1915] Hardware and software used

[1916] Device: PC or smartphone

[1917] Generative AI models: e.g., GPT-4

[1918] Sentiment analysis engine: e.g., Google AutoML Natural Language

[1919] Database: License Database

[1920] API: RESTful API

[1921] Specific examples

[1922] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[1923] 2. The device converts the data into JSON and image format and sends it to the server.

[1924] 3. The server analyzes the received data using the generated AI model and emotion analysis engine to verify the document and determine the user's emotions.

[1925] 4. If there is a problem, the server will send a friendly message to the user requesting corrections and prompting them to try again.

[1926] 5. Once the data is verified, the server updates the license database and a new license is prepared.

[1927] 6. A new license is issued and a notification that "your new license has been sent" is sent to the user in an emotionally sensitive manner.

[1928] Example prompt sentence:

[1929] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[1930] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1931] Processing steps for the Incident Registration function

[1932] Step 1:

[1933] A user accesses the online form from a smartphone or PC, enters details about the accident (type of accident, date and time, location, information about the people involved, etc.), and presses the "Submit" button. The entered data is captured as form fields.

[1934] Input: Accident details (text form)

[1935] Output: Detailed information about the entered accident (text data)

[1936] Step 2:

[1937] The device converts the detailed information of the accident entered by the user into JSON format, and the converted JSON data is sent to the server via the API.

[1938] Input: Detailed information about the accident entered (text data)

[1939] Output: Accident data in JSON format

[1940] Step 3:

[1941] The server stores the received JSON data in a temporary data store, and then uses the generative AI model to analyze the accident information. Specifically, the details of the accident and related information are analyzed, and incomplete parts are filled in.

[1942] Input: Accident data in JSON format

[1943] Output: Analysis results (text data)

[1944] Step 4:

[1945] The server uses an emotion analysis engine to analyze the input data and evaluate the user's emotions, for example, whether the user is feeling stressed or anxious, and selects an appropriate response.

[1946] Input: Detailed information about the accident entered (JSON data)

[1947] Output: Emotion evaluation results (text data)

[1948] Step 5:

[1949] The server integrates the analyzed accident information with the emotion evaluation results and stores them in the accident registration database. Based on the analysis results, notifications are sent to relevant authorities (e.g., police stations).

[1950] Input: Analysis results and emotion evaluation results

[1951] Output: Accident information stored in the database

[1952] Step 6:

[1953] Based on the results of the emotion analysis, the server sends a notification to the user that registration has been completed. The notification message is sensitive to the user's emotions and is sent via email or an online messaging system.

[1954] Input: Registration completion message

[1955] Output: Notification sent to the user

[1956] License-related procedure processing steps

[1957] Step 1:

[1958] The user accesses the police's online form on a PC or smartphone, enters the necessary information for license renewal (name, address, license number, etc.), and uploads the necessary documents (identification, photo, etc.).

[1959] Input: Required license information and documents (text and image format)

[1960] Output: Input and uploaded data

[1961] Step 2:

[1962] The device converts input data and uploaded documents into JSON and image formats, which are then sent to the server via API.

[1963] Input: License information and documents (text and image formats)

[1964] Output: JSON and image data

[1965] Step 3:

[1966] The server stores the received data in a temporary data store and analyzes it using a generative AI model and a sentiment analysis engine. The generative AI model checks the document content, and the sentiment analysis evaluates the user's sentiment.

[1967] Input: JSON and image data

[1968] Output: Analysis results and emotion evaluation results (text data)

[1969] Step 4:

[1970] The server determines whether the data needs to be corrected based on the analysis results. If so, the server sends a correction request to the user in friendly language based on the emotion analysis results.

[1971] Input: Analysis results and emotion evaluation results

[1972] Output: Correction request message

[1973] Step 5:

[1974] The user corrects the input according to the correction request and submits the data again. The server reanalyzes the data and, if it is consistent, updates the license database.

[1975] Input: Corrected data (text and image format)

[1976] Output: Updated license database

[1977] Step 6:

[1978] The server processes the new license and generates a sensitive message to notify the user, which can be sent via email or an online messaging system.

[1979] Input: Result of issuing procedure and notification message

[1980] Output: Notification sent to the user

[1981] Examples of prompt statements

[1982] "Type of accident: Car collision. Date and time: November 1, 2023. Location: Chuo Ward, Osaka City. Details: Cars collided at an intersection."

[1983] "Please enter your information for license renewal. Enter your name, address, license number, and upload any required documents."

[1984] 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.

[1985] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1986] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1987] [Fourth embodiment]

[1988] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1989] 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.

[1990] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1991] 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.

[1992] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1993] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1994] 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.

[1995] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1996] 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.

[1997] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1998] 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.

[1999] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2000] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2001] The present invention is a system that uses generative AI models to digitize police work and automate procedures such as accident registration, license-related procedures, and crime response. Specific embodiments of the present invention are described below.

[2002] The system involves a series of processes for collecting input data from users, analyzing it using a generative AI model, and registering the results in a database.

[2003] Accident registration function embodiment

[2004] 1. The user enters the accident information

[2005] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[2006] 2. The device sends the entered data to the server

[2007] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[2008] 3. The server analyzes the received data

[2009] The server stores the received JSON data in a temporary data store and then analyzes it using a generative AI model, filling in incomplete information and identifying suspicious data.

[2010] 4. The server saves the data to the database

[2011] Based on the analysis results, the final data is stored in the accident registration database, and a notification is sent to the relevant police station based on the location of the accident.

[2012] 5. The server notifies the user that registration is complete

[2013] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[2014] Implementation of license-related procedures

[2015] 1. The user applies for license renewal online

[2016] The user uses the device to access the license renewal application form on the police website, enters the necessary license information (such as name, address, and current license number), and uploads the necessary documents (such as identification and photographs).

[2017] 2. The device sends the entered data and documents to the server

[2018] The device sends the entered data and documents (PDF or image format) to the server, which ensures data integrity and security.

[2019] 3. The server analyzes the received data

[2020] The server analyzes the received data and documents, verifies the document content using a generative AI model, and checks for validity and suitability.

[2021] 4. The server requests that the data be corrected (if necessary)

[2022] If the analysis reveals any defects, the server will send a correction request to the user, who will then correct the data and submit it again.

[2023] 5. The server updates the license database

[2024] If the corrected data is deemed acceptable, the server updates the license database, which prepares the new license for issuance.

[2025] 6. The server issues a new license and sends the user instructions on how to mail it.

[2026] The server will issue a new driver's license and send instructions for mailing the new license to the user via email. The user will then receive the instructions and the new license will be mailed to the registered address.

[2027] Specific examples

[2028] Specific examples of accident registration

[2029] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[2030] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[2031] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[2032] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[2033] 5. Send an email notification to the user stating that the accident registration has been completed.

[2034] Specific examples of license renewal

[2035] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[2036] 2. The device converts the data into PDF and image format and sends it to the server.

[2037] 3. The server analyzes the received data using the generated AI model and verifies the documents.

[2038] 4. If there is a problem, the server sends a correction request to the user and prompts them to re-enter the information.

[2039] 5. Once the data is verified, the server updates the license database.

[2040] 6. Your new license will be issued and instructions on how to mail it will be sent to you.

[2041] The above is a specific embodiment of the present invention, which will improve the efficiency of police work and increase public satisfaction.

[2042] The processing flow will be explained below.

[2043] Processing steps for the Incident Registration function

[2044] Step 1:

[2045] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[2046] Step 2:

[2047] The user completes the input and presses the "Submit" button.

[2048] Step 3:

[2049] The terminal converts the input data into JSON format.

[2050] Step 4:

[2051] The device sends the generated JSON data to the server via the API.

[2052] Step 5:

[2053] The server parses the received JSON data and converts it into the appropriate format.

[2054] Step 6:

[2055] The server saves the transformed data to a temporary data store.

[2056] Step 7:

[2057] The server sends the data to the generative AI model and begins analysis.

[2058] Step 8:

[2059] A generative AI model analyzes incoming data and detects incomplete information or anomalous data.

[2060] Step 9:

[2061] Generative AI models generate the additional information needed to complement the data.

[2062] Step 10:

[2063] Based on the analysis results, the server stores the final data in the accident registration database.

[2064] Step 11:

[2065] The server sends a notification to the relevant police station.

[2066] Step 12:

[2067] The server sends a notification to the user that the accident registration has been completed.

[2068] License-related procedure processing steps

[2069] Step 1:

[2070] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[2071] Step 2:

[2072] The user uploads the required documents (ID, photo, etc.).

[2073] Step 3:

[2074] The user completes the input and upload and presses the "Submit" button.

[2075] Step 4:

[2076] The device converts entered data and uploaded documents into PDF and image formats.

[2077] Step 5:

[2078] The terminal sends the generated data and documents to the server.

[2079] Step 6:

[2080] The server prepares to parse the received data and documents.

[2081] Step 7:

[2082] The server sends the data and documents to the generative AI model and begins analysis.

[2083] Step 8:

[2084] A generative AI model analyzes the content of incoming documents and checks their validity and suitability.

[2085] Step 9:

[2086] Based on the analysis results, the server sends a correction request to the user as necessary.

[2087] Step 10:

[2088] The user makes corrections and submits the data and documents again.

[2089] Step 11:

[2090] The server re-analyzes and verifies the corrected data and documents.

[2091] Step 12:

[2092] The server checks the integrity of the data and then updates the license database.

[2093] Step 13:

[2094] The server processes the issuance of a new license.

[2095] Step 14:

[2096] The server will send the user instructions on how to mail in a new license.

[2097] Example 1

[2098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2099] In conventional police work, procedures related to accident registration, license renewal, etc. are complicated and require a lot of manpower and time. This often leads to delays in procedures, information omissions, and inaccurate data registration. In addition, users have to physically visit the police station, which requires time and effort. It is necessary to solve these issues and improve the efficiency of police work and user convenience.

[2100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2101] In this invention, the server includes means for accepting input data from a user via an online form, means for converting the accepted data into JSON format and sending it to the server, means for saving the accepted data in a temporary data store, means for analyzing the accepted data using a generative AI model and completing incomplete information, means for registering the analysis results in a database, and means for notifying the user of completion of registration, thereby enabling the digitalization and automation of police work.

[2102] 1. "User"

[2103] "User" refers to an individual or organization that uses this system, specifically, a person who provides information related to accident registration and license renewal.

[2104] 2. "Online Form"

[2105] "Online Form" means an electronic input screen for entering and submitting data over the Internet.

[2106] 3. “JSON format”

[2107] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format that describes data in key-value pair format.

[2108] 4. "Server"

[2109] "Server" refers to a centralized computer system that performs a series of processes such as receiving, analyzing, storing, and notifying data.

[2110] 5. "Data Store"

[2111] "Data store" refers to a storage area for temporarily or permanently storing data.

[2112] 6. "Generative AI Models"

[2113] "Generative AI model" refers to an algorithm and its implementation that uses artificial intelligence technology to analyze, complement, and generate data.

[2114] 7. "Database"

[2115] "Database" refers to an electronic information management system for efficiently storing, retrieving, and managing structured information.

[2116] 8. “Notification”

[2117] "Notification" refers to the act of transmitting processing results or necessary information to users or relevant institutions via email or messaging systems.

[2118] 9. "Accident Information"

[2119] "Accident information" refers to detailed data about traffic accidents, etc., including the type of accident, date and time, location, and information about the people involved.

[2120] 10. "License Renewal Application"

[2121] "License Renewal Application" refers to the application process for completing the driver's license renewal procedure online.

[2122] 11. "Identification Card"

[2123] "Identification" refers to an official document used to verify a person's identity, such as a passport or driver's license.

[2124] 12. "Documents"

[2125] "Documents" refers to documents and images required for license-related procedures, including identification documents and photographs.

[2126] 13. “Analysis”

[2127] "Analysis" refers to the process of analyzing received data to extract necessary information or fill in any incomplete parts.

[2128] 14. "Complement"

[2129] "Completion" refers to the act of adding incomplete information or missing data to make the data complete.

[2130] 15. "Defects"

[2131] "Defect" refers to a state in which there are omissions or errors in data or documents and the required conditions are not met.

[2132] This invention is a system that supports the digitization and automation of police operations such as accident registration, license renewal, etc. This system collects input data from users, analyzes it using a generative AI model, and provides a series of processes for registering the data in a database and notifying users.

[2133] The system uses a user's device (such as a PC or smartphone), a central server, a data store, and a generative AI model. Specific embodiments are described below.

[2134] Accident registration function embodiment

[2135] 1. The user enters the accident information

[2136] The user uses the device to access the police's online form. For example, the user logs in to the police's official website through a web browser and enters detailed information about the accident (such as the type of accident, date and time, location, and information about the people involved) into the form. For example, this includes input such as "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[2137] 2. The device sends the entered data to the server

[2138] The terminal converts the data entered by the user into JSON format and sends it to the server via API, which ensures efficient and secure data transfer, specifically using the HTTPS protocol.

[2139] 3. The server analyzes the received data

[2140] The server stores the received JSON data in a temporary data store, for example, using cloud storage such as Amazon S3 or Google Cloud Storage. The server then analyzes the data using a generative AI model to fill in any incomplete information. This process fills in any data detected as unknown or missing.

[2141] 4. The server saves the data to the database

[2142] Based on the analysis results, the server formats the final data and stores it in the accident registration database, using a relational database such as MySQL or PostgreSQL, and also uses SMS, email, and webhooks to send automatic notifications to the relevant police stations based on the location of the accident.

[2143] 5. The server notifies the user that registration is complete

[2144] The server generates a message indicating the successful registration and sends a notification to the user's email address or messaging platform. An example notification might include "Accident registration completed. Registration number is 12345." This process uses the SMTP protocol and a Push notification API.

[2145] Implementation of license-related procedures

[2146] 1. The user applies for license renewal online

[2147] The user accesses the police website using a device and opens the license renewal application form. The form prompts the user to enter or upload the necessary license information (such as name, address, and current license number) and necessary documents (such as identification and photographs). For example, the user may enter information such as "Name: Taro Yamada," "Address: Chiyoda-ku, Tokyo," and "License number: 1234567890."

[2148] 2. The device sends the entered data and documents to the server

[2149] The terminal converts the entered data and documents into a digital format (PDF or image) and sends them to the server via API, which ensures data integrity and security, also using the HTTPS protocol.

[2150] 3. The server analyzes the received data

[2151] The server analyzes the received data and documents and uses a generative AI model to check for validity and compatibility, for example, ensuring the ID photo is clear and the name and license number match.

[2152] 4. The server requests that the data be corrected (if necessary)

[2153] If the analysis reveals any errors, the server notifies the user of the specific details and requests corrections. For example, requests for corrections may include "the photo is unclear" or "the name and license number do not match." The user then resubmits the corrected data.

[2154] 5. The server updates the license database

[2155] After correction, if the data is deemed to be correct, the server updates the license database. The new license information is confirmed and the next step is taken.

[2156] 6. The server issues a new license and sends the user instructions on how to mail it.

[2157] The server initiates the issuance process for the new driver's license and sends the user a notification to mail it to the registered address. For example, the notification may include something like, "Your new driver's license has been issued. It will be mailed to your registered address within a few days." This process also uses the SMTP protocol and the Push notification API.

[2158] Example prompt

[2159] Specific examples of accident registration

[2160] The user enters the following information into their smartphone: "Type of accident: car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo," and then submits it.

[2161] Specific examples of license renewal

[2162] The user accesses the license renewal form on the police website on their PC and uploads the required information (e.g., "Name: Yamada Taro," "Address: Chiyoda-ku, Tokyo," "License number: 1234567890") and documents.

[2163] In this way, the present invention realizes improved efficiency in police work and increased public satisfaction.

[2164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2165] Processing steps for the Incident Registration function

[2166] Step 1: User enters incident information

[2167] Input: The user accesses the police's online form using a smartphone or PC and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) For example, the user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo."

[2168] Output: The information you enter becomes the data sent from the device.

[2169] Step 2: The device sends the entered data to the server

[2170] Input: The incident information entered by the user into the form.

[2171] Data processing and data calculation: The terminal converts the input accident information into JSON format and sends it to the server via API using the HTTPS protocol.

[2172] Specific operation: For example, the device generates JSON data such as {"accident type":"car collision", "date and time":"October 12, 2023", "location":"Shinjuku-ku, Tokyo"} and sends it to the server.

[2173] Output: The JSON data sent to the server.

[2174] Step 3: Parse the data received by the server

[2175] Input: JSON data received from the terminal.

[2176] Data processing and data calculation: The server stores the received JSON data in a temporary data store and then runs the generative AI model to analyze the data. The generative AI model completes incomplete information and detects abnormal data. For example, if the "type of accident" is missing, it completes the information as "car collision."

[2177] How it works: The server stores the data in storage such as Amazon S3 or Google Cloud Storage, then runs the generative AI model to analyze it.

[2178] Output: Imputed data as the analysis result.

[2179] Step 4: The server saves the data to the database

[2180] Input: Data parsed and imputed by the generative AI model.

[2181] Data Processing and Data Calculation: The server formats the final data and stores it in the accident registration database, and also sends notifications to the relevant police stations based on the accident location information.

[2182] What it does: The server stores the data in a relational database such as MySQL or PostgreSQL and sends notifications via SMS or email to relevant organizations.

[2183] Output: Accident information stored in the database and notifications sent.

[2184] Step 5: The server notifies the user that registration is complete

[2185] Input: Data stored in the accident registration database.

[2186] Data processing and data calculation: The server generates a registration completion notification message and sends it to the user's email address or messaging system.

[2187] Specific operation: The server sends an email using the SMTP protocol or sends a message using the Push notification API.

[2188] Output: Notification message "Accident registration completed."

[2189] License-related procedure processing steps

[2190] Step 1: User applies for license renewal online

[2191] Input: The user accesses the license renewal application form on the police website using a PC or smartphone, and enters and uploads the required information (name, address, current license number, etc.) and required documents (e.g., ID, photo, etc.).

[2192] Specific operation: For example, enter "Name: Yamada Taro", "Address: Chiyoda-ku, Tokyo", "License number: 1234567890", and upload an image file of the required identification.

[2193] Output: Input and uploaded data.

[2194] Step 2: The device sends the entered data and documents to the server

[2195] Input: Information entered and uploaded by users.

[2196] Data processing and data calculation: The terminal converts the entered data and documents into a digital format (PDF or image) and sends it to the server through an API using the HTTPS protocol.

[2197] Specific operation: The terminal converts the input data into PDF or image format and sends it to the server.

[2198] Output: Data and documents sent to the server.

[2199] Step 3: The server parses the received data

[2200] Input: Data and documents received from the terminal.

[2201] Data processing and data calculation: The server analyzes the incoming data and documents, and uses a generative AI model to verify the document contents and check for validity and compatibility, for example, whether the photo on the ID card is clear and whether the name and license number match.

[2202] What it does: The server runs the generative AI model to analyze data and documents.

[2203] Output: Analysis results (check results of validity and conformity of data and documents).

[2204] Step 4: Server requests data correction (if necessary)

[2205] Input: If there are any errors in the analysis results.

[2206] Data Processing and Data Arithmetic: The server identifies specific deficiencies and generates and sends a message to the user requesting corrections, such as "The photo is blurry" or "The name and license number do not match."

[2207] Specific operation: The server notifies the user by email using the SMTP protocol.

[2208] Output: A notification message of the correction request containing the details of the deficiency.

[2209] Step 5: The server updates the license database

[2210] Input: Data and documents submitted again after correction.

[2211] Data processing and data calculation: If the data is determined to be problem-free, the server registers and updates the new license information in the license database.

[2212] Specific operation: The server registers and updates data in a database such as MySQL or PostgreSQL.

[2213] Output: Updated license database.

[2214] Step 6: The server issues a new license and sends the user instructions on how to mail it.

[2215] Input: Updated license data.

[2216] Data processing and data calculation: The server initiates the new license issuance procedure, generates instructions for mailing the license, and notifies the user.

[2217] Specific operation: The server sends an invitation message using the SMTP protocol or the Push notification API.

[2218] Output: Notification of new driver's license issuance procedure.

[2219] In this way, this system will improve the efficiency of police work and increase public satisfaction.

[2220] (Application example 1)

[2221] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2222] In traditional police and security services, incident reports and accident information from users are often collected and analyzed manually, which not only lacks efficiency but also makes them prone to human error. Furthermore, speeding up incident response and providing feedback to users is also an issue. To solve these problems, there is a need for data analysis and automatic notification systems using generative AI models.

[2223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2224] In this invention, the server includes means for analyzing input data from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for collecting detailed information about the security incident from the user, means for converting the collected incident information into JSON format and sending it to the server, means for analyzing the received incident information using a generative AI model and completing the detailed information, and means for sending notifications to relevant personnel based on the analysis results. This automates the collection and analysis of security incident and accident information, enabling fast and efficient responses.

[2225] "Generative AI" is an artificial intelligence model that analyzes input data from users and, if necessary, fills in missing information or generates new data.

[2226] A "database" is a system for systematically storing and managing collected and analyzed data.

[2227] A "notification" is a message or alert that informs a user or relevant personnel of some information.

[2228] "Online Form" means a web-based interface for entering and submitting data over the Internet.

[2229] The "JSON format" is a format for expressing data in a structured text-based format, making it easier to exchange data.

[2230] A "server" is a computer system that provides various services such as data storage, analysis, and notification via a network.

[2231] A "security incident" is a safety-related occurrence such as unauthorized access, vandalism, or noise problems.

[2232] "Person in Charge" refers to the person or professional responsible for responding to and managing incidents and accidents.

[2233] "Incident Information" means detailed data about a security incident, including the date, time, location, people involved, and a detailed description.

[2234] The present invention is a system that automates reporting, analysis, and notification of security incidents using a generative AI model. Specific embodiments of the present invention are described below.

[2235] Security Incident Reporting Practices

[2236] 1. The user enters the security incident information

[2237] Users access the security service's application on their smartphones, enter details about the incident (type of incident, date and time, location, relevant information), and then press the "send" button.

[2238] 2. The device sends the entered data to the server

[2239] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[2240] 3. The server analyzes the received data

[2241] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model, completing incomplete information and identifying suspicious data.

[2242] 4. The server saves the data to the database

[2243] Based on the analysis results, the final data is saved in the incident registration database, and notifications are sent to relevant personnel based on the incident location information.

[2244] 5. The server notifies the user that registration is complete

[2245] A message indicating successful registration is generated and sent to the user's email address or online messaging system.

[2246] Generate complete details

[2247] The server uses the generative AI model to generate appropriate detailed information if the incident information provided by the user is insufficient. For example, if the "detailed incident description" is missing, the server generates additional information based on each data element.

[2248] Hardware and software used

[2249] Hardware:

[2250] Smartphone: A device for users to enter incident information.

[2251] Server: A computer system for receiving, analyzing, storing, and notifying data.

[2252] software:

[2253] Generative AI model (e.g. GPT-3): Analyzes the incoming data and generates the necessary details.

[2254] JSON format: A format used to structure and transfer data.

[2255] API: An interface for exchanging data between a device and a server.

[2256] Database (e.g. MySQL): A system for storing analysis results.

[2257] Specific examples

[2258] Please use the following examples to further understand the functionality of this system.

[2259] Example 1: Security Incident Reporting

[2260] 1. The user enters "Type of incident: Vandalism," "Date and time: October 11, 2023," and "Location: Chuo-ku, Osaka City" on their smartphone and submits the information.

[2261] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[2262] 3. The server analyzes the received data using a generative AI model to complement incomplete information or generate additional information.

[2263] 4. Store the final data in the incident registration database and send notifications to relevant personnel.

[2264] 5. Send an email notification to the user stating that the incident report has been completed.

[2265] Example prompt sentence:

[2266] Please complete any missing or incomplete information based on the following security incident data:

[2267] {

[2268] "incident_type": "vandalism",

[2269] "date_time": "2023-10-11 14:00",

[2270] "location": "Chuo-ku, Osaka",

[2271] "details": "The window of the convenience store was broken",

[2272] "reporter_contact": "example@example.com"

[2273] }

[2274] The above is a specific embodiment for carrying out the present invention, which automates the collection and analysis of security incident and accident information, enabling a quick and efficient response.

[2275] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2276] Step 1:

[2277] The user enters information about a security incident. The user accesses the application using a smartphone, enters the type of incident, date and time, location, and related information into an online form, and then presses the "Submit" button. The input here is specific details of the incident in text format.

[2278] Step 2:

[2279] The device sends the entered data to the server. The device (smartphone) converts the entered data into JSON format and sends it to the server via the API. At this time, the data is sent to the server in an appropriate format via the API, so data transfer is efficient and accurate.

[2280] Step 3:

[2281] The server saves the received data in a temporary data store. The server saves the received JSON data in a temporary data store for subsequent analysis. The input here is JSON format data, and the output is saved to the temporary data store.

[2282] Step 4:

[2283] The server analyzes the data using a generative AI model. The server inputs the saved JSON data into a generative AI model (e.g., GPT-3) and analyzes the data. During the analysis process, incomplete information is completed and suspicious data is identified. The input is JSON data, and the output is the completed detailed information and analysis results.

[2284] Step 5:

[2285] The server stores the analysis results in a database. The server then organizes the analysis results obtained by the generative AI model as final data and stores them in the incident registration database. The input here is the analysis results, and the output is the registered data stored in the database.

[2286] Step 6:

[2287] The server sends notifications to relevant personnel based on the analysis results. The server automatically sends notifications to relevant security personnel based on the data stored in the incident registration database. The input here is the registered data, and the output is a notification message to the personnel. Specific operations include automatically sending email notifications to the personnel's email addresses and push notifications to dedicated applications.

[2288] Step 7:

[2289] The server notifies the user that registration is complete. The server generates a message indicating that the incident report has been successfully completed and sends it to the user's email address or to a notification system via an application. The input here is the registration completion information, and the output is a notification message to the user.

[2290] This will enable the realization of a system that efficiently and quickly collects, analyzes, registers, and notifies security incidents through all processing steps.

[2291] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2292] The present invention is a system that combines a generative AI model and an emotion engine to digitize police work and automate the analysis of data entered by users, accident registration, license-related procedures, etc. Specific embodiments of the present invention are described below.

[2293] Accident registration function embodiment

[2294] 1. The user enters the accident information

[2295] The user accesses the police's online form using a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about the people involved, etc.) Once the information is complete, the user presses the "Submit" button.

[2296] 2. The device sends the entered data to the server

[2297] The device converts the input data into JSON format and sends it to the server via API, which ensures efficient data transfer.

[2298] 3. The server analyzes the received data

[2299] The server stores the received JSON data in a temporary data store and then analyzes the data using a generative AI model and emotion engine. The generative AI model analyzes the content of the data, and the emotion engine analyzes the user's emotions when they input. For example, if the user is feeling very stressed or anxious, the system will select an appropriate response.

[2300] 4. The server saves the data to the database

[2301] Based on the analysis results, the final data is stored in the accident registration database. The results of the sentiment analysis are also recorded and notifications are sent to the relevant police stations if necessary.

[2302] 5. The server notifies the user that registration is complete

[2303] A message indicating the completion of registration is generated and sent to the user's email address or online messaging system. The emotion engine selects the message taking into account the user's feelings.

[2304] Implementation of license-related procedures

[2305] 1. The user applies for license renewal online

[2306] The user uses the device to access the license renewal application form on the police website, enters the required license information (name, address, current license number, etc.), and uploads the required documents (ID, photo, etc.).

[2307] 2. The device sends the entered data and documents to the server

[2308] The terminal sends the entered data and uploaded documents (PDF or image format) to the server.

[2309] 3. The server analyzes the received data

[2310] The server analyzes the received data and documents using a generative AI model and an emotion engine. The generative AI model checks the contents of the document, and the emotion engine analyzes the user's emotions. For example, if the user is feeling anxious, the server returns a reassuring message.

[2311] 4. The server requests that the data be corrected (if necessary)

[2312] If the analysis reveals any deficiencies, the server will send a correction request to the user. The emotion engine selects correction requests that take into account the user's emotions. The user then makes the corrections and submits the data and documents again.

[2313] 5. The server updates the database

[2314] If the data is verified as consistent, the server updates the license database and prepares to issue a new license.

[2315] 6. The server issues a new license and notifies you.

[2316] The server processes the issuance of a new driver's license and sends the user instructions on how to mail it. The emotion engine also ensures that the notification message is sensitive to the user's emotions.

[2317] Specific examples

[2318] Specific examples of accident registration

[2319] 1. The user enters "Type of accident: Car collision," "Date and time: October 12, 2023," and "Location: Shinjuku Ward, Tokyo" on their smartphone and submits the information.

[2320] 2. The terminal converts the input data into JSON format and sends it to the server via the API.

[2321] 3. The server analyzes the received data using a generative AI model and emotion engine to complement incomplete information and analyze the user's emotions.

[2322] 4. Store the final data in the accident registration database and send a notification to the relevant police station.

[2323] 5. Send the user an emotionally sensitive message saying, "Accident registration has been completed. Thank you for your cooperation."

[2324] Specific examples of license renewal

[2325] 1. The user accesses the license renewal form on the police website on their PC and uploads the required information and documents.

[2326] 2. The device converts the data into PDF and image format and sends it to the server.

[2327] 3. The server analyzes the received data using the generated AI model and emotion engine, and checks the document and the user's emotions.

[2328] 4. If there is a problem, the server will send a friendly request to the user to correct the problem and prompt them to re-enter the information.

[2329] 5. Once the data is verified, the server updates the license database.

[2330] 6. A new license is issued and a "new license has been sent" notification is sent to the user in an emotionally sensitive manner.

[2331] The above is a concrete example of how the present invention can be implemented. This system will improve the efficiency of police work and increase public satisfaction.

[2332] The processing flow will be explained below.

[2333] Processing steps of the accident registration function combined with the emotion engine

[2334] Step 1:

[2335] The user accesses the police's online form on a device (PC or smartphone) and enters detailed information about the accident (type of accident, date and time, location, information about those involved, etc.).

[2336] Step 2:

[2337] The user completes the input and presses the "Submit" button.

[2338] Step 3:

[2339] The terminal converts the input data into JSON format.

[2340] Step 4:

[2341] The device sends the generated JSON data to the server via the API.

[2342] Step 5:

[2343] The server saves the received JSON data in a temporary data store.

[2344] Step 6:

[2345] The server sends the data to the generative AI model and begins analysis.

[2346] Step 7:

[2347] Generative AI models analyze, classify, and complete data.

[2348] Step 8:

[2349] The server sends the data to the emotion engine, which analyzes the emotion of the user's input.

[2350] Step 9:

[2351] The emotion engine analyzes the user's emotions and detects, for example, impatience or stress.

[2352] Step 10:

[2353] The generative AI model complements the data by combining the analysis results of the emotion engine.

[2354] Step 11:

[2355] The server stores the data in the accident registration database based on the analysis results.

[2356] Step 12:

[2357] The server sends a notification of the accident information to the relevant police station.

[2358] Step 13:

[2359] The server creates a message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[2360] Step 14:

[2361] The server sends a notification to the user that the accident registration has been completed.

[2362] License-related procedure processing steps combined with an emotion engine

[2363] Step 1:

[2364] The user uses a device to access the license renewal application form on the police website and enters the required license information (such as name, address, and current license number).

[2365] Step 2:

[2366] The user uploads the required documents (ID, photo, etc.).

[2367] Step 3:

[2368] The user completes the input and upload and presses the "Submit" button.

[2369] Step 4:

[2370] The device converts entered data and uploaded documents into PDF and image formats.

[2371] Step 5:

[2372] The terminal sends the generated data and documents to the server.

[2373] Step 6:

[2374] The server stores the received data and documents in a temporary data store.

[2375] Step 7:

[2376] The server sends the data and documents to the generative AI model, which begins the analysis.

[2377] Step 8:

[2378] A generative AI model analyzes the document content and checks for compatibility.

[2379] Step 9:

[2380] The server sends the data to the emotion engine to analyze the user's emotions.

[2381] Step 10:

[2382] The emotion engine analyzes the user's emotions and detects, for example, anxiety or doubt.

[2383] Step 11:

[2384] The generative AI model complements the data by combining the analysis results of the emotion engine.

[2385] Step 12:

[2386] The server updates the data in the license database based on the analysis results.

[2387] Step 13:

[2388] If correction is necessary, the server creates a correction request message that takes into consideration the user's emotions based on the analysis results of the emotion engine.

[2389] Step 14:

[2390] The user receives a message requesting correction, and re-enters and re-uploads the data.

[2391] Step 15:

[2392] The terminal sends the corrected data and documents back to the server.

[2393] Step 16:

[2394] The server regenerates the corrected data and documents and analyzes them using an AI model and emotion engine.

[2395] Step 17:

[2396] After the data integrity is verified, the server finally updates the license database.

[2397] Step 18:

[2398] The server initiates the process of issuing a new license.

[2399] Step 19:

[2400] The server creates a notification message for the user that takes into consideration emotions based on the analysis results of the emotion engine.

[2401] Step 20:

[2402] The server will send the user instructions on how to mail in a new license.

[2403] Example 2

[2404] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2405] In modern police work, accident registration and license-related procedures are typically performed manually, requiring a great deal of time and effort. Furthermore, the lack of appropriate responses based on user emotions can lead to a poor user experience. These issues significantly hinder work efficiency and public satisfaction. The present invention aims to solve these issues, digitize and automate police work, and improve the user experience.

[2406] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2407] In this invention, the server includes means for analyzing data input from a user using a generative AI, means for registering the analysis results in a database, means for notifying the user of registration completion, means for analyzing the data input by the user using an emotion engine, means for adjusting the notification content based on the analyzed emotions, and storage means for temporarily saving the data and the analysis results. This automates accident registration and license-related procedures, enabling responses based on user emotions, improving the efficiency of police work and increasing public satisfaction.

[2408] ...

Claims

1. A means for analyzing input data from a user using generative AI; A means of registering the analysis results in a database, The system includes a means for notifying the user that registration is complete.

2. A means for receiving detailed information about the accident from users through an online form; A means to convert the received accident information into JSON format and send it to the server; The system of claim 1, further comprising means for analyzing and registering received accident information using a generative AI model.

3. A means of accepting license-related renewal applications online; A means to analyze and verify uploaded documents such as IDs and photos using a generative AI model, 2. The system according to claim 1, further comprising means for updating the license database with the analyzed data.

Images