system

Abstract

A system is provided.SOLUTION: The system includes a means for inputting specific information (name, address, physical feature, etc.) of a missing person and extracting existence confirmation information by using natural language processing, a means for collecting external information such as a report from a shelter, SNS data, and a news article and automatically analyzing and arranging it, and a means for quickly notifying a person concerned of the collated existence confirmation information.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] When a natural disaster occurs, it can be difficult to quickly and accurately confirm the safety of victims and their families living far away. Traditional methods often require time to collect and confirm information on the safety of victims, resulting in delays before appropriate information reaches families. Furthermore, with the need to protect personal information, there is a significant risk of information leaks and false reports. For this reason, there is a growing demand for a system that can quickly and accurately confirm the safety of victims and their families. [Means for solving the problem]

[0005] This invention solves the problems by providing the following means. First, it provides a means for inputting specific information about a missing person (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing. Second, it provides a means for collecting external information such as reports from evacuation centers, social media data, and news articles, and automatically analyzing and organizing it. This makes it possible to quickly confirm and verify the safety of missing people based on the acquired information. Furthermore, it provides a means for quickly notifying relevant parties of the verified survival confirmation information, and by supporting various notification formats such as email, SMS, and push notification, it is possible to provide information to relevant parties at an appropriate time. In this way, it provides a system that enables quick and accurate safety confirmation of disaster victims and their families.

[0006] "Identifying information of a missing person" means information that can be used to identify a missing person, including, for example, name, address, and physical description.

[0007] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language, and uses this to extract and analyze specific information.

[0008] "Survival confirmation information" refers to information about the current survival status and whereabouts of disaster victims, and is obtained from evacuation center reports, social media, and news articles.

[0009] "Reports from evacuation centers" are official information that records the status and number of disaster victims in evacuation centers.

[0010] "SNS data" refers to digital information such as messages, posts, and comments sent by users on social networking services.

[0011] A "news article" is an article published by a news organization that records the occurrence of a disaster and the situation of the victims.

[0012] "External information" refers to all information collected from outside sources, such as evacuation center reports, social media data, and news articles, that is necessary to confirm the survival of missing persons.

[0013] "Analysis and organization" refers to the process of analyzing collected information and classifying and organizing it into necessary data.

[0014] A "database" is a system that systematically organizes a collection of information so that it can be managed and searched.

[0015] A "notification system" is a system for communicating information to users in the form of email, SMS, push notifications, etc.

[0016] "Concerned Person" refers to a family member, friend, or other person who should receive appropriate information about the whereabouts of a missing person. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, specifically, a system for comparing the missing person's identifying information with information confirming their survival using natural language processing and notifying relevant parties. The system of the present invention is implemented by the following program.

[0039] System configuration

[0040] server

[0041] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles, and generates survival confirmation information based on that data.

[0042] Terminal

[0043] The device provides an interface for users to enter missing person information, which is then sent to a server for verification. The device also displays notifications that relevant parties can receive. For example, notifications can be received through a smartphone application or a web browser.

[0044] User

[0045] The user acts as a family member or relative of the missing person and inputs information about the missing person through the terminal and receives notifications.

[0046] Program processing explanation

[0047] Inputting missing person information and extracting survival confirmation information

[0048] User

[0049] The user enters information such as the missing person's name, address, and physical characteristics into an input form on the terminal.

[0050] Terminal

[0051] The terminal converts the entered missing person information into JSON format and sends it to the server's specified API endpoint.

[0052] server

[0053] The server parses the received JSON data and stores the missing person information in a database.

[0054] The stored information is passed to a natural language processing module, which searches and extracts relevant liveness information from existing information in the database.

[0055] The extracted survival confirmation information is stored in a database.

[0056] Collection and analysis of external information

[0057] server

[0058] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[0059] The collected data is passed to the NLP module for analysis.

[0060] The extracted survival confirmation information is then compared with the missing person information.

[0061] Sending alive notification

[0062] server

[0063] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[0064] Use the notification system to send liveness information to relevant parties via email, SMS, or push notification.

[0065] Terminal

[0066] Display notifications on the devices of those involved.

[0067] User

[0068] The user receives the notification on the terminal and checks the survival confirmation information.

[0069] Specific examples

[0070] For example, to confirm that relative B is missing, user A enters the name "Tanaka Taro," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into his device. The device sends this information to the server. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information on whether "Tanaka Taro" is alive and well from evacuation shelters, social media, and news. As a result of the comparison, the information "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo" is generated, and an email notification is sent to user A. User A receives the notification on his device and can confirm that relative B is safe.

[0071] As described above, the system of the present invention can quickly and accurately collate information on missing persons and notify relevant parties, thereby providing peace of mind to disaster victims and their families.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User

[0075] The user enters information such as the missing person's name, address, and physical characteristics into an input form provided on the terminal.

[0076] For example, enter the name "Yamada Hanako," the address "Sumida Ward, Tokyo," and the physical characteristics "height 160 cm, black hair, glasses."

[0077] Step 2:

[0078] Terminal

[0079] The terminal converts the missing person information entered by the user into JSON format.

[0080] The converted JSON data is sent to the server's API endpoint using an HTTP POST request.

[0081] Step 3:

[0082] server

[0083] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0084] The stored information is passed to a natural language processing (NLP) module, which searches and extracts relevant liveness information from existing information in the database.

[0085] The extracted survival confirmation information is stored in a database.

[0086] Step 4:

[0087] server

[0088] The server runs scripts to periodically collect reports from evacuation centers, social media data, and news articles.

[0089] The information collected from each data source is passed to the NLP module for analysis.

[0090] For example, extract the information that "Yamada Hanako is at a shelter in Sumida Ward, Tokyo."

[0091] Step 5:

[0092] server

[0093] The extracted survival confirmation information is compared with the missing person's identifying information, and if there is a match, the information is stored in a database.

[0094] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[0095] Step 6:

[0096] server

[0097] Use the notification system to notify relevant parties of liveness confirmation information.

[0098] Select the appropriate notification format: email, SMS, or push notification.

[0099] For example, a notification is sent to the user's email address stating, "Yamada Hanako has safely evacuated to a shelter in Sumida Ward, Tokyo."

[0100] Step 7:

[0101] Terminal

[0102] The relevant person's device receives the notification and displays the notification content.

[0103] For example, a push notification on a smartphone might display the message "Yamada Hanako is safe."

[0104] Step 8:

[0105] User

[0106] The user checks the notification on their device and receives information about the missing person who has been confirmed safe.

[0107] This provides peace of mind to the user.

[0108] Through the above processing steps, information on missing persons is quickly and accurately collated and notified to the relevant parties, making it possible to quickly confirm the safety of the victims' families and other relevant parties.

[0109] Example 1

[0110] 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."

[0111] Rapid and accurate confirmation of the safety of missing persons is an extremely difficult task, especially during disasters or sudden incidents. Conventional systems rely primarily on manual confirmation work, requiring a significant amount of time for information collection and analysis, which often results in delays in confirming the person's actual survival and in notifying them. This frequently causes unnecessary worry for missing persons, their families, and other concerned parties. Therefore, there is a need for a system that can quickly and automatically check the missing person's survival based on the missing person's identifying information and promptly notify those concerned.

[0112] 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.

[0113] In this invention, the server includes means for inputting specific information of missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing technology, means for automatically collecting, analyzing, and organizing external information such as reports from evacuation centers, social networking service data, and news articles, and means for quickly notifying relevant parties of the collated survival confirmation information. This makes it possible to quickly and accurately collate information on missing persons and quickly provide relevant parties with survival confirmation information.

[0114] "Identifying information of a missing person" refers to basic information needed to identify a missing person, such as name, address, and physical characteristics.

[0115] "Natural language processing technology" is a technology that allows computers to understand human language and perform analysis and generation.

[0116] "Survival confirmation information" is information indicating that a missing person is currently alive, and can be obtained from evacuation center reports, social media data, news articles, etc.

[0117] "Reports from evacuation shelters" are information reporting the situation of people gathered at evacuation shelters during disasters or emergencies.

[0118] "Social networking service data" refers to data such as text, images, and videos posted on social media platforms.

[0119] A "news article" is the text of news reported in newspapers, television, online media, etc.

[0120] "Means for automatically collecting, analyzing, and organizing" refers to means for automatically collecting external information using a program and analyzing and organizing it using natural language processing technology, etc.

[0121] "Matched survival confirmation information" is survival confirmation information that matches the identifying information of a missing person, and is based on existing information in a database or information collected from outside.

[0122] "Means of notification" refers to the means of communicating information to relevant parties, such as by email, SMS, or push notification.

[0123] A "database" is a system for systematically storing and managing digital information.

[0124] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, and more specifically, to a system for comparing the missing person's identifying information with information confirming their survival using natural language processing technology and notifying relevant parties. An embodiment of the system will be described below.

[0125] System configuration

[0126] server

[0127] The server receives specific information about missing persons (such as name, address, and physical characteristics) entered by the user and stores it in a database. A typical relational database (such as MySQL or PostgreSQL) or a NoSQL database (such as MongoDB) can be used as the database. The server also includes a natural language processing (NLP) module, which includes the Python library spaCy and a Transformer-based model. The NLP module is used to analyze the received information and extract information confirming the person's survival. Furthermore, the server periodically runs a script that collects external information, such as reports from evacuation centers, social networking service (SNS) data, and news articles. The collected data is also analyzed by the NLP module, and survival information is generated based on that data.

[0128] Terminal

[0129] The device provides an interface for users to input specific information about missing people. The interface is provided through a smartphone application or web browser. The device converts the input information into JSON format and sends it to the server via an HTTP request. The device also has a function to display notifications received by relevant parties. This allows relevant parties to receive information confirming the person's survival in the form of push notifications, emails, SMS, etc.

[0130] User

[0131] The user, as a family member or associate of a missing person, plays the role of entering specific information about the missing person through the terminal. The specific information entered by the user includes the missing person's name, address, physical characteristics, etc. For example, to confirm the disappearance of relative B, user A might enter the name "Taro Tanaka," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into the terminal.

[0132] Specific examples

[0133] For example, to confirm the disappearance of relative B, user A enters "Name: Tanaka Taro, Address: Shinjuku Ward, Tokyo, Physical Characteristics: Height 170 cm, Black Hair, Glasses." The device converts this information into JSON format and sends it to the server via an HTTP request. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information confirming the survival of "Tanaka Taro" from evacuation shelter reports, social media, and news articles. A specific example might be a prompt: "We are looking for a missing person named Tanaka Taro. His address is in Shinjuku Ward, Tokyo, and his physical characteristics are height 170 cm, black hair, and glasses. Please find information confirming his survival." The matching result is generated as "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo," and an email notification is sent to user A. User A receives the notification on their device and can confirm that relative B is safe.

[0134] As described above, the system of the present invention can quickly and accurately check the survival confirmation information based on the missing person's identifying information and notify the relevant parties, thereby providing peace of mind to the missing person and their family and related parties.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1: Enter missing person information

[0137] User

[0138] The user enters the missing person's identifying information (such as name, address, and physical characteristics) into an input form on the device. For example, User A enters information such as "Taro Tanaka, Shinjuku Ward, Tokyo, height 170 cm, black hair, glasses." The input data at this point is the missing person's identifying information.

[0139] Step 2: Send data to the server

[0140] Terminal

[0141] The terminal converts the missing person identification information entered by the user into JSON format. Then, it sends the converted JSON data to the server in the form of an HTTP request. Specifically, it sends the data by specifying an endpoint (e.g., " / api / v1 / missing_persons"). The output at this point is the JSON data sent to the server.

[0142] Step 3: Store and analyze missing person information

[0143] server

[0144] The server extracts JSON data from the received HTTP request, parses it, and stores it in a database, such as MongoDB or MySQL. Once stored in this database, the data is passed to a natural language processing (NLP) module, which uses an NLP module (e.g., spaCy or a Transformer-based model) to parse the received data and extract the survival information. The output is a database entry containing the missing person information and the NLP analysis results.

[0145] Step 4: Collect and analyze external data

[0146] server

[0147] The server periodically runs a script that collects external data. This script automatically collects data from evacuation shelter reports, social networking service (SNS) posts, news articles, etc. The collected data is then passed to the same NLP module for analysis and extraction. For example, the data is scraped using Python's BeautifulSoup or Scrapy, and then analyzed by the NLP module. The output at this point is the collected and analyzed survival confirmation information.

[0148] Step 5: Generating and notifying liveness information

[0149] server

[0150] The server compares the analysis results from the NLP module with the missing person information and generates final survival confirmation information. For example, it generates information such as "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." Next, it retrieves the contact information of the relevant parties from the database and uses a notification system (e.g., Twilio or Firebase) to send the survival confirmation information in the form of email, SMS, or push notification. The output at this point is the notification message sent to the relevant parties.

[0151] Step 6: Receive live notification

[0152] Terminal

[0153] The device receives the notification sent from the server and displays it to the user. For example, a message may be displayed in the notification area of ​​a smartphone or within an application. The output at this point is a liveness confirmation notification displayed on the device.

[0154] User

[0155] The user checks the notification and receives the survival confirmation information. Specifically, the user can feel relieved when they see the notification that "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." The final output here is the survival confirmation information that the user receives.

[0156] As a result, this system can quickly and accurately confirm the safety of missing people and notify relevant parties.

[0157] (Application example 1)

[0158] 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."

[0159] In order to quickly and accurately confirm the safety of missing persons, it is necessary to collect and analyze data from multiple information sources and notify relevant parties in a timely manner. However, with conventional systems, it takes time to collect and analyze information, making it particularly difficult to provide information in real time. Furthermore, there is a problem that on-site confirmation work is time-consuming, making it difficult to respond efficiently. The present invention aims to solve these problems and realize the provision of quick and accurate safety confirmation information.

[0160] 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.

[0161] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation centers, social media data, and news articles, means for quickly notifying relevant parties of the collated survival confirmation information, means for displaying and confirming missing person information in real time using visual display devices worn by security guards, and means for obtaining new information from evacuation centers and social media and notifying them by audio and visual alerts. This enables rapid and accurate data collection and analysis from multiple sources, as well as real-time confirmation on site.

[0162] "Identifying information of a missing person" is information that can be used to identify a missing person, such as name, address, and physical characteristics.

[0163] "Natural language processing" is a technology that enables computers to understand, interpret, and manipulate human language.

[0164] "Survival confirmation information" is information indicating that a missing person is safe.

[0165] "Reports from evacuation shelters" refers to the provision of information regarding missing persons at evacuation shelters.

[0166] "SNS Data" is information obtained from posts and comments on social networking services.

[0167] A "news article" is news content provided by a media outlet.

[0168] "External information" is any information collected from outside the system.

[0169] A "visual display device" is a device for visually displaying information, and in this context refers to a device worn by a security guard.

[0170] "Audio and visual alerts" are methods of warning or notifying through audio and visual means.

[0171] The present invention is a system for quickly and accurately confirming the safety of missing persons. This system is composed of the following main components: a server, a terminal, and a user.

[0172] server

[0173] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract information confirming their survival. It also periodically collects external information, such as reports from evacuation centers, social media data, and news articles, and analyzes and organizes it using the NLP module. The server then promptly notifies relevant parties of the results of these comparisons.

[0174] Specifically, when missing person information is sent to the server, natural language processing technology is used to search for and extract related survival confirmation information. Spacy is used as the NLP module to perform data analysis. Evacuation shelter information and social media data are collected regularly and automatically processed on the server. Matched survival confirmation information is quickly distributed to relevant contacts via the notification system.

[0175] Terminal

[0176] The device provides an interface for users to input missing person information. The input information is sent to a server, where the missing person information is compared with information confirming their survival. The device also displays notifications to relevant parties. Devices include smartphones, smart glasses, and head-mounted displays (HMDs).

[0177] Specifically, when security guards patrol wearing head-mounted displays (HMDs), specific information about missing persons is visually displayed, and audio and visual alerts are issued when new information is updated in real time.

[0178] User

[0179] Users, as family members or relatives of the missing person, input information about the missing person through the device and receive notifications. When users use voice input, the voice data is converted into text data and sent to the server. It is then quickly collated and analyzed, and the results are notified.

[0180] Specific examples of prompts include:

[0181] Enter the missing person's information (name, address, physical characteristics). For example, "Taro Tanaka, Shinjuku Ward, Tokyo, height 170cm, black hair, glasses." The entered information will be analyzed using natural language processing, and information on whether the person is alive will be displayed.

[0182] This system makes it possible to quickly confirm the safety of missing persons, providing peace of mind to those involved.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] A user uses a terminal to input information about a missing person, such as name, address, and physical characteristics. The input information is converted into JSON format, and this data is sent to a specified API endpoint on the server.

[0186] Step 2:

[0187] The server parses the received JSON data and stores it in the database, checking the format and integrity of the data to ensure that the entered missing person information is stored accurately.

[0188] Step 3:

[0189] The server passes the stored missing person information to a natural language processing (NLP) module, which searches and extracts relevant information from existing information in the database. Data processing here includes text analysis and keyword extraction.

[0190] Step 4:

[0191] The server periodically runs a script that collects external information such as reports from evacuation centers, social media data, and news articles. This data is also passed to the NLP module for analysis, resulting in additional survival information.

[0192] Step 5:

[0193] The server stores the extracted liveness information in a database and generates a matching result, which is then prepared for immediate notification to relevant parties, whose contact information is also retrieved from the database.

[0194] Step 6:

[0195] The server sends information to relevant parties to confirm the person's survival via email, SMS, push notification, etc. The notification includes the missing person's status and location information.

[0196] Step 7:

[0197] The terminal has the ability to display information in real time on the guard's HMD, which visually displays specific information about missing persons, and issues audio and visual alerts when new information is reported.

[0198] Step 8:

[0199] The user receives a notification on their device and checks it. This notification includes information on the safety of the missing person and is displayed in real time, allowing the user to take prompt action based on this information.

[0200] This processing flow enables the safety of missing persons to be confirmed quickly and accurately, and the information is promptly notified to relevant parties.

[0201] 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.

[0202] The present invention is a system for quickly and accurately confirming the safety of missing persons, and is particularly equipped with a function that recognizes the user's emotions to optimize the response. Specifically, the system uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and further recognizes the user's emotions using an emotion engine to optimize the content and timing of notifications, thereby providing information to relevant parties quickly and appropriately.

[0203] System configuration

[0204] server

[0205] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0206] Terminal

[0207] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0208] User

[0209] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[0210] Program processing explanation

[0211] Inputting missing person information and extracting survival confirmation information

[0212] User

[0213] The user enters the missing person's name, address, physical characteristics, etc. into an input form on the terminal.

[0214] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0215] Terminal

[0216] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[0217] server

[0218] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0219] The stored information is passed to the NLP module to search and extract liveness information.

[0220] The extracted survival confirmation information is stored in a database.

[0221] Collection and analysis of external information

[0222] server

[0223] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[0224] Each collected data is passed to the NLP module for analysis.

[0225] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[0226] Emotion engine for recognizing user emotions and optimizing notifications

[0227] server

[0228] The emotion engine analyzes the information entered by the user and their reactions to past notifications to recognize their emotions.

[0229] For example, if a user frequently enters anxious comments, the next notification will contain reassuring content.

[0230] Terminal

[0231] Customize notification content based on feedback from the emotion engine.

[0232] For example, in addition to the notification "Ichiro Tanaka is safe," a reassuring message such as "Detailed location confirmation has been obtained" is sent.

[0233] Sending alive notification

[0234] server

[0235] Notifications are generated based on the matching results and feedback from the emotion engine.

[0236] Sending generated notifications to interested parties in the form of email, SMS, and push notifications.

[0237] Terminal

[0238] The relevant person's device receives the notification and displays the notification content.

[0239] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safely evacuated to a shelter in Osaka City."

[0240] User

[0241] The user checks the notification on their device and confirms that the missing person is safe.

[0242] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[0243] By using the above processing steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] User

[0247] The user opens a form on the terminal to enter the missing person's name, address, physical characteristics, etc.

[0248] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0249] Step 2:

[0250] Terminal

[0251] The terminal converts the input information into JSON format data.

[0252] The converted JSON data is sent to the server's API using an HTTP POST request.

[0253] Step 3:

[0254] server

[0255] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0256] The stored specific information is passed directly to a natural language processing (NLP) module, which searches and extracts relevant information from existing liveness confirmation information.

[0257] The extracted survival confirmation information is stored in a database.

[0258] Step 4:

[0259] server

[0260] The server periodically runs scripts to collect evacuation shelter reports, social media data, and news articles.

[0261] The collected data is passed to an NLP module, which parses and extracts relevant liveness information.

[0262] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[0263] Step 5:

[0264] server

[0265] The extracted survival confirmation information is compared with the missing person's identification information.

[0266] The matched information is stored in the database.

[0267] Step 6:

[0268] server

[0269] The server uses an emotion engine to recognize the user's emotions based on the user's input information and reaction data to past notifications.

[0270] For example, if a user enters "I'm very worried" into an input form, the emotion engine will detect this as an emotion indicating anxiety.

[0271] Step 7:

[0272] server

[0273] Optimize the content of notifications based on feedback from the emotion engine.

[0274] For example, additional information that provides a sense of security could be added, such as "Ichiro Tanaka is safely evacuated to a shelter in Osaka City. His detailed whereabouts have also been confirmed."

[0275] Step 8:

[0276] server

[0277] Notifications generated based on matching results and feedback from the sentiment engine are sent to relevant parties via email, SMS, or push notifications.

[0278] Step 9:

[0279] Terminal

[0280] The terminals of the people involved receive the notification and display the notification content in a way that gives the user a sense of security.

[0281] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safe at a shelter in Osaka City. His detailed whereabouts have been confirmed."

[0282] Step 10:

[0283] User

[0284] The user checks the notification on their device and knows that the missing person is safe.

[0285] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification, giving the user a greater sense of security.

[0286] By using the above steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0287] Example 2

[0288] 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."

[0289] The challenge is to provide information about missing people quickly and accurately, so that those involved can feel at ease. Furthermore, conventional systems do not provide information that takes into account the user's feelings, and this point also needs to be improved.

[0290] 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.

[0291] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles, and means for analyzing input information from users and responses to past notifications to recognize emotions and optimize the content and timing of notifications. This makes it possible to provide missing person information quickly and accurately and to notify at the optimal timing taking into account the user's emotions.

[0292] "Identifying information of a missing person" is information such as name, address, and physical characteristics that can be used to identify a missing person.

[0293] "Natural language processing" is a technology that enables computers to understand and analyze text written in natural language.

[0294] "Survival confirmation information" is information used to confirm that missing persons are still alive, and includes reports from evacuation centers, social media data, news articles, etc.

[0295] "Reports from evacuation centers" refers to information collected at evacuation centers regarding the status of missing persons.

[0296] "SNS data" refers to information about missing persons posted on social networking services.

[0297] A "news article" is an article about a missing person published by a news organization.

[0298] The "emotion engine" is a component that analyzes the user's input information and reactions to past notifications to recognize the user's emotions.

[0299] "Optimizing notification content and timing" is the process of adjusting the content and timing of notifications based on the user's emotional state.

[0300] A "database" is a system for efficiently storing, managing, and retrieving data.

[0301] "JSON format" stands for JavaScript Object Notation and is a lightweight data exchange format.

[0302] An "HTTP POST request" is a request method in the HTTP protocol for a client to send data to a server.

[0303] A "push notification" is a notification message that is actively sent from a server to a client device.

[0304] "Email" is a digital message sent over the Internet.

[0305] "SMS" stands for Short Message Service, a service that sends short text messages via mobile phones.

[0306] The present invention provides a system for quickly and accurately confirming the safety of missing people, and is particularly equipped with a function for recognizing the user's emotions and optimizing the response. The system includes components of a server, a terminal, and a user.

[0307] server

[0308] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0309] As a concrete example, information about a missing person named "Ichiro Tanaka" is received, and based on that information, information confirming the person's survival, such as "Ichiro Tanaka is at a shelter in Osaka City," is extracted. Also, in cases where a user frequently enters anxious comments, the next notification will include information that provides reassurance.

[0310] Terminal

[0311] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0312] For example, when a user enters information about "Ichiro Tanaka," the device converts this information into JSON format and sends it to the server. When a notification is received from the server, the device displays a reassuring message saying, "Ichiro Tanaka is safely evacuated to an evacuation shelter in Osaka City."

[0313] User

[0314] Users, as family members or relatives, enter information about the missing person through their device and receive a notification. The user's emotions are recorded, and this emotional data is reflected in the next notification. For example, if a user enters a comment when feeling anxious, the emotion engine will analyze it, and the next notification will have a more reassuring message.

[0315] Prompt Sentence Examples

[0316] Generate a description of a missing person safety confirmation system. Include each processing step: inputting missing person information, collecting external information, recognizing emotions using an emotion engine, and sending a survival confirmation notification.

[0317] With the above configuration, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1:

[0320] User enters missing person information and submits it to the server

[0321] Specific actions

[0322] The user inputs information such as the name, address, and physical characteristics of the missing person into an input form on the terminal. For example, the user inputs the name "Ichiro Tanaka," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0323] input:

[0324] Missing person information entered by the user.

[0325] output:

[0326] Missing person information converted to JSON format on the device.

[0327] Data processing / calculation:

[0328] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[0329] Step 2:

[0330] Storage and analysis of missing person information on the server

[0331] Specific actions

[0332] The server parses the JSON data received from the device. The parsed missing person information is stored in a database. The stored information is then passed to the NLP module to search for and extract information confirming survival.

[0333] input:

[0334] JSON data sent from the terminal.

[0335] output:

[0336] Missing person information stored in the database and extracted survival confirmation information.

[0337] Data processing / calculation:

[0338] The server parses the JSON data and stores the missing person's specific information in a database, which is then passed to an NLP module to extract information on whether the person is alive or not.

[0339] Step 3:

[0340] Collection and analysis of external information

[0341] Specific actions

[0342] The server periodically runs scripts to collect external information such as reports from evacuation centers, social media data, and news articles. The collected data is passed to an NLP module for analysis. For example, it extracts information such as "Ichiro Tanaka is at an evacuation center in Osaka City."

[0343] input:

[0344] External information (shelter reports, social media data, news articles).

[0345] output:

[0346] Survival confirmation information analyzed by the NLP module.

[0347] Data processing / calculation:

[0348] The server collects external information and analyzes it using the NLP module. The analyzed information is extracted as survival confirmation information.

[0349] Step 4:

[0350] Optimizing user emotion recognition and notification with an implemented emotion engine

[0351] Specific actions

[0352] The server's emotion engine analyzes the user's input information and reactions to past notifications. Based on the analysis results, it recognizes the user's emotions and optimizes the content and timing of the next notification. For example, if a user frequently enters anxious comments, the next notification will include reassuring content.

[0353] input:

[0354] User input information and response data to past notifications.

[0355] output:

[0356] Optimized notification content and timing.

[0357] Data processing / calculation:

[0358] The emotion engine analyzes user input and recognizes emotions, optimizing the content and timing of the next notification.

[0359] Step 5:

[0360] Generate and send heartbeat notifications

[0361] Specific actions

[0362] The server generates a liveness notification based on the matching results and feedback from the emotion engine, and the notification is sent to the relevant parties in the form of email, SMS, or push notification.

[0363] input:

[0364] Matching results and emotion engine feedback.

[0365] output:

[0366] The generated heartbeat notification.

[0367] Data processing / calculation:

[0368] The server generates a liveness notification and sends it to the interested parties in the appropriate format (email, SMS, push notification).

[0369] Step 6:

[0370] User acknowledgement of notifications and emotional feedback

[0371] Specific actions

[0372] The user checks the notification on their device and confirms that the missing person is safe. The user's reaction is fed back to the emotion engine and reflected in the content of the next notification. For example, the user's relieved reaction is recorded and the emotion data is updated.

[0373] input:

[0374] A live notification from the server.

[0375] output:

[0376] User notification confirmation and emotional data feedback.

[0377] Data processing / calculation:

[0378] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[0379] As described above, each processing step of this system works together to confirm the safety of missing people and optimize the system based on user sentiment, allowing those involved to receive information quickly and with peace of mind.

[0380] (Application example 2)

[0381] 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."

[0382] While conventional systems for confirming the safety of missing persons are somewhat effective in collecting and analyzing information, they do not optimize notifications that take into account the user's emotions, and therefore do not reduce the psychological burden on those involved. Furthermore, there are issues with the ability to efficiently store input missing person information in a database and notify those involved at the appropriate time.

[0383] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting identifying information (such as name, address, and physical characteristics) of a missing person and extracting survival confirmation information using natural language processing; means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles; means for quickly notifying relevant parties of the collated survival confirmation information; means including an emotion engine that recognizes the user's emotions and optimizes the content and timing of the notification; means for converting the user's input information into JSON format and sending it to the server using an HTTP POST request; and means for customizing the notification content and displaying a reassuring message that corresponds to the user's emotions. This reduces the psychological burden on the user and enables quick and reassuring safety confirmation.

[0384] "Identifying information of a missing person" is information that can individually identify a missing person, such as their name, address, or physical description.

[0385] "Natural language processing" is a technology that analyzes input text data, understands meaning and information, and extracts it.

[0386] "Confirmation of survival information" is information indicating that a missing person is safe, and is obtained from evacuation center reports, social media data, news articles, etc.

[0387] "Reports from evacuation centers" are information about the situation and safety of people at evacuation centers.

[0388] "SNS Data" refers to information such as posts, comments, and messages shared by users on social networking services.

[0389] A "news article" is an article of information reported in media such as newspapers or websites.

[0390] An "emotion-recognizing emotion engine" is a technology that analyzes and recognizes emotions from the user's input information and reactions.

[0391] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that structures and represents data in a lightweight manner.

[0392] An "HTTP POST request" is a method of the HTTP protocol used by a web client to send data to a server.

[0393] "Customizing notifications" refers to tailoring the content and format of notifications based on the user's needs and emotions.

[0394] A "reassuring message" is a contact message whose content is designed to make the user feel at ease.

[0395] This invention is a system that quickly and accurately confirms the safety of missing people. In particular, it uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and also has the function of recognizing the user's emotions with an emotion engine to optimize the content and timing of notifications. This allows for quick and appropriate provision of information to relevant parties.

[0396] System configuration

[0397] server

[0398] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0399] For example, the server receives specific information for "Ichiro Tanaka" entered by the user and stores it in a database. The NLP module then analyzes evacuation shelters, social media data, and news articles based on the information for "Ichiro Tanaka," and extracts information confirming his survival, such as "Ichiro Tanaka is at an evacuation shelter."

[0400] Terminal

[0401] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0402] As a specific example of use, a user enters specific information for "Ichiro Tanaka" through a smartphone application and sends it to the server. The server generates confirmation information stating "Ichiro Tanaka is safe" and sends it to the user's device as a push notification. If the emotion engine recognizes "anxiety" at this time, it adds a reassuring message saying "We have also confirmed Ichiro Tanaka's detailed whereabouts."

[0403] User

[0404] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[0405] For example, based on the information entered by the user as "Ichiro Tanaka," the user receives a notification that "Ichiro Tanaka is safe at the evacuation center." If the user has previously entered a worrying comment, the next notification will include a message that says, "Detailed location information for Ichiro Tanaka has also been obtained," to provide a sense of security.

[0406] Hardware and software used

[0407] Hardware

[0408] Smartphone (iOS, Android)

[0409] Server (Cloud service: AWS, GCP, etc.)

[0410] software

[0411] Frontend: React Native (for smartphones)

[0412] Backend: Python (Django, Flask)

[0413] API communication: HTTP, JSON

[0414] Natural Language Processing (NLP): spaCy, NLTK (Python packages)

[0415] Emotion Engine: Machine learning model using tensorflow and keras

[0416] Prompt Sentence Examples

[0417] Enter missing person information:

[0418] Name: "Ichiro Tanaka"

[0419] Address: "City"

[0420] Physical Description: "175cm tall, black hair"

[0421] NLP analysis results:

[0422] Ichiro Tanaka was identified at the evacuation center.

[0423] Emotion Engine Analysis Results:

[0424] The user felt uneasy after receiving the contact.

[0425] Optimized notifications:

[0426] "We have confirmed the identity of Ichiro Tanaka. We have also received detailed information on his whereabouts."

[0427] With the above-described configuration, the present invention can quickly and accurately collate information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[0428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0429] Step 1:

[0430] User enters specific information about the missing person

[0431] Users enter identifying information about a missing person, such as name, address, and physical characteristics, into a smartphone application. The information is then converted into JSON format, which structures the user's input and converts it into a format that can be sent to the server.

[0432] input:

[0433] Name: "Ichiro Tanaka"

[0434] Address: "City"

[0435] Physical Description: "175cm tall, black hair"

[0436] output:

[0437] Missing person information in JSON format

[0438] Step 2:

[0439] The device sends the input information to the server

[0440] The device sends the generated JSON-formatted data to the server using an HTTP POST request, which sends the data to an API endpoint at a specific URL.

[0441] input:

[0442] Missing person information in JSON format

[0443] output:

[0444] HTTP POST request to the server

[0445] Step 3:

[0446] The server receives the missing person information and stores it in a database

[0447] The server parses the received JSON data and stores the missing person's identifying information in a database, which then uses the structured data for subsequent natural language processing (NLP) and matching.

[0448] input:

[0449] Missing person information sent in an HTTP POST request

[0450] output:

[0451] Missing person information stored in a database

[0452] Step 4:

[0453] The server collects and analyzes external information

[0454] The server runs a script that periodically collects reports from evacuation centers, social media data, and news articles. The collected data is then passed to an NLP module for analysis, which extracts relevant survival information.

[0455] input:

[0456] Evacuation shelter reporting data

[0457] Social media data

[0458] News Article

[0459] output:

[0460] Extracted survival confirmation information

[0461] Specific operation:

[0462] For example, the NLP module extracts information from a news article that says, "Ichiro Tanaka is at a shelter."

[0463] Step 5:

[0464] Emotion engine recognizes user emotions

[0465] The server analyzes the user's input information and reactions to past notifications, and uses an emotion engine to recognize the user's emotions. Past comments and reaction data are used to reflect the user's emotions in the next notification.

[0466] input:

[0467] User's past comments and reaction data

[0468] output:

[0469] Recognized user emotion data

[0470] Step 6:

[0471] The server generates and sends the notification content

[0472] The server generates notifications based on the matching results and feedback from the emotion engine and sends them to relevant parties in the form of email, SMS, or push notifications, with messages customized according to the user's emotions.

[0473] input:

[0474] Matching result (survival confirmation information)

[0475] Emotion Engine Feedback

[0476] output:

[0477] Customized notification messages

[0478] Specific operation:

[0479] The notification "Ichiro Tanaka is safe in the evacuation center" will be supplemented with a reassuring message that "Detailed location confirmation has also been obtained."

[0480] Step 7:

[0481] The device receives and displays the notification.

[0482] The terminal receives the notification sent from the server and displays it on the terminal screen of the person involved, allowing the person involved to check the information immediately.

[0483] input:

[0484] Notification messages sent by the server

[0485] output:

[0486] Notification message displayed on the device of the person involved

[0487] Specific operation:

[0488] A push notification will appear on your smartphone with the message "Ichiro Tanaka is safely evacuated to the evacuation shelter."

[0489] Through the above processing steps, the system can quickly and accurately collate information on missing people and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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."

[0506] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, specifically, a system for comparing the missing person's identifying information with information confirming their survival using natural language processing and notifying relevant parties. The system of the present invention is implemented by the following program.

[0507] System configuration

[0508] server

[0509] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles, and generates survival confirmation information based on that data.

[0510] Terminal

[0511] The device provides an interface for users to enter missing person information, which is then sent to a server for verification. The device also displays notifications that relevant parties can receive. For example, notifications can be received through a smartphone application or a web browser.

[0512] User

[0513] The user acts as a family member or relative of the missing person and inputs information about the missing person through the terminal and receives notifications.

[0514] Program processing explanation

[0515] Inputting missing person information and extracting survival confirmation information

[0516] User

[0517] The user enters information such as the missing person's name, address, and physical characteristics into an input form on the terminal.

[0518] Terminal

[0519] The terminal converts the entered missing person information into JSON format and sends it to the server's specified API endpoint.

[0520] server

[0521] The server parses the received JSON data and stores the missing person information in a database.

[0522] The stored information is passed to a natural language processing module, which searches and extracts relevant liveness information from existing information in the database.

[0523] The extracted survival confirmation information is stored in a database.

[0524] Collection and analysis of external information

[0525] server

[0526] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[0527] The collected data is passed to the NLP module for analysis.

[0528] The extracted survival confirmation information is then compared with the missing person information.

[0529] Sending alive notification

[0530] server

[0531] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[0532] Use the notification system to send liveness information to relevant parties via email, SMS, or push notification.

[0533] Terminal

[0534] Display notifications on the devices of those involved.

[0535] User

[0536] The user receives the notification on the terminal and checks the survival confirmation information.

[0537] Specific examples

[0538] For example, to confirm that relative B is missing, user A enters the name "Tanaka Taro," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into his device. The device sends this information to the server. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information on whether "Tanaka Taro" is alive and well from evacuation shelters, social media, and news. As a result of the comparison, the information "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo" is generated, and an email notification is sent to user A. User A receives the notification on his device and can confirm that relative B is safe.

[0539] As described above, the system of the present invention can quickly and accurately collate information on missing persons and notify relevant parties, thereby providing peace of mind to disaster victims and their families.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] User

[0543] The user enters information such as the missing person's name, address, and physical characteristics into an input form provided on the terminal.

[0544] For example, enter the name "Yamada Hanako," the address "Sumida Ward, Tokyo," and the physical characteristics "height 160 cm, black hair, glasses."

[0545] Step 2:

[0546] Terminal

[0547] The terminal converts the missing person information entered by the user into JSON format.

[0548] The converted JSON data is sent to the server's API endpoint using an HTTP POST request.

[0549] Step 3:

[0550] server

[0551] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0552] The stored information is passed to a natural language processing (NLP) module, which searches and extracts relevant liveness information from existing information in the database.

[0553] The extracted survival confirmation information is stored in a database.

[0554] Step 4:

[0555] server

[0556] The server runs scripts to periodically collect reports from evacuation centers, social media data, and news articles.

[0557] The information collected from each data source is passed to the NLP module for analysis.

[0558] For example, extract the information that "Yamada Hanako is at a shelter in Sumida Ward, Tokyo."

[0559] Step 5:

[0560] server

[0561] The extracted survival confirmation information is compared with the missing person's identifying information, and if there is a match, the information is stored in a database.

[0562] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[0563] Step 6:

[0564] server

[0565] Use the notification system to notify relevant parties of liveness confirmation information.

[0566] Select the appropriate notification format: email, SMS, or push notification.

[0567] For example, a notification is sent to the user's email address stating, "Yamada Hanako has safely evacuated to a shelter in Sumida Ward, Tokyo."

[0568] Step 7:

[0569] Terminal

[0570] The relevant person's device receives the notification and displays the notification content.

[0571] For example, a push notification on a smartphone might display the message "Yamada Hanako is safe."

[0572] Step 8:

[0573] User

[0574] The user checks the notification on their device and receives information about the missing person who has been confirmed safe.

[0575] This provides peace of mind to the user.

[0576] Through the above processing steps, information on missing persons is quickly and accurately collated and notified to the relevant parties, making it possible to quickly confirm the safety of the victims' families and other relevant parties.

[0577] Example 1

[0578] 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."

[0579] Rapid and accurate confirmation of the safety of missing persons is an extremely difficult task, especially during disasters or sudden incidents. Conventional systems rely primarily on manual confirmation work, requiring a significant amount of time for information collection and analysis, which often results in delays in confirming the person's actual survival and in notifying them. This frequently causes unnecessary worry for missing persons, their families, and other concerned parties. Therefore, there is a need for a system that can quickly and automatically check the missing person's survival based on the missing person's identifying information and promptly notify those concerned.

[0580] 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.

[0581] In this invention, the server includes means for inputting specific information of missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing technology, means for automatically collecting, analyzing, and organizing external information such as reports from evacuation centers, social networking service data, and news articles, and means for quickly notifying relevant parties of the collated survival confirmation information. This makes it possible to quickly and accurately collate information on missing persons and quickly provide relevant parties with survival confirmation information.

[0582] "Identifying information of a missing person" refers to basic information needed to identify a missing person, such as name, address, and physical characteristics.

[0583] "Natural language processing technology" is a technology that allows computers to understand human language and perform analysis and generation.

[0584] "Survival confirmation information" is information indicating that a missing person is currently alive, and can be obtained from evacuation center reports, social media data, news articles, etc.

[0585] "Reports from evacuation shelters" are information reporting the situation of people gathered at evacuation shelters during disasters or emergencies.

[0586] "Social networking service data" refers to data such as text, images, and videos posted on social media platforms.

[0587] A "news article" is the text of news reported in newspapers, television, online media, etc.

[0588] "Means for automatically collecting, analyzing, and organizing" refers to means for automatically collecting external information using a program and analyzing and organizing it using natural language processing technology, etc.

[0589] "Matched survival confirmation information" is survival confirmation information that matches the identifying information of a missing person, and is based on existing information in a database or information collected from outside.

[0590] "Means of notification" refers to the means of communicating information to relevant parties, such as by email, SMS, or push notification.

[0591] A "database" is a system for systematically storing and managing digital information.

[0592] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, and more specifically, to a system for comparing the missing person's identifying information with information confirming their survival using natural language processing technology and notifying relevant parties. An embodiment of the system will be described below.

[0593] System configuration

[0594] server

[0595] The server receives specific information about missing persons (such as name, address, and physical characteristics) entered by the user and stores it in a database. A typical relational database (such as MySQL or PostgreSQL) or a NoSQL database (such as MongoDB) can be used as the database. The server also includes a natural language processing (NLP) module, which includes the Python library spaCy and a Transformer-based model. The NLP module is used to analyze the received information and extract information confirming the person's survival. Furthermore, the server periodically runs a script that collects external information, such as reports from evacuation centers, social networking service (SNS) data, and news articles. The collected data is also analyzed by the NLP module, and survival information is generated based on that data.

[0596] Terminal

[0597] The device provides an interface for users to input specific information about missing people. The interface is provided through a smartphone application or web browser. The device converts the input information into JSON format and sends it to the server via an HTTP request. The device also has a function to display notifications received by relevant parties. This allows relevant parties to receive information confirming the person's survival in the form of push notifications, emails, SMS, etc.

[0598] User

[0599] The user, as a family member or associate of a missing person, plays the role of entering specific information about the missing person through the terminal. The specific information entered by the user includes the missing person's name, address, physical characteristics, etc. For example, to confirm the disappearance of relative B, user A might enter the name "Taro Tanaka," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into the terminal.

[0600] Specific examples

[0601] For example, to confirm the disappearance of relative B, user A enters "Name: Tanaka Taro, Address: Shinjuku Ward, Tokyo, Physical Characteristics: Height 170 cm, Black Hair, Glasses." The device converts this information into JSON format and sends it to the server via an HTTP request. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information confirming the survival of "Tanaka Taro" from evacuation shelter reports, social media, and news articles. A specific example might be a prompt: "We are looking for a missing person named Tanaka Taro. His address is in Shinjuku Ward, Tokyo, and his physical characteristics are height 170 cm, black hair, and glasses. Please find information confirming his survival." The matching result is generated as "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo," and an email notification is sent to user A. User A receives the notification on their device and can confirm that relative B is safe.

[0602] As described above, the system of the present invention can quickly and accurately check the survival confirmation information based on the missing person's identifying information and notify the relevant parties, thereby providing peace of mind to the missing person and their family and related parties.

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

[0604] Step 1: Enter missing person information

[0605] User

[0606] The user enters the missing person's identifying information (such as name, address, and physical characteristics) into an input form on the device. For example, User A enters information such as "Taro Tanaka, Shinjuku Ward, Tokyo, height 170 cm, black hair, glasses." The input data at this point is the missing person's identifying information.

[0607] Step 2: Send data to the server

[0608] Terminal

[0609] The terminal converts the missing person identification information entered by the user into JSON format. Then, it sends the converted JSON data to the server in the form of an HTTP request. Specifically, it sends the data by specifying an endpoint (e.g., " / api / v1 / missing_persons"). The output at this point is the JSON data sent to the server.

[0610] Step 3: Store and analyze missing person information

[0611] server

[0612] The server extracts JSON data from the received HTTP request, parses it, and stores it in a database, such as MongoDB or MySQL. Once stored in this database, the data is passed to a natural language processing (NLP) module, which uses an NLP module (e.g., spaCy or a Transformer-based model) to parse the received data and extract the survival information. The output is a database entry containing the missing person information and the NLP analysis results.

[0613] Step 4: Collect and analyze external data

[0614] server

[0615] The server periodically runs a script that collects external data. This script automatically collects data from evacuation shelter reports, social networking service (SNS) posts, news articles, etc. The collected data is then passed to the same NLP module for analysis and extraction. For example, the data is scraped using Python's BeautifulSoup or Scrapy, and then analyzed by the NLP module. The output at this point is the collected and analyzed survival confirmation information.

[0616] Step 5: Generating and notifying liveness information

[0617] server

[0618] The server compares the analysis results from the NLP module with the missing person information and generates final survival confirmation information. For example, it generates information such as "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." Next, it retrieves the contact information of the relevant parties from the database and uses a notification system (e.g., Twilio or Firebase) to send the survival confirmation information in the form of email, SMS, or push notification. The output at this point is the notification message sent to the relevant parties.

[0619] Step 6: Receive live notification

[0620] Terminal

[0621] The device receives the notification sent from the server and displays it to the user. For example, a message may be displayed in the notification area of ​​a smartphone or within an application. The output at this point is a liveness confirmation notification displayed on the device.

[0622] User

[0623] The user checks the notification and receives the survival confirmation information. Specifically, the user can feel relieved when they see the notification that "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." The final output here is the survival confirmation information that the user receives.

[0624] As a result, this system can quickly and accurately confirm the safety of missing people and notify relevant parties.

[0625] (Application example 1)

[0626] 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."

[0627] In order to quickly and accurately confirm the safety of missing persons, it is necessary to collect and analyze data from multiple information sources and notify relevant parties in a timely manner. However, with conventional systems, it takes time to collect and analyze information, making it particularly difficult to provide information in real time. Furthermore, there is a problem that on-site confirmation work is time-consuming, making it difficult to respond efficiently. The present invention aims to solve these problems and realize the provision of quick and accurate safety confirmation information.

[0628] 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.

[0629] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation centers, social media data, and news articles, means for quickly notifying relevant parties of the collated survival confirmation information, means for displaying and confirming missing person information in real time using visual display devices worn by security guards, and means for obtaining new information from evacuation centers and social media and notifying them by audio and visual alerts. This enables rapid and accurate data collection and analysis from multiple sources, as well as real-time confirmation on site.

[0630] "Identifying information of a missing person" is information that can be used to identify a missing person, such as name, address, and physical characteristics.

[0631] "Natural language processing" is a technology that enables computers to understand, interpret, and manipulate human language.

[0632] "Survival confirmation information" is information indicating that a missing person is safe.

[0633] "Reports from evacuation shelters" refers to the provision of information regarding missing persons at evacuation shelters.

[0634] "SNS Data" is information obtained from posts and comments on social networking services.

[0635] A "news article" is news content provided by a media outlet.

[0636] "External information" is any information collected from outside the system.

[0637] A "visual display device" is a device for visually displaying information, and in this context refers to a device worn by a security guard.

[0638] "Audio and visual alerts" are methods of warning or notifying through audio and visual means.

[0639] The present invention is a system for quickly and accurately confirming the safety of missing persons. This system is composed of the following main components: a server, a terminal, and a user.

[0640] server

[0641] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract information confirming their survival. It also periodically collects external information, such as reports from evacuation centers, social media data, and news articles, and analyzes and organizes it using the NLP module. The server then promptly notifies relevant parties of the results of these comparisons.

[0642] Specifically, when missing person information is sent to the server, natural language processing technology is used to search for and extract related survival confirmation information. Spacy is used as the NLP module to perform data analysis. Evacuation shelter information and social media data are collected regularly and automatically processed on the server. Matched survival confirmation information is quickly distributed to relevant contacts via the notification system.

[0643] Terminal

[0644] The device provides an interface for users to input missing person information. The input information is sent to a server, where the missing person information is compared with information confirming their survival. The device also displays notifications to relevant parties. Devices include smartphones, smart glasses, and head-mounted displays (HMDs).

[0645] Specifically, when security guards patrol wearing head-mounted displays (HMDs), specific information about missing persons is visually displayed, and audio and visual alerts are issued when new information is updated in real time.

[0646] User

[0647] Users, as family members or relatives of the missing person, input information about the missing person through the device and receive notifications. When users use voice input, the voice data is converted into text data and sent to the server. It is then quickly collated and analyzed, and the results are notified.

[0648] Specific examples of prompts include:

[0649] Enter the missing person's information (name, address, physical characteristics). For example, "Taro Tanaka, Shinjuku Ward, Tokyo, height 170cm, black hair, glasses." The entered information will be analyzed using natural language processing, and information on whether the person is alive will be displayed.

[0650] This system makes it possible to quickly confirm the safety of missing persons, providing peace of mind to those involved.

[0651] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0652] Step 1:

[0653] A user uses a terminal to input information about a missing person, such as name, address, and physical characteristics. The input information is converted into JSON format, and this data is sent to a specified API endpoint on the server.

[0654] Step 2:

[0655] The server parses the received JSON data and stores it in the database, checking the format and integrity of the data to ensure that the entered missing person information is stored accurately.

[0656] Step 3:

[0657] The server passes the stored missing person information to a natural language processing (NLP) module, which searches and extracts relevant information from existing information in the database. Data processing here includes text analysis and keyword extraction.

[0658] Step 4:

[0659] The server periodically runs a script that collects external information such as reports from evacuation centers, social media data, and news articles. This data is also passed to the NLP module for analysis, resulting in additional survival information.

[0660] Step 5:

[0661] The server stores the extracted liveness information in a database and generates a matching result, which is then prepared for immediate notification to relevant parties, whose contact information is also retrieved from the database.

[0662] Step 6:

[0663] The server sends information to relevant parties to confirm the person's survival via email, SMS, push notification, etc. The notification includes the missing person's status and location information.

[0664] Step 7:

[0665] The terminal has the ability to display information in real time on the guard's HMD, which visually displays specific information about missing persons, and issues audio and visual alerts when new information is reported.

[0666] Step 8:

[0667] The user receives a notification on their device and checks it. This notification includes information on the safety of the missing person and is displayed in real time, allowing the user to take prompt action based on this information.

[0668] This processing flow enables the safety of missing persons to be confirmed quickly and accurately, and the information is promptly notified to relevant parties.

[0669] 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.

[0670] The present invention is a system for quickly and accurately confirming the safety of missing persons, and is particularly equipped with a function that recognizes the user's emotions to optimize the response. Specifically, the system uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and further recognizes the user's emotions using an emotion engine to optimize the content and timing of notifications, thereby providing information to relevant parties quickly and appropriately.

[0671] System configuration

[0672] server

[0673] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0674] Terminal

[0675] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0676] User

[0677] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[0678] Program processing explanation

[0679] Inputting missing person information and extracting survival confirmation information

[0680] User

[0681] The user enters the missing person's name, address, physical characteristics, etc. into an input form on the terminal.

[0682] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0683] Terminal

[0684] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[0685] server

[0686] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0687] The stored information is passed to the NLP module to search and extract liveness information.

[0688] The extracted survival confirmation information is stored in a database.

[0689] Collection and analysis of external information

[0690] server

[0691] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[0692] Each collected data is passed to the NLP module for analysis.

[0693] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[0694] Emotion engine for recognizing user emotions and optimizing notifications

[0695] server

[0696] The emotion engine analyzes the information entered by the user and their reactions to past notifications to recognize their emotions.

[0697] For example, if a user frequently enters anxious comments, the next notification will contain reassuring content.

[0698] Terminal

[0699] Customize notification content based on feedback from the emotion engine.

[0700] For example, in addition to the notification "Ichiro Tanaka is safe," a reassuring message such as "Detailed location confirmation has been obtained" is sent.

[0701] Sending alive notification

[0702] server

[0703] Notifications are generated based on the matching results and feedback from the emotion engine.

[0704] Sending generated notifications to interested parties in the form of email, SMS, and push notifications.

[0705] Terminal

[0706] The relevant person's device receives the notification and displays the notification content.

[0707] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safely evacuated to a shelter in Osaka City."

[0708] User

[0709] The user checks the notification on their device and confirms that the missing person is safe.

[0710] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[0711] By using the above processing steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0712] The processing flow will be explained below.

[0713] Step 1:

[0714] User

[0715] The user opens a form on the terminal to enter the missing person's name, address, physical characteristics, etc.

[0716] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0717] Step 2:

[0718] Terminal

[0719] The terminal converts the input information into JSON format data.

[0720] The converted JSON data is sent to the server's API using an HTTP POST request.

[0721] Step 3:

[0722] server

[0723] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[0724] The stored specific information is passed directly to a natural language processing (NLP) module, which searches and extracts relevant information from existing liveness confirmation information.

[0725] The extracted survival confirmation information is stored in a database.

[0726] Step 4:

[0727] server

[0728] The server periodically runs scripts to collect evacuation shelter reports, social media data, and news articles.

[0729] The collected data is passed to an NLP module, which parses and extracts relevant liveness information.

[0730] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[0731] Step 5:

[0732] server

[0733] The extracted survival confirmation information is compared with the missing person's identification information.

[0734] The matched information is stored in the database.

[0735] Step 6:

[0736] server

[0737] The server uses an emotion engine to recognize the user's emotions based on the user's input information and reaction data to past notifications.

[0738] For example, if a user enters "I'm very worried" into an input form, the emotion engine will detect this as an emotion indicating anxiety.

[0739] Step 7:

[0740] server

[0741] Optimize the content of notifications based on feedback from the emotion engine.

[0742] For example, additional information that provides a sense of security could be added, such as "Ichiro Tanaka is safely evacuated to a shelter in Osaka City. His detailed whereabouts have also been confirmed."

[0743] Step 8:

[0744] server

[0745] Notifications generated based on matching results and feedback from the sentiment engine are sent to relevant parties via email, SMS, or push notifications.

[0746] Step 9:

[0747] Terminal

[0748] The terminals of the people involved receive the notification and display the notification content in a way that gives the user a sense of security.

[0749] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safe at a shelter in Osaka City. His detailed whereabouts have been confirmed."

[0750] Step 10:

[0751] User

[0752] The user checks the notification on their device and knows that the missing person is safe.

[0753] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification, giving the user a greater sense of security.

[0754] By using the above steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0755] Example 2

[0756] 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."

[0757] The challenge is to provide information about missing people quickly and accurately, so that those involved can feel at ease. Furthermore, conventional systems do not provide information that takes into account the user's feelings, and this point also needs to be improved.

[0758] 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.

[0759] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles, and means for analyzing input information from users and responses to past notifications to recognize emotions and optimize the content and timing of notifications. This makes it possible to provide missing person information quickly and accurately and to notify at the optimal timing taking into account the user's emotions.

[0760] "Identifying information of a missing person" is information such as name, address, and physical characteristics that can be used to identify a missing person.

[0761] "Natural language processing" is a technology that enables computers to understand and analyze text written in natural language.

[0762] "Survival confirmation information" is information used to confirm that missing persons are still alive, and includes reports from evacuation centers, social media data, news articles, etc.

[0763] "Reports from evacuation centers" refers to information collected at evacuation centers regarding the status of missing persons.

[0764] "SNS data" refers to information about missing persons posted on social networking services.

[0765] A "news article" is an article about a missing person published by a news organization.

[0766] The "emotion engine" is a component that analyzes the user's input information and reactions to past notifications to recognize the user's emotions.

[0767] "Optimizing notification content and timing" is the process of adjusting the content and timing of notifications based on the user's emotional state.

[0768] A "database" is a system for efficiently storing, managing, and retrieving data.

[0769] "JSON format" stands for JavaScript Object Notation and is a lightweight data exchange format.

[0770] An "HTTP POST request" is a request method in the HTTP protocol for a client to send data to a server.

[0771] A "push notification" is a notification message that is actively sent from a server to a client device.

[0772] "Email" is a digital message sent over the Internet.

[0773] "SMS" stands for Short Message Service, a service that sends short text messages via mobile phones.

[0774] The present invention provides a system for quickly and accurately confirming the safety of missing people, and is particularly equipped with a function for recognizing the user's emotions and optimizing the response. The system includes components of a server, a terminal, and a user.

[0775] server

[0776] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0777] As a concrete example, information about a missing person named "Ichiro Tanaka" is received, and based on that information, information confirming the person's survival, such as "Ichiro Tanaka is at a shelter in Osaka City," is extracted. Also, in cases where a user frequently enters anxious comments, the next notification will include information that provides reassurance.

[0778] Terminal

[0779] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0780] For example, when a user enters information about "Ichiro Tanaka," the device converts this information into JSON format and sends it to the server. When a notification is received from the server, the device displays a reassuring message saying, "Ichiro Tanaka is safely evacuated to an evacuation shelter in Osaka City."

[0781] User

[0782] Users, as family members or relatives, enter information about the missing person through their device and receive a notification. The user's emotions are recorded, and this emotional data is reflected in the next notification. For example, if a user enters a comment when feeling anxious, the emotion engine will analyze it, and the next notification will have a more reassuring message.

[0783] Prompt Sentence Examples

[0784] Generate a description of a missing person safety confirmation system. Include each processing step: inputting missing person information, collecting external information, recognizing emotions using an emotion engine, and sending a survival confirmation notification.

[0785] With the above configuration, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[0786] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0787] Step 1:

[0788] User enters missing person information and submits it to the server

[0789] Specific actions

[0790] The user inputs information such as the name, address, and physical characteristics of the missing person into an input form on the terminal. For example, the user inputs the name "Ichiro Tanaka," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[0791] input:

[0792] Missing person information entered by the user.

[0793] output:

[0794] Missing person information converted to JSON format on the device.

[0795] Data processing / calculation:

[0796] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[0797] Step 2:

[0798] Storage and analysis of missing person information on the server

[0799] Specific actions

[0800] The server parses the JSON data received from the device. The parsed missing person information is stored in a database. The stored information is then passed to the NLP module to search for and extract information confirming survival.

[0801] input:

[0802] JSON data sent from the terminal.

[0803] output:

[0804] Missing person information stored in the database and extracted survival confirmation information.

[0805] Data processing / calculation:

[0806] The server parses the JSON data and stores the missing person's specific information in a database, which is then passed to an NLP module to extract information on whether the person is alive or not.

[0807] Step 3:

[0808] Collection and analysis of external information

[0809] Specific actions

[0810] The server periodically runs scripts to collect external information such as reports from evacuation centers, social media data, and news articles. The collected data is passed to an NLP module for analysis. For example, it extracts information such as "Ichiro Tanaka is at an evacuation center in Osaka City."

[0811] input:

[0812] External information (shelter reports, social media data, news articles).

[0813] output:

[0814] Survival confirmation information analyzed by the NLP module.

[0815] Data processing / calculation:

[0816] The server collects external information and analyzes it using the NLP module. The analyzed information is extracted as survival confirmation information.

[0817] Step 4:

[0818] Optimizing user emotion recognition and notification with an implemented emotion engine

[0819] Specific actions

[0820] The server's emotion engine analyzes the user's input information and reactions to past notifications. Based on the analysis results, it recognizes the user's emotions and optimizes the content and timing of the next notification. For example, if a user frequently enters anxious comments, the next notification will include reassuring content.

[0821] input:

[0822] User input information and response data to past notifications.

[0823] output:

[0824] Optimized notification content and timing.

[0825] Data processing / calculation:

[0826] The emotion engine analyzes user input and recognizes emotions, optimizing the content and timing of the next notification.

[0827] Step 5:

[0828] Generate and send heartbeat notifications

[0829] Specific actions

[0830] The server generates a liveness notification based on the matching results and feedback from the emotion engine, and the notification is sent to the relevant parties in the form of email, SMS, or push notification.

[0831] input:

[0832] Matching results and emotion engine feedback.

[0833] output:

[0834] The generated heartbeat notification.

[0835] Data processing / calculation:

[0836] The server generates a liveness notification and sends it to the interested parties in the appropriate format (email, SMS, push notification).

[0837] Step 6:

[0838] User acknowledgement of notifications and emotional feedback

[0839] Specific actions

[0840] The user checks the notification on their device and confirms that the missing person is safe. The user's reaction is fed back to the emotion engine and reflected in the content of the next notification. For example, the user's relieved reaction is recorded and the emotion data is updated.

[0841] input:

[0842] A live notification from the server.

[0843] output:

[0844] User notification confirmation and emotional data feedback.

[0845] Data processing / calculation:

[0846] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[0847] As described above, each processing step of this system works together to confirm the safety of missing people and optimize the system based on user sentiment, allowing those involved to receive information quickly and with peace of mind.

[0848] (Application example 2)

[0849] 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."

[0850] While conventional systems for confirming the safety of missing persons are somewhat effective in collecting and analyzing information, they do not optimize notifications that take into account the user's emotions, and therefore do not reduce the psychological burden on those involved. Furthermore, there are issues with the ability to efficiently store input missing person information in a database and notify those involved at the appropriate time.

[0851] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting identifying information (such as name, address, and physical characteristics) of a missing person and extracting survival confirmation information using natural language processing; means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles; means for quickly notifying relevant parties of the collated survival confirmation information; means including an emotion engine that recognizes the user's emotions and optimizes the content and timing of the notification; means for converting the user's input information into JSON format and sending it to the server using an HTTP POST request; and means for customizing the notification content and displaying a reassuring message that corresponds to the user's emotions. This reduces the psychological burden on the user and enables quick and reassuring safety confirmation.

[0852] "Identifying information of a missing person" is information that can individually identify a missing person, such as their name, address, or physical description.

[0853] "Natural language processing" is a technology that analyzes input text data, understands meaning and information, and extracts it.

[0854] "Confirmation of survival information" is information indicating that a missing person is safe, and is obtained from evacuation center reports, social media data, news articles, etc.

[0855] "Reports from evacuation centers" are information about the situation and safety of people at evacuation centers.

[0856] "SNS Data" refers to information such as posts, comments, and messages shared by users on social networking services.

[0857] A "news article" is an article of information reported in media such as newspapers or websites.

[0858] An "emotion-recognizing emotion engine" is a technology that analyzes and recognizes emotions from the user's input information and reactions.

[0859] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that structures and represents data in a lightweight manner.

[0860] An "HTTP POST request" is a method of the HTTP protocol used by a web client to send data to a server.

[0861] "Customizing notifications" refers to tailoring the content and format of notifications based on the user's needs and emotions.

[0862] A "reassuring message" is a contact message whose content is designed to make the user feel at ease.

[0863] This invention is a system that quickly and accurately confirms the safety of missing people. In particular, it uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and also has the function of recognizing the user's emotions with an emotion engine to optimize the content and timing of notifications. This allows for quick and appropriate provision of information to relevant parties.

[0864] System configuration

[0865] server

[0866] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[0867] For example, the server receives specific information for "Ichiro Tanaka" entered by the user and stores it in a database. The NLP module then analyzes evacuation shelters, social media data, and news articles based on the information for "Ichiro Tanaka," and extracts information confirming his survival, such as "Ichiro Tanaka is at an evacuation shelter."

[0868] Terminal

[0869] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[0870] As a specific example of use, a user enters specific information for "Ichiro Tanaka" through a smartphone application and sends it to the server. The server generates confirmation information stating "Ichiro Tanaka is safe" and sends it to the user's device as a push notification. If the emotion engine recognizes "anxiety" at this time, it adds a reassuring message saying "We have also confirmed Ichiro Tanaka's detailed whereabouts."

[0871] User

[0872] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[0873] For example, based on the information entered by the user as "Ichiro Tanaka," the user receives a notification that "Ichiro Tanaka is safe at the evacuation center." If the user has previously entered a worrying comment, the next notification will include a message that says, "Detailed location information for Ichiro Tanaka has also been obtained," to provide a sense of security.

[0874] Hardware and software used

[0875] Hardware

[0876] Smartphone (iOS, Android)

[0877] Server (Cloud service: AWS, GCP, etc.)

[0878] software

[0879] Frontend: React Native (for smartphones)

[0880] Backend: Python (Django, Flask)

[0881] API communication: HTTP, JSON

[0882] Natural Language Processing (NLP): spaCy, NLTK (Python packages)

[0883] Emotion Engine: Machine learning model using tensorflow and keras

[0884] Prompt Sentence Examples

[0885] Enter missing person information:

[0886] Name: "Ichiro Tanaka"

[0887] Address: "City"

[0888] Physical Description: "175cm tall, black hair"

[0889] NLP analysis results:

[0890] Ichiro Tanaka was identified at the evacuation center.

[0891] Emotion Engine Analysis Results:

[0892] The user felt uneasy after receiving the contact.

[0893] Optimized notifications:

[0894] "We have confirmed the identity of Ichiro Tanaka. We have also received detailed information on his whereabouts."

[0895] With the above-described configuration, the present invention can quickly and accurately collate information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

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

[0897] Step 1:

[0898] User enters specific information about the missing person

[0899] Users enter identifying information about a missing person, such as name, address, and physical characteristics, into a smartphone application. The information is then converted into JSON format, which structures the user's input and converts it into a format that can be sent to the server.

[0900] input:

[0901] Name: "Ichiro Tanaka"

[0902] Address: "City"

[0903] Physical Description: "175cm tall, black hair"

[0904] output:

[0905] Missing person information in JSON format

[0906] Step 2:

[0907] The device sends the input information to the server

[0908] The device sends the generated JSON-formatted data to the server using an HTTP POST request, which sends the data to an API endpoint at a specific URL.

[0909] input:

[0910] Missing person information in JSON format

[0911] output:

[0912] HTTP POST request to the server

[0913] Step 3:

[0914] The server receives the missing person information and stores it in a database

[0915] The server parses the received JSON data and stores the missing person's identifying information in a database, which then uses the structured data for subsequent natural language processing (NLP) and matching.

[0916] input:

[0917] Missing person information sent in an HTTP POST request

[0918] output:

[0919] Missing person information stored in a database

[0920] Step 4:

[0921] The server collects and analyzes external information

[0922] The server runs a script that periodically collects reports from evacuation centers, social media data, and news articles. The collected data is then passed to an NLP module for analysis, which extracts relevant survival information.

[0923] input:

[0924] Evacuation shelter reporting data

[0925] Social media data

[0926] News Article

[0927] output:

[0928] Extracted survival confirmation information

[0929] Specific operation:

[0930] For example, the NLP module extracts information from a news article that says, "Ichiro Tanaka is at a shelter."

[0931] Step 5:

[0932] Emotion engine recognizes user emotions

[0933] The server analyzes the user's input information and reactions to past notifications, and uses an emotion engine to recognize the user's emotions. Past comments and reaction data are used to reflect the user's emotions in the next notification.

[0934] input:

[0935] User's past comments and reaction data

[0936] output:

[0937] Recognized user emotion data

[0938] Step 6:

[0939] The server generates and sends the notification content

[0940] The server generates notifications based on the matching results and feedback from the emotion engine and sends them to relevant parties in the form of email, SMS, or push notifications, with messages customized according to the user's emotions.

[0941] input:

[0942] Matching result (survival confirmation information)

[0943] Emotion Engine Feedback

[0944] output:

[0945] Customized notification messages

[0946] Specific operation:

[0947] The notification "Ichiro Tanaka is safe in the evacuation center" will be supplemented with a reassuring message that "Detailed location confirmation has also been obtained."

[0948] Step 7:

[0949] The device receives and displays the notification.

[0950] The terminal receives the notification sent from the server and displays it on the terminal screen of the person involved, allowing the person involved to check the information immediately.

[0951] input:

[0952] Notification messages sent by the server

[0953] output:

[0954] Notification message displayed on the device of the person involved

[0955] Specific operation:

[0956] A push notification will appear on your smartphone with the message "Ichiro Tanaka is safely evacuated to the evacuation shelter."

[0957] Through the above processing steps, the system can quickly and accurately collate information on missing people and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] [Third embodiment]

[0962] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0963] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0964] 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).

[0965] 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.

[0966] 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.

[0967] 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).

[0968] 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.

[0969] 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.

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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."

[0974] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, specifically, a system for comparing the missing person's identifying information with information confirming their survival using natural language processing and notifying relevant parties. The system of the present invention is implemented by the following program.

[0975] System configuration

[0976] server

[0977] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles, and generates survival confirmation information based on that data.

[0978] Terminal

[0979] The device provides an interface for users to enter missing person information, which is then sent to a server for verification. The device also displays notifications that relevant parties can receive. For example, notifications can be received through a smartphone application or a web browser.

[0980] User

[0981] The user acts as a family member or relative of the missing person and inputs information about the missing person through the terminal and receives notifications.

[0982] Program processing explanation

[0983] Inputting missing person information and extracting survival confirmation information

[0984] User

[0985] The user enters information such as the missing person's name, address, and physical characteristics into an input form on the terminal.

[0986] Terminal

[0987] The terminal converts the entered missing person information into JSON format and sends it to the server's specified API endpoint.

[0988] server

[0989] The server parses the received JSON data and stores the missing person information in a database.

[0990] The stored information is passed to a natural language processing module, which searches and extracts relevant liveness information from existing information in the database.

[0991] The extracted survival confirmation information is stored in a database.

[0992] Collection and analysis of external information

[0993] server

[0994] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[0995] The collected data is passed to the NLP module for analysis.

[0996] The extracted survival confirmation information is then compared with the missing person information.

[0997] Sending alive notification

[0998] server

[0999] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[1000] Use the notification system to send liveness information to relevant parties via email, SMS, or push notification.

[1001] Terminal

[1002] Display notifications on the devices of those involved.

[1003] User

[1004] The user receives the notification on the terminal and checks the survival confirmation information.

[1005] Specific examples

[1006] For example, to confirm that relative B is missing, user A enters the name "Tanaka Taro," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into his device. The device sends this information to the server. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information on whether "Tanaka Taro" is alive and well from evacuation shelters, social media, and news. As a result of the comparison, the information "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo" is generated, and an email notification is sent to user A. User A receives the notification on his device and can confirm that relative B is safe.

[1007] As described above, the system of the present invention can quickly and accurately collate information on missing persons and notify relevant parties, thereby providing peace of mind to disaster victims and their families.

[1008] The processing flow will be explained below.

[1009] Step 1:

[1010] User

[1011] The user enters information such as the missing person's name, address, and physical characteristics into an input form provided on the terminal.

[1012] For example, enter the name "Yamada Hanako," the address "Sumida Ward, Tokyo," and the physical characteristics "height 160 cm, black hair, glasses."

[1013] Step 2:

[1014] Terminal

[1015] The terminal converts the missing person information entered by the user into JSON format.

[1016] The converted JSON data is sent to the server's API endpoint using an HTTP POST request.

[1017] Step 3:

[1018] server

[1019] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1020] The stored information is passed to a natural language processing (NLP) module, which searches and extracts relevant liveness information from existing information in the database.

[1021] The extracted survival confirmation information is stored in a database.

[1022] Step 4:

[1023] server

[1024] The server runs scripts to periodically collect reports from evacuation centers, social media data, and news articles.

[1025] The information collected from each data source is passed to the NLP module for analysis.

[1026] For example, extract the information that "Yamada Hanako is at a shelter in Sumida Ward, Tokyo."

[1027] Step 5:

[1028] server

[1029] The extracted survival confirmation information is compared with the missing person's identifying information, and if there is a match, the information is stored in a database.

[1030] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[1031] Step 6:

[1032] server

[1033] Use the notification system to notify relevant parties of liveness confirmation information.

[1034] Select the appropriate notification format: email, SMS, or push notification.

[1035] For example, a notification is sent to the user's email address stating, "Yamada Hanako has safely evacuated to a shelter in Sumida Ward, Tokyo."

[1036] Step 7:

[1037] Terminal

[1038] The relevant person's device receives the notification and displays the notification content.

[1039] For example, a push notification on a smartphone might display the message "Yamada Hanako is safe."

[1040] Step 8:

[1041] User

[1042] The user checks the notification on their device and receives information about the missing person who has been confirmed safe.

[1043] This provides peace of mind to the user.

[1044] Through the above processing steps, information on missing persons is quickly and accurately collated and notified to the relevant parties, making it possible to quickly confirm the safety of the victims' families and other relevant parties.

[1045] Example 1

[1046] 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."

[1047] Rapid and accurate confirmation of the safety of missing persons is an extremely difficult task, especially during disasters or sudden incidents. Conventional systems rely primarily on manual confirmation work, requiring a significant amount of time for information collection and analysis, which often results in delays in confirming the person's actual survival and in notifying them. This frequently causes unnecessary worry for missing persons, their families, and other concerned parties. Therefore, there is a need for a system that can quickly and automatically check the missing person's survival based on the missing person's identifying information and promptly notify those concerned.

[1048] 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.

[1049] In this invention, the server includes means for inputting specific information of missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing technology, means for automatically collecting, analyzing, and organizing external information such as reports from evacuation centers, social networking service data, and news articles, and means for quickly notifying relevant parties of the collated survival confirmation information. This makes it possible to quickly and accurately collate information on missing persons and quickly provide relevant parties with survival confirmation information.

[1050] "Identifying information of a missing person" refers to basic information needed to identify a missing person, such as name, address, and physical characteristics.

[1051] "Natural language processing technology" is a technology that allows computers to understand human language and perform analysis and generation.

[1052] "Survival confirmation information" is information indicating that a missing person is currently alive, and can be obtained from evacuation center reports, social media data, news articles, etc.

[1053] "Reports from evacuation shelters" are information reporting the situation of people gathered at evacuation shelters during disasters or emergencies.

[1054] "Social networking service data" refers to data such as text, images, and videos posted on social media platforms.

[1055] A "news article" is the text of news reported in newspapers, television, online media, etc.

[1056] "Means for automatically collecting, analyzing, and organizing" refers to means for automatically collecting external information using a program and analyzing and organizing it using natural language processing technology, etc.

[1057] "Matched survival confirmation information" is survival confirmation information that matches the identifying information of a missing person, and is based on existing information in a database or information collected from outside.

[1058] "Means of notification" refers to the means of communicating information to relevant parties, such as by email, SMS, or push notification.

[1059] A "database" is a system for systematically storing and managing digital information.

[1060] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, and more specifically, to a system for comparing the missing person's identifying information with information confirming their survival using natural language processing technology and notifying relevant parties. An embodiment of the system will be described below.

[1061] System configuration

[1062] server

[1063] The server receives specific information about missing persons (such as name, address, and physical characteristics) entered by the user and stores it in a database. A typical relational database (such as MySQL or PostgreSQL) or a NoSQL database (such as MongoDB) can be used as the database. The server also includes a natural language processing (NLP) module, which includes the Python library spaCy and a Transformer-based model. The NLP module is used to analyze the received information and extract information confirming the person's survival. Furthermore, the server periodically runs a script that collects external information, such as reports from evacuation centers, social networking service (SNS) data, and news articles. The collected data is also analyzed by the NLP module, and survival information is generated based on that data.

[1064] Terminal

[1065] The device provides an interface for users to input specific information about missing people. The interface is provided through a smartphone application or web browser. The device converts the input information into JSON format and sends it to the server via an HTTP request. The device also has a function to display notifications received by relevant parties. This allows relevant parties to receive information confirming the person's survival in the form of push notifications, emails, SMS, etc.

[1066] User

[1067] The user, as a family member or associate of a missing person, plays the role of entering specific information about the missing person through the terminal. The specific information entered by the user includes the missing person's name, address, physical characteristics, etc. For example, to confirm the disappearance of relative B, user A might enter the name "Taro Tanaka," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into the terminal.

[1068] Specific examples

[1069] For example, to confirm the disappearance of relative B, user A enters "Name: Tanaka Taro, Address: Shinjuku Ward, Tokyo, Physical Characteristics: Height 170 cm, Black Hair, Glasses." The device converts this information into JSON format and sends it to the server via an HTTP request. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information confirming the survival of "Tanaka Taro" from evacuation shelter reports, social media, and news articles. A specific example might be a prompt: "We are looking for a missing person named Tanaka Taro. His address is in Shinjuku Ward, Tokyo, and his physical characteristics are height 170 cm, black hair, and glasses. Please find information confirming his survival." The matching result is generated as "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo," and an email notification is sent to user A. User A receives the notification on their device and can confirm that relative B is safe.

[1070] As described above, the system of the present invention can quickly and accurately check the survival confirmation information based on the missing person's identifying information and notify the relevant parties, thereby providing peace of mind to the missing person and their family and related parties.

[1071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1072] Step 1: Enter missing person information

[1073] User

[1074] The user enters the missing person's identifying information (such as name, address, and physical characteristics) into an input form on the device. For example, User A enters information such as "Taro Tanaka, Shinjuku Ward, Tokyo, height 170 cm, black hair, glasses." The input data at this point is the missing person's identifying information.

[1075] Step 2: Send data to the server

[1076] Terminal

[1077] The terminal converts the missing person identification information entered by the user into JSON format. Then, it sends the converted JSON data to the server in the form of an HTTP request. Specifically, it sends the data by specifying an endpoint (e.g., " / api / v1 / missing_persons"). The output at this point is the JSON data sent to the server.

[1078] Step 3: Store and analyze missing person information

[1079] server

[1080] The server extracts JSON data from the received HTTP request, parses it, and stores it in a database, such as MongoDB or MySQL. Once stored in this database, the data is passed to a natural language processing (NLP) module, which uses an NLP module (e.g., spaCy or a Transformer-based model) to parse the received data and extract the survival information. The output is a database entry containing the missing person information and the NLP analysis results.

[1081] Step 4: Collect and analyze external data

[1082] server

[1083] The server periodically runs a script that collects external data. This script automatically collects data from evacuation shelter reports, social networking service (SNS) posts, news articles, etc. The collected data is then passed to the same NLP module for analysis and extraction. For example, the data is scraped using Python's BeautifulSoup or Scrapy, and then analyzed by the NLP module. The output at this point is the collected and analyzed survival confirmation information.

[1084] Step 5: Generating and notifying liveness information

[1085] server

[1086] The server compares the analysis results from the NLP module with the missing person information and generates final survival confirmation information. For example, it generates information such as "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." Next, it retrieves the contact information of the relevant parties from the database and uses a notification system (e.g., Twilio or Firebase) to send the survival confirmation information in the form of email, SMS, or push notification. The output at this point is the notification message sent to the relevant parties.

[1087] Step 6: Receive live notification

[1088] Terminal

[1089] The device receives the notification sent from the server and displays it to the user. For example, a message may be displayed in the notification area of ​​a smartphone or within an application. The output at this point is a liveness confirmation notification displayed on the device.

[1090] User

[1091] The user checks the notification and receives the survival confirmation information. Specifically, the user can feel relieved when they see the notification that "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." The final output here is the survival confirmation information that the user receives.

[1092] As a result, this system can quickly and accurately confirm the safety of missing people and notify relevant parties.

[1093] (Application example 1)

[1094] 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."

[1095] In order to quickly and accurately confirm the safety of missing persons, it is necessary to collect and analyze data from multiple information sources and notify relevant parties in a timely manner. However, with conventional systems, it takes time to collect and analyze information, making it particularly difficult to provide information in real time. Furthermore, there is a problem that on-site confirmation work is time-consuming, making it difficult to respond efficiently. The present invention aims to solve these problems and realize the provision of quick and accurate safety confirmation information.

[1096] 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.

[1097] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation centers, social media data, and news articles, means for quickly notifying relevant parties of the collated survival confirmation information, means for displaying and confirming missing person information in real time using visual display devices worn by security guards, and means for obtaining new information from evacuation centers and social media and notifying them by audio and visual alerts. This enables rapid and accurate data collection and analysis from multiple sources, as well as real-time confirmation on site.

[1098] "Identifying information of a missing person" is information that can be used to identify a missing person, such as name, address, and physical characteristics.

[1099] "Natural language processing" is a technology that enables computers to understand, interpret, and manipulate human language.

[1100] "Survival confirmation information" is information indicating that a missing person is safe.

[1101] "Reports from evacuation shelters" refers to the provision of information regarding missing persons at evacuation shelters.

[1102] "SNS Data" is information obtained from posts and comments on social networking services.

[1103] A "news article" is news content provided by a media outlet.

[1104] "External information" is any information collected from outside the system.

[1105] A "visual display device" is a device for visually displaying information, and in this context refers to a device worn by a security guard.

[1106] "Audio and visual alerts" are methods of warning or notifying through audio and visual means.

[1107] The present invention is a system for quickly and accurately confirming the safety of missing persons. This system is composed of the following main components: a server, a terminal, and a user.

[1108] server

[1109] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract information confirming their survival. It also periodically collects external information, such as reports from evacuation centers, social media data, and news articles, and analyzes and organizes it using the NLP module. The server then promptly notifies relevant parties of the results of these comparisons.

[1110] Specifically, when missing person information is sent to the server, natural language processing technology is used to search for and extract related survival confirmation information. Spacy is used as the NLP module to perform data analysis. Evacuation shelter information and social media data are collected regularly and automatically processed on the server. Matched survival confirmation information is quickly distributed to relevant contacts via the notification system.

[1111] Terminal

[1112] The device provides an interface for users to input missing person information. The input information is sent to a server, where the missing person information is compared with information confirming their survival. The device also displays notifications to relevant parties. Devices include smartphones, smart glasses, and head-mounted displays (HMDs).

[1113] Specifically, when security guards patrol wearing head-mounted displays (HMDs), specific information about missing persons is visually displayed, and audio and visual alerts are issued when new information is updated in real time.

[1114] User

[1115] Users, as family members or relatives of the missing person, input information about the missing person through the device and receive notifications. When users use voice input, the voice data is converted into text data and sent to the server. It is then quickly collated and analyzed, and the results are notified.

[1116] Specific examples of prompts include:

[1117] Enter the missing person's information (name, address, physical characteristics). For example, "Taro Tanaka, Shinjuku Ward, Tokyo, height 170cm, black hair, glasses." The entered information will be analyzed using natural language processing, and information on whether the person is alive will be displayed.

[1118] This system makes it possible to quickly confirm the safety of missing persons, providing peace of mind to those involved.

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

[1120] Step 1:

[1121] A user uses a terminal to input information about a missing person, such as name, address, and physical characteristics. The input information is converted into JSON format, and this data is sent to a specified API endpoint on the server.

[1122] Step 2:

[1123] The server parses the received JSON data and stores it in the database, checking the format and integrity of the data to ensure that the entered missing person information is stored accurately.

[1124] Step 3:

[1125] The server passes the stored missing person information to a natural language processing (NLP) module, which searches and extracts relevant information from existing information in the database. Data processing here includes text analysis and keyword extraction.

[1126] Step 4:

[1127] The server periodically runs a script that collects external information such as reports from evacuation centers, social media data, and news articles. This data is also passed to the NLP module for analysis, resulting in additional survival information.

[1128] Step 5:

[1129] The server stores the extracted liveness information in a database and generates a matching result, which is then prepared for immediate notification to relevant parties, whose contact information is also retrieved from the database.

[1130] Step 6:

[1131] The server sends information to relevant parties to confirm the person's survival via email, SMS, push notification, etc. The notification includes the missing person's status and location information.

[1132] Step 7:

[1133] The terminal has the ability to display information in real time on the guard's HMD, which visually displays specific information about missing persons, and issues audio and visual alerts when new information is reported.

[1134] Step 8:

[1135] The user receives a notification on their device and checks it. This notification includes information on the safety of the missing person and is displayed in real time, allowing the user to take prompt action based on this information.

[1136] This processing flow enables the safety of missing persons to be confirmed quickly and accurately, and the information is promptly notified to relevant parties.

[1137] 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.

[1138] The present invention is a system for quickly and accurately confirming the safety of missing persons, and is particularly equipped with a function that recognizes the user's emotions to optimize the response. Specifically, the system uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and further recognizes the user's emotions using an emotion engine to optimize the content and timing of notifications, thereby providing information to relevant parties quickly and appropriately.

[1139] System configuration

[1140] server

[1141] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1142] Terminal

[1143] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1144] User

[1145] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[1146] Program processing explanation

[1147] Inputting missing person information and extracting survival confirmation information

[1148] User

[1149] The user enters the missing person's name, address, physical characteristics, etc. into an input form on the terminal.

[1150] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1151] Terminal

[1152] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[1153] server

[1154] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1155] The stored information is passed to the NLP module to search and extract liveness information.

[1156] The extracted survival confirmation information is stored in a database.

[1157] Collection and analysis of external information

[1158] server

[1159] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[1160] Each collected data is passed to the NLP module for analysis.

[1161] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[1162] Emotion engine for recognizing user emotions and optimizing notifications

[1163] server

[1164] The emotion engine analyzes the information entered by the user and their reactions to past notifications to recognize their emotions.

[1165] For example, if a user frequently enters anxious comments, the next notification will contain reassuring content.

[1166] Terminal

[1167] Customize notification content based on feedback from the emotion engine.

[1168] For example, in addition to the notification "Ichiro Tanaka is safe," a reassuring message such as "Detailed location confirmation has been obtained" is sent.

[1169] Sending alive notification

[1170] server

[1171] Notifications are generated based on the matching results and feedback from the emotion engine.

[1172] Sending generated notifications to interested parties in the form of email, SMS, and push notifications.

[1173] Terminal

[1174] The relevant person's device receives the notification and displays the notification content.

[1175] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safely evacuated to a shelter in Osaka City."

[1176] User

[1177] The user checks the notification on their device and confirms that the missing person is safe.

[1178] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[1179] By using the above processing steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1180] The processing flow will be explained below.

[1181] Step 1:

[1182] User

[1183] The user opens a form on the terminal to enter the missing person's name, address, physical characteristics, etc.

[1184] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1185] Step 2:

[1186] Terminal

[1187] The terminal converts the input information into JSON format data.

[1188] The converted JSON data is sent to the server's API using an HTTP POST request.

[1189] Step 3:

[1190] server

[1191] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1192] The stored specific information is passed directly to a natural language processing (NLP) module, which searches and extracts relevant information from existing liveness confirmation information.

[1193] The extracted survival confirmation information is stored in a database.

[1194] Step 4:

[1195] server

[1196] The server periodically runs scripts to collect evacuation shelter reports, social media data, and news articles.

[1197] The collected data is passed to an NLP module, which parses and extracts relevant liveness information.

[1198] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[1199] Step 5:

[1200] server

[1201] The extracted survival confirmation information is compared with the missing person's identification information.

[1202] The matched information is stored in the database.

[1203] Step 6:

[1204] server

[1205] The server uses an emotion engine to recognize the user's emotions based on the user's input information and reaction data to past notifications.

[1206] For example, if a user enters "I'm very worried" into an input form, the emotion engine will detect this as an emotion indicating anxiety.

[1207] Step 7:

[1208] server

[1209] Optimize the content of notifications based on feedback from the emotion engine.

[1210] For example, additional information that provides a sense of security could be added, such as "Ichiro Tanaka is safely evacuated to a shelter in Osaka City. His detailed whereabouts have also been confirmed."

[1211] Step 8:

[1212] server

[1213] Notifications generated based on matching results and feedback from the sentiment engine are sent to relevant parties via email, SMS, or push notifications.

[1214] Step 9:

[1215] Terminal

[1216] The terminals of the people involved receive the notification and display the notification content in a way that gives the user a sense of security.

[1217] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safe at a shelter in Osaka City. His detailed whereabouts have been confirmed."

[1218] Step 10:

[1219] User

[1220] The user checks the notification on their device and knows that the missing person is safe.

[1221] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification, giving the user a greater sense of security.

[1222] By using the above steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1223] Example 2

[1224] 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."

[1225] The challenge is to provide information about missing people quickly and accurately, so that those involved can feel at ease. Furthermore, conventional systems do not provide information that takes into account the user's feelings, and this point also needs to be improved.

[1226] 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.

[1227] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles, and means for analyzing input information from users and responses to past notifications to recognize emotions and optimize the content and timing of notifications. This makes it possible to provide missing person information quickly and accurately and to notify at the optimal timing taking into account the user's emotions.

[1228] "Identifying information of a missing person" is information such as name, address, and physical characteristics that can be used to identify a missing person.

[1229] "Natural language processing" is a technology that enables computers to understand and analyze text written in natural language.

[1230] "Survival confirmation information" is information used to confirm that missing persons are still alive, and includes reports from evacuation centers, social media data, news articles, etc.

[1231] "Reports from evacuation centers" refers to information collected at evacuation centers regarding the status of missing persons.

[1232] "SNS data" refers to information about missing persons posted on social networking services.

[1233] A "news article" is an article about a missing person published by a news organization.

[1234] The "emotion engine" is a component that analyzes the user's input information and reactions to past notifications to recognize the user's emotions.

[1235] "Optimizing notification content and timing" is the process of adjusting the content and timing of notifications based on the user's emotional state.

[1236] A "database" is a system for efficiently storing, managing, and retrieving data.

[1237] "JSON format" stands for JavaScript Object Notation and is a lightweight data exchange format.

[1238] An "HTTP POST request" is a request method in the HTTP protocol for a client to send data to a server.

[1239] A "push notification" is a notification message that is actively sent from a server to a client device.

[1240] "Email" is a digital message sent over the Internet.

[1241] "SMS" stands for Short Message Service, a service that sends short text messages via mobile phones.

[1242] The present invention provides a system for quickly and accurately confirming the safety of missing people, and is particularly equipped with a function for recognizing the user's emotions and optimizing the response. The system includes components of a server, a terminal, and a user.

[1243] server

[1244] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1245] As a concrete example, information about a missing person named "Ichiro Tanaka" is received, and based on that information, information confirming the person's survival, such as "Ichiro Tanaka is at a shelter in Osaka City," is extracted. Also, in cases where a user frequently enters anxious comments, the next notification will include information that provides reassurance.

[1246] Terminal

[1247] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1248] For example, when a user enters information about "Ichiro Tanaka," the device converts this information into JSON format and sends it to the server. When a notification is received from the server, the device displays a reassuring message saying, "Ichiro Tanaka is safely evacuated to an evacuation shelter in Osaka City."

[1249] User

[1250] Users, as family members or relatives, enter information about the missing person through their device and receive a notification. The user's emotions are recorded, and this emotional data is reflected in the next notification. For example, if a user enters a comment when feeling anxious, the emotion engine will analyze it, and the next notification will have a more reassuring message.

[1251] Prompt Sentence Examples

[1252] Generate a description of a missing person safety confirmation system. Include each processing step: inputting missing person information, collecting external information, recognizing emotions using an emotion engine, and sending a survival confirmation notification.

[1253] With the above configuration, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1:

[1256] User enters missing person information and submits it to the server

[1257] Specific actions

[1258] The user inputs information such as the name, address, and physical characteristics of the missing person into an input form on the terminal. For example, the user inputs the name "Ichiro Tanaka," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1259] input:

[1260] Missing person information entered by the user.

[1261] output:

[1262] Missing person information converted to JSON format on the device.

[1263] Data processing / calculation:

[1264] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[1265] Step 2:

[1266] Storage and analysis of missing person information on the server

[1267] Specific actions

[1268] The server parses the JSON data received from the device. The parsed missing person information is stored in a database. The stored information is then passed to the NLP module to search for and extract information confirming survival.

[1269] input:

[1270] JSON data sent from the terminal.

[1271] output:

[1272] Missing person information stored in the database and extracted survival confirmation information.

[1273] Data processing / calculation:

[1274] The server parses the JSON data and stores the missing person's specific information in a database, which is then passed to an NLP module to extract information on whether the person is alive or not.

[1275] Step 3:

[1276] Collection and analysis of external information

[1277] Specific actions

[1278] The server periodically runs scripts to collect external information such as reports from evacuation centers, social media data, and news articles. The collected data is passed to an NLP module for analysis. For example, it extracts information such as "Ichiro Tanaka is at an evacuation center in Osaka City."

[1279] input:

[1280] External information (shelter reports, social media data, news articles).

[1281] output:

[1282] Survival confirmation information analyzed by the NLP module.

[1283] Data processing / calculation:

[1284] The server collects external information and analyzes it using the NLP module. The analyzed information is extracted as survival confirmation information.

[1285] Step 4:

[1286] Optimizing user emotion recognition and notification with an implemented emotion engine

[1287] Specific actions

[1288] The server's emotion engine analyzes the user's input information and reactions to past notifications. Based on the analysis results, it recognizes the user's emotions and optimizes the content and timing of the next notification. For example, if a user frequently enters anxious comments, the next notification will include reassuring content.

[1289] input:

[1290] User input information and response data to past notifications.

[1291] output:

[1292] Optimized notification content and timing.

[1293] Data processing / calculation:

[1294] The emotion engine analyzes user input and recognizes emotions, optimizing the content and timing of the next notification.

[1295] Step 5:

[1296] Generate and send heartbeat notifications

[1297] Specific actions

[1298] The server generates a liveness notification based on the matching results and feedback from the emotion engine, and the notification is sent to the relevant parties in the form of email, SMS, or push notification.

[1299] input:

[1300] Matching results and emotion engine feedback.

[1301] output:

[1302] The generated heartbeat notification.

[1303] Data processing / calculation:

[1304] The server generates a liveness notification and sends it to the interested parties in the appropriate format (email, SMS, push notification).

[1305] Step 6:

[1306] User acknowledgement of notifications and emotional feedback

[1307] Specific actions

[1308] The user checks the notification on their device and confirms that the missing person is safe. The user's reaction is fed back to the emotion engine and reflected in the content of the next notification. For example, the user's relieved reaction is recorded and the emotion data is updated.

[1309] input:

[1310] A live notification from the server.

[1311] output:

[1312] User notification confirmation and emotional data feedback.

[1313] Data processing / calculation:

[1314] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[1315] As described above, each processing step of this system works together to confirm the safety of missing people and optimize the system based on user sentiment, allowing those involved to receive information quickly and with peace of mind.

[1316] (Application example 2)

[1317] 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."

[1318] While conventional systems for confirming the safety of missing persons are somewhat effective in collecting and analyzing information, they do not optimize notifications that take into account the user's emotions, and therefore do not reduce the psychological burden on those involved. Furthermore, there are issues with the ability to efficiently store input missing person information in a database and notify those involved at the appropriate time.

[1319] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting identifying information (such as name, address, and physical characteristics) of a missing person and extracting survival confirmation information using natural language processing; means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles; means for quickly notifying relevant parties of the collated survival confirmation information; means including an emotion engine that recognizes the user's emotions and optimizes the content and timing of the notification; means for converting the user's input information into JSON format and sending it to the server using an HTTP POST request; and means for customizing the notification content and displaying a reassuring message that corresponds to the user's emotions. This reduces the psychological burden on the user and enables quick and reassuring safety confirmation.

[1320] "Identifying information of a missing person" is information that can individually identify a missing person, such as their name, address, or physical description.

[1321] "Natural language processing" is a technology that analyzes input text data, understands meaning and information, and extracts it.

[1322] "Confirmation of survival information" is information indicating that a missing person is safe, and is obtained from evacuation center reports, social media data, news articles, etc.

[1323] "Reports from evacuation centers" are information about the situation and safety of people at evacuation centers.

[1324] "SNS Data" refers to information such as posts, comments, and messages shared by users on social networking services.

[1325] A "news article" is an article of information reported in media such as newspapers or websites.

[1326] An "emotion-recognizing emotion engine" is a technology that analyzes and recognizes emotions from the user's input information and reactions.

[1327] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that structures and represents data in a lightweight manner.

[1328] An "HTTP POST request" is a method of the HTTP protocol used by a web client to send data to a server.

[1329] "Customizing notifications" refers to tailoring the content and format of notifications based on the user's needs and emotions.

[1330] A "reassuring message" is a contact message whose content is designed to make the user feel at ease.

[1331] This invention is a system that quickly and accurately confirms the safety of missing people. In particular, it uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and also has the function of recognizing the user's emotions with an emotion engine to optimize the content and timing of notifications. This allows for quick and appropriate provision of information to relevant parties.

[1332] System configuration

[1333] server

[1334] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1335] For example, the server receives specific information for "Ichiro Tanaka" entered by the user and stores it in a database. The NLP module then analyzes evacuation shelters, social media data, and news articles based on the information for "Ichiro Tanaka," and extracts information confirming his survival, such as "Ichiro Tanaka is at an evacuation shelter."

[1336] Terminal

[1337] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1338] As a specific example of use, a user enters specific information for "Ichiro Tanaka" through a smartphone application and sends it to the server. The server generates confirmation information stating "Ichiro Tanaka is safe" and sends it to the user's device as a push notification. If the emotion engine recognizes "anxiety" at this time, it adds a reassuring message saying "We have also confirmed Ichiro Tanaka's detailed whereabouts."

[1339] User

[1340] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[1341] For example, based on the information entered by the user as "Ichiro Tanaka," the user receives a notification that "Ichiro Tanaka is safe at the evacuation center." If the user has previously entered a worrying comment, the next notification will include a message that says, "Detailed location information for Ichiro Tanaka has also been obtained," to provide a sense of security.

[1342] Hardware and software used

[1343] Hardware

[1344] Smartphone (iOS, Android)

[1345] Server (Cloud service: AWS, GCP, etc.)

[1346] software

[1347] Frontend: React Native (for smartphones)

[1348] Backend: Python (Django, Flask)

[1349] API communication: HTTP, JSON

[1350] Natural Language Processing (NLP): spaCy, NLTK (Python packages)

[1351] Emotion Engine: Machine learning model using tensorflow and keras

[1352] Prompt Sentence Examples

[1353] Enter missing person information:

[1354] Name: "Ichiro Tanaka"

[1355] Address: "City"

[1356] Physical Description: "175cm tall, black hair"

[1357] NLP analysis results:

[1358] Ichiro Tanaka was identified at the evacuation center.

[1359] Emotion Engine Analysis Results:

[1360] The user felt uneasy after receiving the contact.

[1361] Optimized notifications:

[1362] "We have confirmed the identity of Ichiro Tanaka. We have also received detailed information on his whereabouts."

[1363] With the above-described configuration, the present invention can quickly and accurately collate information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[1364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1365] Step 1:

[1366] User enters specific information about the missing person

[1367] Users enter identifying information about a missing person, such as name, address, and physical characteristics, into a smartphone application. The information is then converted into JSON format, which structures the user's input and converts it into a format that can be sent to the server.

[1368] input:

[1369] Name: "Ichiro Tanaka"

[1370] Address: "City"

[1371] Physical Description: "175cm tall, black hair"

[1372] output:

[1373] Missing person information in JSON format

[1374] Step 2:

[1375] The device sends the input information to the server

[1376] The device sends the generated JSON-formatted data to the server using an HTTP POST request, which sends the data to an API endpoint at a specific URL.

[1377] input:

[1378] Missing person information in JSON format

[1379] output:

[1380] HTTP POST request to the server

[1381] Step 3:

[1382] The server receives the missing person information and stores it in a database

[1383] The server parses the received JSON data and stores the missing person's identifying information in a database, which then uses the structured data for subsequent natural language processing (NLP) and matching.

[1384] input:

[1385] Missing person information sent in an HTTP POST request

[1386] output:

[1387] Missing person information stored in a database

[1388] Step 4:

[1389] The server collects and analyzes external information

[1390] The server runs a script that periodically collects reports from evacuation centers, social media data, and news articles. The collected data is then passed to an NLP module for analysis, which extracts relevant survival information.

[1391] input:

[1392] Evacuation shelter reporting data

[1393] Social media data

[1394] News Article

[1395] output:

[1396] Extracted survival confirmation information

[1397] Specific operation:

[1398] For example, the NLP module extracts information from a news article that says, "Ichiro Tanaka is at a shelter."

[1399] Step 5:

[1400] Emotion engine recognizes user emotions

[1401] The server analyzes the user's input information and reactions to past notifications, and uses an emotion engine to recognize the user's emotions. Past comments and reaction data are used to reflect the user's emotions in the next notification.

[1402] input:

[1403] User's past comments and reaction data

[1404] output:

[1405] Recognized user emotion data

[1406] Step 6:

[1407] The server generates and sends the notification content

[1408] The server generates notifications based on the matching results and feedback from the emotion engine and sends them to relevant parties in the form of email, SMS, or push notifications, with messages customized according to the user's emotions.

[1409] input:

[1410] Matching result (survival confirmation information)

[1411] Emotion Engine Feedback

[1412] output:

[1413] Customized notification messages

[1414] Specific operation:

[1415] The notification "Ichiro Tanaka is safe in the evacuation center" will be supplemented with a reassuring message that "Detailed location confirmation has also been obtained."

[1416] Step 7:

[1417] The device receives and displays the notification.

[1418] The terminal receives the notification sent from the server and displays it on the terminal screen of the person involved, allowing the person involved to check the information immediately.

[1419] input:

[1420] Notification messages sent by the server

[1421] output:

[1422] Notification message displayed on the device of the person involved

[1423] Specific operation:

[1424] A push notification will appear on your smartphone with the message "Ichiro Tanaka is safely evacuated to the evacuation shelter."

[1425] Through the above processing steps, the system can quickly and accurately collate information on missing people and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] [Fourth embodiment]

[1430] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1431] 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.

[1432] 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).

[1433] 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.

[1434] 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.

[1435] 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).

[1436] 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.

[1437] 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.

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] 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."

[1443] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, specifically, a system for comparing the missing person's identifying information with information confirming their survival using natural language processing and notifying relevant parties. The system of the present invention is implemented by the following program.

[1444] System configuration

[1445] server

[1446] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles, and generates survival confirmation information based on that data.

[1447] Terminal

[1448] The device provides an interface for users to enter missing person information, which is then sent to a server for verification. The device also displays notifications that relevant parties can receive. For example, notifications can be received through a smartphone application or a web browser.

[1449] User

[1450] The user acts as a family member or relative of the missing person and inputs information about the missing person through the terminal and receives notifications.

[1451] Program processing explanation

[1452] Inputting missing person information and extracting survival confirmation information

[1453] User

[1454] The user enters information such as the missing person's name, address, and physical characteristics into an input form on the terminal.

[1455] Terminal

[1456] The terminal converts the entered missing person information into JSON format and sends it to the server's specified API endpoint.

[1457] server

[1458] The server parses the received JSON data and stores the missing person information in a database.

[1459] The stored information is passed to a natural language processing module, which searches and extracts relevant liveness information from existing information in the database.

[1460] The extracted survival confirmation information is stored in a database.

[1461] Collection and analysis of external information

[1462] server

[1463] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[1464] The collected data is passed to the NLP module for analysis.

[1465] The extracted survival confirmation information is then compared with the missing person information.

[1466] Sending alive notification

[1467] server

[1468] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[1469] Use the notification system to send liveness information to relevant parties via email, SMS, or push notification.

[1470] Terminal

[1471] Display notifications on the devices of those involved.

[1472] User

[1473] The user receives the notification on the terminal and checks the survival confirmation information.

[1474] Specific examples

[1475] For example, to confirm that relative B is missing, user A enters the name "Tanaka Taro," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into his device. The device sends this information to the server. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information on whether "Tanaka Taro" is alive and well from evacuation shelters, social media, and news. As a result of the comparison, the information "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo" is generated, and an email notification is sent to user A. User A receives the notification on his device and can confirm that relative B is safe.

[1476] As described above, the system of the present invention can quickly and accurately collate information on missing persons and notify relevant parties, thereby providing peace of mind to disaster victims and their families.

[1477] The processing flow will be explained below.

[1478] Step 1:

[1479] User

[1480] The user enters information such as the missing person's name, address, and physical characteristics into an input form provided on the terminal.

[1481] For example, enter the name "Yamada Hanako," the address "Sumida Ward, Tokyo," and the physical characteristics "height 160 cm, black hair, glasses."

[1482] Step 2:

[1483] Terminal

[1484] The terminal converts the missing person information entered by the user into JSON format.

[1485] The converted JSON data is sent to the server's API endpoint using an HTTP POST request.

[1486] Step 3:

[1487] server

[1488] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1489] The stored information is passed to a natural language processing (NLP) module, which searches and extracts relevant liveness information from existing information in the database.

[1490] The extracted survival confirmation information is stored in a database.

[1491] Step 4:

[1492] server

[1493] The server runs scripts to periodically collect reports from evacuation centers, social media data, and news articles.

[1494] The information collected from each data source is passed to the NLP module for analysis.

[1495] For example, extract the information that "Yamada Hanako is at a shelter in Sumida Ward, Tokyo."

[1496] Step 5:

[1497] server

[1498] The extracted survival confirmation information is compared with the missing person's identifying information, and if there is a match, the information is stored in a database.

[1499] Based on the matching results, survival confirmation information is generated and contact information of the relevant parties is obtained from the database.

[1500] Step 6:

[1501] server

[1502] Use the notification system to notify relevant parties of liveness confirmation information.

[1503] Select the appropriate notification format: email, SMS, or push notification.

[1504] For example, a notification is sent to the user's email address stating, "Yamada Hanako has safely evacuated to a shelter in Sumida Ward, Tokyo."

[1505] Step 7:

[1506] Terminal

[1507] The relevant person's device receives the notification and displays the notification content.

[1508] For example, a push notification on a smartphone might display the message "Yamada Hanako is safe."

[1509] Step 8:

[1510] User

[1511] The user checks the notification on their device and receives information about the missing person who has been confirmed safe.

[1512] This provides peace of mind to the user.

[1513] Through the above processing steps, information on missing persons is quickly and accurately collated and notified to the relevant parties, making it possible to quickly confirm the safety of the victims' families and other relevant parties.

[1514] Example 1

[1515] 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."

[1516] Rapid and accurate confirmation of the safety of missing persons is an extremely difficult task, especially during disasters or sudden incidents. Conventional systems rely primarily on manual confirmation work, requiring a significant amount of time for information collection and analysis, which often results in delays in confirming the person's actual survival and in notifying them. This frequently causes unnecessary worry for missing persons, their families, and other concerned parties. Therefore, there is a need for a system that can quickly and automatically check the missing person's survival based on the missing person's identifying information and promptly notify those concerned.

[1517] 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.

[1518] In this invention, the server includes means for inputting specific information of missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing technology, means for automatically collecting, analyzing, and organizing external information such as reports from evacuation centers, social networking service data, and news articles, and means for quickly notifying relevant parties of the collated survival confirmation information. This makes it possible to quickly and accurately collate information on missing persons and quickly provide relevant parties with survival confirmation information.

[1519] "Identifying information of a missing person" refers to basic information needed to identify a missing person, such as name, address, and physical characteristics.

[1520] "Natural language processing technology" is a technology that allows computers to understand human language and perform analysis and generation.

[1521] "Survival confirmation information" is information indicating that a missing person is currently alive, and can be obtained from evacuation center reports, social media data, news articles, etc.

[1522] "Reports from evacuation shelters" are information reporting the situation of people gathered at evacuation shelters during disasters or emergencies.

[1523] "Social networking service data" refers to data such as text, images, and videos posted on social media platforms.

[1524] A "news article" is the text of news reported in newspapers, television, online media, etc.

[1525] "Means for automatically collecting, analyzing, and organizing" refers to means for automatically collecting external information using a program and analyzing and organizing it using natural language processing technology, etc.

[1526] "Matched survival confirmation information" is survival confirmation information that matches the identifying information of a missing person, and is based on existing information in a database or information collected from outside.

[1527] "Means of notification" refers to the means of communicating information to relevant parties, such as by email, SMS, or push notification.

[1528] A "database" is a system for systematically storing and managing digital information.

[1529] The present invention relates to a system for quickly and accurately confirming the safety of a missing person, and more specifically, to a system for comparing the missing person's identifying information with information confirming their survival using natural language processing technology and notifying relevant parties. An embodiment of the system will be described below.

[1530] System configuration

[1531] server

[1532] The server receives specific information about missing persons (such as name, address, and physical characteristics) entered by the user and stores it in a database. A typical relational database (such as MySQL or PostgreSQL) or a NoSQL database (such as MongoDB) can be used as the database. The server also includes a natural language processing (NLP) module, which includes the Python library spaCy and a Transformer-based model. The NLP module is used to analyze the received information and extract information confirming the person's survival. Furthermore, the server periodically runs a script that collects external information, such as reports from evacuation centers, social networking service (SNS) data, and news articles. The collected data is also analyzed by the NLP module, and survival information is generated based on that data.

[1533] Terminal

[1534] The device provides an interface for users to input specific information about missing people. The interface is provided through a smartphone application or web browser. The device converts the input information into JSON format and sends it to the server via an HTTP request. The device also has a function to display notifications received by relevant parties. This allows relevant parties to receive information confirming the person's survival in the form of push notifications, emails, SMS, etc.

[1535] User

[1536] The user, as a family member or associate of a missing person, plays the role of entering specific information about the missing person through the terminal. The specific information entered by the user includes the missing person's name, address, physical characteristics, etc. For example, to confirm the disappearance of relative B, user A might enter the name "Taro Tanaka," address "Shinjuku Ward, Tokyo," and physical characteristics "height 170 cm, black hair, glasses" into the terminal.

[1537] Specific examples

[1538] For example, to confirm the disappearance of relative B, user A enters "Name: Tanaka Taro, Address: Shinjuku Ward, Tokyo, Physical Characteristics: Height 170 cm, Black Hair, Glasses." The device converts this information into JSON format and sends it to the server via an HTTP request. The server stores this information in a database and analyzes it using an NLP module. The server then collects and analyzes information confirming the survival of "Tanaka Taro" from evacuation shelter reports, social media, and news articles. A specific example might be a prompt: "We are looking for a missing person named Tanaka Taro. His address is in Shinjuku Ward, Tokyo, and his physical characteristics are height 170 cm, black hair, and glasses. Please find information confirming his survival." The matching result is generated as "Tanaka Taro is at the Shinjuku evacuation shelter in Tokyo," and an email notification is sent to user A. User A receives the notification on their device and can confirm that relative B is safe.

[1539] As described above, the system of the present invention can quickly and accurately check the survival confirmation information based on the missing person's identifying information and notify the relevant parties, thereby providing peace of mind to the missing person and their family and related parties.

[1540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1541] Step 1: Enter missing person information

[1542] User

[1543] The user enters the missing person's identifying information (such as name, address, and physical characteristics) into an input form on the device. For example, User A enters information such as "Taro Tanaka, Shinjuku Ward, Tokyo, height 170 cm, black hair, glasses." The input data at this point is the missing person's identifying information.

[1544] Step 2: Send data to the server

[1545] Terminal

[1546] The terminal converts the missing person identification information entered by the user into JSON format. Then, it sends the converted JSON data to the server in the form of an HTTP request. Specifically, it sends the data by specifying an endpoint (e.g., " / api / v1 / missing_persons"). The output at this point is the JSON data sent to the server.

[1547] Step 3: Store and analyze missing person information

[1548] server

[1549] The server extracts JSON data from the received HTTP request, parses it, and stores it in a database, such as MongoDB or MySQL. Once stored in this database, the data is passed to a natural language processing (NLP) module, which uses an NLP module (e.g., spaCy or a Transformer-based model) to parse the received data and extract the survival information. The output is a database entry containing the missing person information and the NLP analysis results.

[1550] Step 4: Collect and analyze external data

[1551] server

[1552] The server periodically runs a script that collects external data. This script automatically collects data from evacuation shelter reports, social networking service (SNS) posts, news articles, etc. The collected data is then passed to the same NLP module for analysis and extraction. For example, the data is scraped using Python's BeautifulSoup or Scrapy, and then analyzed by the NLP module. The output at this point is the collected and analyzed survival confirmation information.

[1553] Step 5: Generating and notifying liveness information

[1554] server

[1555] The server compares the analysis results from the NLP module with the missing person information and generates final survival confirmation information. For example, it generates information such as "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." Next, it retrieves the contact information of the relevant parties from the database and uses a notification system (e.g., Twilio or Firebase) to send the survival confirmation information in the form of email, SMS, or push notification. The output at this point is the notification message sent to the relevant parties.

[1556] Step 6: Receive live notification

[1557] Terminal

[1558] The device receives the notification sent from the server and displays it to the user. For example, a message may be displayed in the notification area of ​​a smartphone or within an application. The output at this point is a liveness confirmation notification displayed on the device.

[1559] User

[1560] The user checks the notification and receives the survival confirmation information. Specifically, the user can feel relieved when they see the notification that "Taro Tanaka is at the Shinjuku evacuation shelter in Tokyo." The final output here is the survival confirmation information that the user receives.

[1561] As a result, this system can quickly and accurately confirm the safety of missing people and notify relevant parties.

[1562] (Application example 1)

[1563] 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."

[1564] In order to quickly and accurately confirm the safety of missing persons, it is necessary to collect and analyze data from multiple information sources and notify relevant parties in a timely manner. However, with conventional systems, it takes time to collect and analyze information, making it particularly difficult to provide information in real time. Furthermore, there is a problem that on-site confirmation work is time-consuming, making it difficult to respond efficiently. The present invention aims to solve these problems and realize the provision of quick and accurate safety confirmation information.

[1565] 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.

[1566] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation centers, social media data, and news articles, means for quickly notifying relevant parties of the collated survival confirmation information, means for displaying and confirming missing person information in real time using visual display devices worn by security guards, and means for obtaining new information from evacuation centers and social media and notifying them by audio and visual alerts. This enables rapid and accurate data collection and analysis from multiple sources, as well as real-time confirmation on site.

[1567] "Identifying information of a missing person" is information that can be used to identify a missing person, such as name, address, and physical characteristics.

[1568] "Natural language processing" is a technology that enables computers to understand, interpret, and manipulate human language.

[1569] "Survival confirmation information" is information indicating that a missing person is safe.

[1570] "Reports from evacuation shelters" refers to the provision of information regarding missing persons at evacuation shelters.

[1571] "SNS Data" is information obtained from posts and comments on social networking services.

[1572] A "news article" is news content provided by a media outlet.

[1573] "External information" is any information collected from outside the system.

[1574] A "visual display device" is a device for visually displaying information, and in this context refers to a device worn by a security guard.

[1575] "Audio and visual alerts" are methods of warning or notifying through audio and visual means.

[1576] The present invention is a system for quickly and accurately confirming the safety of missing persons. This system is composed of the following main components: a server, a terminal, and a user.

[1577] server

[1578] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract information confirming their survival. It also periodically collects external information, such as reports from evacuation centers, social media data, and news articles, and analyzes and organizes it using the NLP module. The server then promptly notifies relevant parties of the results of these comparisons.

[1579] Specifically, when missing person information is sent to the server, natural language processing technology is used to search for and extract related survival confirmation information. Spacy is used as the NLP module to perform data analysis. Evacuation shelter information and social media data are collected regularly and automatically processed on the server. Matched survival confirmation information is quickly distributed to relevant contacts via the notification system.

[1580] Terminal

[1581] The device provides an interface for users to input missing person information. The input information is sent to a server, where the missing person information is compared with information confirming their survival. The device also displays notifications to relevant parties. Devices include smartphones, smart glasses, and head-mounted displays (HMDs).

[1582] Specifically, when security guards patrol wearing head-mounted displays (HMDs), specific information about missing persons is visually displayed, and audio and visual alerts are issued when new information is updated in real time.

[1583] User

[1584] Users, as family members or relatives of the missing person, input information about the missing person through the device and receive notifications. When users use voice input, the voice data is converted into text data and sent to the server. It is then quickly collated and analyzed, and the results are notified.

[1585] Specific examples of prompts include:

[1586] Enter the missing person's information (name, address, physical characteristics). For example, "Taro Tanaka, Shinjuku Ward, Tokyo, height 170cm, black hair, glasses." The entered information will be analyzed using natural language processing, and information on whether the person is alive will be displayed.

[1587] This system makes it possible to quickly confirm the safety of missing persons, providing peace of mind to those involved.

[1588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1589] Step 1:

[1590] A user uses a terminal to input information about a missing person, such as name, address, and physical characteristics. The input information is converted into JSON format, and this data is sent to a specified API endpoint on the server.

[1591] Step 2:

[1592] The server parses the received JSON data and stores it in the database, checking the format and integrity of the data to ensure that the entered missing person information is stored accurately.

[1593] Step 3:

[1594] The server passes the stored missing person information to a natural language processing (NLP) module, which searches and extracts relevant information from existing information in the database. Data processing here includes text analysis and keyword extraction.

[1595] Step 4:

[1596] The server periodically runs a script that collects external information such as reports from evacuation centers, social media data, and news articles. This data is also passed to the NLP module for analysis, resulting in additional survival information.

[1597] Step 5:

[1598] The server stores the extracted liveness information in a database and generates a matching result, which is then prepared for immediate notification to relevant parties, whose contact information is also retrieved from the database.

[1599] Step 6:

[1600] The server sends information to relevant parties to confirm the person's survival via email, SMS, push notification, etc. The notification includes the missing person's status and location information.

[1601] Step 7:

[1602] The terminal has the ability to display information in real time on the guard's HMD, which visually displays specific information about missing persons, and issues audio and visual alerts when new information is reported.

[1603] Step 8:

[1604] The user receives a notification on their device and checks it. This notification includes information on the safety of the missing person and is displayed in real time, allowing the user to take prompt action based on this information.

[1605] This processing flow enables the safety of missing persons to be confirmed quickly and accurately, and the information is promptly notified to relevant parties.

[1606] 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.

[1607] The present invention is a system for quickly and accurately confirming the safety of missing persons, and is particularly equipped with a function that recognizes the user's emotions to optimize the response. Specifically, the system uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and further recognizes the user's emotions using an emotion engine to optimize the content and timing of notifications, thereby providing information to relevant parties quickly and appropriately.

[1608] System configuration

[1609] server

[1610] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1611] Terminal

[1612] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1613] User

[1614] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[1615] Program processing explanation

[1616] Inputting missing person information and extracting survival confirmation information

[1617] User

[1618] The user enters the missing person's name, address, physical characteristics, etc. into an input form on the terminal.

[1619] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1620] Terminal

[1621] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[1622] server

[1623] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1624] The stored information is passed to the NLP module to search and extract liveness information.

[1625] The extracted survival confirmation information is stored in a database.

[1626] Collection and analysis of external information

[1627] server

[1628] The server periodically runs a script that collects reports from evacuation centers, social media data, and news articles.

[1629] Each collected data is passed to the NLP module for analysis.

[1630] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[1631] Emotion engine for recognizing user emotions and optimizing notifications

[1632] server

[1633] The emotion engine analyzes the information entered by the user and their reactions to past notifications to recognize their emotions.

[1634] For example, if a user frequently enters anxious comments, the next notification will contain reassuring content.

[1635] Terminal

[1636] Customize notification content based on feedback from the emotion engine.

[1637] For example, in addition to the notification "Ichiro Tanaka is safe," a reassuring message such as "Detailed location confirmation has been obtained" is sent.

[1638] Sending alive notification

[1639] server

[1640] Notifications are generated based on the matching results and feedback from the emotion engine.

[1641] Sending generated notifications to interested parties in the form of email, SMS, and push notifications.

[1642] Terminal

[1643] The relevant person's device receives the notification and displays the notification content.

[1644] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safely evacuated to a shelter in Osaka City."

[1645] User

[1646] The user checks the notification on their device and confirms that the missing person is safe.

[1647] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[1648] By using the above processing steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1649] The processing flow will be explained below.

[1650] Step 1:

[1651] User

[1652] The user opens a form on the terminal to enter the missing person's name, address, physical characteristics, etc.

[1653] For example, enter the name "Tanaka Ichiro," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1654] Step 2:

[1655] Terminal

[1656] The terminal converts the input information into JSON format data.

[1657] The converted JSON data is sent to the server's API using an HTTP POST request.

[1658] Step 3:

[1659] server

[1660] The server analyzes the received JSON data and stores the specific information of the missing person in a database.

[1661] The stored specific information is passed directly to a natural language processing (NLP) module, which searches and extracts relevant information from existing liveness confirmation information.

[1662] The extracted survival confirmation information is stored in a database.

[1663] Step 4:

[1664] server

[1665] The server periodically runs scripts to collect evacuation shelter reports, social media data, and news articles.

[1666] The collected data is passed to an NLP module, which parses and extracts relevant liveness information.

[1667] For example, extract the information that "Ichiro Tanaka is at a shelter in Osaka City."

[1668] Step 5:

[1669] server

[1670] The extracted survival confirmation information is compared with the missing person's identification information.

[1671] The matched information is stored in the database.

[1672] Step 6:

[1673] server

[1674] The server uses an emotion engine to recognize the user's emotions based on the user's input information and reaction data to past notifications.

[1675] For example, if a user enters "I'm very worried" into an input form, the emotion engine will detect this as an emotion indicating anxiety.

[1676] Step 7:

[1677] server

[1678] Optimize the content of notifications based on feedback from the emotion engine.

[1679] For example, additional information that provides a sense of security could be added, such as "Ichiro Tanaka is safely evacuated to a shelter in Osaka City. His detailed whereabouts have also been confirmed."

[1680] Step 8:

[1681] server

[1682] Notifications generated based on matching results and feedback from the sentiment engine are sent to relevant parties via email, SMS, or push notifications.

[1683] Step 9:

[1684] Terminal

[1685] The terminals of the people involved receive the notification and display the notification content in a way that gives the user a sense of security.

[1686] For example, a push notification on a smartphone might display the message, "Ichiro Tanaka is safe at a shelter in Osaka City. His detailed whereabouts have been confirmed."

[1687] Step 10:

[1688] User

[1689] The user checks the notification on their device and knows that the missing person is safe.

[1690] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification, giving the user a greater sense of security.

[1691] By using the above steps, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1692] Example 2

[1693] 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."

[1694] The challenge is to provide information about missing people quickly and accurately, so that those involved can feel at ease. Furthermore, conventional systems do not provide information that takes into account the user's feelings, and this point also needs to be improved.

[1695] 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.

[1696] In this invention, the server includes means for inputting specific information about missing persons (such as name, address, and physical characteristics) and extracting survival confirmation information using natural language processing, means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles, and means for analyzing input information from users and responses to past notifications to recognize emotions and optimize the content and timing of notifications. This makes it possible to provide missing person information quickly and accurately and to notify at the optimal timing taking into account the user's emotions.

[1697] "Identifying information of a missing person" is information such as name, address, and physical characteristics that can be used to identify a missing person.

[1698] "Natural language processing" is a technology that enables computers to understand and analyze text written in natural language.

[1699] "Survival confirmation information" is information used to confirm that missing persons are still alive, and includes reports from evacuation centers, social media data, news articles, etc.

[1700] "Reports from evacuation centers" refers to information collected at evacuation centers regarding the status of missing persons.

[1701] "SNS data" refers to information about missing persons posted on social networking services.

[1702] A "news article" is an article about a missing person published by a news organization.

[1703] The "emotion engine" is a component that analyzes the user's input information and reactions to past notifications to recognize the user's emotions.

[1704] "Optimizing notification content and timing" is the process of adjusting the content and timing of notifications based on the user's emotional state.

[1705] A "database" is a system for efficiently storing, managing, and retrieving data.

[1706] "JSON format" stands for JavaScript Object Notation and is a lightweight data exchange format.

[1707] An "HTTP POST request" is a request method in the HTTP protocol for a client to send data to a server.

[1708] A "push notification" is a notification message that is actively sent from a server to a client device.

[1709] "Email" is a digital message sent over the Internet.

[1710] "SMS" stands for Short Message Service, a service that sends short text messages via mobile phones.

[1711] The present invention provides a system for quickly and accurately confirming the safety of missing people, and is particularly equipped with a function for recognizing the user's emotions and optimizing the response. The system includes components of a server, a terminal, and a user.

[1712] server

[1713] The server receives specific information about missing persons and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1714] As a concrete example, information about a missing person named "Ichiro Tanaka" is received, and based on that information, information confirming the person's survival, such as "Ichiro Tanaka is at a shelter in Osaka City," is extracted. Also, in cases where a user frequently enters anxious comments, the next notification will include information that provides reassurance.

[1715] Terminal

[1716] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1717] For example, when a user enters information about "Ichiro Tanaka," the device converts this information into JSON format and sends it to the server. When a notification is received from the server, the device displays a reassuring message saying, "Ichiro Tanaka is safely evacuated to an evacuation shelter in Osaka City."

[1718] User

[1719] Users, as family members or relatives, enter information about the missing person through their device and receive a notification. The user's emotions are recorded, and this emotional data is reflected in the next notification. For example, if a user enters a comment when feeling anxious, the emotion engine will analyze it, and the next notification will have a more reassuring message.

[1720] Prompt Sentence Examples

[1721] Generate a description of a missing person safety confirmation system. Include each processing step: inputting missing person information, collecting external information, recognizing emotions using an emotion engine, and sending a survival confirmation notification.

[1722] With the above configuration, the system of the present invention can quickly and accurately check information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing a quick and reassuring confirmation of the safety of disaster victims' families and related parties.

[1723] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1724] Step 1:

[1725] User enters missing person information and submits it to the server

[1726] Specific actions

[1727] The user inputs information such as the name, address, and physical characteristics of the missing person into an input form on the terminal. For example, the user inputs the name "Ichiro Tanaka," the address "Osaka City," and the physical characteristics "height 175 cm, black hair."

[1728] input:

[1729] Missing person information entered by the user.

[1730] output:

[1731] Missing person information converted to JSON format on the device.

[1732] Data processing / calculation:

[1733] The device converts the entered information into JSON format and sends it to the server using an HTTP POST request.

[1734] Step 2:

[1735] Storage and analysis of missing person information on the server

[1736] Specific actions

[1737] The server parses the JSON data received from the device. The parsed missing person information is stored in a database. The stored information is then passed to the NLP module to search for and extract information confirming survival.

[1738] input:

[1739] JSON data sent from the terminal.

[1740] output:

[1741] Missing person information stored in the database and extracted survival confirmation information.

[1742] Data processing / calculation:

[1743] The server parses the JSON data and stores the missing person's specific information in a database, which is then passed to an NLP module to extract information on whether the person is alive or not.

[1744] Step 3:

[1745] Collection and analysis of external information

[1746] Specific actions

[1747] The server periodically runs scripts to collect external information such as reports from evacuation centers, social media data, and news articles. The collected data is passed to an NLP module for analysis. For example, it extracts information such as "Ichiro Tanaka is at an evacuation center in Osaka City."

[1748] input:

[1749] External information (shelter reports, social media data, news articles).

[1750] output:

[1751] Survival confirmation information analyzed by the NLP module.

[1752] Data processing / calculation:

[1753] The server collects external information and analyzes it using the NLP module. The analyzed information is extracted as survival confirmation information.

[1754] Step 4:

[1755] Optimizing user emotion recognition and notification with an implemented emotion engine

[1756] Specific actions

[1757] The server's emotion engine analyzes the user's input information and reactions to past notifications. Based on the analysis results, it recognizes the user's emotions and optimizes the content and timing of the next notification. For example, if a user frequently enters anxious comments, the next notification will include reassuring content.

[1758] input:

[1759] User input information and response data to past notifications.

[1760] output:

[1761] Optimized notification content and timing.

[1762] Data processing / calculation:

[1763] The emotion engine analyzes user input and recognizes emotions, optimizing the content and timing of the next notification.

[1764] Step 5:

[1765] Generate and send heartbeat notifications

[1766] Specific actions

[1767] The server generates a liveness notification based on the matching results and feedback from the emotion engine, and the notification is sent to the relevant parties in the form of email, SMS, or push notification.

[1768] input:

[1769] Matching results and emotion engine feedback.

[1770] output:

[1771] The generated heartbeat notification.

[1772] Data processing / calculation:

[1773] The server generates a liveness notification and sends it to the interested parties in the appropriate format (email, SMS, push notification).

[1774] Step 6:

[1775] User acknowledgement of notifications and emotional feedback

[1776] Specific actions

[1777] The user checks the notification on their device and confirms that the missing person is safe. The user's reaction is fed back to the emotion engine and reflected in the content of the next notification. For example, the user's relieved reaction is recorded and the emotion data is updated.

[1778] input:

[1779] A live notification from the server.

[1780] output:

[1781] User notification confirmation and emotional data feedback.

[1782] Data processing / calculation:

[1783] The user's reaction is fed back to the emotion engine and reflected in the content of the next notification.

[1784] As described above, each processing step of this system works together to confirm the safety of missing people and optimize the system based on user sentiment, allowing those involved to receive information quickly and with peace of mind.

[1785] (Application example 2)

[1786] 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."

[1787] While conventional systems for confirming the safety of missing persons are somewhat effective in collecting and analyzing information, they do not optimize notifications that take into account the user's emotions, and therefore do not reduce the psychological burden on those involved. Furthermore, there are issues with the ability to efficiently store input missing person information in a database and notify those involved at the appropriate time.

[1788] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting identifying information (such as name, address, and physical characteristics) of a missing person and extracting survival confirmation information using natural language processing; means for collecting and automatically analyzing and organizing external information such as reports from evacuation shelters, social media data, and news articles; means for quickly notifying relevant parties of the collated survival confirmation information; means including an emotion engine that recognizes the user's emotions and optimizes the content and timing of the notification; means for converting the user's input information into JSON format and sending it to the server using an HTTP POST request; and means for customizing the notification content and displaying a reassuring message that corresponds to the user's emotions. This reduces the psychological burden on the user and enables quick and reassuring safety confirmation.

[1789] "Identifying information of a missing person" is information that can individually identify a missing person, such as their name, address, or physical description.

[1790] "Natural language processing" is a technology that analyzes input text data, understands meaning and information, and extracts it.

[1791] "Confirmation of survival information" is information indicating that a missing person is safe, and is obtained from evacuation center reports, social media data, news articles, etc.

[1792] "Reports from evacuation centers" are information about the situation and safety of people at evacuation centers.

[1793] "SNS Data" refers to information such as posts, comments, and messages shared by users on social networking services.

[1794] A "news article" is an article of information reported in media such as newspapers or websites.

[1795] An "emotion-recognizing emotion engine" is a technology that analyzes and recognizes emotions from the user's input information and reactions.

[1796] "JSON format" is an abbreviation for JavaScript Object Notation, and is a format that structures and represents data in a lightweight manner.

[1797] An "HTTP POST request" is a method of the HTTP protocol used by a web client to send data to a server.

[1798] "Customizing notifications" refers to tailoring the content and format of notifications based on the user's needs and emotions.

[1799] A "reassuring message" is a contact message whose content is designed to make the user feel at ease.

[1800] This invention is a system that quickly and accurately confirms the safety of missing people. In particular, it uses natural language processing (NLP) technology to match the missing person's specific information with information confirming their survival, and also has the function of recognizing the user's emotions with an emotion engine to optimize the content and timing of notifications. This allows for quick and appropriate provision of information to relevant parties.

[1801] System configuration

[1802] server

[1803] The server receives specific information about missing people and stores it in a database. It also uses a natural language processing (NLP) module to analyze the received information and extract and organize survival confirmation information. It also periodically collects and analyzes external information such as reports from evacuation centers, social media data, and news articles to generate survival confirmation information. The server also includes an emotion engine that recognizes emotions from user input and past responses, allowing it to customize the content and timing of notifications.

[1804] For example, the server receives specific information for "Ichiro Tanaka" entered by the user and stores it in a database. The NLP module then analyzes evacuation shelters, social media data, and news articles based on the information for "Ichiro Tanaka," and extracts information confirming his survival, such as "Ichiro Tanaka is at an evacuation shelter."

[1805] Terminal

[1806] The device provides an interface for users to input information about missing people, which is then sent to a server. The device also receives notifications, allowing relevant parties to review the information in a timely manner. The device receives feedback from the emotion engine and customizes the notification content, displaying messages that take the user's emotions into account.

[1807] As a specific example of use, a user enters specific information for "Ichiro Tanaka" through a smartphone application and sends it to the server. The server generates confirmation information stating "Ichiro Tanaka is safe" and sends it to the user's device as a push notification. If the emotion engine recognizes "anxiety" at this time, it adds a reassuring message saying "We have also confirmed Ichiro Tanaka's detailed whereabouts."

[1808] User

[1809] Users, as family members or relatives, input information about the missing person through the device and receive notifications. The user's emotions are recorded, and the emotional data is reflected in the next notification.

[1810] For example, based on the information entered by the user as "Ichiro Tanaka," the user receives a notification that "Ichiro Tanaka is safe at the evacuation center." If the user has previously entered a worrying comment, the next notification will include a message that says, "Detailed location information for Ichiro Tanaka has also been obtained," to provide a sense of security.

[1811] Hardware and software used

[1812] Hardware

[1813] Smartphone (iOS, Android)

[1814] Server (Cloud service: AWS, GCP, etc.)

[1815] software

[1816] Frontend: React Native (for smartphones)

[1817] Backend: Python (Django, Flask)

[1818] API communication: HTTP, JSON

[1819] Natural Language Processing (NLP): spaCy, NLTK (Python packages)

[1820] Emotion Engine: Machine learning model using tensorflow and keras

[1821] Prompt Sentence Examples

[1822] Enter missing person information:

[1823] Name: "Ichiro Tanaka"

[1824] Address: "City"

[1825] Physical Description: "175cm tall, black hair"

[1826] NLP analysis results:

[1827] Ichiro Tanaka was identified at the evacuation center.

[1828] Emotion Engine Analysis Results:

[1829] The user felt uneasy after receiving the contact.

[1830] Optimized notifications:

[1831] "We have confirmed the identity of Ichiro Tanaka. We have also received detailed information on his whereabouts."

[1832] With the above-described configuration, the present invention can quickly and accurately collate information on missing persons and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[1833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1834] Step 1:

[1835] User enters specific information about the missing person

[1836] Users enter identifying information about a missing person, such as name, address, and physical characteristics, into a smartphone application. The information is then converted into JSON format, which structures the user's input and converts it into a format that can be sent to the server.

[1837] input:

[1838] Name: "Ichiro Tanaka"

[1839] Address: "City"

[1840] Physical Description: "175cm tall, black hair"

[1841] output:

[1842] Missing person information in JSON format

[1843] Step 2:

[1844] The device sends the input information to the server

[1845] The device sends the generated JSON-formatted data to the server using an HTTP POST request, which sends the data to an API endpoint at a specific URL.

[1846] input:

[1847] Missing person information in JSON format

[1848] output:

[1849] HTTP POST request to the server

[1850] Step 3:

[1851] The server receives the missing person information and stores it in a database

[1852] The server parses the received JSON data and stores the missing person's identifying information in a database, which then uses the structured data for subsequent natural language processing (NLP) and matching.

[1853] input:

[1854] Missing person information sent in an HTTP POST request

[1855] output:

[1856] Missing person information stored in a database

[1857] Step 4:

[1858] The server collects and analyzes external information

[1859] The server runs a script that periodically collects reports from evacuation centers, social media data, and news articles. The collected data is then passed to an NLP module for analysis, which extracts relevant survival information.

[1860] input:

[1861] Evacuation shelter reporting data

[1862] Social media data

[1863] News Article

[1864] output:

[1865] Extracted survival confirmation information

[1866] Specific operation:

[1867] For example, the NLP module extracts information from a news article that says, "Ichiro Tanaka is at a shelter."

[1868] Step 5:

[1869] Emotion engine recognizes user emotions

[1870] The server analyzes the user's input information and reactions to past notifications, and uses an emotion engine to recognize the user's emotions. Past comments and reaction data are used to reflect the user's emotions in the next notification.

[1871] input:

[1872] User's past comments and reaction data

[1873] output:

[1874] Recognized user emotion data

[1875] Step 6:

[1876] The server generates and sends the notification content

[1877] The server generates notifications based on the matching results and feedback from the emotion engine and sends them to relevant parties in the form of email, SMS, or push notifications, with messages customized according to the user's emotions.

[1878] input:

[1879] Matching result (survival confirmation information)

[1880] Emotion Engine Feedback

[1881] output:

[1882] Customized notification messages

[1883] Specific operation:

[1884] The notification "Ichiro Tanaka is safe in the evacuation center" will be supplemented with a reassuring message that "Detailed location confirmation has also been obtained."

[1885] Step 7:

[1886] The device receives and displays the notification.

[1887] The terminal receives the notification sent from the server and displays it on the terminal screen of the person involved, allowing the person involved to check the information immediately.

[1888] input:

[1889] Notification messages sent by the server

[1890] output:

[1891] Notification message displayed on the device of the person involved

[1892] Specific operation:

[1893] A push notification will appear on your smartphone with the message "Ichiro Tanaka is safely evacuated to the evacuation shelter."

[1894] Through the above processing steps, the system can quickly and accurately collate information on missing people and notify relevant parties in an appropriate manner that takes into account the user's feelings, thereby providing relevant parties with a quick and reassuring way to confirm their safety.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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).

[1902] 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.

[1903] 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."

[1904] 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.

[1905] 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).

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] The following is further disclosed regarding the above embodiment.

[1917] (Claim 1)

[1918] A means for inputting specific information of a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing;

[1919] A means to collect, automatically analyze, and organize external information such as reports from evacuation centers, social media data, and news articles.

[1920] A means for promptly notifying relevant parties of the collated survival confirmation information;

[1921] A system including:

[1922] (Claim 2)

[1923] The system of claim 1, wherein specific information about missing persons is stored in a database and related survival confirmation information is searched for and extracted using a natural language processing module.

[1924] (Claim 3)

[1925] The system according to claim 1, which periodically collects evacuation center information, social media data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology.

[1926] (Claim 4)

[1927] The system of claim 1, wherein the system notifies relevant parties of information on the survival of a missing person in the form of email, SMS, or push notification.

[1928] "Example 1"

[1929] (Claim 1)

[1930] A means of inputting specific information about a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing technology;

[1931] A means to automatically collect, analyze, and organize external information such as reports from evacuation centers, social networking service data, and news articles;

[1932] A means for promptly notifying relevant parties of the collated survival confirmation information;

[1933] A system including:

[1934] (Claim 2)

[1935] The system of claim 1, wherein specific information about missing persons is stored in a database and related survival confirmation information is searched for and extracted using a natural language processing module.

[1936] (Claim 3)

[1937] The system according to claim 1, which periodically collects evacuation shelter information, social networking service data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology.

[1938] "Application Example 1"

[1939] (Claim 1)

[1940] A means for inputting specific information of a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing;

[1941] A means to collect, automatically analyze, and organize external information such as reports from evacuation centers, social media data, and news articles.

[1942] A means for promptly notifying relevant parties of the collated survival confirmation information;

[1943] A means for displaying and checking missing person information in real time using visual display devices worn by security guards;

[1944] A means to obtain new information from evacuation centers and social media and notify you with audio and visual alerts,

[1945] A system including:

[1946] (Claim 2)

[1947] The system of claim 1, wherein specific information about missing persons is stored in a database and related survival confirmation information is searched for and extracted using a natural language processing module.

[1948] (Claim 3)

[1949] The system according to claim 1, which periodically collects evacuation center information, social media data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology.

[1950] "Example 2: Combining Emotion Engines"

[1951] (Claim 1)

[1952] A means for inputting specific information of a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing;

[1953] A means to collect, automatically analyze, and organize external information such as reports from evacuation centers, social media data, and news articles.

[1954] A means for recognizing emotions by analyzing input information from users and responses to past notifications, and optimizing the content and timing of notifications;

[1955] A means for promptly notifying relevant parties of the collated survival confirmation information;

[1956] A system including:

[1957] (Claim 2)

[1958] The system of claim 1, wherein specific information about missing persons is stored in a database and related survival confirmation information is searched for and extracted using a natural language processing module.

[1959] (Claim 3)

[1960] The system according to claim 1, which periodically collects evacuation center information, social media data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology.

[1961] "Application example 2 when combining emotion engines"

[1962] (Claim 1)

[1963] A means for inputting specific information of a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing;

[1964] A means to collect, automatically analyze, and organize external information such as reports from evacuation centers, social media data, and news articles.

[1965] A means for promptly notifying relevant parties of the collated survival confirmation information;

[1966] A means including an emotion engine that recognizes the emotion of a user and optimizes the content and timing of notifications;

[1967] A means to convert the user input information into JSON format and send it to the server using an HTTP POST request;

[1968] A means for customizing the notification content and displaying a reassuring message according to the user's emotions;

[1969] A system including:

[1970] (Claim 2)

[1971] The system of claim 1, wherein specific information about missing persons is stored in a database and related survival confirmation information is searched for and extracted using a natural language processing module.

[1972] (Claim 3)

[1973] The system according to claim 1, which periodically collects evacuation center information, social media data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology. [Explanation of symbols]

[1974] 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 specific information of a missing person (such as name, address, and physical characteristics) and extracting information confirming their survival using natural language processing; A means to collect, automatically analyze, and organize external information such as reports from evacuation centers, social media data, and news articles, and A means for promptly notifying relevant parties of the collated survival confirmation information; A system including:

2. 2. The system according to claim 1, wherein the system stores the specific information of the missing person in a database and searches and extracts related information confirming the person's survival using a natural language processing module.

3. The system according to claim 1, which periodically collects evacuation shelter information, social media data, and news articles, and extracts and analyzes survival confirmation information using natural language processing technology.

4. The system according to claim 1, wherein the system notifies relevant parties of information on the survival of a missing person in the form of email, SMS, or push notification.

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