System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting incident details; means for transmitting the incident details to a server; means for receiving the incident details and generating a preliminary paper using a AI model; means for returning the generated preliminary paper to a client; and means for displaying the returned preliminary paper.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] The traditional police report preparation process is primarily manual, requiring a significant amount of time and effort. In particular, accurately and quickly documenting the outline of a case requires advanced expertise and great care. As a result, preparing reports takes time, making it difficult to conduct prompt investigations and respond to crimes. Furthermore, there is a high possibility of errors or information omissions, reducing the reliability of the quality of the reports. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention relates to a system that includes a means for a user to input case details and send them to a server, a means for the server to generate an initial report using an AI model based on the case details received, a means for returning the generated initial report to a client, and a means for displaying the returned initial report. Specifically, the system analyzes case details using natural language processing technology and automatically generates a draft report from the information entered by the user. This method significantly reduces the time and effort required for report creation, improves the quality and reliability of reports, and enables rapid investigations and criminal response. The system allows users to intuitively input case details through a provided graphical user interface and easily review and modify the generated draft, thereby achieving efficient report creation.

[0006] The "means for inputting details of an incident" refers to a method or device that allows a user to input specific circumstances and information about an incident.

[0007] "Means for transmitting case details to the server" refers to the method or technology for transmitting the entered case details to the server over the Internet or a network.

[0008] An "AI model" is an algorithm or program that uses artificial intelligence techniques to perform a specific task.

[0009] An "initial report" is a draft police report generated by an AI model and is a primary document based on user input.

[0010] The "means for returning the generated initial paper to the client" refers to a method or technology for sending the generated initial paper from the server to the user's terminal.

[0011] The "means for displaying the returned initial paper" refers to a method or device for visually displaying the initial paper received from the server on the user's terminal.

[0012] "Natural language processing technology" refers to the field and technology of computer science for understanding, analyzing, and generating human language.

[0013] A "graphical user interface" is an interface that allows a user to interact with a computer by manipulating visual elements (buttons, text boxes, icons, etc.). [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] This invention relates to a system that allows police officers to efficiently generate police reports. In this system, a user inputs details of a case through a terminal and sends them to a server. An AI model automatically generates an initial report, which is then returned and displayed to the user. This system significantly reduces the time and effort required compared to the traditional manual process of creating reports, and also improves the accuracy and reliability of the reports.

[0036] System configuration

[0037] The system includes the following components:

[0038] 1. Input method for entering details of the case

[0039] 2. Means of transmission for transmitting details of the incident

[0040] 3. A server that processes the received case details and generates an initial report using an AI model.

[0041] 4. A means of returning the generated initial workpaper to the client

[0042] 5. Display means for displaying the returned initial report

[0043] User operations

[0044] The user (police officer) uses their device to open a web browser and access a dedicated page in the report generation system. A text area for entering details of the incident will be displayed, where the user can enter a summary of the incident and the circumstances. For example, they might write, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0045] Once the input is complete, the user clicks the "Generate Report" button, which sends the entered case details from the device to the server.

[0046] Server Processing

[0047] The server receives the details of the case sent from the device and passes them to an AI model to generate an initial report, which uses natural language processing techniques to generate appropriate documents based on the input information.

[0048] The server receives the initial report generated by the AI ​​model and sends it back to the device using an HTTP response, with the generated report sent in JSON format.

[0049] Terminal display

[0050] The terminal receives the initial report sent back from the server and displays it in the browser, with the generated draft report displayed below the text entered by the user, allowing the user to review it and make any necessary corrections.

[0051] Specific examples

[0052] For example, a user enters the following incident details:

[0053] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0054] Based on this, the AI ​​model generates an initial report that looks like this:

[0055] At 3:00 PM on October 1, 2023, a traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car, injured, and transported to XX Hospital. The driver, a man in his 20s, was confirmed at the scene to be suspected of drunk driving.

[0056] The user checks the generated report and adds or corrects information as necessary to complete the final report.

[0057] The above is a specific embodiment of the system of the present invention, which allows police officers to significantly reduce the time it takes to prepare reports and quickly prepare more accurate and consistent documents.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0061] Step 2:

[0062] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0063] Step 3:

[0064] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the incident details entered in the text area.

[0065] Step 4:

[0066] The device converts the retrieved incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user.

[0067] Step 5:

[0068] The server receives the HTTP POST request sent from the terminal, extracts the details of the incident from the request body, and prepares it for further processing.

[0069] Step 6:

[0070] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0071] Step 7:

[0072] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0073] Step 8:

[0074] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0075] Step 9:

[0076] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0077] Step 10:

[0078] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0079] Step 11:

[0080] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0081] Example 1

[0082] 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."

[0083] Conventional report preparation work requires a lot of time and effort, and because it is done manually, there are issues with accuracy and consistency. As a result, the quality of the reports can vary, which can hinder efficient investigations. The purpose of this invention is to solve these issues and automate the report preparation process, making it more efficient.

[0084] 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.

[0085] In this invention, the server includes a means for a user to input details of the case through a terminal, a means for transmitting the input details of the case to the server, a means for the server to convert the received details of the case into a prompt sentence and pass it to a generative AI model to generate an initial report, a means for returning the generated initial report to the terminal in JSON format, and a means for displaying the returned initial report in a browser. This allows police officers to efficiently prepare reports, significantly reducing time and effort while also improving the accuracy and reliability of the reports.

[0086] "User" refers to the individual, primarily a police officer, who uses the terminal to enter details of the case.

[0087] "Terminal" refers to an electronic device used by a user, such as a personal computer, tablet, or smartphone.

[0088] "Incident details" refers to specific information about a particular incident, such as the date, time, location, information about the people involved, and the course of events.

[0089] "Server" refers to the central management system that receives case details sent by users and generates initial reports using AI models.

[0090] "Prompt sentence" refers to an input sentence containing details of the incident that has been converted into a format that is easy for the AI ​​model to understand.

[0091] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate an initial report from an input prompt.

[0092] An "initial report" refers to a document containing basic information about the incident that is automatically created by a generative AI model.

[0093] "JSON format" is a text format for structuring and describing data, and is a format that is often used when exchanging data between servers and terminals.

[0094] "Browser" refers to a software application that enables users to access and use web pages on the Internet.

[0095] "Graphical User Interface" means a visual interface designed to be intuitive to the user and to facilitate the entry of case details.

[0096] MODE FOR CARRYING OUT THE INVENTION

[0097] System Configuration

[0098] The system of the present invention is designed to help police officers efficiently generate case reports. The system consists of the following components:

[0099] 1. Input means (terminal) for entering details of the case

[0100] 2. A means of transmission (a communication interface between the terminal and the server) for transmitting the entered case details to the server.

[0101] 3. A server that converts received case details into prompt text and generates an initial report using a generative AI model.

[0102] 4. Means for returning the generated initial record to the terminal (communication interface between the server and the terminal)

[0103] 5. A means (browser) for displaying the returned initial report

[0104] User operations

[0105] The police officer user first opens a web browser on their device and accesses the dedicated page of the report generation system. A login screen will appear, and the user must enter their authentication information to log in. A text area will then appear on the screen where the police officer can enter details of the case.

[0106] The user enters a summary of the incident and the circumstances in the text area. For example, they could enter details such as, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." Once they have finished entering the information, the user clicks the "Generate Report" button.

[0107] Server Processing

[0108] The server receives the details of the incident sent from the device. The received data is sent to the server in JSON format. The server parses this data and converts it into a prompt. The prompt is then formatted in a format that the generative AI model can understand. Specifically, the following prompt is generated:

[0109] Generate a draft of a traffic accident report based on the following text:

[0110] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0111] The server then sends this prompt to a generative AI model, such as OpenAI's GPT-3 or GPT-4. The generative AI model generates an initial report based on the prompt and returns the generated initial report in JSON format to the server.

[0112] Terminal display

[0113] The terminal receives the initial report sent back from the server and displays it in the user's browser. The generated draft report is displayed below the text entered by the user. The user can review the displayed report and make corrections as necessary. Once the corrections are complete, the report is saved as the final report.

[0114] The above is an embodiment of the system of the present invention, which enables police officers to significantly reduce the time and effort required to prepare reports and improve the accuracy and reliability of reports.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user accesses a dedicated page in the device's web browser. They enter a URL to go to the login screen, then enter their authentication information to log in to the system. This allows the user to access a screen where they can enter details of the incident. The input is in text format, and the details are displayed in a text area in the browser.

[0118] Step 2:

[0119] The user enters details of the incident in the text area provided. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." This input will form the basis for subsequent data processing.

[0120] Step 3:

[0121] The user clicks the "Generate Report" button. By pressing this button, the details of the incident that were entered are sent from the device to the server as an HTTP POST request. Specifically, the entered text is converted to JSON format and sent to the server as transmission data.

[0122] Step 4:

[0123] The server receives the HTTP POST request sent from the terminal. The server extracts data containing details of the incident from this request and analyzes it. Specifically, the content of the received data is checked and converted into a prompt. The input text is first received as a string and then a formatted prompt is created.

[0124] Example: "Generate a draft of a traffic accident report based on the following text: October 1, 2023, 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0125] Step 5:

[0126] The server sends the generated prompt to an AI model (such as OpenAI's GPT-3 or GPT-4), which generates an initial document based on the prompt. The prompt is passed as input to the AI ​​model, which then uses natural language processing techniques to generate an appropriate document.

[0127] Step 6:

[0128] The generative AI model generates an initial report based on the prompt text. The generated initial report is sent back to the server. The server receives the result, formats it in JSON format, and sends it back to the device. At this stage, the generated initial report is sent as data from the server to the device.

[0129] Step 7:

[0130] The terminal receives the initial report in JSON format from the server and displays it in the user's browser. Specifically, it parses the received data and displays it in a format appropriate for the current operating environment. The generated draft report is displayed below the text entered by the user.

[0131] Step 8:

[0132] The user can review the draft report displayed in the browser and make any necessary edits, such as adding additional information or correcting incorrect information. Once the final report is complete, it can be saved and submitted to the appropriate systems.

[0133] The above is the flow of the process in which a user uses a device to automatically generate a transcript using the generative AI model via the server, and the results are displayed in a browser. This system improves the efficiency and accuracy of transcript creation.

[0134] (Application example 1)

[0135] 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."

[0136] In traditional security services, it is difficult for security guards to quickly and accurately generate detailed incident reports from the field, especially when the report is created manually, which takes time and effort. Furthermore, errors or incomplete information entered can cause problems with the accuracy of the report. To solve this problem, a new system is needed.

[0137] 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.

[0138] In this invention, the server includes means for receiving incident details and generating an initial report using a generative AI model, means for returning the generated initial report to the client, means for displaying the returned initial report, and means for security guards to input incident details by voice or visually on-site, thereby enabling security guards to efficiently and accurately create incident reports on-site.

[0139] "Means for inputting incident details" refers to a device or interface through which a security officer inputs incident details.

[0140] "Means for transmitting case details to the server" refers to a communications device or protocol for transmitting the entered case details to the server.

[0141] A "generative AI model" refers to an algorithm or software that automatically generates initial reports using natural language processing technology.

[0142] "Means for generating an initial report" means a process or system that uses a generative AI model to create an initial report from case details.

[0143] "Means for sending the generated initial working paper back to the client" refers to a communication device or protocol that sends the initial working paper generated by the server to the client device.

[0144] "Means for displaying returned initial working paper" refers to a device or interface for visually displaying the received initial working paper at the client device.

[0145] "Smart glasses" refers to wearable devices that display information visually and support voice input and gesture control.

[0146] A "head-mounted display" is a device worn by a user to display visual information, and refers to a display device that is worn on the head.

[0147] "Ability for security guards to input incident details via voice or visual input on-site" refers to a feature that allows security guards to input incident details via voice or visual input on-site using a smart device.

[0148] A system for implementing this invention allows security guards to efficiently and accurately generate incident reports from the field. The system primarily includes the following components:

[0149] 1. Enter the details of the incident:

[0150] Security guards use devices such as smartphones, smart glasses, or head-mounted displays to input details of the incident, either via voice or text.

[0151] 2. Means of sending incident details to the server:

[0152] The details of the incident entered on the device are sent to a server over the Internet using an HTTP POST request.

[0153] 3. How to generate an initial report using a generative AI model:

[0154] Based on the received case details, the server generates an initial report using a generative AI model, which uses natural language processing techniques and applies advanced models such as BERT.

[0155] 4. Means of returning the generated initial workpaper to the client:

[0156] The initial report generated by the server is sent to the client device in JSON format, and is returned using an HTTP response.

[0157] 5. How to view the returned initial report:

[0158] The client device displays the initial report received from the server to the user through a browser or application, and the security officer can review the displayed initial report and enter corrections or additional information as necessary.

[0159] Examples:

[0160] For example, a security guard dictates the following incident details:

[0161] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0162] This input data is sent to the server, and the generative AI model generates an initial report like this:

[0163] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0164] This generated initial report is sent back to the client device for visual review by the guard.

[0165] Example prompt sentence:

[0166] Please generate an incident report based on the following details:

[0167] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0168] The system allows security guards to quickly and accurately generate incident reports from the field, and the versatility of the devices they can use significantly improves their work efficiency.

[0169] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0170] Step 1:

[0171] The user uses a smart device (smartphone, smart glasses, head-mounted display) to input details of the incident. For example, they might enter, "At 5:00 PM on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s. He refused to show identification, so the police were called." The input data is temporarily stored on the device.

[0172] Step 2:

[0173] The device sends the details of the incident that was entered to the server using an HTTP POST request, and the input data is sent to the server in text format using JSON as the specific data format.

[0174] Step 3:

[0175] The server analyzes the details of the incident received from the device and inputs them into the generative AI model. Text information is passed to the AI ​​model as input data, and the AI ​​model generates an initial report using natural language processing techniques. Specifically, the AI ​​model analyzes the meaning of each sentence and reconstructs it in an appropriate format.

[0176] Step 4:

[0177] The server receives the generated initial statement and returns it to the client device using an HTTP response, sending the generated statement in JSON format. In particular, this could be in the following format:

[0178] text

[0179] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0180] Step 5:

[0181] The client device receives the returned initial work report and displays it to the user. This can be done using a browser or a dedicated application, allowing the user to review the generated work report and enter corrections or additional information as needed. The displayed form is formatted to be easy for the user to understand.

[0182] This process allows security guards to quickly and accurately generate incident reports from the scene.The system enables efficient data processing and information sharing by linking the server, terminals, and users.

[0183] 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.

[0184] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0185] System configuration

[0186] The system includes the following components:

[0187] 1. Input method for entering details of the case

[0188] 2. Means of transmission for transmitting details of the incident

[0189] 3. A server that processes the received case details and generates an initial report using an AI model.

[0190] 4. A means of returning the generated initial workpaper to the client

[0191] 5. A means of viewing the returned initial report

[0192] 6. Emotion engine that recognizes user emotions

[0193] User operations

[0194] The user (police officer) uses a terminal to access the report generation system's webpage and enter details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0195] Server Processing

[0196] Once the input is complete, the user clicks the "Generate transcript" button. This action causes the device's client-side script to convert the input information into JSON format and send it to the server. The server then uses an AI model to generate an initial transcript based on the received information. This AI model uses natural language processing technology to analyze the input information and generate the appropriate document.

[0197] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0198] The server returns the generated initial statement and the statement adjusted by the emotion engine to the client. The returned statement is displayed on the terminal, where the user can check it and modify it if necessary.

[0199] Terminal display

[0200] The device receives the report sent back from the server and displays it in the browser. The user can review it and make any necessary edits. The report displayed on the device reflects the details of the incident, as well as a style and tone that is tailored to the user's emotions.

[0201] Specific examples

[0202] For example, a user enters the following incident details:

[0203] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0204] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the AI ​​model will generate an initial report in the following style:

[0205] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0206] The user can check the generated report, make corrections as necessary, and complete the final report.

[0207] The above is a specific embodiment of the system of the present invention. By combining it with an emotion engine, it becomes possible to generate more human-like reports that are adapted to the user's emotions, improving the work efficiency of police officers and the quality of reports.

[0208] The processing flow will be explained below.

[0209] Step 1:

[0210] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0211] Step 2:

[0212] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0213] Step 3:

[0214] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the entered incident details.

[0215] Step 4:

[0216] The device converts the acquired incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user, as well as audio and video data from when the details were entered.

[0217] Step 5:

[0218] The server receives the HTTP POST request sent from the terminal, extracts the incident details and audio and video data from the request body, and prepares them for further processing.

[0219] Step 6:

[0220] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0221] Step 7:

[0222] The server passes the extracted audio and video data to the emotion engine, which then recognizes the user's emotion. The emotion engine determines the user's emotional state based on the user's tone of voice and facial expressions.

[0223] Step 8:

[0224] The server feeds back the emotion data recognized by the emotion engine to the AI ​​model's report generation process, adjusting the style and tone of the report generated based on the user's emotions.

[0225] Step 9:

[0226] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0227] Step 10:

[0228] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0229] Step 11:

[0230] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0231] Step 12:

[0232] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0233] Step 13:

[0234] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0235] This is the processing flow for a system that combines an emotion engine. This makes it possible to generate police reports based on the user's emotions, improving the work efficiency of police officers and the quality of the reports.

[0236] Example 2

[0237] 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."

[0238] Conventional report generation systems take a long time to complete, from entering details of the case to generating and displaying the report, and it is difficult to consider emotional factors. Manual report creation is prone to human error, resulting in unstable report quality. Furthermore, the lack of adjustments to style and tone to reflect the user's emotions makes it difficult to generate detailed, human-like reports.

[0239] 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.

[0240] In this invention, the server includes a means for analyzing the details of the case and generating an initial report using a generative AI model, a means including an emotion engine for recognizing the user's emotions, and a means for returning the generated initial report to the client. This enables the generation of detailed, human-like reports that take the user's emotions into consideration, thereby improving work efficiency and stabilizing the quality of the reports.

[0241] The "means for inputting details of the incident" is an element that provides an interface for the user to input specific circumstances of the incident.

[0242] The "means for transmitting the entered case details to the server" is an element that converts the case information entered by the user into a data format and transmits it to the server via the network.

[0243] "Means for analyzing received case details and generating an initial report using a generative AI model" refers to an element that analyzes case information sent to the server and automatically generates an initial report using AI technology.

[0244] The "means for returning the generated initial record to the client" is an element that sends the record generated by the server back to the client (user's terminal).

[0245] The "means for displaying the returned initial record" is an element that visually displays the record sent to the client so that the user can check it.

[0246] The "means including an emotion engine for recognizing the user's emotion" refers to an element including dedicated hardware and software for analyzing the audio and video data input by the user and recognizing the user's emotion.

[0247] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to generate appropriate documents from input data.

[0248] A "prompt" is text that describes the document format and instructions for providing input data to an AI model.

[0249] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0250] First, the user (police officer) uses a terminal to access the webpage of the report generation system and enters details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0251] Next, when the user clicks the "Generate transcript" button, the terminal's client-side script converts the input information into JSON format and sends it to the server. The server then uses the received information to generate an initial transcript using a generative AI model. This AI model uses natural language processing technology to analyze the input information and generate an appropriate document. The hardware used is a server equipped with a GPU, and the software used includes TensorFlow and PyTorch.

[0252] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion engine uses librosa for analyzing voice data and OpenCV for analyzing video data. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0253] The server returns the generated initial report and the report adjusted by the emotion engine to the client. The returned report is displayed on the terminal, where the user can review and modify it as necessary. The report displayed on the terminal reflects the details of the case, as well as the style and tone adjusted based on the user's emotions.

[0254] As an example, a user enters the following incident details:

[0255] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0256] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the generative AI model will generate an initial report in the following style:

[0257] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0258] By combining this with an emotion engine, it becomes possible to generate more human-like reports that adapt to the user's emotions, improving the efficiency of police officers' work and the quality of reports.

[0259] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0260] Step 1:

[0261] The user enters the details of the incident.

[0262] The user uses a terminal to access the web page of the record generation system and inputs the specific circumstances of the case into the text area.

[0263] Input: Details of the incident (e.g., "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital.")

[0264] Output: Detailed incident data entered in the text area

[0265] Step 2:

[0266] The user clicks the "Generate Work Report" button.

[0267] This operation activates a client-side script on the device, converting the input information into JSON format, which is then sent to the server.

[0268] Input: Case details entered in the text area

[0269] Output: JSON formatted data sent to the server

[0270] Specific operation: Using JavaScript, the input data is converted to JSON format using JSON.stringify, and a POST request is sent to the server using the fetch API.

[0271] Step 3:

[0272] The server analyzes the details of the incident received.

[0273] The server parses and analyzes the received JSON-formatted data, which is then used as input to a generative AI model.

[0274] Input: Case details in JSON format

[0275] Output: Input data to the AI ​​model (parsed case details)

[0276] Specific operation: The JSON data is deserialized using a server-side script (e.g., Python), and the parsed results are input into the generative AI model.

[0277] Step 4:

[0278] The server generates an initial report using the generative AI model.

[0279] The server provides input data to the generative AI model to generate an initial report, which is then output in text format.

[0280] Input: Input data to the AI ​​model (parsed case details)

[0281] Output: Text data of the generated initial report

[0282] Specific operation: Input data in the form of prompt sentences is provided to a natural language processing model built using TensorFlow and PyTorch, and the generated report text is obtained.

[0283] Example prompt sentence:

[0284] Generate a report based on the following incident details: "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0285] Step 5:

[0286] The server applies the emotion engine.

[0287] The server analyzes the audio and video data input by the user to obtain emotional data, and adjusts the initial record based on the obtained emotional data.

[0288] Input: User's audio and video data, generated initial transcript

[0289] Output: Adjusted transcript text reflecting emotion data

[0290] Specific operation: Audio data is analyzed using librosa, and video data is analyzed using OpenCV to determine emotions, and based on this data, the style of the transcript is readjusted using a generative AI model.

[0291] Step 6:

[0292] The server returns the adjusted record to the terminal.

[0293] The server converts the adjusted report text into JSON format and sends it to the terminal as an HTTP response.

[0294] Input: Adjusted transcript text reflecting emotion data

[0295] Output: JSON formatted report data returned to the terminal

[0296] Specific operation: The report text is serialized into JSON format using a server script such as Python and returned as an HTTP response.

[0297] Step 7:

[0298] The terminal receives and displays the returned record.

[0299] The terminal receives the data returned from the server and displays it in the browser. The user can check it and modify it as necessary.

[0300] Input: JSON formatted report data returned from the server

[0301] Output: Text of the report displayed in the browser

[0302] Specific operation: Parse the returned JSON data using JavaScript and display it in the browser using DOM manipulation.

[0303] (Application example 2)

[0304] 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."

[0305] The present invention aims to solve the problems of police officers generating reports efficiently and recognizing the emotions of customers in physical stores in real time and providing appropriate responses. In particular, the objective is to realize a system that can provide more accurate and appropriate reports and customer service methods by recognizing the emotions of users (police officers and store clerks) and adjusting the writing style and tone based on that.

[0306] 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.

[0307] In this invention, the server includes means for recognizing a user's emotions using an emotion engine and adjusting the style and tone of the report, means for recognizing a customer's emotions in real time and generating an appropriate customer service method, and means for receiving details of the incident and generating an initial report using an AI model. This allows police officers to easily generate reports adapted to the user's emotions, and enables store clerks in physical stores to instantly provide appropriate customer service based on the customer's emotions.

[0308] The "means for inputting detailed incident information" is a system component that provides an interface through which a user can input specific information about an incident.

[0309] The "means for transmitting case details to the server" is a system component that has communications capabilities for transferring the case details entered by the user to the server.

[0310] The "means for generating an initial report using an AI model" is a component of a system that uses artificial intelligence technology to automatically create an initial report based on detailed case information received.

[0311] The "means for returning the generated initial report to the client" is a system component that has a communication function for sending the initial report generated by the AI ​​model to the user's device.

[0312] The "means for displaying the returned initial record" is a system component having a display function that allows the client to visually check the received initial record.

[0313] "Means for recognizing the user's emotions using an emotion engine and adjusting the style and tone of the transcript" is a component of a system that has the function of analyzing the user's emotions and appropriately modifying the way the transcript is expressed based on the results.

[0314] "Means for recognizing customer emotions in real time and generating appropriate customer service methods" is a component of a system that has the function of detecting a customer's current emotions in a physical store and generating customer service methods that are appropriate to those emotions.

[0315] A "graphical user interface" is an interface that provides visual elements that allow users to operate intuitively.

[0316] The present invention is a system that recognizes a user's emotions and generates appropriate records and customer service methods. This system includes the following components:

[0317] 1. How to enter details of the incident

[0318] The system provides a graphical user interface (GUI) for users to input details of the case. Using this GUI, users can input specific information about the case on the screen of, for example, a PC or smartphone.

[0319] 2. A means of sending incident details to the server

[0320] The entered details of the incident are converted into a data format such as JSON and sent to a server, using Internet Protocol for communication.

[0321] 3. A method for generating initial records using AI models

[0322] The server generates an initial report based on the received case details using a generative AI model, such as OpenAI's GPT-3, a natural language processing technology, which converts the input information into an appropriate document.

[0323] 4. Using an emotion engine to recognize user emotions and adjust the style and tone of written documentation

[0324] The server also analyzes the input audio and video data and uses an emotion engine, such as the Emotion Recognition API, to recognize the user's emotions. This emotion data is fed back into the initial transcript generation process to adjust the style and tone of the transcript.

[0325] 5. Means of returning generated initial work papers to the client

[0326] The adjusted initial report is then sent back to the client, who uses software such as a browser to receive and display it.

[0327] 6. A means of viewing the returned initial report

[0328] Users can check the returned records and make corrections as necessary. The records are displayed on a computer monitor or smartphone screen.

[0329] 7. A means of recognizing customer emotions in real time and generating appropriate customer service methods

[0330] In a physical store, the user (store clerk) captures a customer's facial image using a smartphone camera and recognizes their emotions. Emotion recognition is performed using OpenCV or the Emotion Recognition API, for example. The recognized emotional information is used as a prompt for a generative AI model, which generates an appropriate way to serve the customer.

[0331] Hardware and software used

[0332] Hardware: PC, smartphone, camera

[0333] Software: GUI, OpenAI API, Emotion Recognition API, OpenCV, Browser

[0334] Specific processing of the program

[0335] When a user enters details of a case using a GUI on their computer or smartphone, the entered information is converted to JSON format and sent to the server. The server uses a generative AI model based on the received information to generate an initial report, and then uses an emotion engine to adjust the style and tone based on the user's emotions. The adjusted initial report is then returned to the client, who then reviews and modifies it. In physical stores, store staff use smartphone cameras to recognize customers' emotions in real time and generate appropriate customer service methods based on that emotional information. For example, the following prompt might be generated for a sad customer:

[0336] Prompt Sentence Examples

[0337] When a customer is sad, suggest appropriate ways to serve them.

[0338] This application example enables police officers to easily generate reports that adapt to the user's emotions, and enables store clerks to instantly provide appropriate customer service based on the customer's emotions.

[0339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0340] Step 1:

[0341] Users enter details of the incident using a GUI on their computer or smartphone. The information entered may include, for example:

[0342] Example: "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s and was suspected of drunk driving at the scene."

[0343] Step 2:

[0344] The device converts the incident details entered by the user into JSON format, which reorganizes the entered text data into a structured data format, such as the date, location, and circumstances of the incident, expressed in key-value pairs.

[0345] Step 3:

[0346] The device sends the converted JSON data to the server. HTTP or HTTPS is used as the communication protocol. In this step, the device connects to the server via network communication and sends the data.

[0347] Step 4:

[0348] The server parses the incoming JSON data to extract details about the incident, using a library such as Python's json library. This parsing process allows the server to split the incoming data into individual fields and format it in a way that can be processed.

[0349] Step 5:

[0350] The server generates an initial report using a generative AI model based on the analyzed details of the case. The generative AI model used is OpenAI's GPT-3. The server inputs the analyzed details as prompts into the AI ​​model, which then generates the appropriate document.

[0351] Step 6:

[0352] The server uses an emotion engine to analyze the input audio and video data and recognize the user's emotions. The emotion engine uses the Emotion Recognition API, etc. In this step, audio and image analysis technologies are used to identify the user's emotional state.

[0353] Step 7:

[0354] The emotion data recognized by the emotion engine is fed back into the initial report generation process. The server uses this emotion data to adjust the style and tone of the generated initial report. This adjustment results in a more appropriate report that is adapted to the user's emotions.

[0355] Step 8:

[0356] The server returns the generated adjusted initial report to the client. The communication protocol is again HTTP or HTTPS. The server converts the report into JSON, XML, or other format and sends it to the client.

[0357] Step 9:

[0358] The terminal receives the report sent back from the server and displays it using a browser or a dedicated application. The user can check the report and make corrections as necessary.

[0359] Step 10:

[0360] In a physical store, a user (a store clerk) captures a customer's face image using a smartphone camera. In this process, the OpenCV library is used to acquire the video data from the camera and extract the face image.

[0361] Step 11:

[0362] The device sends the captured facial image to the emotion recognition API, which analyzes facial expressions and other biometric information to identify emotions, generating emotion data as a result of the analysis.

[0363] Step 12:

[0364] The server receives the recognized emotion data and uses a generative AI model to generate an appropriate customer service method based on this. For example, a prompt sentence such as "Please suggest an appropriate way to serve a customer when they are sad" is used. Based on this prompt, the AI ​​generates an appropriate customer service method.

[0365] Step 13:

[0366] The server sends the generated service method to the terminal, which displays it. The store clerk checks the displayed service method and applies it to actual customer service. This enables service that adapts to the customer's emotions.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] [Second embodiment]

[0371] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0372] 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.

[0373] 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).

[0374] 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.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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."

[0383] This invention relates to a system that allows police officers to efficiently generate police reports. In this system, a user inputs details of a case through a terminal and sends them to a server. An AI model automatically generates an initial report, which is then returned and displayed to the user. This system significantly reduces the time and effort required compared to the traditional manual process of creating reports, and also improves the accuracy and reliability of the reports.

[0384] System configuration

[0385] The system includes the following components:

[0386] 1. Input method for entering details of the case

[0387] 2. Means of transmission for transmitting details of the incident

[0388] 3. A server that processes the received case details and generates an initial report using an AI model.

[0389] 4. A means of returning the generated initial workpaper to the client

[0390] 5. Display means for displaying the returned initial report

[0391] User operations

[0392] The user (police officer) uses their device to open a web browser and access a dedicated page in the report generation system. A text area for entering details of the incident will be displayed, where the user can enter a summary of the incident and the circumstances. For example, they might write, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0393] Once the input is complete, the user clicks the "Generate Report" button, which sends the entered case details from the device to the server.

[0394] Server Processing

[0395] The server receives the details of the case sent from the device and passes them to an AI model to generate an initial report, which uses natural language processing techniques to generate appropriate documents based on the input information.

[0396] The server receives the initial report generated by the AI ​​model and sends it back to the device using an HTTP response, with the generated report sent in JSON format.

[0397] Terminal display

[0398] The terminal receives the initial report sent back from the server and displays it in the browser, with the generated draft report displayed below the text entered by the user, allowing the user to review it and make any necessary corrections.

[0399] Specific examples

[0400] For example, a user enters the following incident details:

[0401] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0402] Based on this, the AI ​​model generates an initial report that looks like this:

[0403] At 3:00 PM on October 1, 2023, a traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car, injured, and transported to XX Hospital. The driver, a man in his 20s, was confirmed at the scene to be suspected of drunk driving.

[0404] The user checks the generated report and adds or corrects information as necessary to complete the final report.

[0405] The above is a specific embodiment of the system of the present invention, which allows police officers to significantly reduce the time it takes to prepare reports and quickly prepare more accurate and consistent documents.

[0406] The processing flow will be explained below.

[0407] Step 1:

[0408] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0409] Step 2:

[0410] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0411] Step 3:

[0412] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the incident details entered in the text area.

[0413] Step 4:

[0414] The device converts the retrieved incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user.

[0415] Step 5:

[0416] The server receives the HTTP POST request sent from the terminal, extracts the details of the incident from the request body, and prepares it for further processing.

[0417] Step 6:

[0418] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0419] Step 7:

[0420] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0421] Step 8:

[0422] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0423] Step 9:

[0424] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0425] Step 10:

[0426] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0427] Step 11:

[0428] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0429] Example 1

[0430] 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."

[0431] Conventional report preparation work requires a lot of time and effort, and because it is done manually, there are issues with accuracy and consistency. As a result, the quality of the reports can vary, which can hinder efficient investigations. The purpose of this invention is to solve these issues and automate the report preparation process, making it more efficient.

[0432] 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.

[0433] In this invention, the server includes a means for a user to input details of the case through a terminal, a means for transmitting the input details of the case to the server, a means for the server to convert the received details of the case into a prompt sentence and pass it to a generative AI model to generate an initial report, a means for returning the generated initial report to the terminal in JSON format, and a means for displaying the returned initial report in a browser. This allows police officers to efficiently prepare reports, significantly reducing time and effort while also improving the accuracy and reliability of the reports.

[0434] "User" refers to the individual, primarily a police officer, who uses the terminal to enter details of the case.

[0435] "Terminal" refers to an electronic device used by a user, such as a personal computer, tablet, or smartphone.

[0436] "Incident details" refers to specific information about a particular incident, such as the date, time, location, information about the people involved, and the course of events.

[0437] "Server" refers to the central management system that receives case details sent by users and generates initial reports using AI models.

[0438] "Prompt sentence" refers to an input sentence containing details of the incident that has been converted into a format that is easy for the AI ​​model to understand.

[0439] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate an initial report from an input prompt.

[0440] An "initial report" refers to a document containing basic information about the incident that is automatically created by a generative AI model.

[0441] "JSON format" is a text format for structuring and describing data, and is a format that is often used when exchanging data between servers and terminals.

[0442] "Browser" refers to a software application that enables users to access and use web pages on the Internet.

[0443] "Graphical User Interface" means a visual interface designed to be intuitive to the user and to facilitate the entry of case details.

[0444] MODE FOR CARRYING OUT THE INVENTION

[0445] System Configuration

[0446] The system of the present invention is designed to help police officers efficiently generate case reports. The system consists of the following components:

[0447] 1. Input means (terminal) for entering details of the case

[0448] 2. A means of transmission (a communication interface between the terminal and the server) for transmitting the entered case details to the server.

[0449] 3. A server that converts received case details into prompt text and generates an initial report using a generative AI model.

[0450] 4. Means for returning the generated initial record to the terminal (communication interface between the server and the terminal)

[0451] 5. A means (browser) for displaying the returned initial report

[0452] User operations

[0453] The police officer user first opens a web browser on their device and accesses the dedicated page of the report generation system. A login screen will appear, and the user must enter their authentication information to log in. A text area will then appear on the screen where the police officer can enter details of the case.

[0454] The user enters a summary of the incident and the circumstances in the text area. For example, they could enter details such as, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." Once they have finished entering the information, the user clicks the "Generate Report" button.

[0455] Server Processing

[0456] The server receives the details of the incident sent from the device. The received data is sent to the server in JSON format. The server parses this data and converts it into a prompt. The prompt is then formatted in a format that the generative AI model can understand. Specifically, the following prompt is generated:

[0457] Generate a draft of a traffic accident report based on the following text:

[0458] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0459] The server then sends this prompt to a generative AI model, such as OpenAI's GPT-3 or GPT-4. The generative AI model generates an initial report based on the prompt and returns the generated initial report in JSON format to the server.

[0460] Terminal display

[0461] The terminal receives the initial report sent back from the server and displays it in the user's browser. The generated draft report is displayed below the text entered by the user. The user can review the displayed report and make corrections as necessary. Once the corrections are complete, the report is saved as the final report.

[0462] The above is an embodiment of the system of the present invention, which enables police officers to significantly reduce the time and effort required to prepare reports and improve the accuracy and reliability of reports.

[0463] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0464] Step 1:

[0465] The user accesses a dedicated page in the device's web browser. They enter a URL to go to the login screen, then enter their authentication information to log in to the system. This allows the user to access a screen where they can enter details of the incident. The input is in text format, and the details are displayed in a text area in the browser.

[0466] Step 2:

[0467] The user enters details of the incident in the text area provided. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." This input will form the basis for subsequent data processing.

[0468] Step 3:

[0469] The user clicks the "Generate Report" button. By pressing this button, the details of the incident that were entered are sent from the device to the server as an HTTP POST request. Specifically, the entered text is converted to JSON format and sent to the server as transmission data.

[0470] Step 4:

[0471] The server receives the HTTP POST request sent from the terminal. The server extracts data containing details of the incident from this request and analyzes it. Specifically, the content of the received data is checked and converted into a prompt. The input text is first received as a string and then a formatted prompt is created.

[0472] Example: "Generate a draft of a traffic accident report based on the following text: October 1, 2023, 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0473] Step 5:

[0474] The server sends the generated prompt to an AI model (such as OpenAI's GPT-3 or GPT-4), which generates an initial document based on the prompt. The prompt is passed as input to the AI ​​model, which then uses natural language processing techniques to generate an appropriate document.

[0475] Step 6:

[0476] The generative AI model generates an initial report based on the prompt text. The generated initial report is sent back to the server. The server receives the result, formats it in JSON format, and sends it back to the device. At this stage, the generated initial report is sent as data from the server to the device.

[0477] Step 7:

[0478] The terminal receives the initial report in JSON format from the server and displays it in the user's browser. Specifically, it parses the received data and displays it in a format appropriate for the current operating environment. The generated draft report is displayed below the text entered by the user.

[0479] Step 8:

[0480] The user can review the draft report displayed in the browser and make any necessary edits, such as adding additional information or correcting incorrect information. Once the final report is complete, it can be saved and submitted to the appropriate systems.

[0481] The above is the flow of the process in which a user uses a device to automatically generate a transcript using the generative AI model via the server, and the results are displayed in a browser. This system improves the efficiency and accuracy of transcript creation.

[0482] (Application example 1)

[0483] 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."

[0484] In traditional security services, it is difficult for security guards to quickly and accurately generate detailed incident reports from the field, especially when the report is created manually, which takes time and effort. Furthermore, errors or incomplete information entered can cause problems with the accuracy of the report. To solve this problem, a new system is needed.

[0485] 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.

[0486] In this invention, the server includes means for receiving incident details and generating an initial report using a generative AI model, means for returning the generated initial report to the client, means for displaying the returned initial report, and means for security guards to input incident details by voice or visually on-site, thereby enabling security guards to efficiently and accurately create incident reports on-site.

[0487] "Means for inputting incident details" refers to a device or interface through which a security officer inputs incident details.

[0488] "Means for transmitting case details to the server" refers to a communications device or protocol for transmitting the entered case details to the server.

[0489] A "generative AI model" refers to an algorithm or software that automatically generates initial reports using natural language processing technology.

[0490] "Means for generating an initial report" means a process or system that uses a generative AI model to create an initial report from case details.

[0491] "Means for sending the generated initial working paper back to the client" refers to a communication device or protocol that sends the initial working paper generated by the server to the client device.

[0492] "Means for displaying returned initial working paper" refers to a device or interface for visually displaying the received initial working paper at the client device.

[0493] "Smart glasses" refers to wearable devices that display information visually and support voice input and gesture control.

[0494] A "head-mounted display" is a device worn by a user to display visual information, and refers to a display device that is worn on the head.

[0495] "Ability for security guards to input incident details via voice or visual input on-site" refers to a feature that allows security guards to input incident details via voice or visual input on-site using a smart device.

[0496] A system for implementing this invention allows security guards to efficiently and accurately generate incident reports from the field. The system primarily includes the following components:

[0497] 1. Enter the details of the incident:

[0498] Security guards use devices such as smartphones, smart glasses, or head-mounted displays to input details of the incident, either via voice or text.

[0499] 2. Means of sending incident details to the server:

[0500] The details of the incident entered on the device are sent to a server over the Internet using an HTTP POST request.

[0501] 3. How to generate an initial report using a generative AI model:

[0502] Based on the received case details, the server generates an initial report using a generative AI model, which uses natural language processing techniques and applies advanced models such as BERT.

[0503] 4. Means of returning the generated initial workpaper to the client:

[0504] The initial report generated by the server is sent to the client device in JSON format, and is returned using an HTTP response.

[0505] 5. How to view the returned initial report:

[0506] The client device displays the initial report received from the server to the user through a browser or application, and the security officer can review the displayed initial report and enter corrections or additional information as necessary.

[0507] Examples:

[0508] For example, a security guard dictates the following incident details:

[0509] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0510] This input data is sent to the server, and the generative AI model generates an initial report like this:

[0511] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0512] This generated initial report is sent back to the client device for visual review by the guard.

[0513] Example prompt sentence:

[0514] Please generate an incident report based on the following details:

[0515] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0516] The system allows security guards to quickly and accurately generate incident reports from the field, and the versatility of the devices they can use significantly improves their work efficiency.

[0517] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0518] Step 1:

[0519] The user uses a smart device (smartphone, smart glasses, head-mounted display) to input details of the incident. For example, they might enter, "At 5:00 PM on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s. He refused to show identification, so the police were called." The input data is temporarily stored on the device.

[0520] Step 2:

[0521] The device sends the details of the incident that was entered to the server using an HTTP POST request, and the input data is sent to the server in text format using JSON as the specific data format.

[0522] Step 3:

[0523] The server analyzes the details of the incident received from the device and inputs them into the generative AI model. Text information is passed to the AI ​​model as input data, and the AI ​​model generates an initial report using natural language processing techniques. Specifically, the AI ​​model analyzes the meaning of each sentence and reconstructs it in an appropriate format.

[0524] Step 4:

[0525] The server receives the generated initial statement and returns it to the client device using an HTTP response, sending the generated statement in JSON format. In particular, this could be in the following format:

[0526] text

[0527] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0528] Step 5:

[0529] The client device receives the returned initial work report and displays it to the user. This can be done using a browser or a dedicated application, allowing the user to review the generated work report and enter corrections or additional information as needed. The displayed form is formatted to be easy for the user to understand.

[0530] This process allows security guards to quickly and accurately generate incident reports from the scene.The system enables efficient data processing and information sharing by linking the server, terminals, and users.

[0531] 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.

[0532] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0533] System configuration

[0534] The system includes the following components:

[0535] 1. Input method for entering details of the case

[0536] 2. Means of transmission for transmitting details of the incident

[0537] 3. A server that processes the received case details and generates an initial report using an AI model.

[0538] 4. A means of returning the generated initial workpaper to the client

[0539] 5. A means of viewing the returned initial report

[0540] 6. Emotion engine that recognizes user emotions

[0541] User operations

[0542] The user (police officer) uses a terminal to access the report generation system's webpage and enter details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0543] Server Processing

[0544] Once the input is complete, the user clicks the "Generate transcript" button. This action causes the device's client-side script to convert the input information into JSON format and send it to the server. The server then uses an AI model to generate an initial transcript based on the received information. This AI model uses natural language processing technology to analyze the input information and generate the appropriate document.

[0545] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0546] The server returns the generated initial statement and the statement adjusted by the emotion engine to the client. The returned statement is displayed on the terminal, where the user can check it and modify it if necessary.

[0547] Terminal display

[0548] The device receives the report sent back from the server and displays it in the browser. The user can review it and make any necessary edits. The report displayed on the device reflects the details of the incident, as well as a style and tone that is tailored to the user's emotions.

[0549] Specific examples

[0550] For example, a user enters the following incident details:

[0551] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0552] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the AI ​​model will generate an initial report in the following style:

[0553] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0554] The user can check the generated report, make corrections as necessary, and complete the final report.

[0555] The above is a specific embodiment of the system of the present invention. By combining it with an emotion engine, it becomes possible to generate more human-like reports that are adapted to the user's emotions, improving the work efficiency of police officers and the quality of reports.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0559] Step 2:

[0560] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0561] Step 3:

[0562] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the entered incident details.

[0563] Step 4:

[0564] The device converts the acquired incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user, as well as audio and video data from when the details were entered.

[0565] Step 5:

[0566] The server receives the HTTP POST request sent from the terminal, extracts the incident details and audio and video data from the request body, and prepares them for further processing.

[0567] Step 6:

[0568] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0569] Step 7:

[0570] The server passes the extracted audio and video data to the emotion engine, which then recognizes the user's emotion. The emotion engine determines the user's emotional state based on the user's tone of voice and facial expressions.

[0571] Step 8:

[0572] The server feeds back the emotion data recognized by the emotion engine to the AI ​​model's report generation process, adjusting the style and tone of the report generated based on the user's emotions.

[0573] Step 9:

[0574] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0575] Step 10:

[0576] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0577] Step 11:

[0578] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0579] Step 12:

[0580] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0581] Step 13:

[0582] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0583] This is the processing flow for a system that combines an emotion engine. This makes it possible to generate police reports based on the user's emotions, improving the work efficiency of police officers and the quality of the reports.

[0584] Example 2

[0585] 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."

[0586] Conventional report generation systems take a long time to complete, from entering details of the case to generating and displaying the report, and it is difficult to consider emotional factors. Manual report creation is prone to human error, resulting in unstable report quality. Furthermore, the lack of adjustments to style and tone to reflect the user's emotions makes it difficult to generate detailed, human-like reports.

[0587] 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.

[0588] In this invention, the server includes a means for analyzing the details of the case and generating an initial report using a generative AI model, a means including an emotion engine for recognizing the user's emotions, and a means for returning the generated initial report to the client. This enables the generation of detailed, human-like reports that take the user's emotions into consideration, thereby improving work efficiency and stabilizing the quality of the reports.

[0589] The "means for inputting details of the incident" is an element that provides an interface for the user to input specific circumstances of the incident.

[0590] The "means for transmitting the entered case details to the server" is an element that converts the case information entered by the user into a data format and transmits it to the server via the network.

[0591] "Means for analyzing received case details and generating an initial report using a generative AI model" refers to an element that analyzes case information sent to the server and automatically generates an initial report using AI technology.

[0592] The "means for returning the generated initial record to the client" is an element that sends the record generated by the server back to the client (user's terminal).

[0593] The "means for displaying the returned initial record" is an element that visually displays the record sent to the client so that the user can check it.

[0594] The "means including an emotion engine for recognizing the user's emotion" refers to an element including dedicated hardware and software for analyzing the audio and video data input by the user and recognizing the user's emotion.

[0595] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to generate appropriate documents from input data.

[0596] A "prompt" is text that describes the document format and instructions for providing input data to an AI model.

[0597] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0598] First, the user (police officer) uses a terminal to access the webpage of the report generation system and enters details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0599] Next, when the user clicks the "Generate transcript" button, the terminal's client-side script converts the input information into JSON format and sends it to the server. The server then uses the received information to generate an initial transcript using a generative AI model. This AI model uses natural language processing technology to analyze the input information and generate an appropriate document. The hardware used is a server equipped with a GPU, and the software used includes TensorFlow and PyTorch.

[0600] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion engine uses librosa for analyzing voice data and OpenCV for analyzing video data. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0601] The server returns the generated initial report and the report adjusted by the emotion engine to the client. The returned report is displayed on the terminal, where the user can review and modify it as necessary. The report displayed on the terminal reflects the details of the case, as well as the style and tone adjusted based on the user's emotions.

[0602] As an example, a user enters the following incident details:

[0603] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0604] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the generative AI model will generate an initial report in the following style:

[0605] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0606] By combining this with an emotion engine, it becomes possible to generate more human-like reports that adapt to the user's emotions, improving the efficiency of police officers' work and the quality of reports.

[0607] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] The user enters the details of the incident.

[0610] The user uses a terminal to access the web page of the record generation system and inputs the specific circumstances of the case into the text area.

[0611] Input: Details of the incident (e.g., "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital.")

[0612] Output: Detailed incident data entered in the text area

[0613] Step 2:

[0614] The user clicks the "Generate Work Report" button.

[0615] This operation activates a client-side script on the device, converting the input information into JSON format, which is then sent to the server.

[0616] Input: Case details entered in the text area

[0617] Output: JSON formatted data sent to the server

[0618] Specific operation: Using JavaScript, the input data is converted to JSON format using JSON.stringify, and a POST request is sent to the server using the fetch API.

[0619] Step 3:

[0620] The server analyzes the details of the incident received.

[0621] The server parses and analyzes the received JSON-formatted data, which is then used as input to a generative AI model.

[0622] Input: Case details in JSON format

[0623] Output: Input data to the AI ​​model (parsed case details)

[0624] Specific operation: The JSON data is deserialized using a server-side script (e.g., Python), and the parsed results are input into the generative AI model.

[0625] Step 4:

[0626] The server generates an initial report using the generative AI model.

[0627] The server provides input data to the generative AI model to generate an initial report, which is then output in text format.

[0628] Input: Input data to the AI ​​model (parsed case details)

[0629] Output: Text data of the generated initial report

[0630] Specific operation: Input data in the form of prompt sentences is provided to a natural language processing model built using TensorFlow and PyTorch, and the generated report text is obtained.

[0631] Example prompt sentence:

[0632] Generate a report based on the following incident details: "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0633] Step 5:

[0634] The server applies the emotion engine.

[0635] The server analyzes the audio and video data input by the user to obtain emotional data, and adjusts the initial record based on the obtained emotional data.

[0636] Input: User's audio and video data, generated initial transcript

[0637] Output: Adjusted transcript text reflecting emotion data

[0638] Specific operation: Audio data is analyzed using librosa, and video data is analyzed using OpenCV to determine emotions, and based on this data, the style of the transcript is readjusted using a generative AI model.

[0639] Step 6:

[0640] The server returns the adjusted record to the terminal.

[0641] The server converts the adjusted report text into JSON format and sends it to the terminal as an HTTP response.

[0642] Input: Adjusted transcript text reflecting emotion data

[0643] Output: JSON formatted report data returned to the terminal

[0644] Specific operation: The report text is serialized into JSON format using a server script such as Python and returned as an HTTP response.

[0645] Step 7:

[0646] The terminal receives and displays the returned record.

[0647] The terminal receives the data returned from the server and displays it in the browser. The user can check it and modify it as necessary.

[0648] Input: JSON formatted report data returned from the server

[0649] Output: Text of the report displayed in the browser

[0650] Specific operation: Parse the returned JSON data using JavaScript and display it in the browser using DOM manipulation.

[0651] (Application example 2)

[0652] 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."

[0653] The present invention aims to solve the problems of police officers generating reports efficiently and recognizing the emotions of customers in physical stores in real time and providing appropriate responses. In particular, the objective is to realize a system that can provide more accurate and appropriate reports and customer service methods by recognizing the emotions of users (police officers and store clerks) and adjusting the writing style and tone based on that.

[0654] 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.

[0655] In this invention, the server includes means for recognizing a user's emotions using an emotion engine and adjusting the style and tone of the report, means for recognizing a customer's emotions in real time and generating an appropriate customer service method, and means for receiving details of the incident and generating an initial report using an AI model. This allows police officers to easily generate reports adapted to the user's emotions, and enables store clerks in physical stores to instantly provide appropriate customer service based on the customer's emotions.

[0656] The "means for inputting detailed incident information" is a system component that provides an interface through which a user can input specific information about an incident.

[0657] The "means for transmitting case details to the server" is a system component that has communications capabilities for transferring the case details entered by the user to the server.

[0658] The "means for generating an initial report using an AI model" is a component of a system that uses artificial intelligence technology to automatically create an initial report based on detailed case information received.

[0659] The "means for returning the generated initial report to the client" is a system component that has a communication function for sending the initial report generated by the AI ​​model to the user's device.

[0660] The "means for displaying the returned initial record" is a system component having a display function that allows the client to visually check the received initial record.

[0661] "Means for recognizing the user's emotions using an emotion engine and adjusting the style and tone of the transcript" is a component of a system that has the function of analyzing the user's emotions and appropriately modifying the way the transcript is expressed based on the results.

[0662] "Means for recognizing customer emotions in real time and generating appropriate customer service methods" is a component of a system that has the function of detecting a customer's current emotions in a physical store and generating customer service methods that are appropriate to those emotions.

[0663] A "graphical user interface" is an interface that provides visual elements that allow users to operate intuitively.

[0664] The present invention is a system that recognizes a user's emotions and generates appropriate records and customer service methods. This system includes the following components:

[0665] 1. How to enter details of the incident

[0666] The system provides a graphical user interface (GUI) for users to input details of the case. Using this GUI, users can input specific information about the case on the screen of, for example, a PC or smartphone.

[0667] 2. A means of sending incident details to the server

[0668] The entered details of the incident are converted into a data format such as JSON and sent to a server, using Internet Protocol for communication.

[0669] 3. A method for generating initial records using AI models

[0670] The server generates an initial report based on the received case details using a generative AI model, such as OpenAI's GPT-3, a natural language processing technology, which converts the input information into an appropriate document.

[0671] 4. Using an emotion engine to recognize user emotions and adjust the style and tone of written documentation

[0672] The server also analyzes the input audio and video data and uses an emotion engine, such as the Emotion Recognition API, to recognize the user's emotions. This emotion data is fed back into the initial transcript generation process to adjust the style and tone of the transcript.

[0673] 5. Means of returning generated initial work papers to the client

[0674] The adjusted initial report is then sent back to the client, who uses software such as a browser to receive and display it.

[0675] 6. A means of viewing the returned initial report

[0676] Users can check the returned records and make corrections as necessary. The records are displayed on a computer monitor or smartphone screen.

[0677] 7. A means of recognizing customer emotions in real time and generating appropriate customer service methods

[0678] In a physical store, the user (store clerk) captures a customer's facial image using a smartphone camera and recognizes their emotions. Emotion recognition is performed using OpenCV or the Emotion Recognition API, for example. The recognized emotional information is used as a prompt for a generative AI model, which generates an appropriate way to serve the customer.

[0679] Hardware and software used

[0680] Hardware: PC, smartphone, camera

[0681] Software: GUI, OpenAI API, Emotion Recognition API, OpenCV, Browser

[0682] Specific processing of the program

[0683] When a user enters details of a case using a GUI on their computer or smartphone, the entered information is converted to JSON format and sent to the server. The server uses a generative AI model based on the received information to generate an initial report, and then uses an emotion engine to adjust the style and tone based on the user's emotions. The adjusted initial report is then returned to the client, who then reviews and modifies it. In physical stores, store staff use smartphone cameras to recognize customers' emotions in real time and generate appropriate customer service methods based on that emotional information. For example, the following prompt might be generated for a sad customer:

[0684] Prompt Sentence Examples

[0685] When a customer is sad, suggest appropriate ways to serve them.

[0686] This application example enables police officers to easily generate reports that adapt to the user's emotions, and enables store clerks to instantly provide appropriate customer service based on the customer's emotions.

[0687] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0688] Step 1:

[0689] Users enter details of the incident using a GUI on their computer or smartphone. The information entered may include, for example:

[0690] Example: "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s and was suspected of drunk driving at the scene."

[0691] Step 2:

[0692] The device converts the incident details entered by the user into JSON format, which reorganizes the entered text data into a structured data format, such as the date, location, and circumstances of the incident, expressed in key-value pairs.

[0693] Step 3:

[0694] The device sends the converted JSON data to the server. HTTP or HTTPS is used as the communication protocol. In this step, the device connects to the server via network communication and sends the data.

[0695] Step 4:

[0696] The server parses the incoming JSON data to extract details about the incident, using a library such as Python's json library. This parsing process allows the server to split the incoming data into individual fields and format it in a way that can be processed.

[0697] Step 5:

[0698] The server generates an initial report using a generative AI model based on the analyzed details of the case. The generative AI model used is OpenAI's GPT-3. The server inputs the analyzed details as prompts into the AI ​​model, which then generates the appropriate document.

[0699] Step 6:

[0700] The server uses an emotion engine to analyze the input audio and video data and recognize the user's emotions. The emotion engine uses the Emotion Recognition API, etc. In this step, audio and image analysis technologies are used to identify the user's emotional state.

[0701] Step 7:

[0702] The emotion data recognized by the emotion engine is fed back into the initial report generation process. The server uses this emotion data to adjust the style and tone of the generated initial report. This adjustment results in a more appropriate report that is adapted to the user's emotions.

[0703] Step 8:

[0704] The server returns the generated adjusted initial report to the client. The communication protocol is again HTTP or HTTPS. The server converts the report into JSON, XML, or other format and sends it to the client.

[0705] Step 9:

[0706] The terminal receives the report sent back from the server and displays it using a browser or a dedicated application. The user can check the report and make corrections as necessary.

[0707] Step 10:

[0708] In a physical store, a user (a store clerk) captures a customer's face image using a smartphone camera. In this process, the OpenCV library is used to acquire the video data from the camera and extract the face image.

[0709] Step 11:

[0710] The device sends the captured facial image to the emotion recognition API, which analyzes facial expressions and other biometric information to identify emotions, generating emotion data as a result of the analysis.

[0711] Step 12:

[0712] The server receives the recognized emotion data and uses a generative AI model to generate an appropriate customer service method based on this. For example, a prompt sentence such as "Please suggest an appropriate way to serve a customer when they are sad" is used. Based on this prompt, the AI ​​generates an appropriate customer service method.

[0713] Step 13:

[0714] The server sends the generated service method to the terminal, which displays it. The store clerk checks the displayed service method and applies it to actual customer service. This enables service that adapts to the customer's emotions.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] [Third embodiment]

[0719] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0720] 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.

[0721] 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).

[0722] 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.

[0723] 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.

[0724] 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).

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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."

[0731] This invention relates to a system that allows police officers to efficiently generate police reports. In this system, a user inputs details of a case through a terminal and sends them to a server. An AI model automatically generates an initial report, which is then returned and displayed to the user. This system significantly reduces the time and effort required compared to the traditional manual process of creating reports, and also improves the accuracy and reliability of the reports.

[0732] System configuration

[0733] The system includes the following components:

[0734] 1. Input method for entering details of the case

[0735] 2. Means of transmission for transmitting details of the incident

[0736] 3. A server that processes the received case details and generates an initial report using an AI model.

[0737] 4. A means of returning the generated initial workpaper to the client

[0738] 5. Display means for displaying the returned initial report

[0739] User operations

[0740] The user (police officer) uses their device to open a web browser and access a dedicated page in the report generation system. A text area for entering details of the incident will be displayed, where the user can enter a summary of the incident and the circumstances. For example, they might write, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0741] Once the input is complete, the user clicks the "Generate Report" button, which sends the entered case details from the device to the server.

[0742] Server Processing

[0743] The server receives the details of the case sent from the device and passes them to an AI model to generate an initial report, which uses natural language processing techniques to generate appropriate documents based on the input information.

[0744] The server receives the initial report generated by the AI ​​model and sends it back to the device using an HTTP response, with the generated report sent in JSON format.

[0745] Terminal display

[0746] The terminal receives the initial report sent back from the server and displays it in the browser, with the generated draft report displayed below the text entered by the user, allowing the user to review it and make any necessary corrections.

[0747] Specific examples

[0748] For example, a user enters the following incident details:

[0749] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0750] Based on this, the AI ​​model generates an initial report that looks like this:

[0751] At 3:00 PM on October 1, 2023, a traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car, injured, and transported to XX Hospital. The driver, a man in his 20s, was confirmed at the scene to be suspected of drunk driving.

[0752] The user checks the generated report and adds or corrects information as necessary to complete the final report.

[0753] The above is a specific embodiment of the system of the present invention, which allows police officers to significantly reduce the time it takes to prepare reports and quickly prepare more accurate and consistent documents.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0757] Step 2:

[0758] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0759] Step 3:

[0760] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the incident details entered in the text area.

[0761] Step 4:

[0762] The device converts the retrieved incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user.

[0763] Step 5:

[0764] The server receives the HTTP POST request sent from the terminal, extracts the details of the incident from the request body, and prepares it for further processing.

[0765] Step 6:

[0766] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0767] Step 7:

[0768] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0769] Step 8:

[0770] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0771] Step 9:

[0772] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0773] Step 10:

[0774] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0775] Step 11:

[0776] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0777] Example 1

[0778] 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."

[0779] Conventional report preparation work requires a lot of time and effort, and because it is done manually, there are issues with accuracy and consistency. As a result, the quality of the reports can vary, which can hinder efficient investigations. The purpose of this invention is to solve these issues and automate the report preparation process, making it more efficient.

[0780] 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.

[0781] In this invention, the server includes a means for a user to input details of the case through a terminal, a means for transmitting the input details of the case to the server, a means for the server to convert the received details of the case into a prompt sentence and pass it to a generative AI model to generate an initial report, a means for returning the generated initial report to the terminal in JSON format, and a means for displaying the returned initial report in a browser. This allows police officers to efficiently prepare reports, significantly reducing time and effort while also improving the accuracy and reliability of the reports.

[0782] "User" refers to the individual, primarily a police officer, who uses the terminal to enter details of the case.

[0783] "Terminal" refers to an electronic device used by a user, such as a personal computer, tablet, or smartphone.

[0784] "Incident details" refers to specific information about a particular incident, such as the date, time, location, information about the people involved, and the course of events.

[0785] "Server" refers to the central management system that receives case details sent by users and generates initial reports using AI models.

[0786] "Prompt sentence" refers to an input sentence containing details of the incident that has been converted into a format that is easy for the AI ​​model to understand.

[0787] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate an initial report from an input prompt.

[0788] An "initial report" refers to a document containing basic information about the incident that is automatically created by a generative AI model.

[0789] "JSON format" is a text format for structuring and describing data, and is a format that is often used when exchanging data between servers and terminals.

[0790] "Browser" refers to a software application that enables users to access and use web pages on the Internet.

[0791] "Graphical User Interface" means a visual interface designed to be intuitive to the user and to facilitate the entry of case details.

[0792] MODE FOR CARRYING OUT THE INVENTION

[0793] System Configuration

[0794] The system of the present invention is designed to help police officers efficiently generate case reports. The system consists of the following components:

[0795] 1. Input means (terminal) for entering details of the case

[0796] 2. A means of transmission (a communication interface between the terminal and the server) for transmitting the entered case details to the server.

[0797] 3. A server that converts received case details into prompt text and generates an initial report using a generative AI model.

[0798] 4. Means for returning the generated initial record to the terminal (communication interface between the server and the terminal)

[0799] 5. A means (browser) for displaying the returned initial report

[0800] User operations

[0801] The police officer user first opens a web browser on their device and accesses the dedicated page of the report generation system. A login screen will appear, and the user must enter their authentication information to log in. A text area will then appear on the screen where the police officer can enter details of the case.

[0802] The user enters a summary of the incident and the circumstances in the text area. For example, they could enter details such as, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." Once they have finished entering the information, the user clicks the "Generate Report" button.

[0803] Server Processing

[0804] The server receives the details of the incident sent from the device. The received data is sent to the server in JSON format. The server parses this data and converts it into a prompt. The prompt is then formatted in a format that the generative AI model can understand. Specifically, the following prompt is generated:

[0805] Generate a draft of a traffic accident report based on the following text:

[0806] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0807] The server then sends this prompt to a generative AI model, such as OpenAI's GPT-3 or GPT-4. The generative AI model generates an initial report based on the prompt and returns the generated initial report in JSON format to the server.

[0808] Terminal display

[0809] The terminal receives the initial report sent back from the server and displays it in the user's browser. The generated draft report is displayed below the text entered by the user. The user can review the displayed report and make corrections as necessary. Once the corrections are complete, the report is saved as the final report.

[0810] The above is an embodiment of the system of the present invention, which enables police officers to significantly reduce the time and effort required to prepare reports and improve the accuracy and reliability of reports.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1:

[0813] The user accesses a dedicated page in the device's web browser. They enter a URL to go to the login screen, then enter their authentication information to log in to the system. This allows the user to access a screen where they can enter details of the incident. The input is in text format, and the details are displayed in a text area in the browser.

[0814] Step 2:

[0815] The user enters details of the incident in the text area provided. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." This input will form the basis for subsequent data processing.

[0816] Step 3:

[0817] The user clicks the "Generate Report" button. By pressing this button, the details of the incident that were entered are sent from the device to the server as an HTTP POST request. Specifically, the entered text is converted to JSON format and sent to the server as transmission data.

[0818] Step 4:

[0819] The server receives the HTTP POST request sent from the terminal. The server extracts data containing details of the incident from this request and analyzes it. Specifically, the content of the received data is checked and converted into a prompt. The input text is first received as a string and then a formatted prompt is created.

[0820] Example: "Generate a draft of a traffic accident report based on the following text: October 1, 2023, 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0821] Step 5:

[0822] The server sends the generated prompt to an AI model (such as OpenAI's GPT-3 or GPT-4), which generates an initial document based on the prompt. The prompt is passed as input to the AI ​​model, which then uses natural language processing techniques to generate an appropriate document.

[0823] Step 6:

[0824] The generative AI model generates an initial report based on the prompt text. The generated initial report is sent back to the server. The server receives the result, formats it in JSON format, and sends it back to the device. At this stage, the generated initial report is sent as data from the server to the device.

[0825] Step 7:

[0826] The terminal receives the initial report in JSON format from the server and displays it in the user's browser. Specifically, it parses the received data and displays it in a format appropriate for the current operating environment. The generated draft report is displayed below the text entered by the user.

[0827] Step 8:

[0828] The user can review the draft report displayed in the browser and make any necessary edits, such as adding additional information or correcting incorrect information. Once the final report is complete, it can be saved and submitted to the appropriate systems.

[0829] The above is the flow of the process in which a user uses a device to automatically generate a transcript using the generative AI model via the server, and the results are displayed in a browser. This system improves the efficiency and accuracy of transcript creation.

[0830] (Application example 1)

[0831] 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."

[0832] In traditional security services, it is difficult for security guards to quickly and accurately generate detailed incident reports from the field, especially when the report is created manually, which takes time and effort. Furthermore, errors or incomplete information entered can cause problems with the accuracy of the report. To solve this problem, a new system is needed.

[0833] 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.

[0834] In this invention, the server includes means for receiving incident details and generating an initial report using a generative AI model, means for returning the generated initial report to the client, means for displaying the returned initial report, and means for security guards to input incident details by voice or visually on-site, thereby enabling security guards to efficiently and accurately create incident reports on-site.

[0835] "Means for inputting incident details" refers to a device or interface through which a security officer inputs incident details.

[0836] "Means for transmitting case details to the server" refers to a communications device or protocol for transmitting the entered case details to the server.

[0837] A "generative AI model" refers to an algorithm or software that automatically generates initial reports using natural language processing technology.

[0838] "Means for generating an initial report" means a process or system that uses a generative AI model to create an initial report from case details.

[0839] "Means for sending the generated initial working paper back to the client" refers to a communication device or protocol that sends the initial working paper generated by the server to the client device.

[0840] "Means for displaying returned initial working paper" refers to a device or interface for visually displaying the received initial working paper at the client device.

[0841] "Smart glasses" refers to wearable devices that display information visually and support voice input and gesture control.

[0842] A "head-mounted display" is a device worn by a user to display visual information, and refers to a display device that is worn on the head.

[0843] "Ability for security guards to input incident details via voice or visual input on-site" refers to a feature that allows security guards to input incident details via voice or visual input on-site using a smart device.

[0844] A system for implementing this invention allows security guards to efficiently and accurately generate incident reports from the field. The system primarily includes the following components:

[0845] 1. Enter the details of the incident:

[0846] Security guards use devices such as smartphones, smart glasses, or head-mounted displays to input details of the incident, either via voice or text.

[0847] 2. Means of sending incident details to the server:

[0848] The details of the incident entered on the device are sent to a server over the Internet using an HTTP POST request.

[0849] 3. How to generate an initial report using a generative AI model:

[0850] Based on the received case details, the server generates an initial report using a generative AI model, which uses natural language processing techniques and applies advanced models such as BERT.

[0851] 4. Means of returning the generated initial workpaper to the client:

[0852] The initial report generated by the server is sent to the client device in JSON format, and is returned using an HTTP response.

[0853] 5. How to view the returned initial report:

[0854] The client device displays the initial report received from the server to the user through a browser or application, and the security officer can review the displayed initial report and enter corrections or additional information as necessary.

[0855] Examples:

[0856] For example, a security guard dictates the following incident details:

[0857] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0858] This input data is sent to the server, and the generative AI model generates an initial report like this:

[0859] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0860] This generated initial report is sent back to the client device for visual review by the guard.

[0861] Example prompt sentence:

[0862] Please generate an incident report based on the following details:

[0863] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[0864] The system allows security guards to quickly and accurately generate incident reports from the field, and the versatility of the devices they can use significantly improves their work efficiency.

[0865] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0866] Step 1:

[0867] The user uses a smart device (smartphone, smart glasses, head-mounted display) to input details of the incident. For example, they might enter, "At 5:00 PM on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s. He refused to show identification, so the police were called." The input data is temporarily stored on the device.

[0868] Step 2:

[0869] The device sends the details of the incident that was entered to the server using an HTTP POST request, and the input data is sent to the server in text format using JSON as the specific data format.

[0870] Step 3:

[0871] The server analyzes the details of the incident received from the device and inputs them into the generative AI model. Text information is passed to the AI ​​model as input data, and the AI ​​model generates an initial report using natural language processing techniques. Specifically, the AI ​​model analyzes the meaning of each sentence and reconstructs it in an appropriate format.

[0872] Step 4:

[0873] The server receives the generated initial statement and returns it to the client device using an HTTP response, sending the generated statement in JSON format. In particular, this could be in the following format:

[0874] text

[0875] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[0876] Step 5:

[0877] The client device receives the returned initial work report and displays it to the user. This can be done using a browser or a dedicated application, allowing the user to review the generated work report and enter corrections or additional information as needed. The displayed form is formatted to be easy for the user to understand.

[0878] This process allows security guards to quickly and accurately generate incident reports from the scene.The system enables efficient data processing and information sharing by linking the server, terminals, and users.

[0879] 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.

[0880] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0881] System configuration

[0882] The system includes the following components:

[0883] 1. Input method for entering details of the case

[0884] 2. Means of transmission for transmitting details of the incident

[0885] 3. A server that processes the received case details and generates an initial report using an AI model.

[0886] 4. A means of returning the generated initial workpaper to the client

[0887] 5. A means of viewing the returned initial report

[0888] 6. Emotion engine that recognizes user emotions

[0889] User operations

[0890] The user (police officer) uses a terminal to access the report generation system's webpage and enter details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0891] Server Processing

[0892] Once the input is complete, the user clicks the "Generate transcript" button. This action causes the device's client-side script to convert the input information into JSON format and send it to the server. The server then uses an AI model to generate an initial transcript based on the received information. This AI model uses natural language processing technology to analyze the input information and generate the appropriate document.

[0893] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0894] The server returns the generated initial statement and the statement adjusted by the emotion engine to the client. The returned statement is displayed on the terminal, where the user can check it and modify it if necessary.

[0895] Terminal display

[0896] The device receives the report sent back from the server and displays it in the browser. The user can review it and make any necessary edits. The report displayed on the device reflects the details of the incident, as well as a style and tone that is tailored to the user's emotions.

[0897] Specific examples

[0898] For example, a user enters the following incident details:

[0899] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0900] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the AI ​​model will generate an initial report in the following style:

[0901] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0902] The user can check the generated report, make corrections as necessary, and complete the final report.

[0903] The above is a specific embodiment of the system of the present invention. By combining it with an emotion engine, it becomes possible to generate more human-like reports that are adapted to the user's emotions, improving the work efficiency of police officers and the quality of reports.

[0904] The processing flow will be explained below.

[0905] Step 1:

[0906] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[0907] Step 2:

[0908] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0909] Step 3:

[0910] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the entered incident details.

[0911] Step 4:

[0912] The device converts the acquired incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user, as well as audio and video data from when the details were entered.

[0913] Step 5:

[0914] The server receives the HTTP POST request sent from the terminal, extracts the incident details and audio and video data from the request body, and prepares them for further processing.

[0915] Step 6:

[0916] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[0917] Step 7:

[0918] The server passes the extracted audio and video data to the emotion engine, which then recognizes the user's emotion. The emotion engine determines the user's emotional state based on the user's tone of voice and facial expressions.

[0919] Step 8:

[0920] The server feeds back the emotion data recognized by the emotion engine to the AI ​​model's report generation process, adjusting the style and tone of the report generated based on the user's emotions.

[0921] Step 9:

[0922] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[0923] Step 10:

[0924] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[0925] Step 11:

[0926] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[0927] Step 12:

[0928] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[0929] Step 13:

[0930] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[0931] This is the processing flow for a system that combines an emotion engine. This makes it possible to generate police reports based on the user's emotions, improving the work efficiency of police officers and the quality of the reports.

[0932] Example 2

[0933] 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."

[0934] Conventional report generation systems take a long time to complete, from entering details of the case to generating and displaying the report, and it is difficult to consider emotional factors. Manual report creation is prone to human error, resulting in unstable report quality. Furthermore, the lack of adjustments to style and tone to reflect the user's emotions makes it difficult to generate detailed, human-like reports.

[0935] 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.

[0936] In this invention, the server includes a means for analyzing the details of the case and generating an initial report using a generative AI model, a means including an emotion engine for recognizing the user's emotions, and a means for returning the generated initial report to the client. This enables the generation of detailed, human-like reports that take the user's emotions into consideration, thereby improving work efficiency and stabilizing the quality of the reports.

[0937] The "means for inputting details of the incident" is an element that provides an interface for the user to input specific circumstances of the incident.

[0938] The "means for transmitting the entered case details to the server" is an element that converts the case information entered by the user into a data format and transmits it to the server via the network.

[0939] "Means for analyzing received case details and generating an initial report using a generative AI model" refers to an element that analyzes case information sent to the server and automatically generates an initial report using AI technology.

[0940] The "means for returning the generated initial record to the client" is an element that sends the record generated by the server back to the client (user's terminal).

[0941] The "means for displaying the returned initial record" is an element that visually displays the record sent to the client so that the user can check it.

[0942] The "means including an emotion engine for recognizing the user's emotion" refers to an element including dedicated hardware and software for analyzing the audio and video data input by the user and recognizing the user's emotion.

[0943] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to generate appropriate documents from input data.

[0944] A "prompt" is text that describes the document format and instructions for providing input data to an AI model.

[0945] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[0946] First, the user (police officer) uses a terminal to access the webpage of the report generation system and enters details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[0947] Next, when the user clicks the "Generate transcript" button, the terminal's client-side script converts the input information into JSON format and sends it to the server. The server then uses the received information to generate an initial transcript using a generative AI model. This AI model uses natural language processing technology to analyze the input information and generate an appropriate document. The hardware used is a server equipped with a GPU, and the software used includes TensorFlow and PyTorch.

[0948] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion engine uses librosa for analyzing voice data and OpenCV for analyzing video data. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[0949] The server returns the generated initial report and the report adjusted by the emotion engine to the client. The returned report is displayed on the terminal, where the user can review and modify it as necessary. The report displayed on the terminal reflects the details of the case, as well as the style and tone adjusted based on the user's emotions.

[0950] As an example, a user enters the following incident details:

[0951] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[0952] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the generative AI model will generate an initial report in the following style:

[0953] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[0954] By combining this with an emotion engine, it becomes possible to generate more human-like reports that adapt to the user's emotions, improving the efficiency of police officers' work and the quality of reports.

[0955] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0956] Step 1:

[0957] The user enters the details of the incident.

[0958] The user uses a terminal to access the web page of the record generation system and inputs the specific circumstances of the case into the text area.

[0959] Input: Details of the incident (e.g., "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital.")

[0960] Output: Detailed incident data entered in the text area

[0961] Step 2:

[0962] The user clicks the "Generate Work Report" button.

[0963] This operation activates a client-side script on the device, converting the input information into JSON format, which is then sent to the server.

[0964] Input: Case details entered in the text area

[0965] Output: JSON formatted data sent to the server

[0966] Specific operation: Using JavaScript, the input data is converted to JSON format using JSON.stringify, and a POST request is sent to the server using the fetch API.

[0967] Step 3:

[0968] The server analyzes the details of the incident received.

[0969] The server parses and analyzes the received JSON-formatted data, which is then used as input to a generative AI model.

[0970] Input: Case details in JSON format

[0971] Output: Input data to the AI ​​model (parsed case details)

[0972] Specific operation: The JSON data is deserialized using a server-side script (e.g., Python), and the parsed results are input into the generative AI model.

[0973] Step 4:

[0974] The server generates an initial report using the generative AI model.

[0975] The server provides input data to the generative AI model to generate an initial report, which is then output in text format.

[0976] Input: Input data to the AI ​​model (parsed case details)

[0977] Output: Text data of the generated initial report

[0978] Specific operation: Input data in the form of prompt sentences is provided to a natural language processing model built using TensorFlow and PyTorch, and the generated report text is obtained.

[0979] Example prompt sentence:

[0980] Generate a report based on the following incident details: "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[0981] Step 5:

[0982] The server applies the emotion engine.

[0983] The server analyzes the audio and video data input by the user to obtain emotional data, and adjusts the initial record based on the obtained emotional data.

[0984] Input: User's audio and video data, generated initial transcript

[0985] Output: Adjusted transcript text reflecting emotion data

[0986] Specific operation: Audio data is analyzed using librosa, and video data is analyzed using OpenCV to determine emotions, and based on this data, the style of the transcript is readjusted using a generative AI model.

[0987] Step 6:

[0988] The server returns the adjusted record to the terminal.

[0989] The server converts the adjusted report text into JSON format and sends it to the terminal as an HTTP response.

[0990] Input: Adjusted transcript text reflecting emotion data

[0991] Output: JSON formatted report data returned to the terminal

[0992] Specific operation: The report text is serialized into JSON format using a server script such as Python and returned as an HTTP response.

[0993] Step 7:

[0994] The terminal receives and displays the returned record.

[0995] The terminal receives the data returned from the server and displays it in the browser. The user can check it and modify it as necessary.

[0996] Input: JSON formatted report data returned from the server

[0997] Output: Text of the report displayed in the browser

[0998] Specific operation: Parse the returned JSON data using JavaScript and display it in the browser using DOM manipulation.

[0999] (Application example 2)

[1000] 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."

[1001] The present invention aims to solve the problems of police officers generating reports efficiently and recognizing the emotions of customers in physical stores in real time and providing appropriate responses. In particular, the objective is to realize a system that can provide more accurate and appropriate reports and customer service methods by recognizing the emotions of users (police officers and store clerks) and adjusting the writing style and tone based on that.

[1002] 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.

[1003] In this invention, the server includes means for recognizing a user's emotions using an emotion engine and adjusting the style and tone of the report, means for recognizing a customer's emotions in real time and generating an appropriate customer service method, and means for receiving details of the incident and generating an initial report using an AI model. This allows police officers to easily generate reports adapted to the user's emotions, and enables store clerks in physical stores to instantly provide appropriate customer service based on the customer's emotions.

[1004] The "means for inputting detailed incident information" is a system component that provides an interface through which a user can input specific information about an incident.

[1005] The "means for transmitting case details to the server" is a system component that has communications capabilities for transferring the case details entered by the user to the server.

[1006] The "means for generating an initial report using an AI model" is a component of a system that uses artificial intelligence technology to automatically create an initial report based on detailed case information received.

[1007] The "means for returning the generated initial report to the client" is a system component that has a communication function for sending the initial report generated by the AI ​​model to the user's device.

[1008] The "means for displaying the returned initial record" is a system component having a display function that allows the client to visually check the received initial record.

[1009] "Means for recognizing the user's emotions using an emotion engine and adjusting the style and tone of the transcript" is a component of a system that has the function of analyzing the user's emotions and appropriately modifying the way the transcript is expressed based on the results.

[1010] "Means for recognizing customer emotions in real time and generating appropriate customer service methods" is a component of a system that has the function of detecting a customer's current emotions in a physical store and generating customer service methods that are appropriate to those emotions.

[1011] A "graphical user interface" is an interface that provides visual elements that allow users to operate intuitively.

[1012] The present invention is a system that recognizes a user's emotions and generates appropriate records and customer service methods. This system includes the following components:

[1013] 1. How to enter details of the incident

[1014] The system provides a graphical user interface (GUI) for users to input details of the case. Using this GUI, users can input specific information about the case on the screen of, for example, a PC or smartphone.

[1015] 2. A means of sending incident details to the server

[1016] The entered details of the incident are converted into a data format such as JSON and sent to a server, using Internet Protocol for communication.

[1017] 3. A method for generating initial records using AI models

[1018] The server generates an initial report based on the received case details using a generative AI model, such as OpenAI's GPT-3, a natural language processing technology, which converts the input information into an appropriate document.

[1019] 4. Using an emotion engine to recognize user emotions and adjust the style and tone of written documentation

[1020] The server also analyzes the input audio and video data and uses an emotion engine, such as the Emotion Recognition API, to recognize the user's emotions. This emotion data is fed back into the initial transcript generation process to adjust the style and tone of the transcript.

[1021] 5. Means of returning generated initial work papers to the client

[1022] The adjusted initial report is then sent back to the client, who uses software such as a browser to receive and display it.

[1023] 6. A means of viewing the returned initial report

[1024] Users can check the returned records and make corrections as necessary. The records are displayed on a computer monitor or smartphone screen.

[1025] 7. A means of recognizing customer emotions in real time and generating appropriate customer service methods

[1026] In a physical store, the user (store clerk) captures a customer's facial image using a smartphone camera and recognizes their emotions. Emotion recognition is performed using OpenCV or the Emotion Recognition API, for example. The recognized emotional information is used as a prompt for a generative AI model, which generates an appropriate way to serve the customer.

[1027] Hardware and software used

[1028] Hardware: PC, smartphone, camera

[1029] Software: GUI, OpenAI API, Emotion Recognition API, OpenCV, Browser

[1030] Specific processing of the program

[1031] When a user enters details of a case using a GUI on their computer or smartphone, the entered information is converted to JSON format and sent to the server. The server uses a generative AI model based on the received information to generate an initial report, and then uses an emotion engine to adjust the style and tone based on the user's emotions. The adjusted initial report is then returned to the client, who then reviews and modifies it. In physical stores, store staff use smartphone cameras to recognize customers' emotions in real time and generate appropriate customer service methods based on that emotional information. For example, the following prompt might be generated for a sad customer:

[1032] Prompt Sentence Examples

[1033] When a customer is sad, suggest appropriate ways to serve them.

[1034] This application example enables police officers to easily generate reports that adapt to the user's emotions, and enables store clerks to instantly provide appropriate customer service based on the customer's emotions.

[1035] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1036] Step 1:

[1037] Users enter details of the incident using a GUI on their computer or smartphone. The information entered may include, for example:

[1038] Example: "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s and was suspected of drunk driving at the scene."

[1039] Step 2:

[1040] The device converts the incident details entered by the user into JSON format, which reorganizes the entered text data into a structured data format, such as the date, location, and circumstances of the incident, expressed in key-value pairs.

[1041] Step 3:

[1042] The device sends the converted JSON data to the server. HTTP or HTTPS is used as the communication protocol. In this step, the device connects to the server via network communication and sends the data.

[1043] Step 4:

[1044] The server parses the incoming JSON data to extract details about the incident, using a library such as Python's json library. This parsing process allows the server to split the incoming data into individual fields and format it in a way that can be processed.

[1045] Step 5:

[1046] The server generates an initial report using a generative AI model based on the analyzed details of the case. The generative AI model used is OpenAI's GPT-3. The server inputs the analyzed details as prompts into the AI ​​model, which then generates the appropriate document.

[1047] Step 6:

[1048] The server uses an emotion engine to analyze the input audio and video data and recognize the user's emotions. The emotion engine uses the Emotion Recognition API, etc. In this step, audio and image analysis technologies are used to identify the user's emotional state.

[1049] Step 7:

[1050] The emotion data recognized by the emotion engine is fed back into the initial report generation process. The server uses this emotion data to adjust the style and tone of the generated initial report. This adjustment results in a more appropriate report that is adapted to the user's emotions.

[1051] Step 8:

[1052] The server returns the generated adjusted initial report to the client. The communication protocol is again HTTP or HTTPS. The server converts the report into JSON, XML, or other format and sends it to the client.

[1053] Step 9:

[1054] The terminal receives the report sent back from the server and displays it using a browser or a dedicated application. The user can check the report and make corrections as necessary.

[1055] Step 10:

[1056] In a physical store, a user (a store clerk) captures a customer's face image using a smartphone camera. In this process, the OpenCV library is used to acquire the video data from the camera and extract the face image.

[1057] Step 11:

[1058] The device sends the captured facial image to the emotion recognition API, which analyzes facial expressions and other biometric information to identify emotions, generating emotion data as a result of the analysis.

[1059] Step 12:

[1060] The server receives the recognized emotion data and uses a generative AI model to generate an appropriate customer service method based on this. For example, a prompt sentence such as "Please suggest an appropriate way to serve a customer when they are sad" is used. Based on this prompt, the AI ​​generates an appropriate customer service method.

[1061] Step 13:

[1062] The server sends the generated service method to the terminal, which displays it. The store clerk checks the displayed service method and applies it to actual customer service. This enables service that adapts to the customer's emotions.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] [Fourth embodiment]

[1067] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1068] 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.

[1069] 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).

[1070] 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.

[1071] 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.

[1072] 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).

[1073] 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.

[1074] 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.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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."

[1080] This invention relates to a system that allows police officers to efficiently generate police reports. In this system, a user inputs details of a case through a terminal and sends them to a server. An AI model automatically generates an initial report, which is then returned and displayed to the user. This system significantly reduces the time and effort required compared to the traditional manual process of creating reports, and also improves the accuracy and reliability of the reports.

[1081] System configuration

[1082] The system includes the following components:

[1083] 1. Input method for entering details of the case

[1084] 2. Means of transmission for transmitting details of the incident

[1085] 3. A server that processes the received case details and generates an initial report using an AI model.

[1086] 4. A means of returning the generated initial workpaper to the client

[1087] 5. Display means for displaying the returned initial report

[1088] User operations

[1089] The user (police officer) uses their device to open a web browser and access a dedicated page in the report generation system. A text area for entering details of the incident will be displayed, where the user can enter a summary of the incident and the circumstances. For example, they might write, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[1090] Once the input is complete, the user clicks the "Generate Report" button, which sends the entered case details from the device to the server.

[1091] Server Processing

[1092] The server receives the details of the case sent from the device and passes them to an AI model to generate an initial report, which uses natural language processing techniques to generate appropriate documents based on the input information.

[1093] The server receives the initial report generated by the AI ​​model and sends it back to the device using an HTTP response, with the generated report sent in JSON format.

[1094] Terminal display

[1095] The terminal receives the initial report sent back from the server and displays it in the browser, with the generated draft report displayed below the text entered by the user, allowing the user to review it and make any necessary corrections.

[1096] Specific examples

[1097] For example, a user enters the following incident details:

[1098] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[1099] Based on this, the AI ​​model generates an initial report that looks like this:

[1100] At 3:00 PM on October 1, 2023, a traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car, injured, and transported to XX Hospital. The driver, a man in his 20s, was confirmed at the scene to be suspected of drunk driving.

[1101] The user checks the generated report and adds or corrects information as necessary to complete the final report.

[1102] The above is a specific embodiment of the system of the present invention, which allows police officers to significantly reduce the time it takes to prepare reports and quickly prepare more accurate and consistent documents.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[1106] Step 2:

[1107] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[1108] Step 3:

[1109] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the incident details entered in the text area.

[1110] Step 4:

[1111] The device converts the retrieved incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user.

[1112] Step 5:

[1113] The server receives the HTTP POST request sent from the terminal, extracts the details of the incident from the request body, and prepares it for further processing.

[1114] Step 6:

[1115] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[1116] Step 7:

[1117] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[1118] Step 8:

[1119] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[1120] Step 9:

[1121] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[1122] Step 10:

[1123] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[1124] Step 11:

[1125] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[1126] Example 1

[1127] 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."

[1128] Conventional report preparation work requires a lot of time and effort, and because it is done manually, there are issues with accuracy and consistency. As a result, the quality of the reports can vary, which can hinder efficient investigations. The purpose of this invention is to solve these issues and automate the report preparation process, making it more efficient.

[1129] 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.

[1130] In this invention, the server includes a means for a user to input details of the case through a terminal, a means for transmitting the input details of the case to the server, a means for the server to convert the received details of the case into a prompt sentence and pass it to a generative AI model to generate an initial report, a means for returning the generated initial report to the terminal in JSON format, and a means for displaying the returned initial report in a browser. This allows police officers to efficiently prepare reports, significantly reducing time and effort while also improving the accuracy and reliability of the reports.

[1131] "User" refers to the individual, primarily a police officer, who uses the terminal to enter details of the case.

[1132] "Terminal" refers to an electronic device used by a user, such as a personal computer, tablet, or smartphone.

[1133] "Incident details" refers to specific information about a particular incident, such as the date, time, location, information about the people involved, and the course of events.

[1134] "Server" refers to the central management system that receives case details sent by users and generates initial reports using AI models.

[1135] "Prompt sentence" refers to an input sentence containing details of the incident that has been converted into a format that is easy for the AI ​​model to understand.

[1136] "Generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate an initial report from an input prompt.

[1137] An "initial report" refers to a document containing basic information about the incident that is automatically created by a generative AI model.

[1138] "JSON format" is a text format for structuring and describing data, and is a format that is often used when exchanging data between servers and terminals.

[1139] "Browser" refers to a software application that enables users to access and use web pages on the Internet.

[1140] "Graphical User Interface" means a visual interface designed to be intuitive to the user and to facilitate the entry of case details.

[1141] MODE FOR CARRYING OUT THE INVENTION

[1142] System Configuration

[1143] The system of the present invention is designed to help police officers efficiently generate case reports. The system consists of the following components:

[1144] 1. Input means (terminal) for entering details of the case

[1145] 2. A means of transmission (a communication interface between the terminal and the server) for transmitting the entered case details to the server.

[1146] 3. A server that converts received case details into prompt text and generates an initial report using a generative AI model.

[1147] 4. Means for returning the generated initial record to the terminal (communication interface between the server and the terminal)

[1148] 5. A means (browser) for displaying the returned initial report

[1149] User operations

[1150] The police officer user first opens a web browser on their device and accesses the dedicated page of the report generation system. A login screen will appear, and the user must enter their authentication information to log in. A text area will then appear on the screen where the police officer can enter details of the case.

[1151] The user enters a summary of the incident and the circumstances in the text area. For example, they could enter details such as, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." Once they have finished entering the information, the user clicks the "Generate Report" button.

[1152] Server Processing

[1153] The server receives the details of the incident sent from the device. The received data is sent to the server in JSON format. The server parses this data and converts it into a prompt. The prompt is then formatted in a format that the generative AI model can understand. Specifically, the following prompt is generated:

[1154] Generate a draft of a traffic accident report based on the following text:

[1155] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[1156] The server then sends this prompt to a generative AI model, such as OpenAI's GPT-3 or GPT-4. The generative AI model generates an initial report based on the prompt and returns the generated initial report in JSON format to the server.

[1157] Terminal display

[1158] The terminal receives the initial report sent back from the server and displays it in the user's browser. The generated draft report is displayed below the text entered by the user. The user can review the displayed report and make corrections as necessary. Once the corrections are complete, the report is saved as the final report.

[1159] The above is an embodiment of the system of the present invention, which enables police officers to significantly reduce the time and effort required to prepare reports and improve the accuracy and reliability of reports.

[1160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The user accesses a dedicated page in the device's web browser. They enter a URL to go to the login screen, then enter their authentication information to log in to the system. This allows the user to access a screen where they can enter details of the incident. The input is in text format, and the details are displayed in a text area in the browser.

[1163] Step 2:

[1164] The user enters details of the incident in the text area provided. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene." This input will form the basis for subsequent data processing.

[1165] Step 3:

[1166] The user clicks the "Generate Report" button. By pressing this button, the details of the incident that were entered are sent from the device to the server as an HTTP POST request. Specifically, the entered text is converted to JSON format and sent to the server as transmission data.

[1167] Step 4:

[1168] The server receives the HTTP POST request sent from the terminal. The server extracts data containing details of the incident from this request and analyzes it. Specifically, the content of the received data is checked and converted into a prompt. The input text is first received as a string and then a formatted prompt is created.

[1169] Example: "Generate a draft of a traffic accident report based on the following text: October 1, 2023, 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[1170] Step 5:

[1171] The server sends the generated prompt to an AI model (such as OpenAI's GPT-3 or GPT-4), which generates an initial document based on the prompt. The prompt is passed as input to the AI ​​model, which then uses natural language processing techniques to generate an appropriate document.

[1172] Step 6:

[1173] The generative AI model generates an initial report based on the prompt text. The generated initial report is sent back to the server. The server receives the result, formats it in JSON format, and sends it back to the device. At this stage, the generated initial report is sent as data from the server to the device.

[1174] Step 7:

[1175] The terminal receives the initial report in JSON format from the server and displays it in the user's browser. Specifically, it parses the received data and displays it in a format appropriate for the current operating environment. The generated draft report is displayed below the text entered by the user.

[1176] Step 8:

[1177] The user can review the draft report displayed in the browser and make any necessary edits, such as adding additional information or correcting incorrect information. Once the final report is complete, it can be saved and submitted to the appropriate systems.

[1178] The above is the flow of the process in which a user uses a device to automatically generate a transcript using the generative AI model via the server, and the results are displayed in a browser. This system improves the efficiency and accuracy of transcript creation.

[1179] (Application example 1)

[1180] 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."

[1181] In traditional security services, it is difficult for security guards to quickly and accurately generate detailed incident reports from the field, especially when the report is created manually, which takes time and effort. Furthermore, errors or incomplete information entered can cause problems with the accuracy of the report. To solve this problem, a new system is needed.

[1182] 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.

[1183] In this invention, the server includes means for receiving incident details and generating an initial report using a generative AI model, means for returning the generated initial report to the client, means for displaying the returned initial report, and means for security guards to input incident details by voice or visually on-site, thereby enabling security guards to efficiently and accurately create incident reports on-site.

[1184] "Means for inputting incident details" refers to a device or interface through which a security officer inputs incident details.

[1185] "Means for transmitting case details to the server" refers to a communications device or protocol for transmitting the entered case details to the server.

[1186] A "generative AI model" refers to an algorithm or software that automatically generates initial reports using natural language processing technology.

[1187] "Means for generating an initial report" means a process or system that uses a generative AI model to create an initial report from case details.

[1188] "Means for sending the generated initial working paper back to the client" refers to a communication device or protocol that sends the initial working paper generated by the server to the client device.

[1189] "Means for displaying returned initial working paper" refers to a device or interface for visually displaying the received initial working paper at the client device.

[1190] "Smart glasses" refers to wearable devices that display information visually and support voice input and gesture control.

[1191] A "head-mounted display" is a device worn by a user to display visual information, and refers to a display device that is worn on the head.

[1192] "Ability for security guards to input incident details via voice or visual input on-site" refers to a feature that allows security guards to input incident details via voice or visual input on-site using a smart device.

[1193] A system for implementing this invention allows security guards to efficiently and accurately generate incident reports from the field. The system primarily includes the following components:

[1194] 1. Enter the details of the incident:

[1195] Security guards use devices such as smartphones, smart glasses, or head-mounted displays to input details of the incident, either via voice or text.

[1196] 2. Means of sending incident details to the server:

[1197] The details of the incident entered on the device are sent to a server over the Internet using an HTTP POST request.

[1198] 3. How to generate an initial report using a generative AI model:

[1199] Based on the received case details, the server generates an initial report using a generative AI model, which uses natural language processing techniques and applies advanced models such as BERT.

[1200] 4. Means of returning the generated initial workpaper to the client:

[1201] The initial report generated by the server is sent to the client device in JSON format, and is returned using an HTTP response.

[1202] 5. How to view the returned initial report:

[1203] The client device displays the initial report received from the server to the user through a browser or application, and the security officer can review the displayed initial report and enter corrections or additional information as necessary.

[1204] Examples:

[1205] For example, a security guard dictates the following incident details:

[1206] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[1207] This input data is sent to the server, and the generative AI model generates an initial report like this:

[1208] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[1209] This generated initial report is sent back to the client device for visual review by the guard.

[1210] Example prompt sentence:

[1211] Please generate an incident report based on the following details:

[1212] At 5:00 pm on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s, who refused to show identification, so they called the police.

[1213] The system allows security guards to quickly and accurately generate incident reports from the field, and the versatility of the devices they can use significantly improves their work efficiency.

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1:

[1216] The user uses a smart device (smartphone, smart glasses, head-mounted display) to input details of the incident. For example, they might enter, "At 5:00 PM on October 1, 2023, a suspicious person was spotted at the entrance to the building. When security guards approached and investigated, they found that the person was a man in his 30s. He refused to show identification, so the police were called." The input data is temporarily stored on the device.

[1217] Step 2:

[1218] The device sends the details of the incident that was entered to the server using an HTTP POST request, and the input data is sent to the server in text format using JSON as the specific data format.

[1219] Step 3:

[1220] The server analyzes the details of the incident received from the device and inputs them into the generative AI model. Text information is passed to the AI ​​model as input data, and the AI ​​model generates an initial report using natural language processing techniques. Specifically, the AI ​​model analyzes the meaning of each sentence and reconstructs it in an appropriate format.

[1221] Step 4:

[1222] The server receives the generated initial statement and returns it to the client device using an HTTP response, sending the generated statement in JSON format. In particular, this could be in the following format:

[1223] text

[1224] At 5:00 pm on October 1, 2023, a suspicious individual (a man in his 30s) was spotted at the entrance to the building and approached by security guards. The individual refused to show identification, and the police were therefore called.

[1225] Step 5:

[1226] The client device receives the returned initial work report and displays it to the user. This can be done using a browser or a dedicated application, allowing the user to review the generated work report and enter corrections or additional information as needed. The displayed form is formatted to be easy for the user to understand.

[1227] This process allows security guards to quickly and accurately generate incident reports from the scene.The system enables efficient data processing and information sharing by linking the server, terminals, and users.

[1228] 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.

[1229] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[1230] System configuration

[1231] The system includes the following components:

[1232] 1. Input method for entering details of the case

[1233] 2. Means of transmission for transmitting details of the incident

[1234] 3. A server that processes the received case details and generates an initial report using an AI model.

[1235] 4. A means of returning the generated initial workpaper to the client

[1236] 5. A means of viewing the returned initial report

[1237] 6. Emotion engine that recognizes user emotions

[1238] User operations

[1239] The user (police officer) uses a terminal to access the report generation system's webpage and enter details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[1240] Server Processing

[1241] Once the input is complete, the user clicks the "Generate transcript" button. This action causes the device's client-side script to convert the input information into JSON format and send it to the server. The server then uses an AI model to generate an initial transcript based on the received information. This AI model uses natural language processing technology to analyze the input information and generate the appropriate document.

[1242] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[1243] The server returns the generated initial statement and the statement adjusted by the emotion engine to the client. The returned statement is displayed on the terminal, where the user can check it and modify it if necessary.

[1244] Terminal display

[1245] The device receives the report sent back from the server and displays it in the browser. The user can review it and make any necessary edits. The report displayed on the device reflects the details of the incident, as well as a style and tone that is tailored to the user's emotions.

[1246] Specific examples

[1247] For example, a user enters the following incident details:

[1248] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[1249] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the AI ​​model will generate an initial report in the following style:

[1250] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[1251] The user can check the generated report, make corrections as necessary, and complete the final report.

[1252] The above is a specific embodiment of the system of the present invention. By combining it with an emotion engine, it becomes possible to generate more human-like reports that are adapted to the user's emotions, improving the work efficiency of police officers and the quality of reports.

[1253] The processing flow will be explained below.

[1254] Step 1:

[1255] A user accesses the web page of the record generation system through a terminal, and the web page displays a text area for entering details of the case.

[1256] Step 2:

[1257] The user enters details of the incident in the text area. For example, "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[1258] Step 3:

[1259] The user clicks the "Generate Report" button, which triggers a client-side script on the device to retrieve the entered incident details.

[1260] Step 4:

[1261] The device converts the acquired incident details into JSON format and sends the information to the server in the body of an HTTP POST request. The transmitted data includes the incident details entered by the user, as well as audio and video data from when the details were entered.

[1262] Step 5:

[1263] The server receives the HTTP POST request sent from the terminal, extracts the incident details and audio and video data from the request body, and prepares them for further processing.

[1264] Step 6:

[1265] The server passes the extracted case details to the AI ​​model and requests it to generate an initial report, which uses natural language processing techniques to generate a draft report from the input information.

[1266] Step 7:

[1267] The server passes the extracted audio and video data to the emotion engine, which then recognizes the user's emotion. The emotion engine determines the user's emotional state based on the user's tone of voice and facial expressions.

[1268] Step 8:

[1269] The server feeds back the emotion data recognized by the emotion engine to the AI ​​model's report generation process, adjusting the style and tone of the report generated based on the user's emotions.

[1270] Step 9:

[1271] The server receives the initial report generated by the AI ​​model, converts it into JSON format, and temporarily stores the generated initial report on the server side.

[1272] Step 10:

[1273] The server returns the converted initial working paper to the client as a JSON response, which includes a draft of the working paper.

[1274] Step 11:

[1275] The terminal receives the JSON response sent back from the server, analyzes the response content, and obtains the generated initial report.

[1276] Step 12:

[1277] The terminal displays the acquired initial report on a web page, where the user can check the report and modify it as necessary.

[1278] Step 13:

[1279] The user checks the generated initial report, makes appropriate corrections, and then saves the final report, which is then used as the official document.

[1280] This is the processing flow for a system that combines an emotion engine. This makes it possible to generate police reports based on the user's emotions, improving the work efficiency of police officers and the quality of the reports.

[1281] Example 2

[1282] 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."

[1283] Conventional report generation systems take a long time to complete, from entering details of the case to generating and displaying the report, and it is difficult to consider emotional factors. Manual report creation is prone to human error, resulting in unstable report quality. Furthermore, the lack of adjustments to style and tone to reflect the user's emotions makes it difficult to generate detailed, human-like reports.

[1284] 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.

[1285] In this invention, the server includes a means for analyzing the details of the case and generating an initial report using a generative AI model, a means including an emotion engine for recognizing the user's emotions, and a means for returning the generated initial report to the client. This enables the generation of detailed, human-like reports that take the user's emotions into consideration, thereby improving work efficiency and stabilizing the quality of the reports.

[1286] The "means for inputting details of the incident" is an element that provides an interface for the user to input specific circumstances of the incident.

[1287] The "means for transmitting the entered case details to the server" is an element that converts the case information entered by the user into a data format and transmits it to the server via the network.

[1288] "Means for analyzing received case details and generating an initial report using a generative AI model" refers to an element that analyzes case information sent to the server and automatically generates an initial report using AI technology.

[1289] The "means for returning the generated initial record to the client" is an element that sends the record generated by the server back to the client (user's terminal).

[1290] The "means for displaying the returned initial record" is an element that visually displays the record sent to the client so that the user can check it.

[1291] The "means including an emotion engine for recognizing the user's emotion" refers to an element including dedicated hardware and software for analyzing the audio and video data input by the user and recognizing the user's emotion.

[1292] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to generate appropriate documents from input data.

[1293] A "prompt" is text that describes the document format and instructions for providing input data to an AI model.

[1294] The present invention relates to a system for police officers to efficiently generate police reports, and in particular, by combining an emotion engine that recognizes the user's emotions, it is possible to more appropriately adjust the generation and display of the report. This system includes a means for inputting details of the incident, a means for transmitting the input information, a means for generating an initial report using an AI model based on the received information, a means for returning and displaying the generated report, and an emotion engine.

[1295] First, the user (police officer) uses a terminal to access the webpage of the report generation system and enters details of the incident. The webpage displays a text area where the user can enter the specific circumstances of the incident. For example, the user might enter, "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s who was suspected of drunk driving at the scene."

[1296] Next, when the user clicks the "Generate transcript" button, the terminal's client-side script converts the input information into JSON format and sends it to the server. The server then uses the received information to generate an initial transcript using a generative AI model. This AI model uses natural language processing technology to analyze the input information and generate an appropriate document. The hardware used is a server equipped with a GPU, and the software used includes TensorFlow and PyTorch.

[1297] Furthermore, the system's emotion engine analyzes the user's voice and video input to recognize their emotions. The emotion engine uses librosa for analyzing voice data and OpenCV for analyzing video data. The emotion data recognized by the emotion engine is fed back into the initial report generation process, adjusting the writing style and tone. For example, if the user has strong emotions about the incident, more careful and detailed language will be used.

[1298] The server returns the generated initial report and the report adjusted by the emotion engine to the client. The returned report is displayed on the terminal, where the user can review and modify it as necessary. The report displayed on the terminal reflects the details of the case, as well as the style and tone adjusted based on the user's emotions.

[1299] As an example, a user enters the following incident details:

[1300] At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian, a man aged XX, was transported to XX Hospital. The driver, a man in his 20s, was suspected of drunk driving at the scene.

[1301] If the emotion engine recognizes the user's emotion as "sadness" in response to this input, the generative AI model will generate an initial report in the following style:

[1302] At 3:00 PM on October 1, 2023, a tragic traffic accident occurred on XX Street in Shibuya Ward, Tokyo. A pedestrian (a man, age XX) was hit by a car and transported to XX Hospital in critical condition. The driver, a man in his 20s, was suspected of drunk driving at the scene and was thoroughly questioned.

[1303] By combining this with an emotion engine, it becomes possible to generate more human-like reports that adapt to the user's emotions, improving the efficiency of police officers' work and the quality of reports.

[1304] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1305] Step 1:

[1306] The user enters the details of the incident.

[1307] The user uses a terminal to access the web page of the record generation system and inputs the specific circumstances of the case into the text area.

[1308] Input: Details of the incident (e.g., "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a XX-year-old man who was transported to XX Hospital.")

[1309] Output: Detailed incident data entered in the text area

[1310] Step 2:

[1311] The user clicks the "Generate Work Report" button.

[1312] This operation activates a client-side script on the device, converting the input information into JSON format, which is then sent to the server.

[1313] Input: Case details entered in the text area

[1314] Output: JSON formatted data sent to the server

[1315] Specific operation: Using JavaScript, the input data is converted to JSON format using JSON.stringify, and a POST request is sent to the server using the fetch API.

[1316] Step 3:

[1317] The server analyzes the details of the incident received.

[1318] The server parses and analyzes the received JSON-formatted data, which is then used as input to a generative AI model.

[1319] Input: Case details in JSON format

[1320] Output: Input data to the AI ​​model (parsed case details)

[1321] Specific operation: The JSON data is deserialized using a server-side script (e.g., Python), and the parsed results are input into the generative AI model.

[1322] Step 4:

[1323] The server generates an initial report using the generative AI model.

[1324] The server provides input data to the generative AI model to generate an initial report, which is then output in text format.

[1325] Input: Input data to the AI ​​model (parsed case details)

[1326] Output: Text data of the generated initial report

[1327] Specific operation: Input data in the form of prompt sentences is provided to a natural language processing model built using TensorFlow and PyTorch, and the generated report text is obtained.

[1328] Example prompt sentence:

[1329] Generate a report based on the following incident details: "On October 1, 2023, at 3:00 PM, a pedestrian was hit by a car on XX Street in Shibuya Ward."

[1330] Step 5:

[1331] The server applies the emotion engine.

[1332] The server analyzes the audio and video data input by the user to obtain emotional data, and adjusts the initial record based on the obtained emotional data.

[1333] Input: User's audio and video data, generated initial transcript

[1334] Output: Adjusted transcript text reflecting emotion data

[1335] Specific operation: Audio data is analyzed using librosa, and video data is analyzed using OpenCV to determine emotions, and based on this data, the style of the transcript is readjusted using a generative AI model.

[1336] Step 6:

[1337] The server returns the adjusted record to the terminal.

[1338] The server converts the adjusted report text into JSON format and sends it to the terminal as an HTTP response.

[1339] Input: Adjusted transcript text reflecting emotion data

[1340] Output: JSON formatted report data returned to the terminal

[1341] Specific operation: The report text is serialized into JSON format using a server script such as Python and returned as an HTTP response.

[1342] Step 7:

[1343] The terminal receives and displays the returned record.

[1344] The terminal receives the data returned from the server and displays it in the browser. The user can check it and modify it as necessary.

[1345] Input: JSON formatted report data returned from the server

[1346] Output: Text of the report displayed in the browser

[1347] Specific operation: Parse the returned JSON data using JavaScript and display it in the browser using DOM manipulation.

[1348] (Application example 2)

[1349] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1350] The present invention aims to solve the problems of police officers generating reports efficiently and recognizing the emotions of customers in physical stores in real time and providing appropriate responses. In particular, the objective is to realize a system that can provide more accurate and appropriate reports and customer service methods by recognizing the emotions of users (police officers and store clerks) and adjusting the writing style and tone based on that.

[1351] 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.

[1352] In this invention, the server includes means for recognizing a user's emotions using an emotion engine and adjusting the style and tone of the report, means for recognizing a customer's emotions in real time and generating an appropriate customer service method, and means for receiving details of the incident and generating an initial report using an AI model. This allows police officers to easily generate reports adapted to the user's emotions, and enables store clerks in physical stores to instantly provide appropriate customer service based on the customer's emotions.

[1353] The "means for inputting detailed incident information" is a system component that provides an interface through which a user can input specific information about an incident.

[1354] The "means for transmitting case details to the server" is a system component that has communications capabilities for transferring the case details entered by the user to the server.

[1355] The "means for generating an initial report using an AI model" is a component of a system that uses artificial intelligence technology to automatically create an initial report based on detailed case information received.

[1356] The "means for returning the generated initial report to the client" is a system component that has a communication function for sending the initial report generated by the AI ​​model to the user's device.

[1357] The "means for displaying the returned initial record" is a system component having a display function that allows the client to visually check the received initial record.

[1358] "Means for recognizing the user's emotions using an emotion engine and adjusting the style and tone of the transcript" is a component of a system that has the function of analyzing the user's emotions and appropriately modifying the way the transcript is expressed based on the results.

[1359] "Means for recognizing customer emotions in real time and generating appropriate customer service methods" is a component of a system that has the function of detecting a customer's current emotions in a physical store and generating customer service methods that are appropriate to those emotions.

[1360] A "graphical user interface" is an interface that provides visual elements that allow users to operate intuitively.

[1361] The present invention is a system that recognizes a user's emotions and generates appropriate records and customer service methods. This system includes the following components:

[1362] 1. How to enter details of the incident

[1363] The system provides a graphical user interface (GUI) for users to input details of the case. Using this GUI, users can input specific information about the case on the screen of, for example, a PC or smartphone.

[1364] 2. A means of sending incident details to the server

[1365] The entered details of the incident are converted into a data format such as JSON and sent to a server, using Internet Protocol for communication.

[1366] 3. A method for generating initial records using AI models

[1367] The server generates an initial report based on the received case details using a generative AI model, such as OpenAI's GPT-3, a natural language processing technology, which converts the input information into an appropriate document.

[1368] 4. Using an emotion engine to recognize user emotions and adjust the style and tone of written documentation

[1369] The server also analyzes the input audio and video data and uses an emotion engine, such as the Emotion Recognition API, to recognize the user's emotions. This emotion data is fed back into the initial transcript generation process to adjust the style and tone of the transcript.

[1370] 5. Means of returning generated initial work papers to the client

[1371] The adjusted initial report is then sent back to the client, who uses software such as a browser to receive and display it.

[1372] 6. A means of viewing the returned initial report

[1373] Users can check the returned records and make corrections as necessary. The records are displayed on a computer monitor or smartphone screen.

[1374] 7. A means of recognizing customer emotions in real time and generating appropriate customer service methods

[1375] In a physical store, the user (store clerk) captures a customer's facial image using a smartphone camera and recognizes their emotions. Emotion recognition is performed using OpenCV or the Emotion Recognition API, for example. The recognized emotional information is used as a prompt for a generative AI model, which generates an appropriate way to serve the customer.

[1376] Hardware and software used

[1377] Hardware: PC, smartphone, camera

[1378] Software: GUI, OpenAI API, Emotion Recognition API, OpenCV, Browser

[1379] Specific processing of the program

[1380] When a user enters details of a case using a GUI on their computer or smartphone, the entered information is converted to JSON format and sent to the server. The server uses a generative AI model based on the received information to generate an initial report, and then uses an emotion engine to adjust the style and tone based on the user's emotions. The adjusted initial report is then returned to the client, who then reviews and modifies it. In physical stores, store staff use smartphone cameras to recognize customers' emotions in real time and generate appropriate customer service methods based on that emotional information. For example, the following prompt might be generated for a sad customer:

[1381] Prompt Sentence Examples

[1382] When a customer is sad, suggest appropriate ways to serve them.

[1383] This application example enables police officers to easily generate reports that adapt to the user's emotions, and enables store clerks to instantly provide appropriate customer service based on the customer's emotions.

[1384] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1385] Step 1:

[1386] Users enter details of the incident using a GUI on their computer or smartphone. The information entered may include, for example:

[1387] Example: "At 3:00 PM on October 1, 2023, a pedestrian was hit by a car on XX Street in Shibuya Ward. The pedestrian was a man XX years old and was transported to XX Hospital. The driver was a man in his 20s and was suspected of drunk driving at the scene."

[1388] Step 2:

[1389] The device converts the incident details entered by the user into JSON format, which reorganizes the entered text data into a structured data format, such as the date, location, and circumstances of the incident, expressed in key-value pairs.

[1390] Step 3:

[1391] The device sends the converted JSON data to the server. HTTP or HTTPS is used as the communication protocol. In this step, the device connects to the server via network communication and sends the data.

[1392] Step 4:

[1393] The server parses the incoming JSON data to extract details about the incident, using a library such as Python's json library. This parsing process allows the server to split the incoming data into individual fields and format it in a way that can be processed.

[1394] Step 5:

[1395] The server generates an initial report using a generative AI model based on the analyzed details of the case. The generative AI model used is OpenAI's GPT-3. The server inputs the analyzed details as prompts into the AI ​​model, which then generates the appropriate document.

[1396] Step 6:

[1397] The server uses an emotion engine to analyze the input audio and video data and recognize the user's emotions. The emotion engine uses the Emotion Recognition API, etc. In this step, audio and image analysis technologies are used to identify the user's emotional state.

[1398] Step 7:

[1399] The emotion data recognized by the emotion engine is fed back into the initial report generation process. The server uses this emotion data to adjust the style and tone of the generated initial report. This adjustment results in a more appropriate report that is adapted to the user's emotions.

[1400] Step 8:

[1401] The server returns the generated adjusted initial report to the client. The communication protocol is again HTTP or HTTPS. The server converts the report into JSON, XML, or other format and sends it to the client.

[1402] Step 9:

[1403] The terminal receives the report sent back from the server and displays it using a browser or a dedicated application. The user can check the report and make corrections as necessary.

[1404] Step 10:

[1405] In a physical store, a user (a store clerk) captures a customer's face image using a smartphone camera. In this process, the OpenCV library is used to acquire the video data from the camera and extract the face image.

[1406] Step 11:

[1407] The device sends the captured facial image to the emotion recognition API, which analyzes facial expressions and other biometric information to identify emotions, generating emotion data as a result of the analysis.

[1408] Step 12:

[1409] The server receives the recognized emotion data and uses a generative AI model to generate an appropriate customer service method based on this. For example, a prompt sentence such as "Please suggest an appropriate way to serve a customer when they are sad" is used. Based on this prompt, the AI ​​generates an appropriate customer service method.

[1410] Step 13:

[1411] The server sends the generated service method to the terminal, which displays it. The store clerk checks the displayed service method and applies it to actual customer service. This enables service that adapts to the customer's emotions.

[1412] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1413] 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.

[1414] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1415] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1416] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1417] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1418] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1419] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1420] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1421] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1422] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1423] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1424] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1425] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1426] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1427] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1428] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1429] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1430] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1431] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1432] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1433] The following is further disclosed regarding the above embodiment.

[1434] (Claim 1)

[1435] a means for inputting details of the incident;

[1436] means for transmitting details of the incident to a server;

[1437] a means for receiving details of the case and generating an initial report using an AI model;

[1438] means for returning the generated initial workpaper to the client;

[1439] A means to view the returned initial report;

[1440] A system including:

[1441] (Claim 2)

[1442] The system of claim 1, wherein the AI ​​model generates the initial report using natural language processing techniques.

[1443] (Claim 3)

[1444] 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details.

[1445] "Example 1"

[1446] (Claim 1)

[1447] a means for a user to input details of the incident via a terminal;

[1448] means for transmitting the entered case details to a server;

[1449] A means for converting the details of the case received by the server into a prompt sentence and passing it to a generative AI model to generate an initial report;

[1450] A means for returning the generated initial report to the terminal in JSON format;

[1451] A means for displaying the returned initial report on a browser;

[1452] A system including:

[1453] (Claim 2)

[1454] The system of claim 1, wherein the generative AI model generates the initial report using natural language processing techniques.

[1455] (Claim 3)

[1456] 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details.

[1457] "Application Example 1"

[1458] (Claim 1)

[1459] a means for inputting details of the incident;

[1460] means for transmitting details of the incident to a server;

[1461] a means for receiving details of the case and generating an initial report using a generative AI model;

[1462] means for returning the generated initial workpaper to the client;

[1463] a means for displaying the returned initial report;

[1464] a means for security personnel to provide audio or visual input of incident details on-site;

[1465] A system including:

[1466] (Claim 2)

[1467] The system of claim 1, wherein the generative AI model generates the initial report using natural language processing techniques.

[1468] (Claim 3)

[1469] 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details and is compatible with a smartphone, smart glasses, or a head-mounted display.

[1470] "Example 2: Combining Emotion Engines"

[1471] (Claim 1)

[1472] a means of entering details of the incident;

[1473] means for transmitting the entered case details to a server;

[1474] a means for analyzing received case details and generating an initial report using a generative AI model;

[1475] means for returning the generated initial workpaper to the client;

[1476] a means for displaying the returned initial report;

[1477] means including an emotion engine for recognizing an emotion of a user;

[1478] A system including:

[1479] (Claim 2)

[1480] The system of claim 1, wherein the AI ​​model generates the initial report using natural language processing techniques.

[1481] (Claim 3)

[1482] 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details.

[1483] "Application example 2 when combining emotion engines"

[1484] (Claim 1)

[1485] a means for inputting details of the incident;

[1486] means for transmitting details of the incident to a server;

[1487] a means for receiving details of the case and generating an initial report using an AI model;

[1488] means for returning the generated initial workpaper to the client;

[1489] a means for displaying the returned initial report;

[1490] a means for recognizing user emotions using an emotion engine and adjusting the style and tone of the transcript;

[1491] A means of recognizing customer emotions in real time and generating appropriate customer service methods;

[1492] A system including:

[1493] (Claim 2)

[1494] The system of claim 1, wherein the AI ​​model generates the initial report using natural language processing techniques.

[1495] (Claim 3)

[1496] 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details. [Explanation of symbols]

[1497] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting details of the incident; means for transmitting details of the incident to a server; a means for receiving details of the case and generating an initial report using an AI model; means for returning the generated initial workpaper to the client; A means to view the returned initial report; A system including:

2. The system of claim 1 , wherein the AI ​​model generates the initial report using natural language processing techniques.

3. 2. The system of claim 1, wherein the incident details input means provides a graphical user interface for a user to input incident details.

Images