System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting accident information; means for receiving and analyzing the inputted accident information; means for storing the analyzed accident information in a database; means for retrieving accident information from the database; and means for providing the retrieved accident information to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] With conventional accident information management systems, each company manages accident information individually, which increases the likelihood of similar accidents occurring and prevents recurrence of such accidents from being shared with other companies. Furthermore, when introducing new services or systems, there is a lack of a way to quickly and accurately refer to past accident information, making it difficult to prevent similar accidents from occurring. This leads to issues such as reduced business safety and efficiency. [Means for solving the problem]

[0006] To solve the above-mentioned problems, the present invention provides a system including a means for inputting accident information, a means for receiving and analyzing the input accident information, a means for storing the analyzed accident information in a database, a means for searching for accident information from the database, and a means for providing the searched accident information to a user. The accident information stored in the database includes the type of accident, cause, recurrence prevention measures, and date of occurrence. The accident information search means uses artificial intelligence to identify related accident information, searches the database based on search keywords, and extracts accident information that matches the conditions. Furthermore, the accident information input means integrates accident information from multiple companies into a single database, promoting information sharing between companies. This invention enables accidents to be prevented and business safety and efficiency to be improved by referring to information on similar past accidents when introducing new services or systems.

[0007] Understood. Below are definitions of important terms contained in the claims.

[0008] "Accident information" is detailed data about accidents that have occurred in organizations such as companies, and specifically includes the type of accident, cause, measures to prevent recurrence, date of occurrence, and the like.

[0009] "Input Means" refers to the interface or device through which a user inputs incident information into the system.

[0010] The "receiving means" refers to a function or device that receives the accident information transmitted from the input means.

[0011] "Analysis means" refers to a function or device that analyzes the accident information obtained from the receiving means and extracts or organizes necessary information.

[0012] "Database" refers to a storage system for systematically managing and storing accident information obtained by analysis means.

[0013] "Storage means" refers to a function or device for recording the accident information obtained from the analysis means in a database.

[0014] "Search means" refers to a function or device that searches for accident information stored in a database based on conditions specified by a user and extracts relevant information.

[0015] The "provision means" refers to a function or device that presents the accident information obtained by the search means to the user.

[0016] "Artificial intelligence" refers to the technology that enables computer systems to mimic human intelligence and learn and reason.

[0017] "Enterprise" means a legal entity or organization established to carry out commercial activities or provide services.

[0018] "Measures to prevent recurrence" refers to specific measures and methods taken to prevent the recurrence of accidents that have occurred in the past.

[0019] "Date of occurrence" refers to the date on which the accident actually occurred. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[0042] The accident information management system of the present invention is for unifying the management of accident information in a company and formulating measures to prevent recurrence. This system has the following main functions.

[0043] 1. Means for inputting accident information

[0044] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for the name of the company where the accident occurred, the type of accident, the cause, measures to prevent recurrence, the date of the accident, etc. When a user enters the necessary information in these fields and clicks the submit button, the information is sent to the server.

[0045] 2. Receiving and analyzing accident information

[0046] The server receives the accident information sent by the user. The received data is analyzed on the server to check the accuracy and completeness of the data. For example, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0047] 3. Saving to the database

[0048] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[0049] 4. Search methods for accident information

[0050] When a user searches for past accident information, they submit a search query to the server, which searches the database to extract accident information that matches the criteria. This search also includes using artificial intelligence to identify relevant accident information.

[0051] 5. Providing search results

[0052] The server organizes the search results and provides them to the user. By reviewing them, users can refer to measures to prevent similar accidents from recurring and take measures to prevent future accidents. In addition, because accident information from multiple companies is integrated into a single database, cross-sectional data analysis is possible, making it possible to formulate more effective measures to prevent recurrence.

[0053] Specific examples

[0054] 1. Enter and save accident information

[0055] Consider the example where a user enters "A data loss incident occurred at CompanyA." The user enters the following information into the form:

[0056] Company Name: "CompanyA"

[0057] Incident Type: "Data Loss"

[0058] Cause: "human error"

[0059] Measures to prevent recurrence: "Perform regular backups"

[0060] Date of occurrence: "May 1, 2023"

[0061] After entering the information, the user clicks the submit button, the server receives it, checks the accuracy of the data, and then saves it in the database.

[0062] 2. Searching for and providing accident information

[0063] Consider an example where a user searches for information on past accidents caused by "human error" when implementing a new system. The user enters "human error" in the search field and sends a search query to the server. The server searches the database and extracts relevant information, such as "data loss accidents that occurred at Company A." The server then organizes this information and provides it to the user.

[0064] This allows users to strengthen measures to prevent recurrence based on past accident information and reduce risks when introducing new systems.

[0065] The processing flow will be explained below.

[0066] Understood. Below is a step-by-step explanation of the process.

[0067] Step 1:

[0068] The user accesses the accident information input form, which contains fields such as "company name," "accident type," "cause," "measures to prevent recurrence," and "occurrence date."

[0069] Step 2:

[0070] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[0071] Step 3:

[0072] The user clicks the send button. This operation sends the entered accident information to the server.

[0073] Step 4:

[0074] The server receives the accident information sent by the user. The received data is sent to the server in JSON format or POST data.

[0075] Step 5:

[0076] The server analyzes the received data and verifies that all required fields are present. For example, it checks that "Company Name," "Type of Accident," "Cause," "Preventive Measures for Recurrence," and "Date of Occurrence" are all present.

[0077] Step 6:

[0078] The server connects to the database and saves the analyzed accident information to the database. When saving, it checks for data consistency and the absence of duplication.

[0079] Step 7:

[0080] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[0081] Step 8:

[0082] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[0083] Step 9:

[0084] A user enters a search term into a search field. For example, they enter "human error."

[0085] Step 10:

[0086] The user clicks the search button, which sends the search keywords to the server.

[0087] Step 11:

[0088] The server receives a search request sent by a user, which includes search keywords and conditions.

[0089] Step 12:

[0090] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[0091] Step 13:

[0092] The server searches the database and retrieves accident information that matches the criteria. For example, it extracts information on past accidents caused by "human error."

[0093] Step 14:

[0094] The server organizes the search results and presents them in a format that is easy for users to view. For example, the server organizes the search results in a list format, displaying the company name, type of accident, cause, measures to prevent recurrence, and date of occurrence for each accident.

[0095] Step 15:

[0096] The server sends the organized search results to the user.

[0097] Step 16:

[0098] The terminal receives the search results sent from the server and displays them.

[0099] Step 17:

[0100] The user checks the search results on the device. For example, a list of information about a "data loss incident" caused by "human error" at "Company A" is displayed.

[0101] By following the above steps, the user can input, save, and search for accident information, and refer to measures to prevent recurrence.

[0102] Example 1

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

[0104] Accident information management in companies is often done individually, making it difficult to centralize information management and share preventative measures. Furthermore, there is a lack of tools to efficiently search for past accident information and formulate appropriate preventative measures. This increases the risk of similar accidents occurring again.

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

[0106] In this invention, the server includes a means for inputting information, a means for receiving and analyzing the input information, and a means for storing the analyzed information in a database, which enables centralized management of information, efficient search, and the formulation of appropriate measures to prevent recurrence.

[0107] "Means for inputting information" refers to the interface within the system through which users can input necessary data such as accident information, and specifically refers to forms on a web browser or mobile applications.

[0108] "Means for receiving and analyzing the input information" refers to a system component that receives data sent by a user on the server side, checks the accuracy and completeness of the data, and performs error handling if necessary.

[0109] "Means for storing the analyzed information in a database" refers to a system component that inserts data that has been verified for accuracy and completeness into a database, where it is systematically recorded for future query and analysis.

[0110] The term "means for retrieving information from the database" refers to a system component that has the function of quickly extracting relevant information from the database in response to a search query from a user.

[0111] "Means for providing the searched information" refers to a system component that has the function of formatting search results and returning them to the terminal in a format that is easily understood and used by the user.

[0112] "Means for identifying relevant information using artificial intelligence" refers to a system component that uses AI technologies such as machine learning and natural language processing to analyze information in the database and identify and extract highly relevant accident information.

[0113] "Type" indicates the category or class of accident information, and specifically includes classifications such as "fire," "data loss," and "machine failure."

[0114] "Cause" refers to the reason or factor behind the occurrence of an accident, and includes, for example, "human error" or "machine failure."

[0115] "Countermeasures" refer to specific actions and measures to prevent the recurrence of accidents, and include "conducting regular inspections" and "introducing backup systems."

[0116] "Date" indicates the specific time when the accident occurred, and refers to information including the year, month, and day.

[0117] The accident information management system of the present invention aims to centrally manage accident information within a company and to formulate measures to prevent recurrence. The main components of this system include a means for inputting information, a means for receiving and analyzing the information, a means for storing the information in a database, a means for searching the database, and a means for providing the search results to the user.

[0118] Program processing

[0119] The program of this system goes through multiple steps to input, analyze, save, search, and provide accident information. The specific process flow is explained below.

[0120] Entering accident information

[0121] The terminal provides an interface for the user to enter incident information. This is implemented as a form that runs in a web browser and contains the following fields:

[0122] Company Name

[0123] Accident type

[0124] cause

[0125] Measures to prevent recurrence

[0126] Date of occurrence

[0127] For example, if a user wants to enter "a data loss incident that occurred at a certain company," they would enter the following:

[0128] Company Name: "A company"

[0129] Incident Type: "Data Loss"

[0130] Cause: "human error"

[0131] Measures to prevent recurrence: "Perform regular backups"

[0132] Date of occurrence: "May 1, 2023"

[0133] Sending accident information

[0134] When the user enters information in all fields and clicks the submit button, the device sends the entered accident information to the server using an HTTP POST request. After sending, the device displays a confirmation message.

[0135] Receiving and analyzing accident information

[0136] The server receives the incident information sent from the device as an HTTP request. The received data is first subjected to a security check by the firewall and web application firewall (WAF). If no abnormalities are found, the server analyzes the data in the next step.

[0137] Hardware and software used

[0138] The hardware used in this system includes:

[0139] Server Computer

[0140] Client terminal (PC, smartphone)

[0141] The software used includes:

[0142] Web Forms (HTML, CSS, JavaScript)

[0143] Server-side programs (Python, Java, Node.js, etc.)

[0144] Database systems (MySQL, PostgreSQL)

[0145] Specific examples

[0146] A specific example of the system's operation is shown below.

[0147] If a user wants to enter "a fire incident at a company", they would enter the following into the form:

[0148] Company Name: "A company"

[0149] Incident type: "Fire"

[0150] Cause: "Electrical failure"

[0151] Measures to prevent recurrence: "Regular equipment inspections"

[0152] Date of occurrence: "April 15, 2023"

[0153] When the user clicks the submit button, the server receives it and checks the accuracy of the data, after which it stores the information in a database.

[0154] Example prompts for generative AI models

[0155] Here are some example prompts to input to the generative AI model:

[0156] Please explain in detail the information that users enter into the web form. The field names to be entered into the form are as follows: company name, type of incident, cause, preventative measures, and date of occurrence. For example, please give a specific example of entering information about a data loss incident that occurred at a certain company.

[0157] This allows the generative AI model to generate specific accident information input procedures for the user.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1: Enter your information

[0160] The terminal provides the user with an interface for entering accident information, specifically a web form with the following fields:

[0161] Company Name

[0162] Accident type

[0163] cause

[0164] Measures to prevent recurrence

[0165] Date of occurrence

[0166] Input: The user enters the required information for each field.

[0167] Output: Data entered into the terminal.

[0168] Step 2: Submit your information

[0169] When the user enters information into all fields and clicks the submit button, the device sends the entered data to the server using an HTTP POST request, with basic validation performed on the data (e.g., whether required fields are filled in).

[0170] Input: Data entered after the user clicks the submit button.

[0171] Output: The HTTP POST request sent to the server.

[0172] Step 3: Receiving information

[0173] The server receives the HTTP POST request sent from the terminal. The received data is first subjected to a security check through the firewall and web application firewall (WAF). If an abnormality is detected in this step, the server generates an error message and returns it to the terminal.

[0174] Input: The HTTP POST request sent from the terminal.

[0175] Output: Security checked data.

[0176] Step 4: Analyze the information

[0177] The server analyzes the received data, specifically checking the following:

[0178] The date of the accident is in the future

[0179] Are the company name and accident type entered in a valid format?

[0180] Are the recurrence prevention measures specific and appropriate?

[0181] Input: Security checked data.

[0182] Output: The validated data. If there are any errors, an error message is generated.

[0183] Step 5: Saving to the Database

[0184] The server stores the data that has passed the analysis in a database, such as MySQL or PostgreSQL. The data is inserted into the appropriate table and assigned a unique identifier (ID).

[0185] Input: Validated data.

[0186] Output: A confirmation message of the data saved in the database.

[0187] Step 6: Finding information

[0188] When a user searches for information about past accidents, the device sends a search query to the server based on the information entered in the search field. For example, if a user searches for information about accidents caused by "human error," the device generates the query "Cause of accident: human error" and sends it as an HTTP GET request.

[0189] Input: The search query that the user entered into the device.

[0190] Output: The HTTP GET request sent to the server.

[0191] Step 7: Serving search results

[0192] The server searches the database based on the received search query. The search process uses SQL queries to extract relevant data. The server organizes this data and returns it to the device in a format such as JSON. The device then displays the data in a user-friendly format.

[0193] Input: The search query sent as an HTTP GET request.

[0194] Output: JSON formatted data containing the search results.

[0195] The above processing steps realize a series of steps from inputting accident information to saving, searching, and providing the information.

[0196] (Application example 1)

[0197] 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."

[0198] In conventional accident information management systems, the input, analysis, storage, and retrieval of accident information are performed separately, resulting in a lack of overall efficiency. Furthermore, the provision of preventive measures based on past accident information is performed manually, making it difficult to respond quickly and appropriately. The present invention aims to solve these problems by providing an efficient system that centrally manages accident information and automatically proposes preventive measures.

[0199] 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.

[0200] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, and means for proposing preventive measures based on the searched accident information, thereby enabling unified management of accident information and rapid proposal of measures to prevent recurrence.

[0201] "Accident information" refers to all data related to an accident, such as the location, type, cause, measures to prevent recurrence, and date of the accident.

[0202] "Input means" refers to the interface through which users input accident information into the system, such as a web form or a smartphone app.

[0203] "Analysis methods" refer to the process of checking the accuracy and completeness of the entered accident information and ensuring the quality of the data.

[0204] "Database" means a data storage system designed to systematically store, search, and retrieve accident information.

[0205] "Search methods" refer to the processes and technologies used to extract data that match specific conditions from stored accident information.

[0206] "Means of provision" refers to the process or function for providing the accident information obtained as a search result to users visually or in other ways.

[0207] "Preventive measures suggestion means" refers to a function that suggests specific measures to prevent similar accidents in the future based on past accident information and analysis results.

[0208] "Artificial intelligence" refers to intellectual tasks such as learning, reasoning, recognition, and adaptation performed by computers.

[0209] In this invention, a specific embodiment necessary for constructing a system for managing accident information and proposing measures to prevent recurrence will be described. This system is composed of the following main components.

[0210] 1. How to input accident information

[0211] The device (e.g., a smartphone or tablet) provides an interface for the user to enter accident information. This interface typically takes the form of a mail form or a mobile application. The user enters information such as the location, type, cause, preventative measures, and date of the accident.

[0212] 2. Methods for analyzing accident information

[0213] The server receives and analyzes the accident information sent from the device. During this process, the accuracy and completeness of the data are checked. For example, it automatically verifies whether the entered date is in the future and whether specific measures to prevent recurrence are described.

[0214] 3. How to store data in a database

[0215] The server stores the analyzed accident information in a database such as SQLite, where the accident information is systematically organized and recorded, enabling quick response for subsequent searches and analysis.

[0216] 4. Search methods for accident information

[0217] When a user searches for specific accident information, they use their device to send a query to the server, which then searches the database to extract accident information that matches the criteria. The search process uses artificial intelligence (AI) to identify relevant data.

[0218] 5. Means of providing search results

[0219] The server organizes the search results and provides them to the device. By checking these, users can take appropriate measures to prevent recurrence based on past accident information.

[0220] 6. Suggested preventive measures

[0221] The server then proposes preventive measures based on the search results. These proposals are derived from information about past accidents and measures to prevent their recurrence. Users can refer to these suggestions and take concrete measures to prevent future accidents.

[0222] Hardware and software used:

[0223] Hardware: smartphones, tablets, servers

[0224] Software: SQLite (database), artificial intelligence model (AI), web or mobile application

[0225] Examples:

[0226] For example, consider a scenario where a user inputs "an accident that occurred during machine maintenance work" and records "thorough periodic inspections" as a recurrence prevention measure. A few months later, another user searches for "machine maintenance accidents" and is presented with the past recurrence prevention measure of "thorough inspections."

[0227] Example prompt sentence:

[0228] "Please give us an example of an implementation where an accident information management system is used to search for past accident information and propose measures to prevent recurrence."

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] The terminal provides an interface for inputting accident information. The user inputs information such as the location, type, cause, preventative measures, and date of the accident. The input information is sent from the terminal to the server.

[0232] Input: Accident information (location, type, cause, recurrence prevention measures, date of occurrence)

[0233] Output: Sending accident information to the server

[0234] Step 2:

[0235] The server analyzes the accident information received and automatically checks the accuracy and completeness of the data. For example, it checks whether the date of occurrence is in the future and whether specific measures to prevent recurrence are described.

[0236] Input: Accident information submitted by the user

[0237] Output: Analyzed accident information

[0238] Step 3:

[0239] The server stores the analyzed accident information in an SQLite database, where the information is systematically organized and stored in a manner that allows for quick retrieval when needed.

[0240] Input: Analyzed accident information

[0241] Output: Accident information stored in the database

[0242] Step 4:

[0243] A user uses a terminal to send a query to search for specific accident information, and the server receives the query and searches the database.

[0244] Input: User search query

[0245] Output: Search query sent to server

[0246] Step 5:

[0247] The server searches the database to extract accident information that matches the criteria, using artificial intelligence (AI) to identify highly relevant data.

[0248] Input: User's search query

[0249] Output: A list of accidents that match the criteria

[0250] Step 6:

[0251] The server organizes the search results and provides them to the user, who then checks the information provided and considers appropriate measures to prevent recurrence.

[0252] Input: Extracted accident information

[0253] Output: Organized search results served to the user

[0254] Step 7:

[0255] The server proposes preventive measures based on past accident data. These proposals are generated based on accident information and measures to prevent recurrence. The user then takes specific measures based on the proposed preventive measures.

[0256] Input: Accident information based on search results

[0257] Output: Preventive measures suggested

[0258] 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.

[0259] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[0260] The present invention combines a system for unifying accident information management and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system has the following main functions:

[0261] 1. Means for inputting accident information

[0262] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for company name, type of accident, cause, preventative measures, date of occurrence, etc. When a user enters information into each field and clicks the submit button, the information is sent to the server.

[0263] 2. Receiving and analyzing accident information

[0264] The server receives and analyzes the accident information sent by the user. The received data is analyzed appropriately on the server to check the accuracy and completeness of the information. Specifically, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0265] 3. Saving to the database

[0266] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[0267] 4. Search methods for accident information

[0268] When a user searches for accident information, they send a search query to the server, which searches the database and extracts accident information that matches the search criteria. At the same time, the server also uses artificial intelligence to identify the most relevant accident information.

[0269] 5. Providing search results

[0270] The server organizes the search results and provides them to the user, who can then review them and take measures to prevent recurrence.

[0271] 6. Introducing the Emotion Engine

[0272] The emotion engine is responsible for analyzing the emotions expressed when users input accident information and when searching. The input emotion information is associated with the accident information and stored in the database. This information enables a more detailed analysis of the cause of the accident. The emotion engine also takes the user's emotions into account when providing search results, providing appropriate feedback.

[0273] Specific explanation of program processing

[0274] 1. Enter and save accident information

[0275] When a user enters accident information into a form and submits information such as "Company A," "data loss," "human error," "regular backup," and "2023-05-01," the data is sent to the server. The server receives the data, checks its integrity, and stores it in a database. At this time, the user's emotions are also detected by the emotion engine and stored along with the data. For example, if the user is feeling stressed while entering information, the emotional data is also saved as "stress."

[0276] 2. Searching for and providing accident information

[0277] When a user searches for information about an accident, they enter a search keyword, such as "human error." The server receives this and searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching and provides appropriate feedback on the search results. For example, if the user is feeling anxious, the system can take action such as highlighting relevant measures to prevent recurrence.

[0278] 3. Visualizing Emotion Data

[0279] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company. For example, if employee stress levels are increasing over a certain period of time, the company can take immediate action based on that information.

[0280] Specific examples

[0281] 1. Entering accident information and saving emotion logs

[0282] When a user enters the accident information for "Data Loss at Company A," the emotion engine recognizes the user's emotion as "stress" through facial recognition and voice analysis. The entered accident information and the emotional data of stress are stored together in the database.

[0283] 2. Accident information search and emotional feedback

[0284] When a user searches for information about accidents related to "human error," the emotion engine recognizes the user's emotions and determines that the user is feeling anxious. Based on this information, the server highlights information about measures to prevent recurrence and provides feedback that reassures the user.

[0285] 3. Visualizing sentiment trends for managers

[0286] Managers can check employee emotional trends through the dashboard. For example, it can visualize that many employees are feeling anxious or stressed during a particular project, and managers can use this information to consider appropriate countermeasures.

[0287] As described above, the system of the present invention not only makes the management and search of accident information more efficient, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[0288] The processing flow will be explained below.

[0289] Understood. Below I will explain the process in concrete steps.

[0290] Step 1:

[0291] The user accesses an accident information input form, which contains fields such as company name, accident type, cause, preventative measures, and date of occurrence.

[0292] Step 2:

[0293] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[0294] Step 3:

[0295] The user clicks the send button. This operation sends the entered accident information to the server.

[0296] Step 4:

[0297] The emotion engine recognizes the user's emotions in real time through facial and voice recognition, specifically identifying stress or anxiety the user is feeling while typing.

[0298] Step 5:

[0299] The server receives the accident information and emotion data sent by the user. The received data is sent to the server in JSON format or POST data.

[0300] Step 6:

[0301] The server analyzes the received data and verifies that all required fields are present. For example, it checks that the "company name," "accident type," "cause," "measures to prevent recurrence," "date of occurrence," and "user emotion data" are all present.

[0302] Step 7:

[0303] The server connects to the database and saves the analyzed accident information and emotion data in the database. When saving, it checks for data consistency and duplication.

[0304] Step 8:

[0305] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[0306] Step 9:

[0307] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[0308] Step 10:

[0309] A user enters a search term into a search field. For example, they enter "human error."

[0310] Step 11:

[0311] The user clicks the search button, which sends the search keywords to the server.

[0312] Step 12:

[0313] The server receives a search request sent by a user, which includes search keywords and conditions.

[0314] Step 13:

[0315] The emotion engine recognizes users' emotions in real time through facial recognition and voice analysis, identifying stress or anxiety users are feeling while searching.

[0316] Step 14:

[0317] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[0318] Step 15:

[0319] The server searches the database to retrieve accident information and related emotion data that match the criteria. As a specific example, it extracts information about accidents caused by "human error" in the past and the emotion data at the time of reporting the accident.

[0320] Step 16:

[0321] The server organizes the search results and presents them in a format that is easy for users to view. For example, the search results are organized in a list format, and the "company name," "type of accident," "cause," "measures to prevent recurrence," "date of occurrence," and "emotions at the time of reporting" for each accident are displayed.

[0322] Step 17:

[0323] The server uses an emotion engine to provide feedback that takes into account the user's emotions during the search. For example, if the user is feeling anxious, information about measures to prevent recurrence will be highlighted.

[0324] Step 18:

[0325] The server sends the organized search results and feedback to the user.

[0326] Step 19:

[0327] The terminal receives the search results sent from the server and displays them.

[0328] Step 20:

[0329] The user checks the search results on the device. As a specific example, information about a "data loss incident" caused by "human error" that occurred at "Company A" and the emotional data at the time of reporting it are displayed in a list.

[0330] Step 21:

[0331] The server receives the data from the sentiment engine and visualizes it on a dashboard for managers, which can be used to understand sentiment trends across the enterprise.

[0332] Step 22:

[0333] Managers can view employee sentiment trends on a dashboard, visualizing, for example, rising employee stress levels over a specific period, allowing managers to take appropriate action based on that information.

[0334] Through these steps, users can input, save, and search accident information, and log and utilize related emotion data.

[0335] Example 2

[0336] 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."

[0337] While conventional accident information management systems improve the efficiency of accident information management and search, they do not take user emotions into consideration, resulting in issues with the user experience. Furthermore, adding emotional information to accident information would enable the formulation of more precise measures to prevent recurrence and the understanding of emotional trends across the company, but this aspect was lacking.

[0338] 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.

[0339] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for saving the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means including an emotion engine for analyzing the user's emotions, means for adding the analyzed emotion information to data and saving it, and means for visualizing the emotion data on a dashboard for an administrator.

[0340] This not only makes the management and search of accident information more efficient, but also makes it possible to take user emotions into account, allowing for the development of more detailed measures to prevent recurrence and the understanding of emotional trends across the entire company.

[0341] "Accident information" is detailed information about an accident entered by the user, and specifically includes the type of accident, cause, measures to prevent recurrence, and date of occurrence.

[0342] The "means for receiving and analyzing" refers to a function that allows the server to receive accident information sent by the user, properly analyze it, and verify the accuracy and completeness of the data.

[0343] The "means for storing in a database" refers to a function that accumulates analyzed accident information and stores it in a database so that it can be easily searched and used later.

[0344] The "search means" is a function that allows a user to search for accident information from a database based on specific conditions.

[0345] The "means of providing" refers to a function that organizes search results and provides them to the user immediately.

[0346] The "emotion engine" has the function of analyzing the user's emotions and adding emotional information to the accident information based on the analysis results.

[0347] The "means for adding analyzed emotional information to data and saving it" has the function of adding the user's emotions analyzed by the emotion engine to the accident information and saving it in a database.

[0348] The "Administrator Dashboard" is an interface that visualizes the emotional data collected by the emotion engine, allowing managers to grasp emotional trends across the entire company.

[0349] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is configured to efficiently perform a series of processes, from inputting accident information to storing it in a database, searching, providing the results to the user, and analyzing the user's emotions and providing feedback.

[0350] Hardware and software used

[0351] The system mainly uses the following hardware and software:

[0352] Server: The central component that receives, analyzes, stores, searches, and serves data. It works in conjunction with the database and emotion engine.

[0353] Terminal: A device on which users enter accident information and view search results. A web browser is typically used.

[0354] Emotion Engine: A software component that analyzes the user's emotions through facial recognition and voice analysis.

[0355] System configuration and specific operation

[0356] Enter and submit accident information

[0357] The user accesses a form for entering accident information via a web browser on the device. This form contains fields for the company name, type of accident, cause, preventive measures, date of occurrence, etc. When the user enters this information and clicks the submit button, the input data is sent to the server.

[0358] Data reception and integrity check

[0359] The server receives the accident information. After receiving it, the server checks the integrity of the data. Specifically, it checks to make sure that the accident date is not in the future and that specific measures to prevent recurrence are included.

[0360] Sentiment analysis and data enrichment

[0361] The server sends the received accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the emotion the user is feeling while typing. For example, if the user is feeling stressed, the engine adds that information to the data as "stress."

[0362] Saving to a database

[0363] The analyzed accident information and emotion data are stored in a database by the server, allowing for later retrieval and analysis.

[0364] Searching for and providing accident information

[0365] When a user searches for information about an accident, they enter a keyword, such as "human error." The device sends this search query to the server, which then searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching, and if the user feels anxious, it takes action such as highlighting information about measures to prevent recurrence.

[0366] Admin Dashboard Visualization

[0367] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company, allowing managers to visually see employee stress levels over a specific period of time.

[0368] Examples of concrete examples and prompts

[0369] Specific examples

[0370] A user inputs the incident information, "Data loss at Company A." The emotion engine detects "stress" from the user's facial expression, and this emotion data is stored in the database along with the incident information.

[0371] Users search for "human error," the server extracts relevant accident information, and the emotion engine detects the user's anxiety and highlights information on measures to prevent recurrence.

[0372] Managers can visually see through a dashboard that many employees are feeling anxious or stressed during a particular project.

[0373] Prompt Sentence Examples

[0374] "Data loss at Company A"

[0375] "Human error"

[0376] The above is a specific embodiment for carrying out the present invention. This system not only improves the efficiency of accident information management and search, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[0377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0378] Step 1:

[0379] Entering accident information

[0380] The user accesses the web form on the device and enters accident information such as the company name, type of accident, cause, preventative measures, date of occurrence, etc. The entered data is sent from the device to the server when the send button is clicked.

[0381] Input: Accident information entered by the user (e.g., "CompanyA," "Data loss," "Human error," "Regular backup," "2023-05-01")

[0382] Output: The entered accident information is sent to the server.

[0383] Step 2:

[0384] Receiving and integrity checking of transmitted data

[0385] The server receives the accident information sent by the user and checks the consistency of the data, for example, checking whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0386] Input: Accident information sent from the device

[0387] Data processing / data calculation: The server analyzes the data received and performs consistency checks

[0388] Output: Accident information with consistency confirmed

[0389] Step 3:

[0390] Sentiment analysis and data enrichment

[0391] The server then sends the confirmed accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the user's emotions. For example, if the user is feeling stressed, the emotion "stress" is added to the data.

[0392] Input: Accident information with confirmed consistency

[0393] Data processing / data calculation: Emotion analysis using an emotion engine and adding that emotion information to accident information

[0394] Output: Accident information with emotional information added

[0395] Step 4:

[0396] Saving to a database

[0397] The server stores the accident information with the added emotional information in a database, which allows for later retrieval and analysis.

[0398] Input: Accident information with emotional information added

[0399] Data processing / data calculation: Data storage process in database

[0400] Output: Accident information stored in the database

[0401] Step 5:

[0402] Accident information search

[0403] When a user searches for specific accident information, for example, they input "human error" as a search keyword, and a search query is sent from the terminal to the server.

[0404] Input: A search query entered by a user into a device (e.g., "human error")

[0405] Output: Search query sent from device to server

[0406] Step 6:

[0407] Providing search results and emotional feedback

[0408] The server searches the database based on the received search query and extracts accident information that matches the criteria. Furthermore, the emotion engine analyzes the user's emotions in real time while searching, and provides feedback such as highlighting information on measures to prevent recurrence if the user is feeling anxious, for example.

[0409] Input: The search query sent to the server

[0410] Data processing / data calculation: database search and user emotion analysis using emotion engine

[0411] Output: Search results with highlighting

[0412] Step 7:

[0413] Visualizing Emotion Data

[0414] The server receives data from the sentiment engine and visualizes it on a dashboard for managers, allowing them to understand sentiment trends across the company and take action if necessary.

[0415] Input: Data from the emotion engine

[0416] Data processing / data calculation: Data visualization processing for dashboards

[0417] Output: Sentiment trend data displayed on an admin dashboard

[0418] (Application example 2)

[0419] 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."

[0420] In modern factories, it is important to efficiently manage accident information and implement measures to prevent recurrence, but conventional systems were unable to take into account the user's emotional information, making it difficult to provide appropriate feedback. Furthermore, the lack of feedback based on emotional state led to problems such as insufficient efforts to improve operators' safety awareness and work efficiency. As a result, issues arose, such as the complicated management of accident information and inappropriate implementation of measures to prevent recurrence.

[0421] 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.

[0422] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means for analyzing a human emotional state when the accident information is input and searched, and means for providing feedback based on the analyzed emotional state. This not only makes the management of accident information more efficient, but also makes it possible to take more appropriate measures to prevent recurrence by taking the user's emotions into consideration.

[0423] "Accident information" refers to detailed information such as the type of accident, cause, measures to prevent recurrence, date of occurrence, and the user's emotional state at the time of inputting the information.

[0424] An "emotion engine" refers to a system that analyzes a user's emotional state (e.g., stress, anxiety, relief, etc.) in real time through facial recognition and voice analysis.

[0425] A "database" refers to a collection of information that allows for the efficient management, search, and extraction of accumulated accident information and related data.

[0426] "Search means" refers to a system that has the function of searching for accident information in a database and extracting relevant information based on conditions specified by the user.

[0427] "Feedback" refers to the act or system of providing appropriate advice or measures tailored to the user's emotional state based on search results and information at the time of input.

[0428] "Means for analysis" refers to a system that has the function of checking the consistency and completeness of input data and appropriately analyzing the entire data, including emotional information.

[0429] "Means for inputting accident information" refers to devices or software that provide an interface for users to input accident information (type of accident, cause, measures to prevent recurrence, date of occurrence, etc.).

[0430] A "factory robot" is an automated mechanical device designed to perform work within a factory, and includes systems that can collect accident information and perform emotion analysis, etc.

[0431] "Artificial intelligence" refers to advanced algorithms and systems that analyze large amounts of data, discover patterns, and make highly accurate predictions and classifications.

[0432] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is installed on factory robots and has the following main functions:

[0433] 1. Enter accident information

[0434] The server provides an interface for inputting accident information. This interface is designed to allow operators to input accident information through the factory robot's touchscreen or voice input function. For example, the operator inputs the "company name," "type of accident," "cause," "measures to prevent recurrence," and "date of occurrence."

[0435] 2. Analysis by emotion engine

[0436] When entering accident information, the emotion engine analyzes the operator's emotions in real time through facial recognition and voice analysis. For example, if the operator is feeling stressed, that emotional data is also recorded. This function uses the emotion_engine module.

[0437] 3. Accident information and emotional data storage

[0438] The server analyzes the input accident information and emotion data, checks the data integrity, and then stores this information in a database using the database_manager module. The stored information includes the type of accident, cause, preventive measures, date of occurrence, and related emotion information.

[0439] 4. Search for accident information

[0440] The server also provides a database search function for accident information. When an operator enters a search query, the AI ​​extracts and provides relevant accident information. At this time, the emotion engine again analyzes the operator's emotional state and adjusts the feedback according to the search results.

[0441] 5. Providing Feedback

[0442] When providing search results, the server provides appropriate feedback based on the operator's emotional state analyzed by the emotion engine. For example, if the operator feels anxious, the server will adjust the search results by highlighting measures to prevent recurrence. This function makes it possible to provide the operator with a sense of security.

[0443] Specific examples

[0444] 1. Entering accident information and saving emotion logs

[0445] When an operator inputs the accident information of "data loss," the emotion engine analyzes the operator's emotions as "stress" through facial recognition and voice analysis, and the information is stored in the database along with the accident information.

[0446] 2. Accident information search and emotional feedback

[0447] When an operator searches for information about accidents related to "human error," the emotion engine analyzes the operator's emotion as "anxiety." Based on this information, the server highlights measures to prevent recurrence and provides search results that give the operator a sense of security.

[0448] Prompt Sentence Examples

[0449] Please enter the accident information. Please include the following information in your response: company name, accident type, cause, preventative measures, date of occurrence, and appropriate emotion labels (e.g., stress, anxiety).

[0450] The system of the present invention not only improves the efficiency of accident information management and search, but also provides feedback that takes into account the user's emotions, thereby providing a safer and more secure working environment.

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] The server accepts input of accident information. The user (operator) uses the factory robot's touchscreen or voice input to enter information such as the company name, type of accident, cause, preventative measures, and date of occurrence. This information is sent to the server. Input data includes, for example, "company name," "data loss," "human error," "regular backup," and "2023-05-01."

[0454] Step 2:

[0455] The server starts the emotion engine and performs facial recognition and voice analysis of the user (operator) while he / she is entering data. The emotion engine (emotion_engine module) is used to analyze the user's emotional state as "stress" or "anxiety," etc. At this time, emotion data is generated by analyzing facial expressions and tone of voice. For example, it is detected that the operator is feeling "stressed" while entering data.

[0456] Step 3:

[0457] The server receives and analyzes the entered accident information and emotion data. Specifically, it checks the consistency and completeness of the data, for example, whether the date of occurrence is in the future or whether the entered information is missing. As a result of this step, data with confirmed consistency is generated. For example, consistent data such as "company name," "data loss," "human error," "regular backup," "2023-05-01," and "stress" is output.

[0458] Step 4:

[0459] The server uses the database_manager module to store the analyzed accident information and emotion data in a database. When storing the data, fields such as the type of accident, cause, preventative measures, date of occurrence, and user emotion information are recorded in the database. For example, records such as "Company Name," "Data Loss," "Human Error," "Regular Backup," "2023-05-01," and "Stress" are added to the database.

[0460] Step 5:

[0461] When a user searches for accident information, the server accepts the user's search query. For example, the user enters the query "human error." This search query is sent to the server.

[0462] Step 6:

[0463] The server restarts the emotion engine and analyzes the user's emotional state during the search. The emotion engine determines that the user is feeling "anxiety." The analysis results are generated as emotion data.

[0464] Step 7:

[0465] The server searches the database and uses artificial intelligence (AI) to identify relevant accident information. For example, it extracts data that matches the search query "human error" for accident information. At this step, accident information that matches the search criteria is output.

[0466] Step 8:

[0467] When providing search results to the user, the server adjusts the feedback based on the user's emotional state analyzed by the emotion engine. If the user is feeling anxious, the server adjusts the feedback to provide a sense of security, such as by highlighting information about measures to prevent recurrence. For example, the server highlights the part of the search results that describes measures to prevent recurrence for "human error" and presents it to the user.

[0468] Step 9:

[0469] The server records the provided feedback and user emotional data to help with future improvements. This information is available to administrators via a dashboard, where it can be used to understand overall emotional trends and consider countermeasures. For example, if many operators are feeling stressed during a specific period, this information can be visualized and appropriate countermeasures can be considered.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] 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."

[0486] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[0487] The accident information management system of the present invention is for unifying the management of accident information in a company and formulating measures to prevent recurrence. This system has the following main functions.

[0488] 1. Means for inputting accident information

[0489] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for the name of the company where the accident occurred, the type of accident, the cause, measures to prevent recurrence, the date of the accident, etc. When a user enters the necessary information in these fields and clicks the submit button, the information is sent to the server.

[0490] 2. Receiving and analyzing accident information

[0491] The server receives the accident information sent by the user. The received data is analyzed on the server to check the accuracy and completeness of the data. For example, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0492] 3. Saving to the database

[0493] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[0494] 4. Search methods for accident information

[0495] When a user searches for past accident information, they submit a search query to the server, which searches the database to extract accident information that matches the criteria. This search also includes using artificial intelligence to identify relevant accident information.

[0496] 5. Providing search results

[0497] The server organizes the search results and provides them to the user. By reviewing them, users can refer to measures to prevent similar accidents from recurring and take measures to prevent future accidents. In addition, because accident information from multiple companies is integrated into a single database, cross-sectional data analysis is possible, making it possible to formulate more effective measures to prevent recurrence.

[0498] Specific examples

[0499] 1. Enter and save accident information

[0500] Consider the example where a user enters "A data loss incident occurred at CompanyA." The user enters the following information into the form:

[0501] Company Name: "CompanyA"

[0502] Incident Type: "Data Loss"

[0503] Cause: "human error"

[0504] Measures to prevent recurrence: "Perform regular backups"

[0505] Date of occurrence: "May 1, 2023"

[0506] After entering the information, the user clicks the submit button, the server receives it, checks the accuracy of the data, and then saves it in the database.

[0507] 2. Searching for and providing accident information

[0508] Consider an example where a user searches for information on past accidents caused by "human error" when implementing a new system. The user enters "human error" in the search field and sends a search query to the server. The server searches the database and extracts relevant information, such as "data loss accidents that occurred at Company A." The server then organizes this information and provides it to the user.

[0509] This allows users to strengthen measures to prevent recurrence based on past accident information and reduce risks when introducing new systems.

[0510] The processing flow will be explained below.

[0511] Understood. Below is a step-by-step explanation of the process.

[0512] Step 1:

[0513] The user accesses the accident information input form, which contains fields such as "company name," "accident type," "cause," "measures to prevent recurrence," and "occurrence date."

[0514] Step 2:

[0515] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[0516] Step 3:

[0517] The user clicks the send button. This operation sends the entered accident information to the server.

[0518] Step 4:

[0519] The server receives the accident information sent by the user. The received data is sent to the server in JSON format or POST data.

[0520] Step 5:

[0521] The server analyzes the received data and verifies that all required fields are present. For example, it checks that "Company Name," "Type of Accident," "Cause," "Preventive Measures for Recurrence," and "Date of Occurrence" are all present.

[0522] Step 6:

[0523] The server connects to the database and saves the analyzed accident information to the database. When saving, it checks for data consistency and the absence of duplication.

[0524] Step 7:

[0525] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[0526] Step 8:

[0527] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[0528] Step 9:

[0529] A user enters a search term into a search field. For example, they enter "human error."

[0530] Step 10:

[0531] The user clicks the search button, which sends the search keywords to the server.

[0532] Step 11:

[0533] The server receives a search request sent by a user, which includes search keywords and conditions.

[0534] Step 12:

[0535] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[0536] Step 13:

[0537] The server searches the database and retrieves accident information that matches the criteria. For example, it extracts information on past accidents caused by "human error."

[0538] Step 14:

[0539] The server organizes the search results and presents them in a format that is easy for users to view. For example, the server organizes the search results in a list format, displaying the company name, type of accident, cause, measures to prevent recurrence, and date of occurrence for each accident.

[0540] Step 15:

[0541] The server sends the organized search results to the user.

[0542] Step 16:

[0543] The terminal receives the search results sent from the server and displays them.

[0544] Step 17:

[0545] The user checks the search results on the device. For example, a list of information about a "data loss incident" caused by "human error" at "Company A" is displayed.

[0546] By following the above steps, the user can input, save, and search for accident information, and refer to measures to prevent recurrence.

[0547] Example 1

[0548] 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."

[0549] Accident information management in companies is often done individually, making it difficult to centralize information management and share preventative measures. Furthermore, there is a lack of tools to efficiently search for past accident information and formulate appropriate preventative measures. This increases the risk of similar accidents occurring again.

[0550] 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.

[0551] In this invention, the server includes a means for inputting information, a means for receiving and analyzing the input information, and a means for storing the analyzed information in a database, which enables centralized management of information, efficient search, and the formulation of appropriate measures to prevent recurrence.

[0552] "Means for inputting information" refers to the interface within the system through which users can input necessary data such as accident information, and specifically refers to forms on a web browser or mobile applications.

[0553] "Means for receiving and analyzing the input information" refers to a system component that receives data sent by a user on the server side, checks the accuracy and completeness of the data, and performs error handling if necessary.

[0554] "Means for storing the analyzed information in a database" refers to a system component that inserts data that has been verified for accuracy and completeness into a database, where it is systematically recorded for future query and analysis.

[0555] The term "means for retrieving information from the database" refers to a system component that has the function of quickly extracting relevant information from the database in response to a search query from a user.

[0556] "Means for providing the searched information" refers to a system component that has the function of formatting search results and returning them to the terminal in a format that is easily understood and used by the user.

[0557] "Means for identifying relevant information using artificial intelligence" refers to a system component that uses AI technologies such as machine learning and natural language processing to analyze information in the database and identify and extract highly relevant accident information.

[0558] "Type" indicates the category or class of accident information, and specifically includes classifications such as "fire," "data loss," and "machine failure."

[0559] "Cause" refers to the reason or factor behind the occurrence of an accident, and includes, for example, "human error" or "machine failure."

[0560] "Countermeasures" refer to specific actions and measures to prevent the recurrence of accidents, and include "conducting regular inspections" and "introducing backup systems."

[0561] "Date" indicates the specific time when the accident occurred, and refers to information including the year, month, and day.

[0562] The accident information management system of the present invention aims to centrally manage accident information within a company and to formulate measures to prevent recurrence. The main components of this system include a means for inputting information, a means for receiving and analyzing the information, a means for storing the information in a database, a means for searching the database, and a means for providing the search results to the user.

[0563] Program processing

[0564] The program of this system goes through multiple steps to input, analyze, save, search, and provide accident information. The specific process flow is explained below.

[0565] Entering accident information

[0566] The terminal provides an interface for the user to enter incident information. This is implemented as a form that runs in a web browser and contains the following fields:

[0567] Company Name

[0568] Accident type

[0569] cause

[0570] Measures to prevent recurrence

[0571] Date of occurrence

[0572] For example, if a user wants to enter "a data loss incident that occurred at a certain company," they would enter the following:

[0573] Company Name: "A company"

[0574] Incident Type: "Data Loss"

[0575] Cause: "human error"

[0576] Measures to prevent recurrence: "Perform regular backups"

[0577] Date of occurrence: "May 1, 2023"

[0578] Sending accident information

[0579] When the user enters information in all fields and clicks the submit button, the device sends the entered accident information to the server using an HTTP POST request. After sending, the device displays a confirmation message.

[0580] Receiving and analyzing accident information

[0581] The server receives the incident information sent from the device as an HTTP request. The received data is first subjected to a security check by the firewall and web application firewall (WAF). If no abnormalities are found, the server analyzes the data in the next step.

[0582] Hardware and software used

[0583] The hardware used in this system includes:

[0584] Server Computer

[0585] Client terminal (PC, smartphone)

[0586] The software used includes:

[0587] Web Forms (HTML, CSS, JavaScript)

[0588] Server-side programs (Python, Java, Node.js, etc.)

[0589] Database systems (MySQL, PostgreSQL)

[0590] Specific examples

[0591] A specific example of the system's operation is shown below.

[0592] If a user wants to enter "a fire incident at a company", they would enter the following into the form:

[0593] Company Name: "A company"

[0594] Incident type: "Fire"

[0595] Cause: "Electrical failure"

[0596] Measures to prevent recurrence: "Regular equipment inspections"

[0597] Date of occurrence: "April 15, 2023"

[0598] When the user clicks the submit button, the server receives it and checks the accuracy of the data, after which it stores the information in a database.

[0599] Example prompts for generative AI models

[0600] Here are some example prompts to input to the generative AI model:

[0601] Please explain in detail the information that users enter into the web form. The field names to be entered into the form are as follows: company name, type of incident, cause, preventative measures, and date of occurrence. For example, please give a specific example of entering information about a data loss incident that occurred at a certain company.

[0602] This allows the generative AI model to generate specific accident information input procedures for the user.

[0603] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0604] Step 1: Enter your information

[0605] The terminal provides the user with an interface for entering accident information, specifically a web form with the following fields:

[0606] Company Name

[0607] Accident type

[0608] cause

[0609] Measures to prevent recurrence

[0610] Date of occurrence

[0611] Input: The user enters the required information for each field.

[0612] Output: Data entered into the terminal.

[0613] Step 2: Submit your information

[0614] When the user enters information into all fields and clicks the submit button, the device sends the entered data to the server using an HTTP POST request, with basic validation performed on the data (e.g., whether required fields are filled in).

[0615] Input: Data entered after the user clicks the submit button.

[0616] Output: The HTTP POST request sent to the server.

[0617] Step 3: Receiving information

[0618] The server receives the HTTP POST request sent from the terminal. The received data is first subjected to a security check through the firewall and web application firewall (WAF). If an abnormality is detected in this step, the server generates an error message and returns it to the terminal.

[0619] Input: The HTTP POST request sent from the terminal.

[0620] Output: Security checked data.

[0621] Step 4: Analyze the information

[0622] The server analyzes the received data, specifically checking the following:

[0623] The date of the accident is in the future

[0624] Are the company name and accident type entered in a valid format?

[0625] Are the recurrence prevention measures specific and appropriate?

[0626] Input: Security checked data.

[0627] Output: The validated data. If there are any errors, an error message is generated.

[0628] Step 5: Saving to the Database

[0629] The server stores the data that has passed the analysis in a database, such as MySQL or PostgreSQL. The data is inserted into the appropriate table and assigned a unique identifier (ID).

[0630] Input: Validated data.

[0631] Output: A confirmation message of the data saved in the database.

[0632] Step 6: Finding information

[0633] When a user searches for information about past accidents, the device sends a search query to the server based on the information entered in the search field. For example, if a user searches for information about accidents caused by "human error," the device generates the query "Cause of accident: human error" and sends it as an HTTP GET request.

[0634] Input: The search query that the user entered into the device.

[0635] Output: The HTTP GET request sent to the server.

[0636] Step 7: Serving search results

[0637] The server searches the database based on the received search query. The search process uses SQL queries to extract relevant data. The server organizes this data and returns it to the device in a format such as JSON. The device then displays the data in a user-friendly format.

[0638] Input: The search query sent as an HTTP GET request.

[0639] Output: JSON formatted data containing the search results.

[0640] The above processing steps realize a series of steps from inputting accident information to saving, searching, and providing the information.

[0641] (Application example 1)

[0642] 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."

[0643] In conventional accident information management systems, the input, analysis, storage, and retrieval of accident information are performed separately, resulting in a lack of overall efficiency. Furthermore, the provision of preventive measures based on past accident information is performed manually, making it difficult to respond quickly and appropriately. The present invention aims to solve these problems by providing an efficient system that centrally manages accident information and automatically proposes preventive measures.

[0644] 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.

[0645] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, and means for proposing preventive measures based on the searched accident information, thereby enabling unified management of accident information and rapid proposal of measures to prevent recurrence.

[0646] "Accident information" refers to all data related to an accident, such as the location, type, cause, measures to prevent recurrence, and date of the accident.

[0647] "Input means" refers to the interface through which users input accident information into the system, such as a web form or a smartphone app.

[0648] "Analysis methods" refer to the process of checking the accuracy and completeness of the entered accident information and ensuring the quality of the data.

[0649] "Database" means a data storage system designed to systematically store, search, and retrieve accident information.

[0650] "Search methods" refer to the processes and technologies used to extract data that match specific conditions from stored accident information.

[0651] "Means of provision" refers to the process or function for providing the accident information obtained as a search result to users visually or in other ways.

[0652] "Preventive measures suggestion means" refers to a function that suggests specific measures to prevent similar accidents in the future based on past accident information and analysis results.

[0653] "Artificial intelligence" refers to intellectual tasks such as learning, reasoning, recognition, and adaptation performed by computers.

[0654] In this invention, a specific embodiment necessary for constructing a system for managing accident information and proposing measures to prevent recurrence will be described. This system is composed of the following main components.

[0655] 1. How to input accident information

[0656] The device (e.g., a smartphone or tablet) provides an interface for the user to enter accident information. This interface typically takes the form of a mail form or a mobile application. The user enters information such as the location, type, cause, preventative measures, and date of the accident.

[0657] 2. Methods for analyzing accident information

[0658] The server receives and analyzes the accident information sent from the device. During this process, the accuracy and completeness of the data are checked. For example, it automatically verifies whether the entered date is in the future and whether specific measures to prevent recurrence are described.

[0659] 3. How to store data in a database

[0660] The server stores the analyzed accident information in a database such as SQLite, where the accident information is systematically organized and recorded, enabling quick response for subsequent searches and analysis.

[0661] 4. Search methods for accident information

[0662] When a user searches for specific accident information, they use their device to send a query to the server, which then searches the database to extract accident information that matches the criteria. The search process uses artificial intelligence (AI) to identify relevant data.

[0663] 5. Means of providing search results

[0664] The server organizes the search results and provides them to the device. By checking these, users can take appropriate measures to prevent recurrence based on past accident information.

[0665] 6. Suggested preventive measures

[0666] The server then proposes preventive measures based on the search results. These proposals are derived from information about past accidents and measures to prevent their recurrence. Users can refer to these suggestions and take concrete measures to prevent future accidents.

[0667] Hardware and software used:

[0668] Hardware: smartphones, tablets, servers

[0669] Software: SQLite (database), artificial intelligence model (AI), web or mobile application

[0670] Examples:

[0671] For example, consider a scenario where a user inputs "an accident that occurred during machine maintenance work" and records "thorough periodic inspections" as a recurrence prevention measure. A few months later, another user searches for "machine maintenance accidents" and is presented with the past recurrence prevention measure of "thorough inspections."

[0672] Example prompt sentence:

[0673] "Please give us an example of an implementation where an accident information management system is used to search for past accident information and propose measures to prevent recurrence."

[0674] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0675] Step 1:

[0676] The terminal provides an interface for inputting accident information. The user inputs information such as the location, type, cause, preventative measures, and date of the accident. The input information is sent from the terminal to the server.

[0677] Input: Accident information (location, type, cause, recurrence prevention measures, date of occurrence)

[0678] Output: Sending accident information to the server

[0679] Step 2:

[0680] The server analyzes the accident information received and automatically checks the accuracy and completeness of the data. For example, it checks whether the date of occurrence is in the future and whether specific measures to prevent recurrence are described.

[0681] Input: Accident information submitted by the user

[0682] Output: Analyzed accident information

[0683] Step 3:

[0684] The server stores the analyzed accident information in an SQLite database, where the information is systematically organized and stored in a manner that allows for quick retrieval when needed.

[0685] Input: Analyzed accident information

[0686] Output: Accident information stored in the database

[0687] Step 4:

[0688] A user uses a terminal to send a query to search for specific accident information, and the server receives the query and searches the database.

[0689] Input: User search query

[0690] Output: Search query sent to server

[0691] Step 5:

[0692] The server searches the database to extract accident information that matches the criteria, using artificial intelligence (AI) to identify highly relevant data.

[0693] Input: User's search query

[0694] Output: A list of accidents that match the criteria

[0695] Step 6:

[0696] The server organizes the search results and provides them to the user, who then checks the information provided and considers appropriate measures to prevent recurrence.

[0697] Input: Extracted accident information

[0698] Output: Organized search results served to the user

[0699] Step 7:

[0700] The server proposes preventive measures based on past accident data. These proposals are generated based on accident information and measures to prevent recurrence. The user then takes specific measures based on the proposed preventive measures.

[0701] Input: Accident information based on search results

[0702] Output: Preventive measures suggested

[0703] 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.

[0704] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[0705] The present invention combines a system for unifying accident information management and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system has the following main functions:

[0706] 1. Means for inputting accident information

[0707] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for company name, type of accident, cause, preventative measures, date of occurrence, etc. When a user enters information into each field and clicks the submit button, the information is sent to the server.

[0708] 2. Receiving and analyzing accident information

[0709] The server receives and analyzes the accident information sent by the user. The received data is analyzed appropriately on the server to check the accuracy and completeness of the information. Specifically, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0710] 3. Saving to the database

[0711] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[0712] 4. Search methods for accident information

[0713] When a user searches for accident information, they send a search query to the server, which searches the database and extracts accident information that matches the search criteria. At the same time, the server also uses artificial intelligence to identify the most relevant accident information.

[0714] 5. Providing search results

[0715] The server organizes the search results and provides them to the user, who can then review them and take measures to prevent recurrence.

[0716] 6. Introducing the Emotion Engine

[0717] The emotion engine is responsible for analyzing the emotions expressed when users input accident information and when searching. The input emotion information is associated with the accident information and stored in the database. This information enables a more detailed analysis of the cause of the accident. The emotion engine also takes the user's emotions into account when providing search results, providing appropriate feedback.

[0718] Specific explanation of program processing

[0719] 1. Enter and save accident information

[0720] When a user enters accident information into a form and submits information such as "Company A," "data loss," "human error," "regular backup," and "2023-05-01," the data is sent to the server. The server receives the data, checks its integrity, and stores it in a database. At this time, the user's emotions are also detected by the emotion engine and stored along with the data. For example, if the user is feeling stressed while entering information, the emotional data is also saved as "stress."

[0721] 2. Searching for and providing accident information

[0722] When a user searches for information about an accident, they enter a search keyword, such as "human error." The server receives this and searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching and provides appropriate feedback on the search results. For example, if the user is feeling anxious, the system can take action such as highlighting relevant measures to prevent recurrence.

[0723] 3. Visualizing Emotion Data

[0724] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company. For example, if employee stress levels are increasing over a certain period of time, the company can take immediate action based on that information.

[0725] Specific examples

[0726] 1. Entering accident information and saving emotion logs

[0727] When a user enters the accident information for "Data Loss at Company A," the emotion engine recognizes the user's emotion as "stress" through facial recognition and voice analysis. The entered accident information and the emotional data of stress are stored together in the database.

[0728] 2. Accident information search and emotional feedback

[0729] When a user searches for information about accidents related to "human error," the emotion engine recognizes the user's emotions and determines that the user is feeling anxious. Based on this information, the server highlights information about measures to prevent recurrence and provides feedback that reassures the user.

[0730] 3. Visualizing sentiment trends for managers

[0731] Managers can check employee emotional trends through the dashboard. For example, it can visualize that many employees are feeling anxious or stressed during a particular project, and managers can use this information to consider appropriate countermeasures.

[0732] As described above, the system of the present invention not only makes the management and search of accident information more efficient, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[0733] The processing flow will be explained below.

[0734] Understood. Below I will explain the process in concrete steps.

[0735] Step 1:

[0736] The user accesses an accident information input form, which contains fields such as company name, accident type, cause, preventative measures, and date of occurrence.

[0737] Step 2:

[0738] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[0739] Step 3:

[0740] The user clicks the send button. This operation sends the entered accident information to the server.

[0741] Step 4:

[0742] The emotion engine recognizes the user's emotions in real time through facial and voice recognition, specifically identifying stress or anxiety the user is feeling while typing.

[0743] Step 5:

[0744] The server receives the accident information and emotion data sent by the user. The received data is sent to the server in JSON format or POST data.

[0745] Step 6:

[0746] The server analyzes the received data and verifies that all required fields are present. For example, it checks that the "company name," "accident type," "cause," "measures to prevent recurrence," "date of occurrence," and "user emotion data" are all present.

[0747] Step 7:

[0748] The server connects to the database and saves the analyzed accident information and emotion data in the database. When saving, it checks for data consistency and duplication.

[0749] Step 8:

[0750] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[0751] Step 9:

[0752] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[0753] Step 10:

[0754] A user enters a search term into a search field. For example, they enter "human error."

[0755] Step 11:

[0756] The user clicks the search button, which sends the search keywords to the server.

[0757] Step 12:

[0758] The server receives a search request sent by a user, which includes search keywords and conditions.

[0759] Step 13:

[0760] The emotion engine recognizes users' emotions in real time through facial recognition and voice analysis, identifying stress or anxiety users are feeling while searching.

[0761] Step 14:

[0762] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[0763] Step 15:

[0764] The server searches the database to retrieve accident information and related emotion data that match the criteria. As a specific example, it extracts information about accidents caused by "human error" in the past and the emotion data at the time of reporting the accident.

[0765] Step 16:

[0766] The server organizes the search results and presents them in a format that is easy for users to view. For example, the search results are organized in a list format, and the "company name," "type of accident," "cause," "measures to prevent recurrence," "date of occurrence," and "emotions at the time of reporting" for each accident are displayed.

[0767] Step 17:

[0768] The server uses an emotion engine to provide feedback that takes into account the user's emotions during the search. For example, if the user is feeling anxious, information about measures to prevent recurrence will be highlighted.

[0769] Step 18:

[0770] The server sends the organized search results and feedback to the user.

[0771] Step 19:

[0772] The terminal receives the search results sent from the server and displays them.

[0773] Step 20:

[0774] The user checks the search results on the device. As a specific example, information about a "data loss incident" caused by "human error" that occurred at "Company A" and the emotional data at the time of reporting it are displayed in a list.

[0775] Step 21:

[0776] The server receives the data from the sentiment engine and visualizes it on a dashboard for managers, which can be used to understand sentiment trends across the enterprise.

[0777] Step 22:

[0778] Managers can view employee sentiment trends on a dashboard, visualizing, for example, rising employee stress levels over a specific period, allowing managers to take appropriate action based on that information.

[0779] Through these steps, users can input, save, and search accident information, and log and utilize related emotion data.

[0780] Example 2

[0781] 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."

[0782] While conventional accident information management systems improve the efficiency of accident information management and search, they do not take user emotions into consideration, resulting in issues with the user experience. Furthermore, adding emotional information to accident information would enable the formulation of more precise measures to prevent recurrence and the understanding of emotional trends across the company, but this aspect was lacking.

[0783] 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.

[0784] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for saving the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means including an emotion engine for analyzing the user's emotions, means for adding the analyzed emotion information to data and saving it, and means for visualizing the emotion data on a dashboard for an administrator.

[0785] This not only makes the management and search of accident information more efficient, but also makes it possible to take user emotions into account, allowing for the development of more detailed measures to prevent recurrence and the understanding of emotional trends across the entire company.

[0786] "Accident information" is detailed information about an accident entered by the user, and specifically includes the type of accident, cause, measures to prevent recurrence, and date of occurrence.

[0787] The "means for receiving and analyzing" refers to a function that allows the server to receive accident information sent by the user, properly analyze it, and verify the accuracy and completeness of the data.

[0788] The "means for storing in a database" refers to a function that accumulates analyzed accident information and stores it in a database so that it can be easily searched and used later.

[0789] The "search means" is a function that allows a user to search for accident information from a database based on specific conditions.

[0790] The "means of providing" refers to a function that organizes search results and provides them to the user immediately.

[0791] The "emotion engine" has the function of analyzing the user's emotions and adding emotional information to the accident information based on the analysis results.

[0792] The "means for adding analyzed emotional information to data and saving it" has the function of adding the user's emotions analyzed by the emotion engine to the accident information and saving it in a database.

[0793] The "Administrator Dashboard" is an interface that visualizes the emotional data collected by the emotion engine, allowing managers to grasp emotional trends across the entire company.

[0794] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is configured to efficiently perform a series of processes, from inputting accident information to storing it in a database, searching, providing the results to the user, and analyzing the user's emotions and providing feedback.

[0795] Hardware and software used

[0796] The system mainly uses the following hardware and software:

[0797] Server: The central component that receives, analyzes, stores, searches, and serves data. It works in conjunction with the database and emotion engine.

[0798] Terminal: A device on which users enter accident information and view search results. A web browser is typically used.

[0799] Emotion Engine: A software component that analyzes the user's emotions through facial recognition and voice analysis.

[0800] System configuration and specific operation

[0801] Enter and submit accident information

[0802] The user accesses a form for entering accident information via a web browser on the device. This form contains fields for the company name, type of accident, cause, preventive measures, date of occurrence, etc. When the user enters this information and clicks the submit button, the input data is sent to the server.

[0803] Data reception and integrity check

[0804] The server receives the accident information. After receiving it, the server checks the integrity of the data. Specifically, it checks to make sure that the accident date is not in the future and that specific measures to prevent recurrence are included.

[0805] Sentiment analysis and data enrichment

[0806] The server sends the received accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the emotion the user is feeling while typing. For example, if the user is feeling stressed, the engine adds that information to the data as "stress."

[0807] Saving to a database

[0808] The analyzed accident information and emotion data are stored in a database by the server, allowing for later retrieval and analysis.

[0809] Searching for and providing accident information

[0810] When a user searches for information about an accident, they enter a keyword, such as "human error." The device sends this search query to the server, which then searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching, and if the user feels anxious, it takes action such as highlighting information about measures to prevent recurrence.

[0811] Admin Dashboard Visualization

[0812] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company, allowing managers to visually see employee stress levels over a specific period of time.

[0813] Examples of concrete examples and prompts

[0814] Specific examples

[0815] A user inputs the incident information, "Data loss at Company A." The emotion engine detects "stress" from the user's facial expression, and this emotion data is stored in the database along with the incident information.

[0816] Users search for "human error," the server extracts relevant accident information, and the emotion engine detects the user's anxiety and highlights information on measures to prevent recurrence.

[0817] Managers can visually see through a dashboard that many employees are feeling anxious or stressed during a particular project.

[0818] Prompt Sentence Examples

[0819] "Data loss at Company A"

[0820] "Human error"

[0821] The above is a specific embodiment for carrying out the present invention. This system not only improves the efficiency of accident information management and search, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Step 1:

[0824] Entering accident information

[0825] The user accesses the web form on the device and enters accident information such as the company name, type of accident, cause, preventative measures, date of occurrence, etc. The entered data is sent from the device to the server when the send button is clicked.

[0826] Input: Accident information entered by the user (e.g., "CompanyA," "Data loss," "Human error," "Regular backup," "2023-05-01")

[0827] Output: The entered accident information is sent to the server.

[0828] Step 2:

[0829] Receiving and integrity checking of transmitted data

[0830] The server receives the accident information sent by the user and checks the consistency of the data, for example, checking whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0831] Input: Accident information sent from the device

[0832] Data processing / data calculation: The server analyzes the data received and performs consistency checks

[0833] Output: Accident information with consistency confirmed

[0834] Step 3:

[0835] Sentiment analysis and data enrichment

[0836] The server then sends the confirmed accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the user's emotions. For example, if the user is feeling stressed, the emotion "stress" is added to the data.

[0837] Input: Accident information with confirmed consistency

[0838] Data processing / data calculation: Emotion analysis using an emotion engine and adding that emotion information to accident information

[0839] Output: Accident information with emotional information added

[0840] Step 4:

[0841] Saving to a database

[0842] The server stores the accident information with the added emotional information in a database, which allows for later retrieval and analysis.

[0843] Input: Accident information with emotional information added

[0844] Data processing / data calculation: Data storage process in database

[0845] Output: Accident information stored in the database

[0846] Step 5:

[0847] Accident information search

[0848] When a user searches for specific accident information, for example, they input "human error" as a search keyword, and a search query is sent from the terminal to the server.

[0849] Input: A search query entered by a user into a device (e.g., "human error")

[0850] Output: Search query sent from device to server

[0851] Step 6:

[0852] Providing search results and emotional feedback

[0853] The server searches the database based on the received search query and extracts accident information that matches the criteria. Furthermore, the emotion engine analyzes the user's emotions in real time while searching, and provides feedback such as highlighting information on measures to prevent recurrence if the user is feeling anxious, for example.

[0854] Input: The search query sent to the server

[0855] Data processing / data calculation: database search and user emotion analysis using emotion engine

[0856] Output: Search results with highlighting

[0857] Step 7:

[0858] Visualizing Emotion Data

[0859] The server receives data from the sentiment engine and visualizes it on a dashboard for managers, allowing them to understand sentiment trends across the company and take action if necessary.

[0860] Input: Data from the emotion engine

[0861] Data processing / data calculation: Data visualization processing for dashboards

[0862] Output: Sentiment trend data displayed on an admin dashboard

[0863] (Application example 2)

[0864] 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."

[0865] In modern factories, it is important to efficiently manage accident information and implement measures to prevent recurrence, but conventional systems were unable to take into account the user's emotional information, making it difficult to provide appropriate feedback. Furthermore, the lack of feedback based on emotional state led to problems such as insufficient efforts to improve operators' safety awareness and work efficiency. As a result, issues arose, such as the complicated management of accident information and inappropriate implementation of measures to prevent recurrence.

[0866] 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.

[0867] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means for analyzing a human emotional state when the accident information is input and searched, and means for providing feedback based on the analyzed emotional state. This not only makes the management of accident information more efficient, but also makes it possible to take more appropriate measures to prevent recurrence by taking the user's emotions into consideration.

[0868] "Accident information" refers to detailed information such as the type of accident, cause, measures to prevent recurrence, date of occurrence, and the user's emotional state at the time of inputting the information.

[0869] An "emotion engine" refers to a system that analyzes a user's emotional state (e.g., stress, anxiety, relief, etc.) in real time through facial recognition and voice analysis.

[0870] A "database" refers to a collection of information that allows for the efficient management, search, and extraction of accumulated accident information and related data.

[0871] "Search means" refers to a system that has the function of searching for accident information in a database and extracting relevant information based on conditions specified by the user.

[0872] "Feedback" refers to the act or system of providing appropriate advice or measures tailored to the user's emotional state based on search results and information at the time of input.

[0873] "Means for analysis" refers to a system that has the function of checking the consistency and completeness of input data and appropriately analyzing the entire data, including emotional information.

[0874] "Means for inputting accident information" refers to devices or software that provide an interface for users to input accident information (type of accident, cause, measures to prevent recurrence, date of occurrence, etc.).

[0875] A "factory robot" is an automated mechanical device designed to perform work within a factory, and includes systems that can collect accident information and perform emotion analysis, etc.

[0876] "Artificial intelligence" refers to advanced algorithms and systems that analyze large amounts of data, discover patterns, and make highly accurate predictions and classifications.

[0877] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is installed on factory robots and has the following main functions:

[0878] 1. Enter accident information

[0879] The server provides an interface for inputting accident information. This interface is designed to allow operators to input accident information through the factory robot's touchscreen or voice input function. For example, the operator inputs the "company name," "type of accident," "cause," "measures to prevent recurrence," and "date of occurrence."

[0880] 2. Analysis by emotion engine

[0881] When entering accident information, the emotion engine analyzes the operator's emotions in real time through facial recognition and voice analysis. For example, if the operator is feeling stressed, that emotional data is also recorded. This function uses the emotion_engine module.

[0882] 3. Accident information and emotional data storage

[0883] The server analyzes the input accident information and emotion data, checks the data integrity, and then stores this information in a database using the database_manager module. The stored information includes the type of accident, cause, preventive measures, date of occurrence, and related emotion information.

[0884] 4. Search for accident information

[0885] The server also provides a database search function for accident information. When an operator enters a search query, the AI ​​extracts and provides relevant accident information. At this time, the emotion engine again analyzes the operator's emotional state and adjusts the feedback according to the search results.

[0886] 5. Providing Feedback

[0887] When providing search results, the server provides appropriate feedback based on the operator's emotional state analyzed by the emotion engine. For example, if the operator feels anxious, the server will adjust the search results by highlighting measures to prevent recurrence. This function makes it possible to provide the operator with a sense of security.

[0888] Specific examples

[0889] 1. Entering accident information and saving emotion logs

[0890] When an operator inputs the accident information of "data loss," the emotion engine analyzes the operator's emotions as "stress" through facial recognition and voice analysis, and the information is stored in the database along with the accident information.

[0891] 2. Accident information search and emotional feedback

[0892] When an operator searches for information about accidents related to "human error," the emotion engine analyzes the operator's emotion as "anxiety." Based on this information, the server highlights measures to prevent recurrence and provides search results that give the operator a sense of security.

[0893] Prompt Sentence Examples

[0894] Please enter the accident information. Please include the following information in your response: company name, accident type, cause, preventative measures, date of occurrence, and appropriate emotion labels (e.g., stress, anxiety).

[0895] The system of the present invention not only improves the efficiency of accident information management and search, but also provides feedback that takes into account the user's emotions, thereby providing a safer and more secure working environment.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Step 1:

[0898] The server accepts input of accident information. The user (operator) uses the factory robot's touchscreen or voice input to enter information such as the company name, type of accident, cause, preventative measures, and date of occurrence. This information is sent to the server. Input data includes, for example, "company name," "data loss," "human error," "regular backup," and "2023-05-01."

[0899] Step 2:

[0900] The server starts the emotion engine and performs facial recognition and voice analysis of the user (operator) while he / she is entering data. The emotion engine (emotion_engine module) is used to analyze the user's emotional state as "stress" or "anxiety," etc. At this time, emotion data is generated by analyzing facial expressions and tone of voice. For example, it is detected that the operator is feeling "stressed" while entering data.

[0901] Step 3:

[0902] The server receives and analyzes the entered accident information and emotion data. Specifically, it checks the consistency and completeness of the data, for example, whether the date of occurrence is in the future or whether the entered information is missing. As a result of this step, data with confirmed consistency is generated. For example, consistent data such as "company name," "data loss," "human error," "regular backup," "2023-05-01," and "stress" is output.

[0903] Step 4:

[0904] The server uses the database_manager module to store the analyzed accident information and emotion data in a database. When storing the data, fields such as the type of accident, cause, preventative measures, date of occurrence, and user emotion information are recorded in the database. For example, records such as "Company Name," "Data Loss," "Human Error," "Regular Backup," "2023-05-01," and "Stress" are added to the database.

[0905] Step 5:

[0906] When a user searches for accident information, the server accepts the user's search query. For example, the user enters the query "human error." This search query is sent to the server.

[0907] Step 6:

[0908] The server restarts the emotion engine and analyzes the user's emotional state during the search. The emotion engine determines that the user is feeling "anxiety." The analysis results are generated as emotion data.

[0909] Step 7:

[0910] The server searches the database and uses artificial intelligence (AI) to identify relevant accident information. For example, it extracts data that matches the search query "human error" for accident information. At this step, accident information that matches the search criteria is output.

[0911] Step 8:

[0912] When providing search results to the user, the server adjusts the feedback based on the user's emotional state analyzed by the emotion engine. If the user is feeling anxious, the server adjusts the feedback to provide a sense of security, such as by highlighting information about measures to prevent recurrence. For example, the server highlights the part of the search results that describes measures to prevent recurrence for "human error" and presents it to the user.

[0913] Step 9:

[0914] The server records the provided feedback and user emotional data to help with future improvements. This information is available to administrators via a dashboard, where it can be used to understand overall emotional trends and consider countermeasures. For example, if many operators are feeling stressed during a specific period, this information can be visualized and appropriate countermeasures can be considered.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] [Third embodiment]

[0919] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0920] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0921] 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).

[0922] 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.

[0923] 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.

[0924] 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).

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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."

[0931] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[0932] The accident information management system of the present invention is for unifying the management of accident information in a company and formulating measures to prevent recurrence. This system has the following main functions.

[0933] 1. Means for inputting accident information

[0934] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for the name of the company where the accident occurred, the type of accident, the cause, measures to prevent recurrence, the date of the accident, etc. When a user enters the necessary information in these fields and clicks the submit button, the information is sent to the server.

[0935] 2. Receiving and analyzing accident information

[0936] The server receives the accident information sent by the user. The received data is analyzed on the server to check the accuracy and completeness of the data. For example, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[0937] 3. Saving to the database

[0938] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[0939] 4. Search methods for accident information

[0940] When a user searches for past accident information, they submit a search query to the server, which searches the database to extract accident information that matches the criteria. This search also includes using artificial intelligence to identify relevant accident information.

[0941] 5. Providing search results

[0942] The server organizes the search results and provides them to the user. By reviewing them, users can refer to measures to prevent similar accidents from recurring and take measures to prevent future accidents. In addition, because accident information from multiple companies is integrated into a single database, cross-sectional data analysis is possible, making it possible to formulate more effective measures to prevent recurrence.

[0943] Specific examples

[0944] 1. Enter and save accident information

[0945] Consider the example where a user enters "A data loss incident occurred at CompanyA." The user enters the following information into the form:

[0946] Company Name: "CompanyA"

[0947] Incident Type: "Data Loss"

[0948] Cause: "human error"

[0949] Measures to prevent recurrence: "Perform regular backups"

[0950] Date of occurrence: "May 1, 2023"

[0951] After entering the information, the user clicks the submit button, the server receives it, checks the accuracy of the data, and then saves it in the database.

[0952] 2. Searching for and providing accident information

[0953] Consider an example where a user searches for information on past accidents caused by "human error" when implementing a new system. The user enters "human error" in the search field and sends a search query to the server. The server searches the database and extracts relevant information, such as "data loss accidents that occurred at Company A." The server then organizes this information and provides it to the user.

[0954] This allows users to strengthen measures to prevent recurrence based on past accident information and reduce risks when introducing new systems.

[0955] The processing flow will be explained below.

[0956] Understood. Below is a step-by-step explanation of the process.

[0957] Step 1:

[0958] The user accesses the accident information input form, which contains fields such as "company name," "accident type," "cause," "measures to prevent recurrence," and "occurrence date."

[0959] Step 2:

[0960] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[0961] Step 3:

[0962] The user clicks the send button. This operation sends the entered accident information to the server.

[0963] Step 4:

[0964] The server receives the accident information sent by the user. The received data is sent to the server in JSON format or POST data.

[0965] Step 5:

[0966] The server analyzes the received data and verifies that all required fields are present. For example, it checks that "Company Name," "Type of Accident," "Cause," "Preventive Measures for Recurrence," and "Date of Occurrence" are all present.

[0967] Step 6:

[0968] The server connects to the database and saves the analyzed accident information to the database. When saving, it checks for data consistency and the absence of duplication.

[0969] Step 7:

[0970] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[0971] Step 8:

[0972] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[0973] Step 9:

[0974] A user enters a search term into a search field. For example, they enter "human error."

[0975] Step 10:

[0976] The user clicks the search button, which sends the search keywords to the server.

[0977] Step 11:

[0978] The server receives a search request sent by a user, which includes search keywords and conditions.

[0979] Step 12:

[0980] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[0981] Step 13:

[0982] The server searches the database and retrieves accident information that matches the criteria. For example, it extracts information on past accidents caused by "human error."

[0983] Step 14:

[0984] The server organizes the search results and presents them in a format that is easy for users to view. For example, the server organizes the search results in a list format, displaying the company name, type of accident, cause, measures to prevent recurrence, and date of occurrence for each accident.

[0985] Step 15:

[0986] The server sends the organized search results to the user.

[0987] Step 16:

[0988] The terminal receives the search results sent from the server and displays them.

[0989] Step 17:

[0990] The user checks the search results on the device. For example, a list of information about a "data loss incident" caused by "human error" at "Company A" is displayed.

[0991] By following the above steps, the user can input, save, and search for accident information, and refer to measures to prevent recurrence.

[0992] Example 1

[0993] 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."

[0994] Accident information management in companies is often done individually, making it difficult to centralize information management and share preventative measures. Furthermore, there is a lack of tools to efficiently search for past accident information and formulate appropriate preventative measures. This increases the risk of similar accidents occurring again.

[0995] 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.

[0996] In this invention, the server includes a means for inputting information, a means for receiving and analyzing the input information, and a means for storing the analyzed information in a database, which enables centralized management of information, efficient search, and the formulation of appropriate measures to prevent recurrence.

[0997] "Means for inputting information" refers to the interface within the system through which users can input necessary data such as accident information, and specifically refers to forms on a web browser or mobile applications.

[0998] "Means for receiving and analyzing the input information" refers to a system component that receives data sent by a user on the server side, checks the accuracy and completeness of the data, and performs error handling if necessary.

[0999] "Means for storing the analyzed information in a database" refers to a system component that inserts data that has been verified for accuracy and completeness into a database, where it is systematically recorded for future query and analysis.

[1000] The term "means for retrieving information from the database" refers to a system component that has the function of quickly extracting relevant information from the database in response to a search query from a user.

[1001] "Means for providing the searched information" refers to a system component that has the function of formatting search results and returning them to the terminal in a format that is easily understood and used by the user.

[1002] "Means for identifying relevant information using artificial intelligence" refers to a system component that uses AI technologies such as machine learning and natural language processing to analyze information in the database and identify and extract highly relevant accident information.

[1003] "Type" indicates the category or class of accident information, and specifically includes classifications such as "fire," "data loss," and "machine failure."

[1004] "Cause" refers to the reason or factor behind the occurrence of an accident, and includes, for example, "human error" or "machine failure."

[1005] "Countermeasures" refer to specific actions and measures to prevent the recurrence of accidents, and include "conducting regular inspections" and "introducing backup systems."

[1006] "Date" indicates the specific time when the accident occurred, and refers to information including the year, month, and day.

[1007] The accident information management system of the present invention aims to centrally manage accident information within a company and to formulate measures to prevent recurrence. The main components of this system include a means for inputting information, a means for receiving and analyzing the information, a means for storing the information in a database, a means for searching the database, and a means for providing the search results to the user.

[1008] Program processing

[1009] The program of this system goes through multiple steps to input, analyze, save, search, and provide accident information. The specific process flow is explained below.

[1010] Entering accident information

[1011] The terminal provides an interface for the user to enter incident information. This is implemented as a form that runs in a web browser and contains the following fields:

[1012] Company Name

[1013] Accident type

[1014] cause

[1015] Measures to prevent recurrence

[1016] Date of occurrence

[1017] For example, if a user wants to enter "a data loss incident that occurred at a certain company," they would enter the following:

[1018] Company Name: "A company"

[1019] Incident Type: "Data Loss"

[1020] Cause: "human error"

[1021] Measures to prevent recurrence: "Perform regular backups"

[1022] Date of occurrence: "May 1, 2023"

[1023] Sending accident information

[1024] When the user enters information in all fields and clicks the submit button, the device sends the entered accident information to the server using an HTTP POST request. After sending, the device displays a confirmation message.

[1025] Receiving and analyzing accident information

[1026] The server receives the incident information sent from the device as an HTTP request. The received data is first subjected to a security check by the firewall and web application firewall (WAF). If no abnormalities are found, the server analyzes the data in the next step.

[1027] Hardware and software used

[1028] The hardware used in this system includes:

[1029] Server Computer

[1030] Client terminal (PC, smartphone)

[1031] The software used includes:

[1032] Web Forms (HTML, CSS, JavaScript)

[1033] Server-side programs (Python, Java, Node.js, etc.)

[1034] Database systems (MySQL, PostgreSQL)

[1035] Specific examples

[1036] A specific example of the system's operation is shown below.

[1037] If a user wants to enter "a fire incident at a company", they would enter the following into the form:

[1038] Company Name: "A company"

[1039] Incident type: "Fire"

[1040] Cause: "Electrical failure"

[1041] Measures to prevent recurrence: "Regular equipment inspections"

[1042] Date of occurrence: "April 15, 2023"

[1043] When the user clicks the submit button, the server receives it and checks the accuracy of the data, after which it stores the information in a database.

[1044] Example prompts for generative AI models

[1045] Here are some example prompts to input to the generative AI model:

[1046] Please explain in detail the information that users enter into the web form. The field names to be entered into the form are as follows: company name, type of incident, cause, preventative measures, and date of occurrence. For example, please give a specific example of entering information about a data loss incident that occurred at a certain company.

[1047] This allows the generative AI model to generate specific accident information input procedures for the user.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1: Enter your information

[1050] The terminal provides the user with an interface for entering accident information, specifically a web form with the following fields:

[1051] Company Name

[1052] Accident type

[1053] cause

[1054] Measures to prevent recurrence

[1055] Date of occurrence

[1056] Input: The user enters the required information for each field.

[1057] Output: Data entered into the terminal.

[1058] Step 2: Submit your information

[1059] When the user enters information into all fields and clicks the submit button, the device sends the entered data to the server using an HTTP POST request, with basic validation performed on the data (e.g., whether required fields are filled in).

[1060] Input: Data entered after the user clicks the submit button.

[1061] Output: The HTTP POST request sent to the server.

[1062] Step 3: Receiving information

[1063] The server receives the HTTP POST request sent from the terminal. The received data is first subjected to a security check through the firewall and web application firewall (WAF). If an abnormality is detected in this step, the server generates an error message and returns it to the terminal.

[1064] Input: The HTTP POST request sent from the terminal.

[1065] Output: Security checked data.

[1066] Step 4: Analyze the information

[1067] The server analyzes the received data, specifically checking the following:

[1068] The date of the accident is in the future

[1069] Are the company name and accident type entered in a valid format?

[1070] Are the recurrence prevention measures specific and appropriate?

[1071] Input: Security checked data.

[1072] Output: The validated data. If there are any errors, an error message is generated.

[1073] Step 5: Saving to the Database

[1074] The server stores the data that has passed the analysis in a database, such as MySQL or PostgreSQL. The data is inserted into the appropriate table and assigned a unique identifier (ID).

[1075] Input: Validated data.

[1076] Output: A confirmation message of the data saved in the database.

[1077] Step 6: Finding information

[1078] When a user searches for information about past accidents, the device sends a search query to the server based on the information entered in the search field. For example, if a user searches for information about accidents caused by "human error," the device generates the query "Cause of accident: human error" and sends it as an HTTP GET request.

[1079] Input: The search query that the user entered into the device.

[1080] Output: The HTTP GET request sent to the server.

[1081] Step 7: Serving search results

[1082] The server searches the database based on the received search query. The search process uses SQL queries to extract relevant data. The server organizes this data and returns it to the device in a format such as JSON. The device then displays the data in a user-friendly format.

[1083] Input: The search query sent as an HTTP GET request.

[1084] Output: JSON formatted data containing the search results.

[1085] The above processing steps realize a series of steps from inputting accident information to saving, searching, and providing the information.

[1086] (Application example 1)

[1087] 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."

[1088] In conventional accident information management systems, the input, analysis, storage, and retrieval of accident information are performed separately, resulting in a lack of overall efficiency. Furthermore, the provision of preventive measures based on past accident information is performed manually, making it difficult to respond quickly and appropriately. The present invention aims to solve these problems by providing an efficient system that centrally manages accident information and automatically proposes preventive measures.

[1089] 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.

[1090] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, and means for proposing preventive measures based on the searched accident information, thereby enabling unified management of accident information and rapid proposal of measures to prevent recurrence.

[1091] "Accident information" refers to all data related to an accident, such as the location, type, cause, measures to prevent recurrence, and date of the accident.

[1092] "Input means" refers to the interface through which users input accident information into the system, such as a web form or a smartphone app.

[1093] "Analysis methods" refer to the process of checking the accuracy and completeness of the entered accident information and ensuring the quality of the data.

[1094] "Database" means a data storage system designed to systematically store, search, and retrieve accident information.

[1095] "Search methods" refer to the processes and technologies used to extract data that match specific conditions from stored accident information.

[1096] "Means of provision" refers to the process or function for providing the accident information obtained as a search result to users visually or in other ways.

[1097] "Preventive measures suggestion means" refers to a function that suggests specific measures to prevent similar accidents in the future based on past accident information and analysis results.

[1098] "Artificial intelligence" refers to intellectual tasks such as learning, reasoning, recognition, and adaptation performed by computers.

[1099] In this invention, a specific embodiment necessary for constructing a system for managing accident information and proposing measures to prevent recurrence will be described. This system is composed of the following main components.

[1100] 1. How to input accident information

[1101] The device (e.g., a smartphone or tablet) provides an interface for the user to enter accident information. This interface typically takes the form of a mail form or a mobile application. The user enters information such as the location, type, cause, preventative measures, and date of the accident.

[1102] 2. Methods for analyzing accident information

[1103] The server receives and analyzes the accident information sent from the device. During this process, the accuracy and completeness of the data are checked. For example, it automatically verifies whether the entered date is in the future and whether specific measures to prevent recurrence are described.

[1104] 3. How to store data in a database

[1105] The server stores the analyzed accident information in a database such as SQLite, where the accident information is systematically organized and recorded, enabling quick response for subsequent searches and analysis.

[1106] 4. Search methods for accident information

[1107] When a user searches for specific accident information, they use their device to send a query to the server, which then searches the database to extract accident information that matches the criteria. The search process uses artificial intelligence (AI) to identify relevant data.

[1108] 5. Means of providing search results

[1109] The server organizes the search results and provides them to the device. By checking these, users can take appropriate measures to prevent recurrence based on past accident information.

[1110] 6. Suggested preventive measures

[1111] The server then proposes preventive measures based on the search results. These proposals are derived from information about past accidents and measures to prevent their recurrence. Users can refer to these suggestions and take concrete measures to prevent future accidents.

[1112] Hardware and software used:

[1113] Hardware: smartphones, tablets, servers

[1114] Software: SQLite (database), artificial intelligence model (AI), web or mobile application

[1115] Examples:

[1116] For example, consider a scenario where a user inputs "an accident that occurred during machine maintenance work" and records "thorough periodic inspections" as a recurrence prevention measure. A few months later, another user searches for "machine maintenance accidents" and is presented with the past recurrence prevention measure of "thorough inspections."

[1117] Example prompt sentence:

[1118] "Please give us an example of an implementation where an accident information management system is used to search for past accident information and propose measures to prevent recurrence."

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] The terminal provides an interface for inputting accident information. The user inputs information such as the location, type, cause, preventative measures, and date of the accident. The input information is sent from the terminal to the server.

[1122] Input: Accident information (location, type, cause, recurrence prevention measures, date of occurrence)

[1123] Output: Sending accident information to the server

[1124] Step 2:

[1125] The server analyzes the accident information received and automatically checks the accuracy and completeness of the data. For example, it checks whether the date of occurrence is in the future and whether specific measures to prevent recurrence are described.

[1126] Input: Accident information submitted by the user

[1127] Output: Analyzed accident information

[1128] Step 3:

[1129] The server stores the analyzed accident information in an SQLite database, where the information is systematically organized and stored in a manner that allows for quick retrieval when needed.

[1130] Input: Analyzed accident information

[1131] Output: Accident information stored in the database

[1132] Step 4:

[1133] A user uses a terminal to send a query to search for specific accident information, and the server receives the query and searches the database.

[1134] Input: User search query

[1135] Output: Search query sent to server

[1136] Step 5:

[1137] The server searches the database to extract accident information that matches the criteria, using artificial intelligence (AI) to identify highly relevant data.

[1138] Input: User's search query

[1139] Output: A list of accidents that match the criteria

[1140] Step 6:

[1141] The server organizes the search results and provides them to the user, who then checks the information provided and considers appropriate measures to prevent recurrence.

[1142] Input: Extracted accident information

[1143] Output: Organized search results served to the user

[1144] Step 7:

[1145] The server proposes preventive measures based on past accident data. These proposals are generated based on accident information and measures to prevent recurrence. The user then takes specific measures based on the proposed preventive measures.

[1146] Input: Accident information based on search results

[1147] Output: Preventive measures suggested

[1148] 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.

[1149] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[1150] The present invention combines a system for unifying accident information management and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system has the following main functions:

[1151] 1. Means for inputting accident information

[1152] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for company name, type of accident, cause, preventative measures, date of occurrence, etc. When a user enters information into each field and clicks the submit button, the information is sent to the server.

[1153] 2. Receiving and analyzing accident information

[1154] The server receives and analyzes the accident information sent by the user. The received data is analyzed appropriately on the server to check the accuracy and completeness of the information. Specifically, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[1155] 3. Saving to the database

[1156] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[1157] 4. Search methods for accident information

[1158] When a user searches for accident information, they send a search query to the server, which searches the database and extracts accident information that matches the search criteria. At the same time, the server also uses artificial intelligence to identify the most relevant accident information.

[1159] 5. Providing search results

[1160] The server organizes the search results and provides them to the user, who can then review them and take measures to prevent recurrence.

[1161] 6. Introducing the Emotion Engine

[1162] The emotion engine is responsible for analyzing the emotions expressed when users input accident information and when searching. The input emotion information is associated with the accident information and stored in the database. This information enables a more detailed analysis of the cause of the accident. The emotion engine also takes the user's emotions into account when providing search results, providing appropriate feedback.

[1163] Specific explanation of program processing

[1164] 1. Enter and save accident information

[1165] When a user enters accident information into a form and submits information such as "Company A," "data loss," "human error," "regular backup," and "2023-05-01," the data is sent to the server. The server receives the data, checks its integrity, and stores it in a database. At this time, the user's emotions are also detected by the emotion engine and stored along with the data. For example, if the user is feeling stressed while entering information, the emotional data is also saved as "stress."

[1166] 2. Searching for and providing accident information

[1167] When a user searches for information about an accident, they enter a search keyword, such as "human error." The server receives this and searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching and provides appropriate feedback on the search results. For example, if the user is feeling anxious, the system can take action such as highlighting relevant measures to prevent recurrence.

[1168] 3. Visualizing Emotion Data

[1169] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company. For example, if employee stress levels are increasing over a certain period of time, the company can take immediate action based on that information.

[1170] Specific examples

[1171] 1. Entering accident information and saving emotion logs

[1172] When a user enters the accident information for "Data Loss at Company A," the emotion engine recognizes the user's emotion as "stress" through facial recognition and voice analysis. The entered accident information and the emotional data of stress are stored together in the database.

[1173] 2. Accident information search and emotional feedback

[1174] When a user searches for information about accidents related to "human error," the emotion engine recognizes the user's emotions and determines that the user is feeling anxious. Based on this information, the server highlights information about measures to prevent recurrence and provides feedback that reassures the user.

[1175] 3. Visualizing sentiment trends for managers

[1176] Managers can check employee emotional trends through the dashboard. For example, it can visualize that many employees are feeling anxious or stressed during a particular project, and managers can use this information to consider appropriate countermeasures.

[1177] As described above, the system of the present invention not only makes the management and search of accident information more efficient, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[1178] The processing flow will be explained below.

[1179] Understood. Below I will explain the process in concrete steps.

[1180] Step 1:

[1181] The user accesses an accident information input form, which contains fields such as company name, accident type, cause, preventative measures, and date of occurrence.

[1182] Step 2:

[1183] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[1184] Step 3:

[1185] The user clicks the send button. This operation sends the entered accident information to the server.

[1186] Step 4:

[1187] The emotion engine recognizes the user's emotions in real time through facial and voice recognition, specifically identifying stress or anxiety the user is feeling while typing.

[1188] Step 5:

[1189] The server receives the accident information and emotion data sent by the user. The received data is sent to the server in JSON format or POST data.

[1190] Step 6:

[1191] The server analyzes the received data and verifies that all required fields are present. For example, it checks that the "company name," "accident type," "cause," "measures to prevent recurrence," "date of occurrence," and "user emotion data" are all present.

[1192] Step 7:

[1193] The server connects to the database and saves the analyzed accident information and emotion data in the database. When saving, it checks for data consistency and duplication.

[1194] Step 8:

[1195] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[1196] Step 9:

[1197] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[1198] Step 10:

[1199] A user enters a search term into a search field. For example, they enter "human error."

[1200] Step 11:

[1201] The user clicks the search button, which sends the search keywords to the server.

[1202] Step 12:

[1203] The server receives a search request sent by a user, which includes search keywords and conditions.

[1204] Step 13:

[1205] The emotion engine recognizes users' emotions in real time through facial recognition and voice analysis, identifying stress or anxiety users are feeling while searching.

[1206] Step 14:

[1207] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[1208] Step 15:

[1209] The server searches the database to retrieve accident information and related emotion data that match the criteria. As a specific example, it extracts information about accidents caused by "human error" in the past and the emotion data at the time of reporting the accident.

[1210] Step 16:

[1211] The server organizes the search results and presents them in a format that is easy for users to view. For example, the search results are organized in a list format, and the "company name," "type of accident," "cause," "measures to prevent recurrence," "date of occurrence," and "emotions at the time of reporting" for each accident are displayed.

[1212] Step 17:

[1213] The server uses an emotion engine to provide feedback that takes into account the user's emotions during the search. For example, if the user is feeling anxious, information about measures to prevent recurrence will be highlighted.

[1214] Step 18:

[1215] The server sends the organized search results and feedback to the user.

[1216] Step 19:

[1217] The terminal receives the search results sent from the server and displays them.

[1218] Step 20:

[1219] The user checks the search results on the device. As a specific example, information about a "data loss incident" caused by "human error" that occurred at "Company A" and the emotional data at the time of reporting it are displayed in a list.

[1220] Step 21:

[1221] The server receives the data from the sentiment engine and visualizes it on a dashboard for managers, which can be used to understand sentiment trends across the enterprise.

[1222] Step 22:

[1223] Managers can view employee sentiment trends on a dashboard, visualizing, for example, rising employee stress levels over a specific period, allowing managers to take appropriate action based on that information.

[1224] Through these steps, users can input, save, and search accident information, and log and utilize related emotion data.

[1225] Example 2

[1226] 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."

[1227] While conventional accident information management systems improve the efficiency of accident information management and search, they do not take user emotions into consideration, resulting in issues with the user experience. Furthermore, adding emotional information to accident information would enable the formulation of more precise measures to prevent recurrence and the understanding of emotional trends across the company, but this aspect was lacking.

[1228] 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.

[1229] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for saving the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means including an emotion engine for analyzing the user's emotions, means for adding the analyzed emotion information to data and saving it, and means for visualizing the emotion data on a dashboard for an administrator.

[1230] This not only makes the management and search of accident information more efficient, but also makes it possible to take user emotions into account, allowing for the development of more detailed measures to prevent recurrence and the understanding of emotional trends across the entire company.

[1231] "Accident information" is detailed information about an accident entered by the user, and specifically includes the type of accident, cause, measures to prevent recurrence, and date of occurrence.

[1232] The "means for receiving and analyzing" refers to a function that allows the server to receive accident information sent by the user, properly analyze it, and verify the accuracy and completeness of the data.

[1233] The "means for storing in a database" refers to a function that accumulates analyzed accident information and stores it in a database so that it can be easily searched and used later.

[1234] The "search means" is a function that allows a user to search for accident information from a database based on specific conditions.

[1235] The "means of providing" refers to a function that organizes search results and provides them to the user immediately.

[1236] The "emotion engine" has the function of analyzing the user's emotions and adding emotional information to the accident information based on the analysis results.

[1237] The "means for adding analyzed emotional information to data and saving it" has the function of adding the user's emotions analyzed by the emotion engine to the accident information and saving it in a database.

[1238] The "Administrator Dashboard" is an interface that visualizes the emotional data collected by the emotion engine, allowing managers to grasp emotional trends across the entire company.

[1239] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is configured to efficiently perform a series of processes, from inputting accident information to storing it in a database, searching, providing the results to the user, and analyzing the user's emotions and providing feedback.

[1240] Hardware and software used

[1241] The system mainly uses the following hardware and software:

[1242] Server: The central component that receives, analyzes, stores, searches, and serves data. It works in conjunction with the database and emotion engine.

[1243] Terminal: A device on which users enter accident information and view search results. A web browser is typically used.

[1244] Emotion Engine: A software component that analyzes the user's emotions through facial recognition and voice analysis.

[1245] System configuration and specific operation

[1246] Enter and submit accident information

[1247] The user accesses a form for entering accident information via a web browser on the device. This form contains fields for the company name, type of accident, cause, preventive measures, date of occurrence, etc. When the user enters this information and clicks the submit button, the input data is sent to the server.

[1248] Data reception and integrity check

[1249] The server receives the accident information. After receiving it, the server checks the integrity of the data. Specifically, it checks to make sure that the accident date is not in the future and that specific measures to prevent recurrence are included.

[1250] Sentiment analysis and data enrichment

[1251] The server sends the received accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the emotion the user is feeling while typing. For example, if the user is feeling stressed, the engine adds that information to the data as "stress."

[1252] Saving to a database

[1253] The analyzed accident information and emotion data are stored in a database by the server, allowing for later retrieval and analysis.

[1254] Searching for and providing accident information

[1255] When a user searches for information about an accident, they enter a keyword, such as "human error." The device sends this search query to the server, which then searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching, and if the user feels anxious, it takes action such as highlighting information about measures to prevent recurrence.

[1256] Admin Dashboard Visualization

[1257] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company, allowing managers to visually see employee stress levels over a specific period of time.

[1258] Examples of concrete examples and prompts

[1259] Specific examples

[1260] A user inputs the incident information, "Data loss at Company A." The emotion engine detects "stress" from the user's facial expression, and this emotion data is stored in the database along with the incident information.

[1261] Users search for "human error," the server extracts relevant accident information, and the emotion engine detects the user's anxiety and highlights information on measures to prevent recurrence.

[1262] Managers can visually see through a dashboard that many employees are feeling anxious or stressed during a particular project.

[1263] Prompt Sentence Examples

[1264] "Data loss at Company A"

[1265] "Human error"

[1266] The above is a specific embodiment for carrying out the present invention. This system not only improves the efficiency of accident information management and search, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[1267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1268] Step 1:

[1269] Entering accident information

[1270] The user accesses the web form on the device and enters accident information such as the company name, type of accident, cause, preventative measures, date of occurrence, etc. The entered data is sent from the device to the server when the send button is clicked.

[1271] Input: Accident information entered by the user (e.g., "CompanyA," "Data loss," "Human error," "Regular backup," "2023-05-01")

[1272] Output: The entered accident information is sent to the server.

[1273] Step 2:

[1274] Receiving and integrity checking of transmitted data

[1275] The server receives the accident information sent by the user and checks the consistency of the data, for example, checking whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[1276] Input: Accident information sent from the device

[1277] Data processing / data calculation: The server analyzes the data received and performs consistency checks

[1278] Output: Accident information with consistency confirmed

[1279] Step 3:

[1280] Sentiment analysis and data enrichment

[1281] The server then sends the confirmed accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the user's emotions. For example, if the user is feeling stressed, the emotion "stress" is added to the data.

[1282] Input: Accident information with confirmed consistency

[1283] Data processing / data calculation: Emotion analysis using an emotion engine and adding that emotion information to accident information

[1284] Output: Accident information with emotional information added

[1285] Step 4:

[1286] Saving to a database

[1287] The server stores the accident information with the added emotional information in a database, which allows for later retrieval and analysis.

[1288] Input: Accident information with emotional information added

[1289] Data processing / data calculation: Data storage process in database

[1290] Output: Accident information stored in the database

[1291] Step 5:

[1292] Accident information search

[1293] When a user searches for specific accident information, for example, they input "human error" as a search keyword, and a search query is sent from the terminal to the server.

[1294] Input: A search query entered by a user into a device (e.g., "human error")

[1295] Output: Search query sent from device to server

[1296] Step 6:

[1297] Providing search results and emotional feedback

[1298] The server searches the database based on the received search query and extracts accident information that matches the criteria. Furthermore, the emotion engine analyzes the user's emotions in real time while searching, and provides feedback such as highlighting information on measures to prevent recurrence if the user is feeling anxious, for example.

[1299] Input: The search query sent to the server

[1300] Data processing / data calculation: database search and user emotion analysis using emotion engine

[1301] Output: Search results with highlighting

[1302] Step 7:

[1303] Visualizing Emotion Data

[1304] The server receives data from the sentiment engine and visualizes it on a dashboard for managers, allowing them to understand sentiment trends across the company and take action if necessary.

[1305] Input: Data from the emotion engine

[1306] Data processing / data calculation: Data visualization processing for dashboards

[1307] Output: Sentiment trend data displayed on an admin dashboard

[1308] (Application example 2)

[1309] 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."

[1310] In modern factories, it is important to efficiently manage accident information and implement measures to prevent recurrence, but conventional systems were unable to take into account the user's emotional information, making it difficult to provide appropriate feedback. Furthermore, the lack of feedback based on emotional state led to problems such as insufficient efforts to improve operators' safety awareness and work efficiency. As a result, issues arose, such as the complicated management of accident information and inappropriate implementation of measures to prevent recurrence.

[1311] 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.

[1312] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means for analyzing a human emotional state when the accident information is input and searched, and means for providing feedback based on the analyzed emotional state. This not only makes the management of accident information more efficient, but also makes it possible to take more appropriate measures to prevent recurrence by taking the user's emotions into consideration.

[1313] "Accident information" refers to detailed information such as the type of accident, cause, measures to prevent recurrence, date of occurrence, and the user's emotional state at the time of inputting the information.

[1314] An "emotion engine" refers to a system that analyzes a user's emotional state (e.g., stress, anxiety, relief, etc.) in real time through facial recognition and voice analysis.

[1315] A "database" refers to a collection of information that allows for the efficient management, search, and extraction of accumulated accident information and related data.

[1316] "Search means" refers to a system that has the function of searching for accident information in a database and extracting relevant information based on conditions specified by the user.

[1317] "Feedback" refers to the act or system of providing appropriate advice or measures tailored to the user's emotional state based on search results and information at the time of input.

[1318] "Means for analysis" refers to a system that has the function of checking the consistency and completeness of input data and appropriately analyzing the entire data, including emotional information.

[1319] "Means for inputting accident information" refers to devices or software that provide an interface for users to input accident information (type of accident, cause, measures to prevent recurrence, date of occurrence, etc.).

[1320] A "factory robot" is an automated mechanical device designed to perform work within a factory, and includes systems that can collect accident information and perform emotion analysis, etc.

[1321] "Artificial intelligence" refers to advanced algorithms and systems that analyze large amounts of data, discover patterns, and make highly accurate predictions and classifications.

[1322] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is installed on factory robots and has the following main functions:

[1323] 1. Enter accident information

[1324] The server provides an interface for inputting accident information. This interface is designed to allow operators to input accident information through the factory robot's touchscreen or voice input function. For example, the operator inputs the "company name," "type of accident," "cause," "measures to prevent recurrence," and "date of occurrence."

[1325] 2. Analysis by emotion engine

[1326] When entering accident information, the emotion engine analyzes the operator's emotions in real time through facial recognition and voice analysis. For example, if the operator is feeling stressed, that emotional data is also recorded. This function uses the emotion_engine module.

[1327] 3. Accident information and emotional data storage

[1328] The server analyzes the input accident information and emotion data, checks the data integrity, and then stores this information in a database using the database_manager module. The stored information includes the type of accident, cause, preventive measures, date of occurrence, and related emotion information.

[1329] 4. Search for accident information

[1330] The server also provides a database search function for accident information. When an operator enters a search query, the AI ​​extracts and provides relevant accident information. At this time, the emotion engine again analyzes the operator's emotional state and adjusts the feedback according to the search results.

[1331] 5. Providing Feedback

[1332] When providing search results, the server provides appropriate feedback based on the operator's emotional state analyzed by the emotion engine. For example, if the operator feels anxious, the server will adjust the search results by highlighting measures to prevent recurrence. This function makes it possible to provide the operator with a sense of security.

[1333] Specific examples

[1334] 1. Entering accident information and saving emotion logs

[1335] When an operator inputs the accident information of "data loss," the emotion engine analyzes the operator's emotions as "stress" through facial recognition and voice analysis, and the information is stored in the database along with the accident information.

[1336] 2. Accident information search and emotional feedback

[1337] When an operator searches for information about accidents related to "human error," the emotion engine analyzes the operator's emotion as "anxiety." Based on this information, the server highlights measures to prevent recurrence and provides search results that give the operator a sense of security.

[1338] Prompt Sentence Examples

[1339] Please enter the accident information. Please include the following information in your response: company name, accident type, cause, preventative measures, date of occurrence, and appropriate emotion labels (e.g., stress, anxiety).

[1340] The system of the present invention not only improves the efficiency of accident information management and search, but also provides feedback that takes into account the user's emotions, thereby providing a safer and more secure working environment.

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] The server accepts input of accident information. The user (operator) uses the factory robot's touchscreen or voice input to enter information such as the company name, type of accident, cause, preventative measures, and date of occurrence. This information is sent to the server. Input data includes, for example, "company name," "data loss," "human error," "regular backup," and "2023-05-01."

[1344] Step 2:

[1345] The server starts the emotion engine and performs facial recognition and voice analysis of the user (operator) while he / she is entering data. The emotion engine (emotion_engine module) is used to analyze the user's emotional state as "stress" or "anxiety," etc. At this time, emotion data is generated by analyzing facial expressions and tone of voice. For example, it is detected that the operator is feeling "stressed" while entering data.

[1346] Step 3:

[1347] The server receives and analyzes the entered accident information and emotion data. Specifically, it checks the consistency and completeness of the data, for example, whether the date of occurrence is in the future or whether the entered information is missing. As a result of this step, data with confirmed consistency is generated. For example, consistent data such as "company name," "data loss," "human error," "regular backup," "2023-05-01," and "stress" is output.

[1348] Step 4:

[1349] The server uses the database_manager module to store the analyzed accident information and emotion data in a database. When storing the data, fields such as the type of accident, cause, preventative measures, date of occurrence, and user emotion information are recorded in the database. For example, records such as "Company Name," "Data Loss," "Human Error," "Regular Backup," "2023-05-01," and "Stress" are added to the database.

[1350] Step 5:

[1351] When a user searches for accident information, the server accepts the user's search query. For example, the user enters the query "human error." This search query is sent to the server.

[1352] Step 6:

[1353] The server restarts the emotion engine and analyzes the user's emotional state during the search. The emotion engine determines that the user is feeling "anxiety." The analysis results are generated as emotion data.

[1354] Step 7:

[1355] The server searches the database and uses artificial intelligence (AI) to identify relevant accident information. For example, it extracts data that matches the search query "human error" for accident information. At this step, accident information that matches the search criteria is output.

[1356] Step 8:

[1357] When providing search results to the user, the server adjusts the feedback based on the user's emotional state analyzed by the emotion engine. If the user is feeling anxious, the server adjusts the feedback to provide a sense of security, such as by highlighting information about measures to prevent recurrence. For example, the server highlights the part of the search results that describes measures to prevent recurrence for "human error" and presents it to the user.

[1358] Step 9:

[1359] The server records the provided feedback and user emotional data to help with future improvements. This information is available to administrators via a dashboard, where it can be used to understand overall emotional trends and consider countermeasures. For example, if many operators are feeling stressed during a specific period, this information can be visualized and appropriate countermeasures can be considered.

[1360] 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.

[1361] 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.

[1362] 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.

[1363] [Fourth embodiment]

[1364] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1365] 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.

[1366] 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).

[1367] 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.

[1368] 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.

[1369] 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).

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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."

[1377] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[1378] The accident information management system of the present invention is for unifying the management of accident information in a company and formulating measures to prevent recurrence. This system has the following main functions.

[1379] 1. Means for inputting accident information

[1380] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for the name of the company where the accident occurred, the type of accident, the cause, measures to prevent recurrence, the date of the accident, etc. When a user enters the necessary information in these fields and clicks the submit button, the information is sent to the server.

[1381] 2. Receiving and analyzing accident information

[1382] The server receives the accident information sent by the user. The received data is analyzed on the server to check the accuracy and completeness of the data. For example, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[1383] 3. Saving to the database

[1384] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[1385] 4. Search methods for accident information

[1386] When a user searches for past accident information, they submit a search query to the server, which searches the database to extract accident information that matches the criteria. This search also includes using artificial intelligence to identify relevant accident information.

[1387] 5. Providing search results

[1388] The server organizes the search results and provides them to the user. By reviewing them, users can refer to measures to prevent similar accidents from recurring and take measures to prevent future accidents. In addition, because accident information from multiple companies is integrated into a single database, cross-sectional data analysis is possible, making it possible to formulate more effective measures to prevent recurrence.

[1389] Specific examples

[1390] 1. Enter and save accident information

[1391] Consider the example where a user enters "A data loss incident occurred at CompanyA." The user enters the following information into the form:

[1392] Company Name: "CompanyA"

[1393] Incident Type: "Data Loss"

[1394] Cause: "human error"

[1395] Measures to prevent recurrence: "Perform regular backups"

[1396] Date of occurrence: "May 1, 2023"

[1397] After entering the information, the user clicks the submit button, the server receives it, checks the accuracy of the data, and then saves it in the database.

[1398] 2. Searching for and providing accident information

[1399] Consider an example where a user searches for information on past accidents caused by "human error" when implementing a new system. The user enters "human error" in the search field and sends a search query to the server. The server searches the database and extracts relevant information, such as "data loss accidents that occurred at Company A." The server then organizes this information and provides it to the user.

[1400] This allows users to strengthen measures to prevent recurrence based on past accident information and reduce risks when introducing new systems.

[1401] The processing flow will be explained below.

[1402] Understood. Below is a step-by-step explanation of the process.

[1403] Step 1:

[1404] The user accesses the accident information input form, which contains fields such as "company name," "accident type," "cause," "measures to prevent recurrence," and "occurrence date."

[1405] Step 2:

[1406] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[1407] Step 3:

[1408] The user clicks the send button. This operation sends the entered accident information to the server.

[1409] Step 4:

[1410] The server receives the accident information sent by the user. The received data is sent to the server in JSON format or POST data.

[1411] Step 5:

[1412] The server analyzes the received data and verifies that all required fields are present. For example, it checks that "Company Name," "Type of Accident," "Cause," "Preventive Measures for Recurrence," and "Date of Occurrence" are all present.

[1413] Step 6:

[1414] The server connects to the database and saves the analyzed accident information to the database. When saving, it checks for data consistency and the absence of duplication.

[1415] Step 7:

[1416] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[1417] Step 8:

[1418] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[1419] Step 9:

[1420] A user enters a search term into a search field. For example, they enter "human error."

[1421] Step 10:

[1422] The user clicks the search button, which sends the search keywords to the server.

[1423] Step 11:

[1424] The server receives a search request sent by a user, which includes search keywords and conditions.

[1425] Step 12:

[1426] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[1427] Step 13:

[1428] The server searches the database and retrieves accident information that matches the criteria. For example, it extracts information on past accidents caused by "human error."

[1429] Step 14:

[1430] The server organizes the search results and presents them in a format that is easy for users to view. For example, the server organizes the search results in a list format, displaying the company name, type of accident, cause, measures to prevent recurrence, and date of occurrence for each accident.

[1431] Step 15:

[1432] The server sends the organized search results to the user.

[1433] Step 16:

[1434] The terminal receives the search results sent from the server and displays them.

[1435] Step 17:

[1436] The user checks the search results on the device. For example, a list of information about a "data loss incident" caused by "human error" at "Company A" is displayed.

[1437] By following the above steps, the user can input, save, and search for accident information, and refer to measures to prevent recurrence.

[1438] Example 1

[1439] 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."

[1440] Accident information management in companies is often done individually, making it difficult to centralize information management and share preventative measures. Furthermore, there is a lack of tools to efficiently search for past accident information and formulate appropriate preventative measures. This increases the risk of similar accidents occurring again.

[1441] 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.

[1442] In this invention, the server includes a means for inputting information, a means for receiving and analyzing the input information, and a means for storing the analyzed information in a database, which enables centralized management of information, efficient search, and the formulation of appropriate measures to prevent recurrence.

[1443] "Means for inputting information" refers to the interface within the system through which users can input necessary data such as accident information, and specifically refers to forms on a web browser or mobile applications.

[1444] "Means for receiving and analyzing the input information" refers to a system component that receives data sent by a user on the server side, checks the accuracy and completeness of the data, and performs error handling if necessary.

[1445] "Means for storing the analyzed information in a database" refers to a system component that inserts data that has been verified for accuracy and completeness into a database, where it is systematically recorded for future query and analysis.

[1446] The term "means for retrieving information from the database" refers to a system component that has the function of quickly extracting relevant information from the database in response to a search query from a user.

[1447] "Means for providing the searched information" refers to a system component that has the function of formatting search results and returning them to the terminal in a format that is easily understood and used by the user.

[1448] "Means for identifying relevant information using artificial intelligence" refers to a system component that uses AI technologies such as machine learning and natural language processing to analyze information in the database and identify and extract highly relevant accident information.

[1449] "Type" indicates the category or class of accident information, and specifically includes classifications such as "fire," "data loss," and "machine failure."

[1450] "Cause" refers to the reason or factor behind the occurrence of an accident, and includes, for example, "human error" or "machine failure."

[1451] "Countermeasures" refer to specific actions and measures to prevent the recurrence of accidents, and include "conducting regular inspections" and "introducing backup systems."

[1452] "Date" indicates the specific time when the accident occurred, and refers to information including the year, month, and day.

[1453] The accident information management system of the present invention aims to centrally manage accident information within a company and to formulate measures to prevent recurrence. The main components of this system include a means for inputting information, a means for receiving and analyzing the information, a means for storing the information in a database, a means for searching the database, and a means for providing the search results to the user.

[1454] Program processing

[1455] The program of this system goes through multiple steps to input, analyze, save, search, and provide accident information. The specific process flow is explained below.

[1456] Entering accident information

[1457] The terminal provides an interface for the user to enter incident information. This is implemented as a form that runs in a web browser and contains the following fields:

[1458] Company Name

[1459] Accident type

[1460] cause

[1461] Measures to prevent recurrence

[1462] Date of occurrence

[1463] For example, if a user wants to enter "a data loss incident that occurred at a certain company," they would enter the following:

[1464] Company Name: "A company"

[1465] Incident Type: "Data Loss"

[1466] Cause: "human error"

[1467] Measures to prevent recurrence: "Perform regular backups"

[1468] Date of occurrence: "May 1, 2023"

[1469] Sending accident information

[1470] When the user enters information in all fields and clicks the submit button, the device sends the entered accident information to the server using an HTTP POST request. After sending, the device displays a confirmation message.

[1471] Receiving and analyzing accident information

[1472] The server receives the incident information sent from the device as an HTTP request. The received data is first subjected to a security check by the firewall and web application firewall (WAF). If no abnormalities are found, the server analyzes the data in the next step.

[1473] Hardware and software used

[1474] The hardware used in this system includes:

[1475] Server Computer

[1476] Client terminal (PC, smartphone)

[1477] The software used includes:

[1478] Web Forms (HTML, CSS, JavaScript)

[1479] Server-side programs (Python, Java, Node.js, etc.)

[1480] Database systems (MySQL, PostgreSQL)

[1481] Specific examples

[1482] A specific example of the system's operation is shown below.

[1483] If a user wants to enter "a fire incident at a company", they would enter the following into the form:

[1484] Company Name: "A company"

[1485] Incident type: "Fire"

[1486] Cause: "Electrical failure"

[1487] Measures to prevent recurrence: "Regular equipment inspections"

[1488] Date of occurrence: "April 15, 2023"

[1489] When the user clicks the submit button, the server receives it and checks the accuracy of the data, after which it stores the information in a database.

[1490] Example prompts for generative AI models

[1491] Here are some example prompts to input to the generative AI model:

[1492] Please explain in detail the information that users enter into the web form. The field names to be entered into the form are as follows: company name, type of incident, cause, preventative measures, and date of occurrence. For example, please give a specific example of entering information about a data loss incident that occurred at a certain company.

[1493] This allows the generative AI model to generate specific accident information input procedures for the user.

[1494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1495] Step 1: Enter your information

[1496] The terminal provides the user with an interface for entering accident information, specifically a web form with the following fields:

[1497] Company Name

[1498] Accident type

[1499] cause

[1500] Measures to prevent recurrence

[1501] Date of occurrence

[1502] Input: The user enters the required information for each field.

[1503] Output: Data entered into the terminal.

[1504] Step 2: Submit your information

[1505] When the user enters information into all fields and clicks the submit button, the device sends the entered data to the server using an HTTP POST request, with basic validation performed on the data (e.g., whether required fields are filled in).

[1506] Input: Data entered after the user clicks the submit button.

[1507] Output: The HTTP POST request sent to the server.

[1508] Step 3: Receiving information

[1509] The server receives the HTTP POST request sent from the terminal. The received data is first subjected to a security check through the firewall and web application firewall (WAF). If an abnormality is detected in this step, the server generates an error message and returns it to the terminal.

[1510] Input: The HTTP POST request sent from the terminal.

[1511] Output: Security checked data.

[1512] Step 4: Analyze the information

[1513] The server analyzes the received data, specifically checking the following:

[1514] The date of the accident is in the future

[1515] Are the company name and accident type entered in a valid format?

[1516] Are the recurrence prevention measures specific and appropriate?

[1517] Input: Security checked data.

[1518] Output: The validated data. If there are any errors, an error message is generated.

[1519] Step 5: Saving to the Database

[1520] The server stores the data that has passed the analysis in a database, such as MySQL or PostgreSQL. The data is inserted into the appropriate table and assigned a unique identifier (ID).

[1521] Input: Validated data.

[1522] Output: A confirmation message of the data saved in the database.

[1523] Step 6: Finding information

[1524] When a user searches for information about past accidents, the device sends a search query to the server based on the information entered in the search field. For example, if a user searches for information about accidents caused by "human error," the device generates the query "Cause of accident: human error" and sends it as an HTTP GET request.

[1525] Input: The search query that the user entered into the device.

[1526] Output: The HTTP GET request sent to the server.

[1527] Step 7: Serving search results

[1528] The server searches the database based on the received search query. The search process uses SQL queries to extract relevant data. The server organizes this data and returns it to the device in a format such as JSON. The device then displays the data in a user-friendly format.

[1529] Input: The search query sent as an HTTP GET request.

[1530] Output: JSON formatted data containing the search results.

[1531] The above processing steps realize a series of steps from inputting accident information to saving, searching, and providing the information.

[1532] (Application example 1)

[1533] 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."

[1534] In conventional accident information management systems, the input, analysis, storage, and retrieval of accident information are performed separately, resulting in a lack of overall efficiency. Furthermore, the provision of preventive measures based on past accident information is performed manually, making it difficult to respond quickly and appropriately. The present invention aims to solve these problems by providing an efficient system that centrally manages accident information and automatically proposes preventive measures.

[1535] 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.

[1536] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, and means for proposing preventive measures based on the searched accident information, thereby enabling unified management of accident information and rapid proposal of measures to prevent recurrence.

[1537] "Accident information" refers to all data related to an accident, such as the location, type, cause, measures to prevent recurrence, and date of the accident.

[1538] "Input means" refers to the interface through which users input accident information into the system, such as a web form or a smartphone app.

[1539] "Analysis methods" refer to the process of checking the accuracy and completeness of the entered accident information and ensuring the quality of the data.

[1540] "Database" means a data storage system designed to systematically store, search, and retrieve accident information.

[1541] "Search methods" refer to the processes and technologies used to extract data that match specific conditions from stored accident information.

[1542] "Means of provision" refers to the process or function for providing the accident information obtained as a search result to users visually or in other ways.

[1543] "Preventive measures suggestion means" refers to a function that suggests specific measures to prevent similar accidents in the future based on past accident information and analysis results.

[1544] "Artificial intelligence" refers to intellectual tasks such as learning, reasoning, recognition, and adaptation performed by computers.

[1545] In this invention, a specific embodiment necessary for constructing a system for managing accident information and proposing measures to prevent recurrence will be described. This system is composed of the following main components.

[1546] 1. How to input accident information

[1547] The device (e.g., a smartphone or tablet) provides an interface for the user to enter accident information. This interface typically takes the form of a mail form or a mobile application. The user enters information such as the location, type, cause, preventative measures, and date of the accident.

[1548] 2. Methods for analyzing accident information

[1549] The server receives and analyzes the accident information sent from the device. During this process, the accuracy and completeness of the data are checked. For example, it automatically verifies whether the entered date is in the future and whether specific measures to prevent recurrence are described.

[1550] 3. How to store data in a database

[1551] The server stores the analyzed accident information in a database such as SQLite, where the accident information is systematically organized and recorded, enabling quick response for subsequent searches and analysis.

[1552] 4. Search methods for accident information

[1553] When a user searches for specific accident information, they use their device to send a query to the server, which then searches the database to extract accident information that matches the criteria. The search process uses artificial intelligence (AI) to identify relevant data.

[1554] 5. Means of providing search results

[1555] The server organizes the search results and provides them to the device. By checking these, users can take appropriate measures to prevent recurrence based on past accident information.

[1556] 6. Suggested preventive measures

[1557] The server then proposes preventive measures based on the search results. These proposals are derived from information about past accidents and measures to prevent their recurrence. Users can refer to these suggestions and take concrete measures to prevent future accidents.

[1558] Hardware and software used:

[1559] Hardware: smartphones, tablets, servers

[1560] Software: SQLite (database), artificial intelligence model (AI), web or mobile application

[1561] Examples:

[1562] For example, consider a scenario where a user inputs "an accident that occurred during machine maintenance work" and records "thorough periodic inspections" as a recurrence prevention measure. A few months later, another user searches for "machine maintenance accidents" and is presented with the past recurrence prevention measure of "thorough inspections."

[1563] Example prompt sentence:

[1564] "Please give us an example of an implementation where an accident information management system is used to search for past accident information and propose measures to prevent recurrence."

[1565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1566] Step 1:

[1567] The terminal provides an interface for inputting accident information. The user inputs information such as the location, type, cause, preventative measures, and date of the accident. The input information is sent from the terminal to the server.

[1568] Input: Accident information (location, type, cause, recurrence prevention measures, date of occurrence)

[1569] Output: Sending accident information to the server

[1570] Step 2:

[1571] The server analyzes the accident information received and automatically checks the accuracy and completeness of the data. For example, it checks whether the date of occurrence is in the future and whether specific measures to prevent recurrence are described.

[1572] Input: Accident information submitted by the user

[1573] Output: Analyzed accident information

[1574] Step 3:

[1575] The server stores the analyzed accident information in an SQLite database, where the information is systematically organized and stored in a manner that allows for quick retrieval when needed.

[1576] Input: Analyzed accident information

[1577] Output: Accident information stored in the database

[1578] Step 4:

[1579] A user uses a terminal to send a query to search for specific accident information, and the server receives the query and searches the database.

[1580] Input: User search query

[1581] Output: Search query sent to server

[1582] Step 5:

[1583] The server searches the database to extract accident information that matches the criteria, using artificial intelligence (AI) to identify highly relevant data.

[1584] Input: User's search query

[1585] Output: A list of accidents that match the criteria

[1586] Step 6:

[1587] The server organizes the search results and provides them to the user, who then checks the information provided and considers appropriate measures to prevent recurrence.

[1588] Input: Extracted accident information

[1589] Output: Organized search results served to the user

[1590] Step 7:

[1591] The server proposes preventive measures based on past accident data. These proposals are generated based on accident information and measures to prevent recurrence. The user then takes specific measures based on the proposed preventive measures.

[1592] Input: Accident information based on search results

[1593] Output: Preventive measures suggested

[1594] 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.

[1595] Understood. Below is the "Mode for Carrying Out the Invention" from the patent specification.

[1596] The present invention combines a system for unifying accident information management and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system has the following main functions:

[1597] 1. Means for inputting accident information

[1598] The interface for users to enter accident information is a form that runs on a web browser. This form has fields for company name, type of accident, cause, preventative measures, date of occurrence, etc. When a user enters information into each field and clicks the submit button, the information is sent to the server.

[1599] 2. Receiving and analyzing accident information

[1600] The server receives and analyzes the accident information sent by the user. The received data is analyzed appropriately on the server to check the accuracy and completeness of the information. Specifically, it checks whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[1601] 3. Saving to the database

[1602] The analyzed accident information is stored in a database by the server, which is designed to systematically record accident information and provide quick responses to various queries.

[1603] 4. Search methods for accident information

[1604] When a user searches for accident information, they send a search query to the server, which searches the database and extracts accident information that matches the search criteria. At the same time, the server also uses artificial intelligence to identify the most relevant accident information.

[1605] 5. Providing search results

[1606] The server organizes the search results and provides them to the user, who can then review them and take measures to prevent recurrence.

[1607] 6. Introducing the Emotion Engine

[1608] The emotion engine is responsible for analyzing the emotions expressed when users input accident information and when searching. The input emotion information is associated with the accident information and stored in the database. This information enables a more detailed analysis of the cause of the accident. The emotion engine also takes the user's emotions into account when providing search results, providing appropriate feedback.

[1609] Specific explanation of program processing

[1610] 1. Enter and save accident information

[1611] When a user enters accident information into a form and submits information such as "Company A," "data loss," "human error," "regular backup," and "2023-05-01," the data is sent to the server. The server receives the data, checks its integrity, and stores it in a database. At this time, the user's emotions are also detected by the emotion engine and stored along with the data. For example, if the user is feeling stressed while entering information, the emotional data is also saved as "stress."

[1612] 2. Searching for and providing accident information

[1613] When a user searches for information about an accident, they enter a search keyword, such as "human error." The server receives this and searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching and provides appropriate feedback on the search results. For example, if the user is feeling anxious, the system can take action such as highlighting relevant measures to prevent recurrence.

[1614] 3. Visualizing Emotion Data

[1615] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company. For example, if employee stress levels are increasing over a certain period of time, the company can take immediate action based on that information.

[1616] Specific examples

[1617] 1. Entering accident information and saving emotion logs

[1618] When a user enters the accident information for "Data Loss at Company A," the emotion engine recognizes the user's emotion as "stress" through facial recognition and voice analysis. The entered accident information and the emotional data of stress are stored together in the database.

[1619] 2. Accident information search and emotional feedback

[1620] When a user searches for information about accidents related to "human error," the emotion engine recognizes the user's emotions and determines that the user is feeling anxious. Based on this information, the server highlights information about measures to prevent recurrence and provides feedback that reassures the user.

[1621] 3. Visualizing sentiment trends for managers

[1622] Managers can check employee emotional trends through the dashboard. For example, it can visualize that many employees are feeling anxious or stressed during a particular project, and managers can use this information to consider appropriate countermeasures.

[1623] As described above, the system of the present invention not only makes the management and search of accident information more efficient, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[1624] The processing flow will be explained below.

[1625] Understood. Below I will explain the process in concrete steps.

[1626] Step 1:

[1627] The user accesses an accident information input form, which contains fields such as company name, accident type, cause, preventative measures, and date of occurrence.

[1628] Step 2:

[1629] The user enters the accident information in each field. For example, the user enters "Company A," "data loss," "human error," "regular backup performed," and "May 1, 2023."

[1630] Step 3:

[1631] The user clicks the send button. This operation sends the entered accident information to the server.

[1632] Step 4:

[1633] The emotion engine recognizes the user's emotions in real time through facial and voice recognition, specifically identifying stress or anxiety the user is feeling while typing.

[1634] Step 5:

[1635] The server receives the accident information and emotion data sent by the user. The received data is sent to the server in JSON format or POST data.

[1636] Step 6:

[1637] The server analyzes the received data and verifies that all required fields are present. For example, it checks that the "company name," "accident type," "cause," "measures to prevent recurrence," "date of occurrence," and "user emotion data" are all present.

[1638] Step 7:

[1639] The server connects to the database and saves the analyzed accident information and emotion data in the database. When saving, it checks for data consistency and duplication.

[1640] Step 8:

[1641] The server will provide feedback to the user that the accident information has been successfully saved. For example, the server will send a message to the user saying "Accident information has been successfully saved."

[1642] Step 9:

[1643] To search for past accident information, a user accesses a search form that has a field for entering search keywords.

[1644] Step 10:

[1645] A user enters a search term into a search field. For example, they enter "human error."

[1646] Step 11:

[1647] The user clicks the search button, which sends the search keywords to the server.

[1648] Step 12:

[1649] The server receives a search request sent by a user, which includes search keywords and conditions.

[1650] Step 13:

[1651] The emotion engine recognizes users' emotions in real time through facial recognition and voice analysis, identifying stress or anxiety users are feeling while searching.

[1652] Step 14:

[1653] The server analyzes the search conditions and determines which conditions to use for the search. For example, let's search for accident information where the "cause" is "human error."

[1654] Step 15:

[1655] The server searches the database to retrieve accident information and related emotion data that match the criteria. As a specific example, it extracts information about accidents caused by "human error" in the past and the emotion data at the time of reporting the accident.

[1656] Step 16:

[1657] The server organizes the search results and presents them in a format that is easy for users to view. For example, the search results are organized in a list format, and the "company name," "type of accident," "cause," "measures to prevent recurrence," "date of occurrence," and "emotions at the time of reporting" for each accident are displayed.

[1658] Step 17:

[1659] The server uses an emotion engine to provide feedback that takes into account the user's emotions during the search. For example, if the user is feeling anxious, information about measures to prevent recurrence will be highlighted.

[1660] Step 18:

[1661] The server sends the organized search results and feedback to the user.

[1662] Step 19:

[1663] The terminal receives the search results sent from the server and displays them.

[1664] Step 20:

[1665] The user checks the search results on the device. As a specific example, information about a "data loss incident" caused by "human error" that occurred at "Company A" and the emotional data at the time of reporting it are displayed in a list.

[1666] Step 21:

[1667] The server receives the data from the sentiment engine and visualizes it on a dashboard for managers, which can be used to understand sentiment trends across the enterprise.

[1668] Step 22:

[1669] Managers can view employee sentiment trends on a dashboard, visualizing, for example, rising employee stress levels over a specific period, allowing managers to take appropriate action based on that information.

[1670] Through these steps, users can input, save, and search accident information, and log and utilize related emotion data.

[1671] Example 2

[1672] 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."

[1673] While conventional accident information management systems improve the efficiency of accident information management and search, they do not take user emotions into consideration, resulting in issues with the user experience. Furthermore, adding emotional information to accident information would enable the formulation of more precise measures to prevent recurrence and the understanding of emotional trends across the company, but this aspect was lacking.

[1674] 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.

[1675] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for saving the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means including an emotion engine for analyzing the user's emotions, means for adding the analyzed emotion information to data and saving it, and means for visualizing the emotion data on a dashboard for an administrator.

[1676] This not only makes the management and search of accident information more efficient, but also makes it possible to take user emotions into account, allowing for the development of more detailed measures to prevent recurrence and the understanding of emotional trends across the entire company.

[1677] "Accident information" is detailed information about an accident entered by the user, and specifically includes the type of accident, cause, measures to prevent recurrence, and date of occurrence.

[1678] The "means for receiving and analyzing" refers to a function that allows the server to receive accident information sent by the user, properly analyze it, and verify the accuracy and completeness of the data.

[1679] The "means for storing in a database" refers to a function that accumulates analyzed accident information and stores it in a database so that it can be easily searched and used later.

[1680] The "search means" is a function that allows a user to search for accident information from a database based on specific conditions.

[1681] The "means of providing" refers to a function that organizes search results and provides them to the user immediately.

[1682] The "emotion engine" has the function of analyzing the user's emotions and adding emotional information to the accident information based on the analysis results.

[1683] The "means for adding analyzed emotional information to data and saving it" has the function of adding the user's emotions analyzed by the emotion engine to the accident information and saving it in a database.

[1684] The "Administrator Dashboard" is an interface that visualizes the emotional data collected by the emotion engine, allowing managers to grasp emotional trends across the entire company.

[1685] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is configured to efficiently perform a series of processes, from inputting accident information to storing it in a database, searching, providing the results to the user, and analyzing the user's emotions and providing feedback.

[1686] Hardware and software used

[1687] The system mainly uses the following hardware and software:

[1688] Server: The central component that receives, analyzes, stores, searches, and serves data. It works in conjunction with the database and emotion engine.

[1689] Terminal: A device on which users enter accident information and view search results. A web browser is typically used.

[1690] Emotion Engine: A software component that analyzes the user's emotions through facial recognition and voice analysis.

[1691] System configuration and specific operation

[1692] Enter and submit accident information

[1693] The user accesses a form for entering accident information via a web browser on the device. This form contains fields for the company name, type of accident, cause, preventive measures, date of occurrence, etc. When the user enters this information and clicks the submit button, the input data is sent to the server.

[1694] Data reception and integrity check

[1695] The server receives the accident information. After receiving it, the server checks the integrity of the data. Specifically, it checks to make sure that the accident date is not in the future and that specific measures to prevent recurrence are included.

[1696] Sentiment analysis and data enrichment

[1697] The server sends the received accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the emotion the user is feeling while typing. For example, if the user is feeling stressed, the engine adds that information to the data as "stress."

[1698] Saving to a database

[1699] The analyzed accident information and emotion data are stored in a database by the server, allowing for later retrieval and analysis.

[1700] Searching for and providing accident information

[1701] When a user searches for information about an accident, they enter a keyword, such as "human error." The device sends this search query to the server, which then searches the database to extract results that match the criteria. The emotion engine analyzes the user's emotions in real time while searching, and if the user feels anxious, it takes action such as highlighting information about measures to prevent recurrence.

[1702] Admin Dashboard Visualization

[1703] The server receives data from the emotion engine and visualizes it on a dashboard for managers, which is used to understand sentiment trends across the company, allowing managers to visually see employee stress levels over a specific period of time.

[1704] Examples of concrete examples and prompts

[1705] Specific examples

[1706] A user inputs the incident information, "Data loss at Company A." The emotion engine detects "stress" from the user's facial expression, and this emotion data is stored in the database along with the incident information.

[1707] Users search for "human error," the server extracts relevant accident information, and the emotion engine detects the user's anxiety and highlights information on measures to prevent recurrence.

[1708] Managers can visually see through a dashboard that many employees are feeling anxious or stressed during a particular project.

[1709] Prompt Sentence Examples

[1710] "Data loss at Company A"

[1711] "Human error"

[1712] The above is a specific embodiment for carrying out the present invention. This system not only improves the efficiency of accident information management and search, but also takes into account the user's emotions, making it possible to provide a safer and more secure environment.

[1713] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1714] Step 1:

[1715] Entering accident information

[1716] The user accesses the web form on the device and enters accident information such as the company name, type of accident, cause, preventative measures, date of occurrence, etc. The entered data is sent from the device to the server when the send button is clicked.

[1717] Input: Accident information entered by the user (e.g., "CompanyA," "Data loss," "Human error," "Regular backup," "2023-05-01")

[1718] Output: The entered accident information is sent to the server.

[1719] Step 2:

[1720] Receiving and integrity checking of transmitted data

[1721] The server receives the accident information sent by the user and checks the consistency of the data, for example, checking whether the date of the accident is in the future and whether specific measures to prevent recurrence are described.

[1722] Input: Accident information sent from the device

[1723] Data processing / data calculation: The server analyzes the data received and performs consistency checks

[1724] Output: Accident information with consistency confirmed

[1725] Step 3:

[1726] Sentiment analysis and data enrichment

[1727] The server then sends the confirmed accident information to the emotion engine, which analyzes the user's emotions. The emotion engine uses facial recognition and voice analysis to identify the user's emotions. For example, if the user is feeling stressed, the emotion "stress" is added to the data.

[1728] Input: Accident information with confirmed consistency

[1729] Data processing / data calculation: Emotion analysis using an emotion engine and adding that emotion information to accident information

[1730] Output: Accident information with emotional information added

[1731] Step 4:

[1732] Saving to a database

[1733] The server stores the accident information with the added emotional information in a database, which allows for later retrieval and analysis.

[1734] Input: Accident information with emotional information added

[1735] Data processing / data calculation: Data storage process in database

[1736] Output: Accident information stored in the database

[1737] Step 5:

[1738] Accident information search

[1739] When a user searches for specific accident information, for example, they input "human error" as a search keyword, and a search query is sent from the terminal to the server.

[1740] Input: A search query entered by a user into a device (e.g., "human error")

[1741] Output: Search query sent from device to server

[1742] Step 6:

[1743] Providing search results and emotional feedback

[1744] The server searches the database based on the received search query and extracts accident information that matches the criteria. Furthermore, the emotion engine analyzes the user's emotions in real time while searching, and provides feedback such as highlighting information on measures to prevent recurrence if the user is feeling anxious, for example.

[1745] Input: The search query sent to the server

[1746] Data processing / data calculation: database search and user emotion analysis using emotion engine

[1747] Output: Search results with highlighting

[1748] Step 7:

[1749] Visualizing Emotion Data

[1750] The server receives data from the sentiment engine and visualizes it on a dashboard for managers, allowing them to understand sentiment trends across the company and take action if necessary.

[1751] Input: Data from the emotion engine

[1752] Data processing / data calculation: Data visualization processing for dashboards

[1753] Output: Sentiment trend data displayed on an admin dashboard

[1754] (Application example 2)

[1755] 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."

[1756] In modern factories, it is important to efficiently manage accident information and implement measures to prevent recurrence, but conventional systems were unable to take into account the user's emotional information, making it difficult to provide appropriate feedback. Furthermore, the lack of feedback based on emotional state led to problems such as insufficient efforts to improve operators' safety awareness and work efficiency. As a result, issues arose, such as the complicated management of accident information and inappropriate implementation of measures to prevent recurrence.

[1757] 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.

[1758] In this invention, the server includes means for inputting accident information, means for receiving and analyzing the input accident information, means for storing the analyzed accident information in a database, means for searching for accident information from the database, means for providing the searched accident information to a user, means for analyzing a human emotional state when the accident information is input and searched, and means for providing feedback based on the analyzed emotional state. This not only makes the management of accident information more efficient, but also makes it possible to take more appropriate measures to prevent recurrence by taking the user's emotions into consideration.

[1759] "Accident information" refers to detailed information such as the type of accident, cause, measures to prevent recurrence, date of occurrence, and the user's emotional state at the time of inputting the information.

[1760] An "emotion engine" refers to a system that analyzes a user's emotional state (e.g., stress, anxiety, relief, etc.) in real time through facial recognition and voice analysis.

[1761] A "database" refers to a collection of information that allows for the efficient management, search, and extraction of accumulated accident information and related data.

[1762] "Search means" refers to a system that has the function of searching for accident information in a database and extracting relevant information based on conditions specified by the user.

[1763] "Feedback" refers to the act or system of providing appropriate advice or measures tailored to the user's emotional state based on search results and information at the time of input.

[1764] "Means for analysis" refers to a system that has the function of checking the consistency and completeness of input data and appropriately analyzing the entire data, including emotional information.

[1765] "Means for inputting accident information" refers to devices or software that provide an interface for users to input accident information (type of accident, cause, measures to prevent recurrence, date of occurrence, etc.).

[1766] A "factory robot" is an automated mechanical device designed to perform work within a factory, and includes systems that can collect accident information and perform emotion analysis, etc.

[1767] "Artificial intelligence" refers to advanced algorithms and systems that analyze large amounts of data, discover patterns, and make highly accurate predictions and classifications.

[1768] This invention combines a system for centrally managing accident information and formulating measures to prevent recurrence with an emotion engine that recognizes and analyzes user emotions. This system is installed on factory robots and has the following main functions:

[1769] 1. Enter accident information

[1770] The server provides an interface for inputting accident information. This interface is designed to allow operators to input accident information through the factory robot's touchscreen or voice input function. For example, the operator inputs the "company name," "type of accident," "cause," "measures to prevent recurrence," and "date of occurrence."

[1771] 2. Analysis by emotion engine

[1772] When entering accident information, the emotion engine analyzes the operator's emotions in real time through facial recognition and voice analysis. For example, if the operator is feeling stressed, that emotional data is also recorded. This function uses the emotion_engine module.

[1773] 3. Accident information and emotional data storage

[1774] The server analyzes the input accident information and emotion data, checks the data integrity, and then stores this information in a database using the database_manager module. The stored information includes the type of accident, cause, preventive measures, date of occurrence, and related emotion information.

[1775] 4. Search for accident information

[1776] The server also provides a database search function for accident information. When an operator enters a search query, the AI ​​extracts and provides relevant accident information. At this time, the emotion engine again analyzes the operator's emotional state and adjusts the feedback according to the search results.

[1777] 5. Providing Feedback

[1778] When providing search results, the server provides appropriate feedback based on the operator's emotional state analyzed by the emotion engine. For example, if the operator feels anxious, the server will adjust the search results by highlighting measures to prevent recurrence. This function makes it possible to provide the operator with a sense of security.

[1779] Specific examples

[1780] 1. Entering accident information and saving emotion logs

[1781] When an operator inputs the accident information of "data loss," the emotion engine analyzes the operator's emotions as "stress" through facial recognition and voice analysis, and the information is stored in the database along with the accident information.

[1782] 2. Accident information search and emotional feedback

[1783] When an operator searches for information about accidents related to "human error," the emotion engine analyzes the operator's emotion as "anxiety." Based on this information, the server highlights measures to prevent recurrence and provides search results that give the operator a sense of security.

[1784] Prompt Sentence Examples

[1785] Please enter the accident information. Please include the following information in your response: company name, accident type, cause, preventative measures, date of occurrence, and appropriate emotion labels (e.g., stress, anxiety).

[1786] The system of the present invention not only improves the efficiency of accident information management and search, but also provides feedback that takes into account the user's emotions, thereby providing a safer and more secure working environment.

[1787] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1788] Step 1:

[1789] The server accepts input of accident information. The user (operator) uses the factory robot's touchscreen or voice input to enter information such as the company name, type of accident, cause, preventative measures, and date of occurrence. This information is sent to the server. Input data includes, for example, "company name," "data loss," "human error," "regular backup," and "2023-05-01."

[1790] Step 2:

[1791] The server starts the emotion engine and performs facial recognition and voice analysis of the user (operator) while he / she is entering data. The emotion engine (emotion_engine module) is used to analyze the user's emotional state as "stress" or "anxiety," etc. At this time, emotion data is generated by analyzing facial expressions and tone of voice. For example, it is detected that the operator is feeling "stressed" while entering data.

[1792] Step 3:

[1793] The server receives and analyzes the entered accident information and emotion data. Specifically, it checks the consistency and completeness of the data, for example, whether the date of occurrence is in the future or whether the entered information is missing. As a result of this step, data with confirmed consistency is generated. For example, consistent data such as "company name," "data loss," "human error," "regular backup," "2023-05-01," and "stress" is output.

[1794] Step 4:

[1795] The server uses the database_manager module to store the analyzed accident information and emotion data in a database. When storing the data, fields such as the type of accident, cause, preventative measures, date of occurrence, and user emotion information are recorded in the database. For example, records such as "Company Name," "Data Loss," "Human Error," "Regular Backup," "2023-05-01," and "Stress" are added to the database.

[1796] Step 5:

[1797] When a user searches for accident information, the server accepts the user's search query. For example, the user enters the query "human error." This search query is sent to the server.

[1798] Step 6:

[1799] The server restarts the emotion engine and analyzes the user's emotional state during the search. The emotion engine determines that the user is feeling "anxiety." The analysis results are generated as emotion data.

[1800] Step 7:

[1801] The server searches the database and uses artificial intelligence (AI) to identify relevant accident information. For example, it extracts data that matches the search query "human error" for accident information. At this step, accident information that matches the search criteria is output.

[1802] Step 8:

[1803] When providing search results to the user, the server adjusts the feedback based on the user's emotional state analyzed by the emotion engine. If the user is feeling anxious, the server adjusts the feedback to provide a sense of security, such as by highlighting information about measures to prevent recurrence. For example, the server highlights the part of the search results that describes measures to prevent recurrence for "human error" and presents it to the user.

[1804] Step 9:

[1805] The server records the provided feedback and user emotional data to help with future improvements. This information is available to administrators via a dashboard, where it can be used to understand overall emotional trends and consider countermeasures. For example, if many operators are feeling stressed during a specific period, this information can be visualized and appropriate countermeasures can be considered.

[1806] 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.

[1807] 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.

[1808] 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.

[1809] 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.

[1810] 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.

[1811] 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.

[1812] 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).

[1813] 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.

[1814] 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."

[1815] 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.

[1816] 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).

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] 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.

[1823] 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.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] The following is further disclosed regarding the above embodiment.

[1828] Understood. The draft claims are as follows:

[1829] (Claim 1)

[1830] a means for inputting accident information;

[1831] means for receiving and analyzing the input accident information;

[1832] means for storing the analyzed accident information in a database;

[1833] means for searching for accident information from the database;

[1834] means for providing the retrieved accident information to a user;

[1835] A system including:

[1836] (Claim 2)

[1837] 2. The system according to claim 1, wherein the accident information stored in the database includes the type of accident, the cause, measures to prevent recurrence, and the date of occurrence.

[1838] (Claim 3)

[1839] The system of claim 1, wherein the accident information retrieval means uses artificial intelligence to identify relevant accident information.

[1840] (Claim 4)

[1841] 2. The system according to claim 1, wherein the search means searches a database based on a search keyword from a user and extracts accident information that matches the search keyword.

[1842] (Claim 5)

[1843] 2. The system according to claim 1, wherein the accident information input means integrates accident information from a plurality of companies into one database.

[1844] "Example 1"

[1845] (Claim 1)

[1846] a means for inputting information;

[1847] means for receiving and analyzing the input information;

[1848] means for storing the analyzed information in a database;

[1849] means for retrieving information from said database;

[1850] means for providing the retrieved information;

[1851] A system including:

[1852] (Claim 2)

[1853] 2. The system of claim 1, wherein the information stored in the database includes type, cause, countermeasure, and date.

[1854] (Claim 3)

[1855] 10. The system of claim 1, wherein the information retrieval means uses artificial intelligence to identify relevant information.

[1856] "Application Example 1"

[1857] (Claim 1)

[1858] a means for inputting accident information;

[1859] means for receiving and analyzing the input accident information;

[1860] means for storing the analyzed accident information in a database;

[1861] means for searching for accident information from the database;

[1862] means for providing the retrieved accident information to a user;

[1863] A means for suggesting preventive measures based on the retrieved accident information;

[1864] A system including:

[1865] (Claim 2)

[1866] 2. The system according to claim 1, wherein the accident information stored in the database includes the type of accident, the cause, measures to prevent recurrence, and the date of occurrence.

[1867] (Claim 3)

[1868] The system of claim 1, wherein the accident information retrieval means uses artificial intelligence to identify relevant accident information.

[1869] "Example 2: Combining Emotion Engines"

[1870] (Claim 1)

[1871] a means for inputting accident information;

[1872] means for receiving and analyzing the input accident information;

[1873] means for storing the analyzed accident information in a database;

[1874] means for searching for accident information from the database;

[1875] means for providing the retrieved accident information to a user;

[1876] means including an emotion engine for analyzing the emotion of a user;

[1877] means for adding the analyzed emotion information to data and storing the same;

[1878] means for visualizing said emotion data on an administrator dashboard;

[1879] A system including:

[1880] (Claim 2)

[1881] 10. The system of claim 1, wherein the emotion engine analyzes the user's emotions using facial recognition or voice analysis of the user.

[1882] (Claim 3)

[1883] 2. The system according to claim 1, wherein the search means performs a process of feeding back search results based on user emotions.

[1884] "Application example 2 when combining emotion engines"

[1885] (Claim 1)

[1886] a means for inputting accident information;

[1887] means for receiving and analyzing the input accident information;

[1888] means for storing the analyzed accident information in a database;

[1889] means for searching for accident information from the database;

[1890] means for providing the retrieved accident information to a user;

[1891] means for analyzing a person's emotional state when the accident information is input and retrieved;

[1892] means for providing feedback based on said analyzed emotional state;

[1893] A system including:

[1894] (Claim 2)

[1895] 2. The system according to claim 1, wherein the accident information stored in the database includes the type of accident, the cause, measures to prevent recurrence, the date of occurrence, and the user's emotional information.

[1896] (Claim 3)

[1897] The system of claim 1, wherein the means for searching for accident information uses artificial intelligence to identify relevant accident information and adjusts search results by taking into account emotional information. [Explanation of symbols]

[1898] 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 accident information; means for receiving and analyzing the input accident information; means for storing the analyzed accident information in a database; means for searching for accident information from the database; means for providing the retrieved accident information to a user; A system including:

2. 2. The system according to claim 1, wherein the accident information stored in the database includes the type of accident, the cause, measures to prevent recurrence, and the date of occurrence.

3. The system of claim 1 , wherein the accident information retrieval means uses artificial intelligence to identify relevant accident information.

4. 2. The system according to claim 1, wherein said search means searches the database based on a search keyword entered by a user, and extracts accident information that matches the search keyword.

5. 2. The system according to claim 1, wherein said accident information input means integrates accident information from a plurality of companies into one database.

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