System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting data from a plurality of information sources on the Internet; means for analyzing the collected data, evaluating reliability, and classifying the information; means for storing reliable information; means for receiving a request from a user and providing reliable information; means for acquiring characteristic information of the user and proposing an appropriate support method based on the characteristic; and means for providing the proposed support method to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] During disasters, information can be confusing and it can be difficult to obtain reliable information. Furthermore, relief efforts undertaken independently by individuals can be inappropriate, or unnecessary supplies can be provided. This not only makes relief less efficient, but can also cause confusion in the affected areas. To solve these problems, there is a need for accurate information gathering and proposals for optimal relief methods for individuals. [Means for solving the problem]

[0005] The present invention provides a system that includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, and means for accumulating highly reliable information. It also includes means for receiving requests from users and providing highly reliable information, means for acquiring user characteristic information and proposing appropriate support methods based on the characteristics, and means for providing the proposed support methods to the users. This makes it possible to provide highly reliable information even during disasters and propose optimal support methods based on individual characteristics.

[0006] "Data" refers to various types of information, such as text, images, and videos, collected from sources on the Internet.

[0007] "Sources" refers to various online platforms where data is collected, such as social media, news sites, and government and local government pages.

[0008] "Collection" refers to the process of obtaining data from the Internet and storing it in temporary data storage.

[0009] "Analysis" refers to the act of processing collected data using AI models and algorithms to evaluate its content and reliability.

[0010] "Reliability" refers to an indicator that evaluates whether collected data is accurate and worthy of use.

[0011] "Classification" refers to the process of dividing information into "high," "medium," or "low reliability" according to an assessment of reliability based on analyzed data.

[0012] "Storage" refers to the process of storing classified, reliable information in a database.

[0013] A "request" refers to a request for information made by a user to the system via a chat interface or the like.

[0014] "Providing" refers to the process of displaying reliable information to a user in response to a user request.

[0015] "Characteristic information" refers to personal information such as the supplies a user can provide, the time available to provide support, and special skills.

[0016] "Support method" refers to the specific support activities that the system proposes based on the user's characteristic information.

[0017] "Proposal" refers to the process in which the system generates the optimal support method based on the user's characteristic information and communicates it to the user.

[0018] "Chat Interface" means an interactive user interface through which a user can submit requests to the system and receive information and suggestions from the system.

[0019] "AI model" refers to an artificial intelligence algorithm used for data analysis, reliability assessment, and generating support methods.

[0020] A "natural language processing model" is a type of artificial intelligence used to analyze and classify collected data, and refers to a model for understanding and processing language data. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[0043] System Configuration

[0044] server

[0045] The server has the function of collecting and analyzing data from multiple sources on the Internet. The server evaluates the reliability of the collected data and categorizes the information. It also stores highly reliable information in a database and provides the necessary information in response to user requests.

[0046] Terminal

[0047] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also acquires user characteristic information and sends it to the server.

[0048] User

[0049] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system via their terminals and receive the necessary information and support methods.

[0050] System Features

[0051] Data collection

[0052] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[0053] Data analysis

[0054] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0055] Accumulation of information

[0056] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[0057] Receiving requests and providing information

[0058] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[0059] Acquisition of characteristic information and proposal of support methods

[0060] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages specific action.

[0061] Specific examples

[0062] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[0063] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server proposes a support method: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0064] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and propose appropriate support methods that suit the characteristics of each individual.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[0068] Step 2:

[0069] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0070] Step 3:

[0071] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[0072] Step 4:

[0073] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[0074] Step 5:

[0075] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[0076] Step 6:

[0077] The terminal displays the highly reliable information received from the server to the user, allowing the user to check the latest information on the disaster area.

[0078] Step 7:

[0079] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[0080] Step 8:

[0081] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[0082] Step 9:

[0083] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[0084] Step 10:

[0085] The server sends the generated support method to the terminal, which displays the support method to the user, enabling the user to provide accurate and effective support activities.

[0086] Example 1

[0087] 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."

[0088] During disasters, there is a need for systems that can quickly collect and analyze reliable information and propose appropriate support methods to users. However, current systems have low information reliability and lack the functionality to propose appropriate support methods based on user characteristics. In addition, it is difficult to collect information in real time, and they are unable to respond promptly to user needs. Therefore, the challenge is to create a system that can efficiently collect and analyze reliable information and quickly provide users with the optimal support methods.

[0089] 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.

[0090] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a natural language processing model, evaluating its reliability and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and providing the latest highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, and means for providing the user with the proposed support method. This makes it possible to collect and analyze highly reliable information in real time and quickly provide an appropriate support method based on the user's characteristics.

[0091] "Multiple sources on the Internet" refers to multiple data sources on the Internet, such as websites, social media, news sites, and official government and local government pages.

[0092] "Means of collecting data" refers to methods or technologies for obtaining data from the Internet, such as web scraping, API access, RSS feeds, etc.

[0093] "Natural language processing model" refers to machine learning algorithms and software tools used to analyze collected text data and evaluate its content and reliability.

[0094] "Means for assessing reliability and classifying information" refers to a method or technology that uses an algorithm to score the reliability of text data and classify the data as "high reliability," "medium reliability," or "low reliability."

[0095] "Means for storing highly reliable information in a database" refers to a method or technology for saving and storing information that has received a high score in the reliability evaluation in a database.

[0096] "Means for receiving requests from users and providing up-to-date, reliable information" refers to a method or technology for receiving information requests entered by users, searching for up-to-date, reliable information from a database according to the content of the requests, and providing the information.

[0097] "Means for acquiring user characteristic information and proposing appropriate support methods based on those characteristics" refers to algorithms and processes that collect information about the user (e.g., available time, available materials, special skills, etc.) and propose the most appropriate support method based on that information.

[0098] The "means for providing the user with the proposed assistance method" refers to a method or technology for notifying and displaying the assistance method generated by the server on the user's terminal.

[0099] "Chat Interface" means an interactive communication interface that enables a User to submit requests or questions to a System in text form and receive responses from the System.

[0100] MODE FOR CARRYING OUT THE INVENTION

[0101] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is mainly composed of a server, terminals, and users.

[0102] Server Roles

[0103] The server is a high-performance server machine that uses Python and natural language processing libraries (e.g., NLTK, SpaCy) to collect data from multiple sources on the Internet. Web scraping tools (e.g., BeautifulSoup, Scrapy) and API access are used to obtain data from social media, news sites, and government and local government pages. This data is stored in temporary data storage (e.g., Redis, MongoDB).

[0104] The server then applies a natural language processing model to the collected data to analyze its content and reliability. The reliability assessment uses an algorithm that compares it with past data and calculates a reliability score. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data that is assessed as highly reliable is then stored in a database (e.g., MySQL, PostgreSQL).

[0105] Device Role

[0106] A terminal is a device used by a user, such as a smartphone or PC, and operates by combining a front-end framework (e.g., React, Vue.js) and a back-end framework (e.g., Flask, Django). The terminal sends requests from the user to the server, receives responses from the server, and displays them.

[0107] For example, if a user requests "Please tell me the latest information on disaster areas," the device will send this request to the server as an API request. The server will search the database for the latest and most reliable information on disaster areas and return it to the device. The device will then display the retrieved information to the user.

[0108] User Roles

[0109] Users are individuals who use the system and wish to provide support to disaster-stricken areas. They send requests to the system through their terminals and receive information and suggestions. For example, if a user asks "What can I do?" through the chat interface, the terminal will ask the user about "what supplies can be provided" and "when you are available."

[0110] Specific examples

[0111] Prompt Sentence Examples

[0112] "Please tell me the latest information on the disaster-stricken areas"

[0113] "What can I do?"

[0114] As a concrete example, let's say a citizen wants to help a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to the server. The server searches its database for reliable information, such as "There is currently a food shortage in a specific area," and provides it. The device then displays this information to the citizen.

[0115] Next, when the citizen asks, "What can I do?", the device asks the citizen about the supplies they can provide, the time they are available, their areas of expertise, etc. If the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this to the server. Based on this characteristic information, the server proposes a method of assistance: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0116] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and quickly propose appropriate support methods tailored to individual characteristics.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] Data collection

[0120] Server Role:

[0121] The server uses web scraping tools (e.g., BeautifulSoup, Scrapy) or API access to collect data from social media sites, news sites, government and local government pages on the Internet. Specifically, the server periodically executes data collection tasks using a job scheduler (e.g., Celery, Cron). For example, the server scrapes news sites every hour to obtain new articles.

[0122] Input: Internet URL

[0123] Output: raw collected text data

[0124] Step 2:

[0125] Data analysis

[0126] Server Role:

[0127] The server analyzes the collected data using natural language processing libraries (e.g., NLTK, SpaCy). The server cleans the collected text data, tokenizes it, and tags it with parts of speech. To analyze reliability, an algorithm is used to compare it with past data and calculate a reliability score.

[0128] Input: Raw collected text data

[0129] Output: Parsed text data (with confidence scores)

[0130] Step 3:

[0131] Classification and storage of information

[0132] Server Role:

[0133] The server classifies the data into "high reliability," "medium reliability," and "low reliability" based on the analysis results. Of the classified data, data evaluated as high reliability is stored in a database (e.g., MySQL, PostgreSQL). During this process, the server generates SQL queries and inserts them into the database.

[0134] Input: Parsed text data (with confidence scores)

[0135] Output: Reliable data stored in a database

[0136] Step 4:

[0137] Receiving a user request

[0138] User role:

[0139] The user uses a terminal to send a request (e.g., "Please tell me the latest information on the disaster area") to the system.

[0140] Device role:

[0141] The device sends the user's request to the server as an API request. When the user enters a request into the chat interface and presses the send button, the device sends the input request to the server.

[0142] Input: User request (prompt)

[0143] Output: API request sent to the server

[0144] Step 5:

[0145] Providing information

[0146] Server Role:

[0147] The server receives the user's request, searches the database for the latest and most reliable information, and provides it to the device. The server generates an SQL query to retrieve the required information from the database and returns it in JSON format to the device.

[0148] Input: User request (prompt)

[0149] Output: Latest reliable information (JSON format)

[0150] Step 6:

[0151] Acquisition of characteristic information and proposal of support methods

[0152] User role:

[0153] The user provides characteristic information (e.g., "available supplies," "available time," "areas of expertise") to the system.

[0154] Device role:

[0155] The device sends the collected characteristic information in JSON format to the server. The user enters the characteristic information in the chat interface, and the device sends it to the server.

[0156] Server Role:

[0157] The server calculates the appropriate support method based on the characteristic information and returns the result to the device. This support method is generated using an algorithm that proposes the optimal support method based on the results of analyzing the user's characteristic information.

[0158] Input: User characteristics information (prompt sentence)

[0159] Output: Proposed support measures (JSON format)

[0160] Step 7:

[0161] View Suggestions

[0162] Device role:

[0163] The terminal displays the support methods sent from the server to the user, allowing the user to confirm the specific action plan.

[0164] Input: Proposed support method (JSON format)

[0165] Output: The specific assistance method displayed to the user

[0166] The above is the specific processing flow of the system program. The seamless coordination of each step allows users to provide support to disaster-stricken areas efficiently and effectively.

[0167] (Application example 1)

[0168] 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."

[0169] Conventional data analysis systems only provide the functionality to collect data from multiple sources on the Internet and evaluate its reliability. Furthermore, they have limitations in providing reliable information in response to user requests. In particular, in the advertising field, not only is data reliability required, but the generation and effective delivery of targeted advertisements based on user characteristics is also required. However, current systems do not adequately predict advertising effectiveness in real time or perform individual targeting. To address these issues, a system is needed that integrates the functions of predicting advertising effectiveness based on reliable data and generating and delivering targeted advertisements based on user characteristics.

[0170] 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.

[0171] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for storing highly reliable information, means for receiving requests from users and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for providing the user with the proposed support method, means for predicting advertising effectiveness from the analyzed data, means for generating appropriate targeted advertisements based on the user characteristic information, and means for delivering the generated advertisements to users. This makes it possible to predict advertising effectiveness based on highly reliable data and to generate and deliver targeted advertisements based on user characteristics.

[0172] "Multiple sources of information on the Internet" refers to information sources that can be accessed via the Internet, such as various websites, social media, news media, and official government and local government pages.

[0173] "Means of collecting data" refers to methods of obtaining data from sources on the Internet, such as web scraping, API usage, and RSS feeds.

[0174] "Means for analyzing data and assessing reliability" refers to methods for analyzing the content of collected data using natural language processing models or machine learning algorithms, and quantifying or classifying the reliability.

[0175] "Means for classifying information" refers to a method for categorizing data into categories such as "high reliability," "medium reliability," and "low reliability" based on the results of the reliability assessment.

[0176] "Means for storing highly reliable information" refers to a method of storing information that has been evaluated as being highly reliable in a database or the like, making it easily accessible later.

[0177] "Means for receiving requests from users" refers to an interface through which users input questions or requests for information to the system, and includes smartphone apps and web applications.

[0178] "Means for providing highly reliable information" refers to a method for searching for highly reliable information stored in response to a user's request and presenting it to the user.

[0179] The "means for acquiring user characteristic information" is a method for collecting information such as the materials that a user can provide, the time available, and areas of expertise.

[0180] The "means for proposing an appropriate support method" is a method for recommending optimal support or actions to a user based on collected characteristic information of the user.

[0181] The "means for providing the user with the proposed support method" is a method for displaying the support method proposed by the server to the user.

[0182] "Means for predicting advertising effectiveness" refers to methods that include algorithms and statistical models for predicting the influence and effectiveness of targeted advertising in advance, based on reliable data.

[0183] The "means for generating targeted advertisements" is a method for automatically creating optimized advertisement content based on user characteristic information.

[0184] The "means for delivering the generated advertisement to the user" refers to a method for displaying the generated advertisement on the user's screen or notifying the user of the advertisement.

[0185] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[0186] System Configuration

[0187] server

[0188] The server has the ability to collect and analyze data from multiple sources on the Internet (e.g., social media, news sites, government and local government pages). The server evaluates the reliability of the collected data and classifies it as "high reliability," "medium reliability," or "low reliability." Highly reliable information is stored in a database and provided upon user request.

[0189] Terminal

[0190] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user. It also acquires user characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server.

[0191] User

[0192] Users are individuals who use the system, and are those who wish to support disaster-stricken areas or are targets for receiving advertisements. Users send requests to the system through their terminals and receive the necessary information and support methods.

[0193] System Features

[0194] Data collection

[0195] The server uses constantly running web crawlers and APIs to collect data in real time from social media sites, news sites, government and local government pages, and other sources on the Internet.

[0196] Data analysis

[0197] The server applies natural language processing models (e.g., Hugging Face Transformers) to the collected data to analyze its content and reliability. Based on the analysis results, the data is classified and only highly reliable information is stored in the database.

[0198] Predicting advertising effectiveness

[0199] Furthermore, the server uses machine learning algorithms (e.g., scikit-learn's Linear Regression) based on reliable data to predict advertising effectiveness.

[0200] Generating and disseminating targeted advertisements

[0201] The server collects user characteristic information and uses a generative AI model to generate advertising content optimized for the user's characteristics. The generated advertisements are then delivered to the user via their device.

[0202] Feedback Loop

[0203] The server monitors the effectiveness of the delivered advertisements in real time, and AI optimizes the advertisements based on the feedback.

[0204] Specific examples

[0205] If a citizen wishes to provide support to a disaster-stricken area, they access the chat interface on their smartphone and type, "Please tell me the latest information about the disaster area." This request is sent from the device to the server, which searches for reliable information from its database and provides it. The device then displays this information to the citizen. If the citizen asks, "What can I do?", the device obtains the citizen's characteristics and sends them to the server. The server performs an analysis and generates a suggestion, such as, "We can deliver batteries and other supplies to the support center," which is displayed on the device.

[0206] Example prompt sentence:

[0207] Based on the following user information, predict what kind of ads will be effective.

[0208] Characteristics information:

[0209] Male in his 20s

[0210] Interests: Sports, technology

[0211] Residence: Tokyo

[0212] The above is a specific embodiment for carrying out the invention. This system makes it possible to predict the effectiveness of advertising based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] Data collection

[0216] The server collects data in real time from multiple sources on the Internet using web scraping techniques or APIs (e.g., NewsAPI, Twitter API). The input is the URLs and API keys of the sources to be collected, and the output is a set of collected raw data.

[0217] Step 2:

[0218] Data analysis

[0219] The collected raw data is sent to a server and analyzed using a natural language processing model (e.g., Hugging Face Transformers). The analysis evaluates the content and reliability of the data, and classifies the data as "high reliability," "medium reliability," or "low reliability." The input is raw data, and the output is classified data.

[0220] Step 3:

[0221] Data storage

[0222] The server stores the data that is rated as reliable in a database. The input is the data that is classified as reliable, and the output is an entry stored in the database.

[0223] Step 4:

[0224] Receiving a request from a user

[0225] A user inputs a request such as "Please tell me the latest information on disaster areas" through a terminal. The input is the user's request text, and the output is a data packet containing the request content sent to the server.

[0226] Step 5:

[0227] Reliable information search

[0228] The server searches the database for reliable information and extracts the most appropriate information for the request. The input is the user's request, and the output is reliable information as a search result.

[0229] Step 6:

[0230] Information provision

[0231] The reliable information provided by the server is sent to the terminal and displayed to the user. The input is the reliable information as a search result, and the output is the information displayed on the user's terminal.

[0232] Step 7:

[0233] Collecting user characteristics information

[0234] When a user inputs a question such as "What kind of assistance do you need?" through the terminal, the terminal collects the user's characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server. The input is the user's characteristic information, and the output is the characteristic information data sent to the server.

[0235] Step 8:

[0236] Proposal of support methods

[0237] The server analyzes the collected characteristic information and generates the optimal support method. The input is the user's characteristic information, and the output is the proposed support method. Specifically, the support method is predicted using a machine learning algorithm (e.g., Linear Regression in scikit-learn).

[0238] Step 9:

[0239] Providing support methods

[0240] The generated assistance method is presented to the user through a terminal. The input is the proposed assistance method, and the output is the assistance proposal displayed on the user's terminal.

[0241] Step 10:

[0242] Predicting advertising effectiveness

[0243] The server predicts advertising effectiveness using a generative AI model (e.g., Hugging Face Transformers) based on the collected reliable data. The input is reliable data and user characteristic information, and the output is the predicted advertising effectiveness.

[0244] Step 11:

[0245] Generate targeted ads

[0246] The server generates a targeted advertisement optimized for user characteristics based on the predicted results of the advertisement effectiveness. The input is the predicted results of the advertisement effectiveness and the user characteristic information, and the output is the generated advertisement content.

[0247] Step 12:

[0248] Ad serving

[0249] The generated targeted advertisement is delivered to the user through the terminal, where the input is the generated advertisement content and the output is the advertisement delivered to the user terminal.

[0250] The above processing steps make it possible to predict advertising effectiveness based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[0251] 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.

[0252] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and suggests appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine. A specific embodiment of the system is described below.

[0253] System Configuration

[0254] server

[0255] The server has the function of collecting and analyzing data from multiple sources on the Internet. It evaluates the reliability of the collected data and classifies the information. It also stores highly reliable information in a database and provides necessary information in response to user requests. It also obtains information about the user's characteristics and proposes appropriate support methods.

[0256] Terminal

[0257] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also has the role of acquiring user characteristic information and sending it to the server.

[0258] User

[0259] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system through their terminals and receive the necessary information and support methods. Furthermore, the system also recognizes the user's emotional state.

[0260] Emotion Engine

[0261] The emotion engine is responsible for assessing the user's emotional state based on the user's input and analysis results. Based on this assessment, the emotion engine suggests appropriate support methods for the user and adjusts the content and method of providing information.

[0262] System Features

[0263] Data collection

[0264] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[0265] Data analysis

[0266] The server applies natural language processing models to the collected data to analyze its content and reliability, and based on the results of the analysis, classifies the data as "high," "medium," or "low reliability."

[0267] Accumulation of information

[0268] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[0269] Receiving requests and providing information

[0270] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[0271] Acquisition of characteristic information and proposal of support methods

[0272] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages them to take specific action.

[0273] Sentiment analysis and support method suggestions

[0274] When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and evaluates the user's emotional state. Based on this evaluation, the server generates appropriate support methods. For example, if the user is feeling stressed, the emotion engine determines that psychological support is needed and suggests support methods. The device displays these support methods to the user.

[0275] Specific examples

[0276] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[0277] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server suggests a support method: "I can deliver batteries and other supplies to the support center." The device then displays this support method to the citizen.

[0278] Furthermore, if a citizen enters a message expressing anxiety, such as "Is this amount of support enough?", the emotion engine will analyze the message and assess that the citizen is feeling anxious. Based on the emotion engine's assessment, the server will determine that psychological support or additional information is necessary, and will display a message on the device saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind."

[0279] The above is a specific embodiment of the present invention. By combining emotion engines, it is possible to provide appropriate support methods and information according to the user's emotional state, thereby promoting more effective support activities.

[0280] The processing flow will be explained below.

[0281] Step 1:

[0282] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[0283] Step 2:

[0284] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0285] Step 3:

[0286] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[0287] Step 4:

[0288] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[0289] Step 5:

[0290] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[0291] Step 6:

[0292] The terminal displays the reliable information received from the server to the user, who then confirms the information.

[0293] Step 7:

[0294] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[0295] Step 8:

[0296] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[0297] Step 9:

[0298] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[0299] Step 10:

[0300] The server sends the generated support method to the terminal, which displays the support method to the user and provides the user with specific instructions for actions.

[0301] Step 11:

[0302] The user enters a message containing emotional content into the chat interface, such as a message expressing anxiety, such as "Is this enough support?"

[0303] Step 12:

[0304] An emotion engine analyzes the user's emotional message and evaluates the user's emotional state, for example, determining whether the user is feeling anxious.

[0305] Step 13:

[0306] Based on the evaluation results of the emotion engine, the server re-proposes appropriate support methods and information to the user. For example, it generates content such as, "Through your support activities, you can contact other volunteers. Please feel free to do so."

[0307] Step 14:

[0308] The server sends the generated re-proposal to the terminal, which displays the re-proposal to the user, giving the user a sense of security.

[0309] These are the specific processing steps of the present invention. In each step, the server, terminal, and user play their respective roles and cooperate with each other in real time, thereby providing highly reliable information and proposing individually optimized support methods. Utilizing an emotion engine also makes it possible to respond flexibly to the user's emotional state.

[0310] Example 2

[0311] 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."

[0312] In today's world, where the diversity of information on the Internet makes it difficult to evaluate its reliability, it is important for users to obtain reliable information quickly and accurately. Furthermore, in specific situations such as disaster relief, it is essential to propose prompt and appropriate support methods. Furthermore, if the user's emotional state affects the effectiveness of support, it is necessary to provide support methods that take emotions into consideration. A system that can address these challenges is needed.

[0313] 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.

[0314] In this invention, the server includes means for collecting data from multiple information sources on the Internet, analyzing the collected data using a natural language processing model, evaluating its reliability, and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and searching for and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for evaluating the user's emotional state using an emotion engine when the user inputs a message containing emotional content through a chat interface, and means for generating an appropriate support method based on the evaluation results and providing the user with the proposed support method. This enables the user to quickly obtain highly reliable information, have an appropriate support method suggested based on the user's characteristics, and is also provided with support that takes the user's emotional state into consideration.

[0315] "Multiple sources of information on the Internet" refers to multiple online information sources with different characteristics, such as social media, news sites, and government and local government websites.

[0316] "Data collection methods" refers to the technologies and processes that automatically obtain the required data from sources on the Internet.

[0317] A "natural language processing model" is an artificial intelligence technology for analyzing and understanding human language, and is used to analyze text data and evaluate its reliability.

[0318] "Reliability assessment" is the process of determining how accurate the collected data is and how trustworthy it is as information.

[0319] "Means of classifying information" refers to techniques for organizing collected information based on its reliability and content and separating it into different categories.

[0320] A "database" is a systematically organized collection of data, a system that facilitates the storage, retrieval, and management of information.

[0321] "User request" refers to a query or request sent by a user to the system.

[0322] "Characteristic information" is information about the characteristics of each user, such as the goods and services that the user can provide, the time available, and special skills.

[0323] The "means for proposing a support method" is a technology that generates and proposes a support method suited to a user based on the user's characteristic information and other data.

[0324] "Chat interface" means an interface through which a user can enter text messages and interact with the system in real time.

[0325] An "emotion engine" is a technology that analyzes text entered by a user and evaluates the user's emotional state based on the content.

[0326] "Means for generating appropriate support methods" refers to technology that automatically creates optimal support methods for users based on collected information and analysis results.

[0327] This invention is a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine.

[0328] System Configuration

[0329] server

[0330] The server collects data from multiple sources on the Internet, specifically from social media, news sites, government and local government pages, etc., using a crawler. The collected data is then stored in temporary data storage.

[0331] Next, the server analyzes the collected data using natural language processing models (e.g., BERT or GPT) and evaluates its reliability. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data evaluated as "high reliability" is stored in a database.

[0332] For example, if the server inputs a news article collected from "https: / / news.example.com" into the BERT model and determines that it is "highly reliable," it stores it in the database.

[0333] Terminal

[0334] The terminal is a device that provides the interface used by the user, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user.

[0335] For example, if a user types "Please tell me the latest information on disaster areas" into a smartphone app, this is sent to the server as an HTTP request. In response to the server's response, the device displays to the user the information that "Evacuation shelters have been opened in City A."

[0336] User

[0337] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send necessary requests to the system via their terminals and receive information and support methods according to their requests.

[0338] For example, when a user asks, "What kind of support do you need?", the device collects information about the user's characteristics (such as the supplies they can provide, the time they have available, and their special skills) and sends it to the server. Based on this information, the server proposes an appropriate method of support, which the device then displays.

[0339] Emotion Engine

[0340] The emotion engine evaluates the user's emotional state based on the user's input and analysis results. When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and recognizes the user's emotional state (e.g., stress, anxiety, joy, etc.).

[0341] For example, if a user types, "Is this enough support?", the emotion engine will detect the emotion "anxiety." Based on this analysis, the server will determine that psychological support is needed and generate a message that reads, "You can contact other volunteers through support activities. Please proceed without worry," and display it on the device.

[0342] Specific examples

[0343] A citizen wants to help support a disaster-stricken area, so they type "Please tell me the latest information about the disaster area" into their smartphone. This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in City A," and provides it. The device then displays this information to the citizen.

[0344] Next, the citizen asks, "What can I do?" The terminal asks the citizen about "what supplies can I provide," "what time is available," "what are my areas of expertise," etc. When the citizen enters "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the terminal sends this to the server.

[0345] Based on this information, the server suggests a method of support, such as "We can deliver batteries and other supplies to the support center," and the terminal displays this method of support to the citizen.

[0346] Furthermore, if a citizen inputs a message containing emotional content such as "Is this amount of support enough?", the emotion engine will recognize the emotion as "anxiety." Based on this result, the server will generate a message saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind," and the device will display this to the citizen.

[0347] Examples of prompt statements

[0348] Prompts for suggesting ways to help

[0349] Please propose the best way to provide support based on the available supplies (batteries), available times (weekends), and area of ​​expertise (driving).

[0350] Sentiment Analysis Prompt

[0351] When a user types "Is this enough support?", analyze the anxiety factor and generate an appropriate response.

[0352] In this way, the present invention enables users to quickly obtain highly reliable information and to be proposed appropriate support methods based on their own characteristics. Furthermore, by providing support methods that take into account the user's emotional state, more effective support activities can be promoted.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Program processing steps

[0355] Step 1: Start collecting data

[0356] The server automatically collects data from social media sites, news sites, government and local government pages, etc. This process is carried out using a crawler. The input is the URL of each source, and the output is the collected text data.

[0357] Step 2: Save your data

[0358] The server stores the collected data in temporary data storage, where preprocessing such as data format conversion and basic filtering is performed. The input is the collected text data, and the output is the preprocessed data.

[0359] Step 3: Applying the natural language processing model

[0360] The server uses a natural language processing model (e.g., BERT or GPT) to analyze the content and reliability of the preprocessed data. In this step, text data is input to the model, and the analysis results are output. The input is the preprocessed data, and the output is the reliability rating and the analysis results.

[0361] Step 4: Classify the data

[0362] Based on the analysis results, the server classifies the data as "high confidence," "medium confidence," or "low confidence." This classification is done automatically based on an algorithm. The input is the analysis results, and the output is the classified data.

[0363] Step 5: Store reliable data

[0364] The server stores the data that is rated as "highly reliable" in a database. In this step, only data that meets certain criteria is registered in the database. The input is the classified data, and the output is a database entry.

[0365] Step 6: Receiving the request

[0366] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." This request is sent from the device to the server as an HTTP request. The input is the user's request, and the output is an HTTP request.

[0367] Step 7: Submitting the request

[0368] The terminal sends the user's request to the server, where the network communication takes place and the request reaches the server. The input is the HTTP request, and the output is the data transmission to the server.

[0369] Step 8: Finding information

[0370] The server searches the database for reliable, up-to-date information. The search query is generated based on the user's request. The input is the user's request, and the output is the search results.

[0371] Step 9: Provide information

[0372] The server sends the searched information to the terminal, which displays it to the user. This is where format conversion and data transmission take place. The input is the search results, and the output is the information displayed on the user's screen.

[0373] Step 10: Obtaining User Characteristics

[0374] When a user asks "What kind of help do you need?", the device obtains the user's characteristic information (available supplies, available time, special skills, etc.). The input is the user's answer, and the output is the collected characteristic information.

[0375] Step 11: Sending characteristic information

[0376] The terminal sends the collected characteristic information to the server, which converts this information into a format required for processing. The input is the collected characteristic information, and the output is the data after format conversion.

[0377] Step 12: Propose ways to help

[0378] The server analyzes the transmitted characteristic information and proposes the optimal assistance method for the user. In this step, the proposal is generated using an AI model. The input is the characteristic information, and the output is the proposed assistance method.

[0379] Step 13: Provide a proposal

[0380] The terminal displays the proposed support methods to the user and encourages specific actions. The input is the content of the proposal, and the output is the proposed information displayed on the user's screen.

[0381] Step 14: Sentiment Analysis

[0382] When a user types a message containing emotional content into the chat interface, the emotion engine analyzes the message: the input is the user's message and the output is the analyzed emotional state.

[0383] Step 15: Generate support methods

[0384] The server generates an appropriate support method based on the evaluation of the emotion engine. In this step, a proposal is made based on the emotion analysis results. The input is the emotion analysis results, and the output is the generated support method.

[0385] Step 16: Providing psychological support

[0386] The terminal displays the generated support method and psychological support information to the user. The input is the generated support method, and the output is the support information displayed to the user.

[0387] (Application example 2)

[0388] 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."

[0389] Modern logistics centers collect large amounts of information daily, and there is a need to select and provide necessary and reliable information from that information. However, it is not easy to efficiently evaluate the reliability of information and provide optimal support methods. It is also important to provide support methods that take into account the emotional state of logistics center staff, but the technology to achieve this in real time is not yet in place. Therefore, it is necessary to develop a system that collects, analyzes, and evaluates reliable information and provides optimal support methods based on the user's emotional state.

[0390] 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 collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for accumulating highly reliable information, means for receiving a request from a user and providing highly reliable information, means for acquiring the user's characteristic information and emotional state and proposing an appropriate support method based thereon, means for providing the user with the proposed support method, means for evaluating the user's emotional state using an emotion engine, means for adjusting the information provision and support method based on the evaluation, and means for proposing psychological support according to the user's emotional state. This makes it possible to efficiently collect, analyze, and evaluate highly reliable information and provide an optimal support method that takes the user's emotional state into consideration.

[0391] "Multiple sources on the internet" refers to the wide range of data sources available online, including websites, social media, news feeds, government and public agency pages, and APIs.

[0392] "Data collection methods" refers to the technologies and processes used to automatically obtain the required information from multiple sources on the Internet.

[0393] "Means of analyzing data, assessing reliability, and classifying information" refers to technology that uses natural language processing and machine learning to analyze collected data, assess its reliability, and organize the data into categories based on the assessment.

[0394] "Means for storing highly reliable information" refers to the processes and technologies for storing evaluated, highly reliable data in a storage device such as a database.

[0395] "Means for receiving requests from users and providing highly reliable information" refers to systems and technologies that receive information requests from users, extract stored, highly reliable information, and provide it.

[0396] "Means for acquiring user characteristic information and emotional state and proposing appropriate support methods based on that" refers to the technology and process for analyzing the user's abilities, state, and emotions, and then deriving and proposing the most appropriate support method based on that.

[0397] "Means for providing the user with the proposed assistance methods" refers to the technology or interface that notifies or presents the user with the assistance methods derived by the system.

[0398] "Means for assessing a user's emotional state using an emotion engine" refers to an engine and its process that automatically determines a user's emotional state using technologies such as text analysis and speech analysis.

[0399] "Means for adjusting the information provided or the support method based on the evaluation" refers to the technology or process for appropriately changing or adjusting the information provided or the support method based on the evaluation results of the emotion engine.

[0400] "Means for suggesting psychological support according to the user's emotional state" refers to a technology or process that suggests psychological support, such as stress reduction or motivation improvement, based on an emotional assessment of the user.

[0401] This invention is a system for improving work efficiency and providing psychological support to staff at a logistics center. This system is composed of a server, terminals, users, and an emotion engine.

[0402] System Configuration

[0403] server

[0404] The server automatically collects data from multiple sources on the Internet and analyzes it using a natural language processing model. The collected data is assessed for reliability and classified as "high reliability," "medium reliability," or "low reliability." Information assessed as "high reliability" is then stored in a database. The server then receives requests from users, searches the database for the latest information, and provides it.

[0405] This allows logistics center staff to quickly and accurately obtain the information they need.

[0406] Terminal

[0407] The device is the interface operated by the user, such as a smartphone or PC. The device sends requests and inputs from the user to the server and displays information provided by the server to the user. The device also acquires information about the user's characteristics and emotional state and sends it to the server.

[0408] User

[0409] The users are staff at a logistics center who use the system to improve work efficiency. They request work information through their terminals and carry out their work based on the information provided. The system also evaluates their emotional state and suggests appropriate support methods.

[0410] Emotion Engine

[0411] The emotion engine analyzes the user's input text and voice data to assess the user's emotional state, for example by using TextBlob or other natural language processing models to detect positive and negative emotions from the input text.

[0412] System Features

[0413] Data collection

[0414] The server collects data in real time from the Internet, including social media, news sites, government and local government pages, etc. This collection is done automatically using the Requests library.

[0415] Data analysis

[0416] The server analyzes the collected data using natural language processing models (e.g., TextBlob, scikit-learn) and evaluates its reliability. Highly reliable information is stored in a database and used in response to future user requests.

[0417] Processing user requests

[0418] When a user sends a request via a terminal saying, "Tell me the latest situation at the logistics center," the server receives the request and searches for and provides reliable information. For example, it might display information from the database such as, "The logistics center's operations are currently at their peak."

[0419] Characteristics information and support methods

[0420] When a user asks, "How can I make my work more efficient?", the device obtains characteristic information from the user (e.g., current work content, areas of expertise, working hours, etc.) and sends it to the server. Based on this information, the server proposes the most suitable support method for the user. For example, it might say, "Since you're good at driving, why don't you take charge of operating logistics vehicles?"

[0421] Sentiment analysis and assistance adjustment

[0422] If a user types, "I'm stressed because my work just isn't going well," the emotion engine analyzes this text and evaluates that the user is feeling stressed. Based on this evaluation, the system will suggest psychological support, such as, "I recommend you take a break to relax."

[0423] Specific examples

[0424] As a specific example of use, if logistics center staff member A enters into the app, "I'm tired because work has been so busy recently," the emotion engine evaluates that emotional state, and the server suggests psychological support. It also provides reliable work information, such as "The logistics center is currently at its peak," and suggests appropriate support methods, such as "Take a break and refresh yourself."

[0425] Example prompt sentence:

[0426] "I've been feeling tired lately because of the heavy workload. How can I improve this?"

[0427] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0428] Step 1:

[0429] The server collects data from sources on the Internet, specifically from social media sites, news sites, government and local government pages, etc., using the Requests library. The input is the URL of each website or API, and the output is the collected raw data.

[0430] Step 2:

[0431] The server analyzes the collected data using a natural language processing model (e.g., TextBlob, scikit-learn). The input is the collected raw data, and the output is the text data as the analysis result and its reliability evaluation. During this process, semantic analysis and sentiment evaluation of the data are performed.

[0432] Step 3:

[0433] The server evaluates the reliability of the information based on the analysis results and classifies the information as "high reliability," "medium reliability," or "low reliability." The input is the analyzed data, and the output is the reliability evaluation and its classification result.

[0434] Step 4:

[0435] The server stores only information that is evaluated as highly reliable in the database. The input is the highly reliable information from the classification results, and the output is the highly reliable data stored in the database.

[0436] Step 5:

[0437] A user sends an information request through a terminal. Specifically, the user types something like "Tell me about the latest situation at the logistics center" into the chat interface. The input is the user's request, and the output is the transmission of the request content to the server.

[0438] Step 6:

[0439] The server receives the request and searches the database for the relevant, up-to-date, and reliable information. The input is the user request, and the output is the search results for the relevant information.

[0440] Step 7:

[0441] The server sends the search result information to the terminal, which then displays it to the user. The input is the search result information, and the output is the information provided to the user.

[0442] Step 8:

[0443] The user types "How can we make our work more efficient?" and the question is sent from the terminal to the server. The input is the user's question, and the output is the transmission of the question to the server.

[0444] Step 9:

[0445] The server acquires characteristic information about the user (e.g., current job content, areas of expertise, working hours, etc.). This information is sent from the terminal to the server. The input is the user's characteristic information, and the output is the transmission of the characteristic information to the server.

[0446] Step 10:

[0447] The server generates the optimal support method for the user based on the acquired characteristic information and reliable information. The input is the characteristic information and information from the database, and the output is the proposed support method.

[0448] Step 11:

[0449] The server sends the generated assistance method to the terminal, which then displays it to the user. The input is the assistance method, and the output is the display of the assistance method to the user.

[0450] Step 12:

[0451] The user inputs emotional content (e.g., "I'm stressed because my work just isn't going well"), and the emotion engine analyzes this text. The input is the emotional text, and the output is the emotion evaluation result.

[0452] Step 13:

[0453] The server generates support methods for psychological support and work adjustment based on the emotion evaluation results. The input is the emotion evaluation results, and the output is proposals for psychological support and work adjustment.

[0454] Step 14:

[0455] The server sends the proposed assistance method to the terminal, which then displays it to the user. The input is the proposed assistance method, and the output is the display to the user.

[0456] In this way, the system allows logistics center staff to quickly and effectively obtain information and receive appropriate assistance depending on their emotional state.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] [Second embodiment]

[0461] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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."

[0473] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[0474] System Configuration

[0475] server

[0476] The server has the function of collecting and analyzing data from multiple sources on the Internet. The server evaluates the reliability of the collected data and categorizes the information. It also stores highly reliable information in a database and provides the necessary information in response to user requests.

[0477] Terminal

[0478] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also acquires user characteristic information and sends it to the server.

[0479] User

[0480] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system via their terminals and receive the necessary information and support methods.

[0481] System Features

[0482] Data collection

[0483] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[0484] Data analysis

[0485] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0486] Accumulation of information

[0487] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[0488] Receiving requests and providing information

[0489] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[0490] Acquisition of characteristic information and proposal of support methods

[0491] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages specific action.

[0492] Specific examples

[0493] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[0494] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server proposes a support method: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0495] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and propose appropriate support methods that suit the characteristics of each individual.

[0496] The processing flow will be explained below.

[0497] Step 1:

[0498] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[0499] Step 2:

[0500] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0501] Step 3:

[0502] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[0503] Step 4:

[0504] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[0505] Step 5:

[0506] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[0507] Step 6:

[0508] The terminal displays the highly reliable information received from the server to the user, allowing the user to check the latest information on the disaster area.

[0509] Step 7:

[0510] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[0511] Step 8:

[0512] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[0513] Step 9:

[0514] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[0515] Step 10:

[0516] The server sends the generated support method to the terminal, which displays the support method to the user, enabling the user to provide accurate and effective support activities.

[0517] Example 1

[0518] 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."

[0519] During disasters, there is a need for systems that can quickly collect and analyze reliable information and propose appropriate support methods to users. However, current systems have low information reliability and lack the functionality to propose appropriate support methods based on user characteristics. In addition, it is difficult to collect information in real time, and they are unable to respond promptly to user needs. Therefore, the challenge is to create a system that can efficiently collect and analyze reliable information and quickly provide users with the optimal support methods.

[0520] 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.

[0521] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a natural language processing model, evaluating its reliability and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and providing the latest highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, and means for providing the user with the proposed support method. This makes it possible to collect and analyze highly reliable information in real time and quickly provide an appropriate support method based on the user's characteristics.

[0522] "Multiple sources on the Internet" refers to multiple data sources on the Internet, such as websites, social media, news sites, and official government and local government pages.

[0523] "Means of collecting data" refers to methods or technologies for obtaining data from the Internet, such as web scraping, API access, RSS feeds, etc.

[0524] "Natural language processing model" refers to machine learning algorithms and software tools used to analyze collected text data and evaluate its content and reliability.

[0525] "Means for assessing reliability and classifying information" refers to a method or technology that uses an algorithm to score the reliability of text data and classify the data as "high reliability," "medium reliability," or "low reliability."

[0526] "Means for storing highly reliable information in a database" refers to a method or technology for saving and storing information that has received a high score in the reliability evaluation in a database.

[0527] "Means for receiving requests from users and providing up-to-date, reliable information" refers to a method or technology for receiving information requests entered by users, searching for up-to-date, reliable information from a database according to the content of the requests, and providing the information.

[0528] "Means for acquiring user characteristic information and proposing appropriate support methods based on those characteristics" refers to algorithms and processes that collect information about the user (e.g., available time, available materials, special skills, etc.) and propose the most appropriate support method based on that information.

[0529] The "means for providing the user with the proposed assistance method" refers to a method or technology for notifying and displaying the assistance method generated by the server on the user's terminal.

[0530] "Chat Interface" means an interactive communication interface that enables a User to submit requests or questions to a System in text form and receive responses from the System.

[0531] MODE FOR CARRYING OUT THE INVENTION

[0532] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is mainly composed of a server, terminals, and users.

[0533] Server Roles

[0534] The server is a high-performance server machine that uses Python and natural language processing libraries (e.g., NLTK, SpaCy) to collect data from multiple sources on the Internet. Web scraping tools (e.g., BeautifulSoup, Scrapy) and API access are used to obtain data from social media, news sites, and government and local government pages. This data is stored in temporary data storage (e.g., Redis, MongoDB).

[0535] The server then applies a natural language processing model to the collected data to analyze its content and reliability. The reliability assessment uses an algorithm that compares it with past data and calculates a reliability score. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data that is assessed as highly reliable is then stored in a database (e.g., MySQL, PostgreSQL).

[0536] Device Role

[0537] A terminal is a device used by a user, such as a smartphone or PC, and operates by combining a front-end framework (e.g., React, Vue.js) and a back-end framework (e.g., Flask, Django). The terminal sends requests from the user to the server, receives responses from the server, and displays them.

[0538] For example, if a user requests "Please tell me the latest information on disaster areas," the device will send this request to the server as an API request. The server will search the database for the latest and most reliable information on disaster areas and return it to the device. The device will then display the retrieved information to the user.

[0539] User Roles

[0540] Users are individuals who use the system and wish to provide support to disaster-stricken areas. They send requests to the system through their terminals and receive information and suggestions. For example, if a user asks "What can I do?" through the chat interface, the terminal will ask the user about "what supplies can be provided" and "when you are available."

[0541] Specific examples

[0542] Prompt Sentence Examples

[0543] "Please tell me the latest information on the disaster-stricken areas"

[0544] "What can I do?"

[0545] As a concrete example, let's say a citizen wants to help a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to the server. The server searches its database for reliable information, such as "There is currently a food shortage in a specific area," and provides it. The device then displays this information to the citizen.

[0546] Next, when the citizen asks, "What can I do?", the device asks the citizen about the supplies they can provide, the time they are available, their areas of expertise, etc. If the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this to the server. Based on this characteristic information, the server proposes a method of assistance: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0547] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and quickly propose appropriate support methods tailored to individual characteristics.

[0548] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0549] Step 1:

[0550] Data collection

[0551] Server Role:

[0552] The server uses web scraping tools (e.g., BeautifulSoup, Scrapy) or API access to collect data from social media sites, news sites, government and local government pages on the Internet. Specifically, the server periodically executes data collection tasks using a job scheduler (e.g., Celery, Cron). For example, the server scrapes news sites every hour to obtain new articles.

[0553] Input: Internet URL

[0554] Output: raw collected text data

[0555] Step 2:

[0556] Data analysis

[0557] Server Role:

[0558] The server analyzes the collected data using natural language processing libraries (e.g., NLTK, SpaCy). The server cleans the collected text data, tokenizes it, and tags it with parts of speech. To analyze reliability, an algorithm is used to compare it with past data and calculate a reliability score.

[0559] Input: Raw collected text data

[0560] Output: Parsed text data (with confidence scores)

[0561] Step 3:

[0562] Classification and storage of information

[0563] Server Role:

[0564] The server classifies the data into "high reliability," "medium reliability," and "low reliability" based on the analysis results. Of the classified data, data evaluated as high reliability is stored in a database (e.g., MySQL, PostgreSQL). During this process, the server generates SQL queries and inserts them into the database.

[0565] Input: Parsed text data (with confidence scores)

[0566] Output: Reliable data stored in a database

[0567] Step 4:

[0568] Receiving a user request

[0569] User role:

[0570] The user uses a terminal to send a request (e.g., "Please tell me the latest information on the disaster area") to the system.

[0571] Device role:

[0572] The device sends the user's request to the server as an API request. When the user enters a request into the chat interface and presses the send button, the device sends the input request to the server.

[0573] Input: User request (prompt)

[0574] Output: API request sent to the server

[0575] Step 5:

[0576] Providing information

[0577] Server Role:

[0578] The server receives the user's request, searches the database for the latest and most reliable information, and provides it to the device. The server generates an SQL query to retrieve the required information from the database and returns it in JSON format to the device.

[0579] Input: User request (prompt)

[0580] Output: Latest reliable information (JSON format)

[0581] Step 6:

[0582] Acquisition of characteristic information and proposal of support methods

[0583] User role:

[0584] The user provides characteristic information (e.g., "available supplies," "available time," "areas of expertise") to the system.

[0585] Device role:

[0586] The device sends the collected characteristic information in JSON format to the server. The user enters the characteristic information in the chat interface, and the device sends it to the server.

[0587] Server Role:

[0588] The server calculates the appropriate support method based on the characteristic information and returns the result to the device. This support method is generated using an algorithm that proposes the optimal support method based on the results of analyzing the user's characteristic information.

[0589] Input: User characteristics information (prompt sentence)

[0590] Output: Proposed support measures (JSON format)

[0591] Step 7:

[0592] View Suggestions

[0593] Device role:

[0594] The terminal displays the support methods sent from the server to the user, allowing the user to confirm the specific action plan.

[0595] Input: Proposed support method (JSON format)

[0596] Output: The specific assistance method displayed to the user

[0597] The above is the specific processing flow of the system program. The seamless coordination of each step allows users to provide support to disaster-stricken areas efficiently and effectively.

[0598] (Application example 1)

[0599] 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."

[0600] Conventional data analysis systems only provide the functionality to collect data from multiple sources on the Internet and evaluate its reliability. Furthermore, they have limitations in providing reliable information in response to user requests. In particular, in the advertising field, not only is data reliability required, but the generation and effective delivery of targeted advertisements based on user characteristics is also required. However, current systems do not adequately predict advertising effectiveness in real time or perform individual targeting. To address these issues, a system is needed that integrates the functions of predicting advertising effectiveness based on reliable data and generating and delivering targeted advertisements based on user characteristics.

[0601] 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.

[0602] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for storing highly reliable information, means for receiving requests from users and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for providing the user with the proposed support method, means for predicting advertising effectiveness from the analyzed data, means for generating appropriate targeted advertisements based on the user characteristic information, and means for delivering the generated advertisements to users. This makes it possible to predict advertising effectiveness based on highly reliable data and to generate and deliver targeted advertisements based on user characteristics.

[0603] "Multiple sources of information on the Internet" refers to information sources that can be accessed via the Internet, such as various websites, social media, news media, and official government and local government pages.

[0604] "Means of collecting data" refers to methods of obtaining data from sources on the Internet, such as web scraping, API usage, and RSS feeds.

[0605] "Means for analyzing data and assessing reliability" refers to methods for analyzing the content of collected data using natural language processing models or machine learning algorithms, and quantifying or classifying the reliability.

[0606] "Means for classifying information" refers to a method for categorizing data into categories such as "high reliability," "medium reliability," and "low reliability" based on the results of the reliability assessment.

[0607] "Means for storing highly reliable information" refers to a method of storing information that has been evaluated as being highly reliable in a database or the like, making it easily accessible later.

[0608] "Means for receiving requests from users" refers to an interface through which users input questions or requests for information to the system, and includes smartphone apps and web applications.

[0609] "Means for providing highly reliable information" refers to a method for searching for highly reliable information stored in response to a user's request and presenting it to the user.

[0610] The "means for acquiring user characteristic information" is a method for collecting information such as the materials that a user can provide, the time available, and areas of expertise.

[0611] The "means for proposing an appropriate support method" is a method for recommending optimal support or actions to a user based on collected characteristic information of the user.

[0612] The "means for providing the user with the proposed support method" is a method for displaying the support method proposed by the server to the user.

[0613] "Means for predicting advertising effectiveness" refers to methods that include algorithms and statistical models for predicting the influence and effectiveness of targeted advertising in advance, based on reliable data.

[0614] The "means for generating targeted advertisements" is a method for automatically creating optimized advertisement content based on user characteristic information.

[0615] The "means for delivering the generated advertisement to the user" refers to a method for displaying the generated advertisement on the user's screen or notifying the user of the advertisement.

[0616] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[0617] System Configuration

[0618] server

[0619] The server has the ability to collect and analyze data from multiple sources on the Internet (e.g., social media, news sites, government and local government pages). The server evaluates the reliability of the collected data and classifies it as "high reliability," "medium reliability," or "low reliability." Highly reliable information is stored in a database and provided upon user request.

[0620] Terminal

[0621] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user. It also acquires user characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server.

[0622] User

[0623] Users are individuals who use the system, and are those who wish to support disaster-stricken areas or are targets for receiving advertisements. Users send requests to the system through their terminals and receive the necessary information and support methods.

[0624] System Features

[0625] Data collection

[0626] The server uses constantly running web crawlers and APIs to collect data in real time from social media sites, news sites, government and local government pages, and other sources on the Internet.

[0627] Data analysis

[0628] The server applies natural language processing models (e.g., Hugging Face Transformers) to the collected data to analyze its content and reliability. Based on the analysis results, the data is classified and only highly reliable information is stored in the database.

[0629] Predicting advertising effectiveness

[0630] Furthermore, the server uses machine learning algorithms (e.g., scikit-learn's Linear Regression) based on reliable data to predict advertising effectiveness.

[0631] Generating and disseminating targeted advertisements

[0632] The server collects user characteristic information and uses a generative AI model to generate advertising content optimized for the user's characteristics. The generated advertisements are then delivered to the user via their device.

[0633] Feedback Loop

[0634] The server monitors the effectiveness of the delivered advertisements in real time, and AI optimizes the advertisements based on the feedback.

[0635] Specific examples

[0636] If a citizen wishes to provide support to a disaster-stricken area, they access the chat interface on their smartphone and type, "Please tell me the latest information about the disaster area." This request is sent from the device to the server, which searches for reliable information from its database and provides it. The device then displays this information to the citizen. If the citizen asks, "What can I do?", the device obtains the citizen's characteristics and sends them to the server. The server performs an analysis and generates a suggestion, such as, "We can deliver batteries and other supplies to the support center," which is displayed on the device.

[0637] Example prompt sentence:

[0638] Based on the following user information, predict what kind of ads will be effective.

[0639] Characteristics information:

[0640] Male in his 20s

[0641] Interests: Sports, technology

[0642] Residence: Tokyo

[0643] The above is a specific embodiment for carrying out the invention. This system makes it possible to predict the effectiveness of advertising based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[0644] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0645] Step 1:

[0646] Data collection

[0647] The server collects data in real time from multiple sources on the Internet using web scraping techniques or APIs (e.g., NewsAPI, Twitter API). The input is the URLs and API keys of the sources to be collected, and the output is a set of collected raw data.

[0648] Step 2:

[0649] Data analysis

[0650] The collected raw data is sent to a server and analyzed using a natural language processing model (e.g., Hugging Face Transformers). The analysis evaluates the content and reliability of the data, and classifies the data as "high reliability," "medium reliability," or "low reliability." The input is raw data, and the output is classified data.

[0651] Step 3:

[0652] Data storage

[0653] The server stores the data that is rated as reliable in a database. The input is the data that is classified as reliable, and the output is an entry stored in the database.

[0654] Step 4:

[0655] Receiving a request from a user

[0656] A user inputs a request such as "Please tell me the latest information on disaster areas" through a terminal. The input is the user's request text, and the output is a data packet containing the request content sent to the server.

[0657] Step 5:

[0658] Reliable information search

[0659] The server searches the database for reliable information and extracts the most appropriate information for the request. The input is the user's request, and the output is reliable information as a search result.

[0660] Step 6:

[0661] Information provision

[0662] The reliable information provided by the server is sent to the terminal and displayed to the user. The input is the reliable information as a search result, and the output is the information displayed on the user's terminal.

[0663] Step 7:

[0664] Collecting user characteristics information

[0665] When a user inputs a question such as "What kind of assistance do you need?" through the terminal, the terminal collects the user's characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server. The input is the user's characteristic information, and the output is the characteristic information data sent to the server.

[0666] Step 8:

[0667] Proposal of support methods

[0668] The server analyzes the collected characteristic information and generates the optimal support method. The input is the user's characteristic information, and the output is the proposed support method. Specifically, the support method is predicted using a machine learning algorithm (e.g., Linear Regression in scikit-learn).

[0669] Step 9:

[0670] Providing support methods

[0671] The generated assistance method is presented to the user through a terminal. The input is the proposed assistance method, and the output is the assistance proposal displayed on the user's terminal.

[0672] Step 10:

[0673] Predicting advertising effectiveness

[0674] The server predicts advertising effectiveness using a generative AI model (e.g., Hugging Face Transformers) based on the collected reliable data. The input is reliable data and user characteristic information, and the output is the predicted advertising effectiveness.

[0675] Step 11:

[0676] Generate targeted ads

[0677] The server generates a targeted advertisement optimized for user characteristics based on the predicted results of the advertisement effectiveness. The input is the predicted results of the advertisement effectiveness and the user characteristic information, and the output is the generated advertisement content.

[0678] Step 12:

[0679] Ad serving

[0680] The generated targeted advertisement is delivered to the user through the terminal, where the input is the generated advertisement content and the output is the advertisement delivered to the user terminal.

[0681] The above processing steps make it possible to predict advertising effectiveness based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[0682] 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.

[0683] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and suggests appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine. A specific embodiment of the system is described below.

[0684] System Configuration

[0685] server

[0686] The server has the function of collecting and analyzing data from multiple sources on the Internet. It evaluates the reliability of the collected data and classifies the information. It also stores highly reliable information in a database and provides necessary information in response to user requests. It also obtains information about the user's characteristics and proposes appropriate support methods.

[0687] Terminal

[0688] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also has the role of acquiring user characteristic information and sending it to the server.

[0689] User

[0690] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system through their terminals and receive the necessary information and support methods. Furthermore, the system also recognizes the user's emotional state.

[0691] Emotion Engine

[0692] The emotion engine is responsible for assessing the user's emotional state based on the user's input and analysis results. Based on this assessment, the emotion engine suggests appropriate support methods for the user and adjusts the content and method of providing information.

[0693] System Features

[0694] Data collection

[0695] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[0696] Data analysis

[0697] The server applies natural language processing models to the collected data to analyze its content and reliability, and based on the results of the analysis, classifies the data as "high," "medium," or "low reliability."

[0698] Accumulation of information

[0699] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[0700] Receiving requests and providing information

[0701] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[0702] Acquisition of characteristic information and proposal of support methods

[0703] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages them to take specific action.

[0704] Sentiment analysis and support method suggestions

[0705] When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and evaluates the user's emotional state. Based on this evaluation, the server generates appropriate support methods. For example, if the user is feeling stressed, the emotion engine determines that psychological support is needed and suggests support methods. The device displays these support methods to the user.

[0706] Specific examples

[0707] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[0708] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server suggests a support method: "I can deliver batteries and other supplies to the support center." The device then displays this support method to the citizen.

[0709] Furthermore, if a citizen enters a message expressing anxiety, such as "Is this amount of support enough?", the emotion engine will analyze the message and assess that the citizen is feeling anxious. Based on the emotion engine's assessment, the server will determine that psychological support or additional information is necessary, and will display a message on the device saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind."

[0710] The above is a specific embodiment of the present invention. By combining emotion engines, it is possible to provide appropriate support methods and information according to the user's emotional state, thereby promoting more effective support activities.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[0714] Step 2:

[0715] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0716] Step 3:

[0717] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[0718] Step 4:

[0719] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[0720] Step 5:

[0721] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[0722] Step 6:

[0723] The terminal displays the reliable information received from the server to the user, who then confirms the information.

[0724] Step 7:

[0725] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[0726] Step 8:

[0727] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[0728] Step 9:

[0729] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[0730] Step 10:

[0731] The server sends the generated support method to the terminal, which displays the support method to the user and provides the user with specific instructions for actions.

[0732] Step 11:

[0733] The user enters a message containing emotional content into the chat interface, such as a message expressing anxiety, such as "Is this enough support?"

[0734] Step 12:

[0735] An emotion engine analyzes the user's emotional message and evaluates the user's emotional state, for example, determining whether the user is feeling anxious.

[0736] Step 13:

[0737] Based on the evaluation results of the emotion engine, the server re-proposes appropriate support methods and information to the user. For example, it generates content such as, "Through your support activities, you can contact other volunteers. Please feel free to do so."

[0738] Step 14:

[0739] The server sends the generated re-proposal to the terminal, which displays the re-proposal to the user, giving the user a sense of security.

[0740] These are the specific processing steps of the present invention. In each step, the server, terminal, and user play their respective roles and cooperate with each other in real time, thereby providing highly reliable information and proposing individually optimized support methods. Utilizing an emotion engine also makes it possible to respond flexibly to the user's emotional state.

[0741] Example 2

[0742] 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."

[0743] In today's world, where the diversity of information on the Internet makes it difficult to evaluate its reliability, it is important for users to obtain reliable information quickly and accurately. Furthermore, in specific situations such as disaster relief, it is essential to propose prompt and appropriate support methods. Furthermore, if the user's emotional state affects the effectiveness of support, it is necessary to provide support methods that take emotions into consideration. A system that can address these challenges is needed.

[0744] 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.

[0745] In this invention, the server includes means for collecting data from multiple information sources on the Internet, analyzing the collected data using a natural language processing model, evaluating its reliability, and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and searching for and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for evaluating the user's emotional state using an emotion engine when the user inputs a message containing emotional content through a chat interface, and means for generating an appropriate support method based on the evaluation results and providing the user with the proposed support method. This enables the user to quickly obtain highly reliable information, have an appropriate support method suggested based on the user's characteristics, and is also provided with support that takes the user's emotional state into consideration.

[0746] "Multiple sources of information on the Internet" refers to multiple online information sources with different characteristics, such as social media, news sites, and government and local government websites.

[0747] "Data collection methods" refers to the technologies and processes that automatically obtain the required data from sources on the Internet.

[0748] A "natural language processing model" is an artificial intelligence technology for analyzing and understanding human language, and is used to analyze text data and evaluate its reliability.

[0749] "Reliability assessment" is the process of determining how accurate the collected data is and how trustworthy it is as information.

[0750] "Means of classifying information" refers to techniques for organizing collected information based on its reliability and content and separating it into different categories.

[0751] A "database" is a systematically organized collection of data, a system that facilitates the storage, retrieval, and management of information.

[0752] "User request" refers to a query or request sent by a user to the system.

[0753] "Characteristic information" is information about the characteristics of each user, such as the goods and services that the user can provide, the time available, and special skills.

[0754] The "means for proposing a support method" is a technology that generates and proposes a support method suited to a user based on the user's characteristic information and other data.

[0755] "Chat interface" means an interface through which a user can enter text messages and interact with the system in real time.

[0756] An "emotion engine" is a technology that analyzes text entered by a user and evaluates the user's emotional state based on the content.

[0757] "Means for generating appropriate support methods" refers to technology that automatically creates optimal support methods for users based on collected information and analysis results.

[0758] This invention is a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine.

[0759] System Configuration

[0760] server

[0761] The server collects data from multiple sources on the Internet, specifically from social media, news sites, government and local government pages, etc., using a crawler. The collected data is then stored in temporary data storage.

[0762] Next, the server analyzes the collected data using natural language processing models (e.g., BERT or GPT) and evaluates its reliability. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data evaluated as "high reliability" is stored in a database.

[0763] For example, if the server inputs a news article collected from "https: / / news.example.com" into the BERT model and determines that it is "highly reliable," it stores it in the database.

[0764] Terminal

[0765] The terminal is a device that provides the interface used by the user, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user.

[0766] For example, if a user types "Please tell me the latest information on disaster areas" into a smartphone app, this is sent to the server as an HTTP request. In response to the server's response, the device displays to the user the information that "Evacuation shelters have been opened in City A."

[0767] User

[0768] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send necessary requests to the system via their terminals and receive information and support methods according to their requests.

[0769] For example, when a user asks, "What kind of support do you need?", the device collects information about the user's characteristics (such as the supplies they can provide, the time they have available, and their special skills) and sends it to the server. Based on this information, the server proposes an appropriate method of support, which the device then displays.

[0770] Emotion Engine

[0771] The emotion engine evaluates the user's emotional state based on the user's input and analysis results. When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and recognizes the user's emotional state (e.g., stress, anxiety, joy, etc.).

[0772] For example, if a user types, "Is this enough support?", the emotion engine will detect the emotion "anxiety." Based on this analysis, the server will determine that psychological support is needed and generate a message that reads, "You can contact other volunteers through support activities. Please proceed without worry," and display it on the device.

[0773] Specific examples

[0774] A citizen wants to help support a disaster-stricken area, so they type "Please tell me the latest information about the disaster area" into their smartphone. This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in City A," and provides it. The device then displays this information to the citizen.

[0775] Next, the citizen asks, "What can I do?" The terminal asks the citizen about "what supplies can I provide," "what time is available," "what are my areas of expertise," etc. When the citizen enters "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the terminal sends this to the server.

[0776] Based on this information, the server suggests a method of support, such as "We can deliver batteries and other supplies to the support center," and the terminal displays this method of support to the citizen.

[0777] Furthermore, if a citizen inputs a message containing emotional content such as "Is this amount of support enough?", the emotion engine will recognize the emotion as "anxiety." Based on this result, the server will generate a message saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind," and the device will display this to the citizen.

[0778] Examples of prompt statements

[0779] Prompts for suggesting ways to help

[0780] Please propose the best way to provide support based on the available supplies (batteries), available times (weekends), and area of ​​expertise (driving).

[0781] Sentiment Analysis Prompt

[0782] When a user types "Is this enough support?", analyze the anxiety factor and generate an appropriate response.

[0783] In this way, the present invention enables users to quickly obtain highly reliable information and to be proposed appropriate support methods based on their own characteristics. Furthermore, by providing support methods that take into account the user's emotional state, more effective support activities can be promoted.

[0784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0785] Program processing steps

[0786] Step 1: Start collecting data

[0787] The server automatically collects data from social media sites, news sites, government and local government pages, etc. This process is carried out using a crawler. The input is the URL of each source, and the output is the collected text data.

[0788] Step 2: Save your data

[0789] The server stores the collected data in temporary data storage, where preprocessing such as data format conversion and basic filtering is performed. The input is the collected text data, and the output is the preprocessed data.

[0790] Step 3: Applying the natural language processing model

[0791] The server uses a natural language processing model (e.g., BERT or GPT) to analyze the content and reliability of the preprocessed data. In this step, text data is input to the model, and the analysis results are output. The input is the preprocessed data, and the output is the reliability rating and the analysis results.

[0792] Step 4: Classify the data

[0793] Based on the analysis results, the server classifies the data as "high confidence," "medium confidence," or "low confidence." This classification is done automatically based on an algorithm. The input is the analysis results, and the output is the classified data.

[0794] Step 5: Store reliable data

[0795] The server stores the data that is rated as "highly reliable" in a database. In this step, only data that meets certain criteria is registered in the database. The input is the classified data, and the output is a database entry.

[0796] Step 6: Receiving the request

[0797] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." This request is sent from the device to the server as an HTTP request. The input is the user's request, and the output is an HTTP request.

[0798] Step 7: Submitting the request

[0799] The terminal sends the user's request to the server, where the network communication takes place and the request reaches the server. The input is the HTTP request, and the output is the data transmission to the server.

[0800] Step 8: Finding information

[0801] The server searches the database for reliable, up-to-date information. The search query is generated based on the user's request. The input is the user's request, and the output is the search results.

[0802] Step 9: Provide information

[0803] The server sends the searched information to the terminal, which displays it to the user. This is where format conversion and data transmission take place. The input is the search results, and the output is the information displayed on the user's screen.

[0804] Step 10: Obtaining User Characteristics

[0805] When a user asks "What kind of help do you need?", the device obtains the user's characteristic information (available supplies, available time, special skills, etc.). The input is the user's answer, and the output is the collected characteristic information.

[0806] Step 11: Sending characteristic information

[0807] The terminal sends the collected characteristic information to the server, which converts this information into a format required for processing. The input is the collected characteristic information, and the output is the data after format conversion.

[0808] Step 12: Propose ways to help

[0809] The server analyzes the transmitted characteristic information and proposes the optimal assistance method for the user. In this step, the proposal is generated using an AI model. The input is the characteristic information, and the output is the proposed assistance method.

[0810] Step 13: Provide a proposal

[0811] The terminal displays the proposed support methods to the user and encourages specific actions. The input is the content of the proposal, and the output is the proposed information displayed on the user's screen.

[0812] Step 14: Sentiment Analysis

[0813] When a user types a message containing emotional content into the chat interface, the emotion engine analyzes the message: the input is the user's message and the output is the analyzed emotional state.

[0814] Step 15: Generate support methods

[0815] The server generates an appropriate support method based on the evaluation of the emotion engine. In this step, a proposal is made based on the emotion analysis results. The input is the emotion analysis results, and the output is the generated support method.

[0816] Step 16: Providing psychological support

[0817] The terminal displays the generated support method and psychological support information to the user. The input is the generated support method, and the output is the support information displayed to the user.

[0818] (Application example 2)

[0819] 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."

[0820] Modern logistics centers collect large amounts of information daily, and there is a need to select and provide necessary and reliable information from that information. However, it is not easy to efficiently evaluate the reliability of information and provide optimal support methods. It is also important to provide support methods that take into account the emotional state of logistics center staff, but the technology to achieve this in real time is not yet in place. Therefore, it is necessary to develop a system that collects, analyzes, and evaluates reliable information and provides optimal support methods based on the user's emotional state.

[0821] 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 collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for accumulating highly reliable information, means for receiving a request from a user and providing highly reliable information, means for acquiring the user's characteristic information and emotional state and proposing an appropriate support method based thereon, means for providing the user with the proposed support method, means for evaluating the user's emotional state using an emotion engine, means for adjusting the information provision and support method based on the evaluation, and means for proposing psychological support according to the user's emotional state. This makes it possible to efficiently collect, analyze, and evaluate highly reliable information and provide an optimal support method that takes the user's emotional state into consideration.

[0822] "Multiple sources on the internet" refers to the wide range of data sources available online, including websites, social media, news feeds, government and public agency pages, and APIs.

[0823] "Data collection methods" refers to the technologies and processes used to automatically obtain the required information from multiple sources on the Internet.

[0824] "Means of analyzing data, assessing reliability, and classifying information" refers to technology that uses natural language processing and machine learning to analyze collected data, assess its reliability, and organize the data into categories based on the assessment.

[0825] "Means for storing highly reliable information" refers to the processes and technologies for storing evaluated, highly reliable data in a storage device such as a database.

[0826] "Means for receiving requests from users and providing highly reliable information" refers to systems and technologies that receive information requests from users, extract stored, highly reliable information, and provide it.

[0827] "Means for acquiring user characteristic information and emotional state and proposing appropriate support methods based on that" refers to the technology and process for analyzing the user's abilities, state, and emotions, and then deriving and proposing the most appropriate support method based on that.

[0828] "Means for providing the user with the proposed assistance methods" refers to the technology or interface that notifies or presents the user with the assistance methods derived by the system.

[0829] "Means for assessing a user's emotional state using an emotion engine" refers to an engine and its process that automatically determines a user's emotional state using technologies such as text analysis and speech analysis.

[0830] "Means for adjusting the information provided or the support method based on the evaluation" refers to the technology or process for appropriately changing or adjusting the information provided or the support method based on the evaluation results of the emotion engine.

[0831] "Means for suggesting psychological support according to the user's emotional state" refers to a technology or process that suggests psychological support, such as stress reduction or motivation improvement, based on an emotional assessment of the user.

[0832] This invention is a system for improving work efficiency and providing psychological support to staff at a logistics center. This system is composed of a server, terminals, users, and an emotion engine.

[0833] System Configuration

[0834] server

[0835] The server automatically collects data from multiple sources on the Internet and analyzes it using a natural language processing model. The collected data is assessed for reliability and classified as "high reliability," "medium reliability," or "low reliability." Information assessed as "high reliability" is then stored in a database. The server then receives requests from users, searches the database for the latest information, and provides it.

[0836] This allows logistics center staff to quickly and accurately obtain the information they need.

[0837] Terminal

[0838] The device is the interface operated by the user, such as a smartphone or PC. The device sends requests and inputs from the user to the server and displays information provided by the server to the user. The device also acquires information about the user's characteristics and emotional state and sends it to the server.

[0839] User

[0840] The users are staff at a logistics center who use the system to improve work efficiency. They request work information through their terminals and carry out their work based on the information provided. The system also evaluates their emotional state and suggests appropriate support methods.

[0841] Emotion Engine

[0842] The emotion engine analyzes the user's input text and voice data to assess the user's emotional state, for example by using TextBlob or other natural language processing models to detect positive and negative emotions from the input text.

[0843] System Features

[0844] Data collection

[0845] The server collects data in real time from the Internet, including social media, news sites, government and local government pages, etc. This collection is done automatically using the Requests library.

[0846] Data analysis

[0847] The server analyzes the collected data using natural language processing models (e.g., TextBlob, scikit-learn) and evaluates its reliability. Highly reliable information is stored in a database and used in response to future user requests.

[0848] Processing user requests

[0849] When a user sends a request via a terminal saying, "Tell me the latest situation at the logistics center," the server receives the request and searches for and provides reliable information. For example, it might display information from the database such as, "The logistics center's operations are currently at their peak."

[0850] Characteristics information and support methods

[0851] When a user asks, "How can I make my work more efficient?", the device obtains characteristic information from the user (e.g., current work content, areas of expertise, working hours, etc.) and sends it to the server. Based on this information, the server proposes the most suitable support method for the user. For example, it might say, "Since you're good at driving, why don't you take charge of operating logistics vehicles?"

[0852] Sentiment analysis and assistance adjustment

[0853] If a user types, "I'm stressed because my work just isn't going well," the emotion engine analyzes this text and evaluates that the user is feeling stressed. Based on this evaluation, the system will suggest psychological support, such as, "I recommend you take a break to relax."

[0854] Specific examples

[0855] As a specific example of use, if logistics center staff member A enters into the app, "I'm tired because work has been so busy recently," the emotion engine evaluates that emotional state, and the server suggests psychological support. It also provides reliable work information, such as "The logistics center is currently at its peak," and suggests appropriate support methods, such as "Take a break and refresh yourself."

[0856] Example prompt sentence:

[0857] "I've been feeling tired lately because of the heavy workload. How can I improve this?"

[0858] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0859] Step 1:

[0860] The server collects data from sources on the Internet, specifically from social media sites, news sites, government and local government pages, etc., using the Requests library. The input is the URL of each website or API, and the output is the collected raw data.

[0861] Step 2:

[0862] The server analyzes the collected data using a natural language processing model (e.g., TextBlob, scikit-learn). The input is the collected raw data, and the output is the text data as the analysis result and its reliability evaluation. During this process, semantic analysis and sentiment evaluation of the data are performed.

[0863] Step 3:

[0864] The server evaluates the reliability of the information based on the analysis results and classifies the information as "high reliability," "medium reliability," or "low reliability." The input is the analyzed data, and the output is the reliability evaluation and its classification result.

[0865] Step 4:

[0866] The server stores only information that is evaluated as highly reliable in the database. The input is the highly reliable information from the classification results, and the output is the highly reliable data stored in the database.

[0867] Step 5:

[0868] A user sends an information request through a terminal. Specifically, the user types something like "Tell me about the latest situation at the logistics center" into the chat interface. The input is the user's request, and the output is the transmission of the request content to the server.

[0869] Step 6:

[0870] The server receives the request and searches the database for the relevant, up-to-date, and reliable information. The input is the user request, and the output is the search results for the relevant information.

[0871] Step 7:

[0872] The server sends the search result information to the terminal, which then displays it to the user. The input is the search result information, and the output is the information provided to the user.

[0873] Step 8:

[0874] The user types "How can we make our work more efficient?" and the question is sent from the terminal to the server. The input is the user's question, and the output is the transmission of the question to the server.

[0875] Step 9:

[0876] The server acquires characteristic information about the user (e.g., current job content, areas of expertise, working hours, etc.). This information is sent from the terminal to the server. The input is the user's characteristic information, and the output is the transmission of the characteristic information to the server.

[0877] Step 10:

[0878] The server generates the optimal support method for the user based on the acquired characteristic information and reliable information. The input is the characteristic information and information from the database, and the output is the proposed support method.

[0879] Step 11:

[0880] The server sends the generated assistance method to the terminal, which then displays it to the user. The input is the assistance method, and the output is the display of the assistance method to the user.

[0881] Step 12:

[0882] The user inputs emotional content (e.g., "I'm stressed because my work just isn't going well"), and the emotion engine analyzes this text. The input is the emotional text, and the output is the emotion evaluation result.

[0883] Step 13:

[0884] The server generates support methods for psychological support and work adjustment based on the emotion evaluation results. The input is the emotion evaluation results, and the output is proposals for psychological support and work adjustment.

[0885] Step 14:

[0886] The server sends the proposed assistance method to the terminal, which then displays it to the user. The input is the proposed assistance method, and the output is the display to the user.

[0887] In this way, the system allows logistics center staff to quickly and effectively obtain information and receive appropriate assistance depending on their emotional state.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] [Third embodiment]

[0892] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0893] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0894] 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).

[0895] 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.

[0896] 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.

[0897] 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).

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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."

[0904] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[0905] System Configuration

[0906] server

[0907] The server has the function of collecting and analyzing data from multiple sources on the Internet. The server evaluates the reliability of the collected data and categorizes the information. It also stores highly reliable information in a database and provides the necessary information in response to user requests.

[0908] Terminal

[0909] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also acquires user characteristic information and sends it to the server.

[0910] User

[0911] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system via their terminals and receive the necessary information and support methods.

[0912] System Features

[0913] Data collection

[0914] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[0915] Data analysis

[0916] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0917] Accumulation of information

[0918] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[0919] Receiving requests and providing information

[0920] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[0921] Acquisition of characteristic information and proposal of support methods

[0922] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages specific action.

[0923] Specific examples

[0924] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[0925] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server proposes a support method: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0926] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and propose appropriate support methods that suit the characteristics of each individual.

[0927] The processing flow will be explained below.

[0928] Step 1:

[0929] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[0930] Step 2:

[0931] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[0932] Step 3:

[0933] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[0934] Step 4:

[0935] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[0936] Step 5:

[0937] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[0938] Step 6:

[0939] The terminal displays the highly reliable information received from the server to the user, allowing the user to check the latest information on the disaster area.

[0940] Step 7:

[0941] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[0942] Step 8:

[0943] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[0944] Step 9:

[0945] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[0946] Step 10:

[0947] The server sends the generated support method to the terminal, which displays the support method to the user, enabling the user to provide accurate and effective support activities.

[0948] Example 1

[0949] 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."

[0950] During disasters, there is a need for systems that can quickly collect and analyze reliable information and propose appropriate support methods to users. However, current systems have low information reliability and lack the functionality to propose appropriate support methods based on user characteristics. In addition, it is difficult to collect information in real time, and they are unable to respond promptly to user needs. Therefore, the challenge is to create a system that can efficiently collect and analyze reliable information and quickly provide users with the optimal support methods.

[0951] 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.

[0952] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a natural language processing model, evaluating its reliability and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and providing the latest highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, and means for providing the user with the proposed support method. This makes it possible to collect and analyze highly reliable information in real time and quickly provide an appropriate support method based on the user's characteristics.

[0953] "Multiple sources on the Internet" refers to multiple data sources on the Internet, such as websites, social media, news sites, and official government and local government pages.

[0954] "Means of collecting data" refers to methods or technologies for obtaining data from the Internet, such as web scraping, API access, RSS feeds, etc.

[0955] "Natural language processing model" refers to machine learning algorithms and software tools used to analyze collected text data and evaluate its content and reliability.

[0956] "Means for assessing reliability and classifying information" refers to a method or technology that uses an algorithm to score the reliability of text data and classify the data as "high reliability," "medium reliability," or "low reliability."

[0957] "Means for storing highly reliable information in a database" refers to a method or technology for saving and storing information that has received a high score in the reliability evaluation in a database.

[0958] "Means for receiving requests from users and providing up-to-date, reliable information" refers to a method or technology for receiving information requests entered by users, searching for up-to-date, reliable information from a database according to the content of the requests, and providing the information.

[0959] "Means for acquiring user characteristic information and proposing appropriate support methods based on those characteristics" refers to algorithms and processes that collect information about the user (e.g., available time, available materials, special skills, etc.) and propose the most appropriate support method based on that information.

[0960] The "means for providing the user with the proposed assistance method" refers to a method or technology for notifying and displaying the assistance method generated by the server on the user's terminal.

[0961] "Chat Interface" means an interactive communication interface that enables a User to submit requests or questions to a System in text form and receive responses from the System.

[0962] MODE FOR CARRYING OUT THE INVENTION

[0963] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is mainly composed of a server, terminals, and users.

[0964] Server Roles

[0965] The server is a high-performance server machine that uses Python and natural language processing libraries (e.g., NLTK, SpaCy) to collect data from multiple sources on the Internet. Web scraping tools (e.g., BeautifulSoup, Scrapy) and API access are used to obtain data from social media, news sites, and government and local government pages. This data is stored in temporary data storage (e.g., Redis, MongoDB).

[0966] The server then applies a natural language processing model to the collected data to analyze its content and reliability. The reliability assessment uses an algorithm that compares it with past data and calculates a reliability score. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data that is assessed as highly reliable is then stored in a database (e.g., MySQL, PostgreSQL).

[0967] Device Role

[0968] A terminal is a device used by a user, such as a smartphone or PC, and operates by combining a front-end framework (e.g., React, Vue.js) and a back-end framework (e.g., Flask, Django). The terminal sends requests from the user to the server, receives responses from the server, and displays them.

[0969] For example, if a user requests "Please tell me the latest information on disaster areas," the device will send this request to the server as an API request. The server will search the database for the latest and most reliable information on disaster areas and return it to the device. The device will then display the retrieved information to the user.

[0970] User Roles

[0971] Users are individuals who use the system and wish to provide support to disaster-stricken areas. They send requests to the system through their terminals and receive information and suggestions. For example, if a user asks "What can I do?" through the chat interface, the terminal will ask the user about "what supplies can be provided" and "when you are available."

[0972] Specific examples

[0973] Prompt Sentence Examples

[0974] "Please tell me the latest information on the disaster-stricken areas"

[0975] "What can I do?"

[0976] As a concrete example, let's say a citizen wants to help a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to the server. The server searches its database for reliable information, such as "There is currently a food shortage in a specific area," and provides it. The device then displays this information to the citizen.

[0977] Next, when the citizen asks, "What can I do?", the device asks the citizen about the supplies they can provide, the time they are available, their areas of expertise, etc. If the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this to the server. Based on this characteristic information, the server proposes a method of assistance: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[0978] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and quickly propose appropriate support methods tailored to individual characteristics.

[0979] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0980] Step 1:

[0981] Data collection

[0982] Server Role:

[0983] The server uses web scraping tools (e.g., BeautifulSoup, Scrapy) or API access to collect data from social media sites, news sites, government and local government pages on the Internet. Specifically, the server periodically executes data collection tasks using a job scheduler (e.g., Celery, Cron). For example, the server scrapes news sites every hour to obtain new articles.

[0984] Input: Internet URL

[0985] Output: raw collected text data

[0986] Step 2:

[0987] Data analysis

[0988] Server Role:

[0989] The server analyzes the collected data using natural language processing libraries (e.g., NLTK, SpaCy). The server cleans the collected text data, tokenizes it, and tags it with parts of speech. To analyze reliability, an algorithm is used to compare it with past data and calculate a reliability score.

[0990] Input: Raw collected text data

[0991] Output: Parsed text data (with confidence scores)

[0992] Step 3:

[0993] Classification and storage of information

[0994] Server Role:

[0995] The server classifies the data into "high reliability," "medium reliability," and "low reliability" based on the analysis results. Of the classified data, data evaluated as high reliability is stored in a database (e.g., MySQL, PostgreSQL). During this process, the server generates SQL queries and inserts them into the database.

[0996] Input: Parsed text data (with confidence scores)

[0997] Output: Reliable data stored in a database

[0998] Step 4:

[0999] Receiving a user request

[1000] User role:

[1001] The user uses a terminal to send a request (e.g., "Please tell me the latest information on the disaster area") to the system.

[1002] Device role:

[1003] The device sends the user's request to the server as an API request. When the user enters a request into the chat interface and presses the send button, the device sends the input request to the server.

[1004] Input: User request (prompt)

[1005] Output: API request sent to the server

[1006] Step 5:

[1007] Providing information

[1008] Server Role:

[1009] The server receives the user's request, searches the database for the latest and most reliable information, and provides it to the device. The server generates an SQL query to retrieve the required information from the database and returns it in JSON format to the device.

[1010] Input: User request (prompt)

[1011] Output: Latest reliable information (JSON format)

[1012] Step 6:

[1013] Acquisition of characteristic information and proposal of support methods

[1014] User role:

[1015] The user provides characteristic information (e.g., "available supplies," "available time," "areas of expertise") to the system.

[1016] Device role:

[1017] The device sends the collected characteristic information in JSON format to the server. The user enters the characteristic information in the chat interface, and the device sends it to the server.

[1018] Server Role:

[1019] The server calculates the appropriate support method based on the characteristic information and returns the result to the device. This support method is generated using an algorithm that proposes the optimal support method based on the results of analyzing the user's characteristic information.

[1020] Input: User characteristics information (prompt sentence)

[1021] Output: Proposed support measures (JSON format)

[1022] Step 7:

[1023] View Suggestions

[1024] Device role:

[1025] The terminal displays the support methods sent from the server to the user, allowing the user to confirm the specific action plan.

[1026] Input: Proposed support method (JSON format)

[1027] Output: The specific assistance method displayed to the user

[1028] The above is the specific processing flow of the system program. The seamless coordination of each step allows users to provide support to disaster-stricken areas efficiently and effectively.

[1029] (Application example 1)

[1030] 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."

[1031] Conventional data analysis systems only provide the functionality to collect data from multiple sources on the Internet and evaluate its reliability. Furthermore, they have limitations in providing reliable information in response to user requests. In particular, in the advertising field, not only is data reliability required, but the generation and effective delivery of targeted advertisements based on user characteristics is also required. However, current systems do not adequately predict advertising effectiveness in real time or perform individual targeting. To address these issues, a system is needed that integrates the functions of predicting advertising effectiveness based on reliable data and generating and delivering targeted advertisements based on user characteristics.

[1032] 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.

[1033] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for storing highly reliable information, means for receiving requests from users and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for providing the user with the proposed support method, means for predicting advertising effectiveness from the analyzed data, means for generating appropriate targeted advertisements based on the user characteristic information, and means for delivering the generated advertisements to users. This makes it possible to predict advertising effectiveness based on highly reliable data and to generate and deliver targeted advertisements based on user characteristics.

[1034] "Multiple sources of information on the Internet" refers to information sources that can be accessed via the Internet, such as various websites, social media, news media, and official government and local government pages.

[1035] "Means of collecting data" refers to methods of obtaining data from sources on the Internet, such as web scraping, API usage, and RSS feeds.

[1036] "Means for analyzing data and assessing reliability" refers to methods for analyzing the content of collected data using natural language processing models or machine learning algorithms, and quantifying or classifying the reliability.

[1037] "Means for classifying information" refers to a method for categorizing data into categories such as "high reliability," "medium reliability," and "low reliability" based on the results of the reliability assessment.

[1038] "Means for storing highly reliable information" refers to a method of storing information that has been evaluated as being highly reliable in a database or the like, making it easily accessible later.

[1039] "Means for receiving requests from users" refers to an interface through which users input questions or requests for information to the system, and includes smartphone apps and web applications.

[1040] "Means for providing highly reliable information" refers to a method for searching for highly reliable information stored in response to a user's request and presenting it to the user.

[1041] The "means for acquiring user characteristic information" is a method for collecting information such as the materials that a user can provide, the time available, and areas of expertise.

[1042] The "means for proposing an appropriate support method" is a method for recommending optimal support or actions to a user based on collected characteristic information of the user.

[1043] The "means for providing the user with the proposed support method" is a method for displaying the support method proposed by the server to the user.

[1044] "Means for predicting advertising effectiveness" refers to methods that include algorithms and statistical models for predicting the influence and effectiveness of targeted advertising in advance, based on reliable data.

[1045] The "means for generating targeted advertisements" is a method for automatically creating optimized advertisement content based on user characteristic information.

[1046] The "means for delivering the generated advertisement to the user" refers to a method for displaying the generated advertisement on the user's screen or notifying the user of the advertisement.

[1047] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[1048] System Configuration

[1049] server

[1050] The server has the ability to collect and analyze data from multiple sources on the Internet (e.g., social media, news sites, government and local government pages). The server evaluates the reliability of the collected data and classifies it as "high reliability," "medium reliability," or "low reliability." Highly reliable information is stored in a database and provided upon user request.

[1051] Terminal

[1052] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user. It also acquires user characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server.

[1053] User

[1054] Users are individuals who use the system, and are those who wish to support disaster-stricken areas or are targets for receiving advertisements. Users send requests to the system through their terminals and receive the necessary information and support methods.

[1055] System Features

[1056] Data collection

[1057] The server uses constantly running web crawlers and APIs to collect data in real time from social media sites, news sites, government and local government pages, and other sources on the Internet.

[1058] Data analysis

[1059] The server applies natural language processing models (e.g., Hugging Face Transformers) to the collected data to analyze its content and reliability. Based on the analysis results, the data is classified and only highly reliable information is stored in the database.

[1060] Predicting advertising effectiveness

[1061] Furthermore, the server uses machine learning algorithms (e.g., scikit-learn's Linear Regression) based on reliable data to predict advertising effectiveness.

[1062] Generating and disseminating targeted advertisements

[1063] The server collects user characteristic information and uses a generative AI model to generate advertising content optimized for the user's characteristics. The generated advertisements are then delivered to the user via their device.

[1064] Feedback Loop

[1065] The server monitors the effectiveness of the delivered advertisements in real time, and AI optimizes the advertisements based on the feedback.

[1066] Specific examples

[1067] If a citizen wishes to provide support to a disaster-stricken area, they access the chat interface on their smartphone and type, "Please tell me the latest information about the disaster area." This request is sent from the device to the server, which searches for reliable information from its database and provides it. The device then displays this information to the citizen. If the citizen asks, "What can I do?", the device obtains the citizen's characteristics and sends them to the server. The server performs an analysis and generates a suggestion, such as, "We can deliver batteries and other supplies to the support center," which is displayed on the device.

[1068] Example prompt sentence:

[1069] Based on the following user information, predict what kind of ads will be effective.

[1070] Characteristics information:

[1071] Male in his 20s

[1072] Interests: Sports, technology

[1073] Residence: Tokyo

[1074] The above is a specific embodiment for carrying out the invention. This system makes it possible to predict the effectiveness of advertising based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[1075] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1076] Step 1:

[1077] Data collection

[1078] The server collects data in real time from multiple sources on the Internet using web scraping techniques or APIs (e.g., NewsAPI, Twitter API). The input is the URLs and API keys of the sources to be collected, and the output is a set of collected raw data.

[1079] Step 2:

[1080] Data analysis

[1081] The collected raw data is sent to a server and analyzed using a natural language processing model (e.g., Hugging Face Transformers). The analysis evaluates the content and reliability of the data, and classifies the data as "high reliability," "medium reliability," or "low reliability." The input is raw data, and the output is classified data.

[1082] Step 3:

[1083] Data storage

[1084] The server stores the data that is rated as reliable in a database. The input is the data that is classified as reliable, and the output is an entry stored in the database.

[1085] Step 4:

[1086] Receiving a request from a user

[1087] A user inputs a request such as "Please tell me the latest information on disaster areas" through a terminal. The input is the user's request text, and the output is a data packet containing the request content sent to the server.

[1088] Step 5:

[1089] Reliable information search

[1090] The server searches the database for reliable information and extracts the most appropriate information for the request. The input is the user's request, and the output is reliable information as a search result.

[1091] Step 6:

[1092] Information provision

[1093] The reliable information provided by the server is sent to the terminal and displayed to the user. The input is the reliable information as a search result, and the output is the information displayed on the user's terminal.

[1094] Step 7:

[1095] Collecting user characteristics information

[1096] When a user inputs a question such as "What kind of assistance do you need?" through the terminal, the terminal collects the user's characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server. The input is the user's characteristic information, and the output is the characteristic information data sent to the server.

[1097] Step 8:

[1098] Proposal of support methods

[1099] The server analyzes the collected characteristic information and generates the optimal support method. The input is the user's characteristic information, and the output is the proposed support method. Specifically, the support method is predicted using a machine learning algorithm (e.g., Linear Regression in scikit-learn).

[1100] Step 9:

[1101] Providing support methods

[1102] The generated assistance method is presented to the user through a terminal. The input is the proposed assistance method, and the output is the assistance proposal displayed on the user's terminal.

[1103] Step 10:

[1104] Predicting advertising effectiveness

[1105] The server predicts advertising effectiveness using a generative AI model (e.g., Hugging Face Transformers) based on the collected reliable data. The input is reliable data and user characteristic information, and the output is the predicted advertising effectiveness.

[1106] Step 11:

[1107] Generate targeted ads

[1108] The server generates a targeted advertisement optimized for user characteristics based on the predicted results of the advertisement effectiveness. The input is the predicted results of the advertisement effectiveness and the user characteristic information, and the output is the generated advertisement content.

[1109] Step 12:

[1110] Ad serving

[1111] The generated targeted advertisement is delivered to the user through the terminal, where the input is the generated advertisement content and the output is the advertisement delivered to the user terminal.

[1112] The above processing steps make it possible to predict advertising effectiveness based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[1113] 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.

[1114] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and suggests appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine. A specific embodiment of the system is described below.

[1115] System Configuration

[1116] server

[1117] The server has the function of collecting and analyzing data from multiple sources on the Internet. It evaluates the reliability of the collected data and classifies the information. It also stores highly reliable information in a database and provides necessary information in response to user requests. It also obtains information about the user's characteristics and proposes appropriate support methods.

[1118] Terminal

[1119] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also has the role of acquiring user characteristic information and sending it to the server.

[1120] User

[1121] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system through their terminals and receive the necessary information and support methods. Furthermore, the system also recognizes the user's emotional state.

[1122] Emotion Engine

[1123] The emotion engine is responsible for assessing the user's emotional state based on the user's input and analysis results. Based on this assessment, the emotion engine suggests appropriate support methods for the user and adjusts the content and method of providing information.

[1124] System Features

[1125] Data collection

[1126] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[1127] Data analysis

[1128] The server applies natural language processing models to the collected data to analyze its content and reliability, and based on the results of the analysis, classifies the data as "high," "medium," or "low reliability."

[1129] Accumulation of information

[1130] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[1131] Receiving requests and providing information

[1132] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[1133] Acquisition of characteristic information and proposal of support methods

[1134] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages them to take specific action.

[1135] Sentiment analysis and support method suggestions

[1136] When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and evaluates the user's emotional state. Based on this evaluation, the server generates appropriate support methods. For example, if the user is feeling stressed, the emotion engine determines that psychological support is needed and suggests support methods. The device displays these support methods to the user.

[1137] Specific examples

[1138] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[1139] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server suggests a support method: "I can deliver batteries and other supplies to the support center." The device then displays this support method to the citizen.

[1140] Furthermore, if a citizen enters a message expressing anxiety, such as "Is this amount of support enough?", the emotion engine will analyze the message and assess that the citizen is feeling anxious. Based on the emotion engine's assessment, the server will determine that psychological support or additional information is necessary, and will display a message on the device saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind."

[1141] The above is a specific embodiment of the present invention. By combining emotion engines, it is possible to provide appropriate support methods and information according to the user's emotional state, thereby promoting more effective support activities.

[1142] The processing flow will be explained below.

[1143] Step 1:

[1144] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[1145] Step 2:

[1146] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[1147] Step 3:

[1148] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[1149] Step 4:

[1150] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[1151] Step 5:

[1152] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[1153] Step 6:

[1154] The terminal displays the reliable information received from the server to the user, who then confirms the information.

[1155] Step 7:

[1156] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[1157] Step 8:

[1158] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[1159] Step 9:

[1160] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[1161] Step 10:

[1162] The server sends the generated support method to the terminal, which displays the support method to the user and provides the user with specific instructions for actions.

[1163] Step 11:

[1164] The user enters a message containing emotional content into the chat interface, such as a message expressing anxiety, such as "Is this enough support?"

[1165] Step 12:

[1166] An emotion engine analyzes the user's emotional message and evaluates the user's emotional state, for example, determining whether the user is feeling anxious.

[1167] Step 13:

[1168] Based on the evaluation results of the emotion engine, the server re-proposes appropriate support methods and information to the user. For example, it generates content such as, "Through your support activities, you can contact other volunteers. Please feel free to do so."

[1169] Step 14:

[1170] The server sends the generated re-proposal to the terminal, which displays the re-proposal to the user, giving the user a sense of security.

[1171] These are the specific processing steps of the present invention. In each step, the server, terminal, and user play their respective roles and cooperate with each other in real time, thereby providing highly reliable information and proposing individually optimized support methods. Utilizing an emotion engine also makes it possible to respond flexibly to the user's emotional state.

[1172] Example 2

[1173] 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."

[1174] In today's world, where the diversity of information on the Internet makes it difficult to evaluate its reliability, it is important for users to obtain reliable information quickly and accurately. Furthermore, in specific situations such as disaster relief, it is essential to propose prompt and appropriate support methods. Furthermore, if the user's emotional state affects the effectiveness of support, it is necessary to provide support methods that take emotions into consideration. A system that can address these challenges is needed.

[1175] 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.

[1176] In this invention, the server includes means for collecting data from multiple information sources on the Internet, analyzing the collected data using a natural language processing model, evaluating its reliability, and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and searching for and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for evaluating the user's emotional state using an emotion engine when the user inputs a message containing emotional content through a chat interface, and means for generating an appropriate support method based on the evaluation results and providing the user with the proposed support method. This enables the user to quickly obtain highly reliable information, have an appropriate support method suggested based on the user's characteristics, and is also provided with support that takes the user's emotional state into consideration.

[1177] "Multiple sources of information on the Internet" refers to multiple online information sources with different characteristics, such as social media, news sites, and government and local government websites.

[1178] "Data collection methods" refers to the technologies and processes that automatically obtain the required data from sources on the Internet.

[1179] A "natural language processing model" is an artificial intelligence technology for analyzing and understanding human language, and is used to analyze text data and evaluate its reliability.

[1180] "Reliability assessment" is the process of determining how accurate the collected data is and how trustworthy it is as information.

[1181] "Means of classifying information" refers to techniques for organizing collected information based on its reliability and content and separating it into different categories.

[1182] A "database" is a systematically organized collection of data, a system that facilitates the storage, retrieval, and management of information.

[1183] "User request" refers to a query or request sent by a user to the system.

[1184] "Characteristic information" is information about the characteristics of each user, such as the goods and services that the user can provide, the time available, and special skills.

[1185] The "means for proposing a support method" is a technology that generates and proposes a support method suited to a user based on the user's characteristic information and other data.

[1186] "Chat interface" means an interface through which a user can enter text messages and interact with the system in real time.

[1187] An "emotion engine" is a technology that analyzes text entered by a user and evaluates the user's emotional state based on the content.

[1188] "Means for generating appropriate support methods" refers to technology that automatically creates optimal support methods for users based on collected information and analysis results.

[1189] This invention is a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine.

[1190] System Configuration

[1191] server

[1192] The server collects data from multiple sources on the Internet, specifically from social media, news sites, government and local government pages, etc., using a crawler. The collected data is then stored in temporary data storage.

[1193] Next, the server analyzes the collected data using natural language processing models (e.g., BERT or GPT) and evaluates its reliability. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data evaluated as "high reliability" is stored in a database.

[1194] For example, if the server inputs a news article collected from "https: / / news.example.com" into the BERT model and determines that it is "highly reliable," it stores it in the database.

[1195] Terminal

[1196] The terminal is a device that provides the interface used by the user, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user.

[1197] For example, if a user types "Please tell me the latest information on disaster areas" into a smartphone app, this is sent to the server as an HTTP request. In response to the server's response, the device displays to the user the information that "Evacuation shelters have been opened in City A."

[1198] User

[1199] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send necessary requests to the system via their terminals and receive information and support methods according to their requests.

[1200] For example, when a user asks, "What kind of support do you need?", the device collects information about the user's characteristics (such as the supplies they can provide, the time they have available, and their special skills) and sends it to the server. Based on this information, the server proposes an appropriate method of support, which the device then displays.

[1201] Emotion Engine

[1202] The emotion engine evaluates the user's emotional state based on the user's input and analysis results. When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and recognizes the user's emotional state (e.g., stress, anxiety, joy, etc.).

[1203] For example, if a user types, "Is this enough support?", the emotion engine will detect the emotion "anxiety." Based on this analysis, the server will determine that psychological support is needed and generate a message that reads, "You can contact other volunteers through support activities. Please proceed without worry," and display it on the device.

[1204] Specific examples

[1205] A citizen wants to help support a disaster-stricken area, so they type "Please tell me the latest information about the disaster area" into their smartphone. This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in City A," and provides it. The device then displays this information to the citizen.

[1206] Next, the citizen asks, "What can I do?" The terminal asks the citizen about "what supplies can I provide," "what time is available," "what are my areas of expertise," etc. When the citizen enters "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the terminal sends this to the server.

[1207] Based on this information, the server suggests a method of support, such as "We can deliver batteries and other supplies to the support center," and the terminal displays this method of support to the citizen.

[1208] Furthermore, if a citizen inputs a message containing emotional content such as "Is this amount of support enough?", the emotion engine will recognize the emotion as "anxiety." Based on this result, the server will generate a message saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind," and the device will display this to the citizen.

[1209] Examples of prompt statements

[1210] Prompts for suggesting ways to help

[1211] Please propose the best way to provide support based on the available supplies (batteries), available times (weekends), and area of ​​expertise (driving).

[1212] Sentiment Analysis Prompt

[1213] When a user types "Is this enough support?", analyze the anxiety factor and generate an appropriate response.

[1214] In this way, the present invention enables users to quickly obtain highly reliable information and to be proposed appropriate support methods based on their own characteristics. Furthermore, by providing support methods that take into account the user's emotional state, more effective support activities can be promoted.

[1215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1216] Program processing steps

[1217] Step 1: Start collecting data

[1218] The server automatically collects data from social media sites, news sites, government and local government pages, etc. This process is carried out using a crawler. The input is the URL of each source, and the output is the collected text data.

[1219] Step 2: Save your data

[1220] The server stores the collected data in temporary data storage, where preprocessing such as data format conversion and basic filtering is performed. The input is the collected text data, and the output is the preprocessed data.

[1221] Step 3: Applying the natural language processing model

[1222] The server uses a natural language processing model (e.g., BERT or GPT) to analyze the content and reliability of the preprocessed data. In this step, text data is input to the model, and the analysis results are output. The input is the preprocessed data, and the output is the reliability rating and the analysis results.

[1223] Step 4: Classify the data

[1224] Based on the analysis results, the server classifies the data as "high confidence," "medium confidence," or "low confidence." This classification is done automatically based on an algorithm. The input is the analysis results, and the output is the classified data.

[1225] Step 5: Store reliable data

[1226] The server stores the data that is rated as "highly reliable" in a database. In this step, only data that meets certain criteria is registered in the database. The input is the classified data, and the output is a database entry.

[1227] Step 6: Receiving the request

[1228] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." This request is sent from the device to the server as an HTTP request. The input is the user's request, and the output is an HTTP request.

[1229] Step 7: Submitting the request

[1230] The terminal sends the user's request to the server, where the network communication takes place and the request reaches the server. The input is the HTTP request, and the output is the data transmission to the server.

[1231] Step 8: Finding information

[1232] The server searches the database for reliable, up-to-date information. The search query is generated based on the user's request. The input is the user's request, and the output is the search results.

[1233] Step 9: Provide information

[1234] The server sends the searched information to the terminal, which displays it to the user. This is where format conversion and data transmission take place. The input is the search results, and the output is the information displayed on the user's screen.

[1235] Step 10: Obtaining User Characteristics

[1236] When a user asks "What kind of help do you need?", the device obtains the user's characteristic information (available supplies, available time, special skills, etc.). The input is the user's answer, and the output is the collected characteristic information.

[1237] Step 11: Sending characteristic information

[1238] The terminal sends the collected characteristic information to the server, which converts this information into a format required for processing. The input is the collected characteristic information, and the output is the data after format conversion.

[1239] Step 12: Propose ways to help

[1240] The server analyzes the transmitted characteristic information and proposes the optimal assistance method for the user. In this step, the proposal is generated using an AI model. The input is the characteristic information, and the output is the proposed assistance method.

[1241] Step 13: Provide a proposal

[1242] The terminal displays the proposed support methods to the user and encourages specific actions. The input is the content of the proposal, and the output is the proposed information displayed on the user's screen.

[1243] Step 14: Sentiment Analysis

[1244] When a user types a message containing emotional content into the chat interface, the emotion engine analyzes the message: the input is the user's message and the output is the analyzed emotional state.

[1245] Step 15: Generate support methods

[1246] The server generates an appropriate support method based on the evaluation of the emotion engine. In this step, a proposal is made based on the emotion analysis results. The input is the emotion analysis results, and the output is the generated support method.

[1247] Step 16: Providing psychological support

[1248] The terminal displays the generated support method and psychological support information to the user. The input is the generated support method, and the output is the support information displayed to the user.

[1249] (Application example 2)

[1250] 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."

[1251] Modern logistics centers collect large amounts of information daily, and there is a need to select and provide necessary and reliable information from that information. However, it is not easy to efficiently evaluate the reliability of information and provide optimal support methods. It is also important to provide support methods that take into account the emotional state of logistics center staff, but the technology to achieve this in real time is not yet in place. Therefore, it is necessary to develop a system that collects, analyzes, and evaluates reliable information and provides optimal support methods based on the user's emotional state.

[1252] 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 collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for accumulating highly reliable information, means for receiving a request from a user and providing highly reliable information, means for acquiring the user's characteristic information and emotional state and proposing an appropriate support method based thereon, means for providing the user with the proposed support method, means for evaluating the user's emotional state using an emotion engine, means for adjusting the information provision and support method based on the evaluation, and means for proposing psychological support according to the user's emotional state. This makes it possible to efficiently collect, analyze, and evaluate highly reliable information and provide an optimal support method that takes the user's emotional state into consideration.

[1253] "Multiple sources on the internet" refers to the wide range of data sources available online, including websites, social media, news feeds, government and public agency pages, and APIs.

[1254] "Data collection methods" refers to the technologies and processes used to automatically obtain the required information from multiple sources on the Internet.

[1255] "Means of analyzing data, assessing reliability, and classifying information" refers to technology that uses natural language processing and machine learning to analyze collected data, assess its reliability, and organize the data into categories based on the assessment.

[1256] "Means for storing highly reliable information" refers to the processes and technologies for storing evaluated, highly reliable data in a storage device such as a database.

[1257] "Means for receiving requests from users and providing highly reliable information" refers to systems and technologies that receive information requests from users, extract stored, highly reliable information, and provide it.

[1258] "Means for acquiring user characteristic information and emotional state and proposing appropriate support methods based on that" refers to the technology and process for analyzing the user's abilities, state, and emotions, and then deriving and proposing the most appropriate support method based on that.

[1259] "Means for providing the user with the proposed assistance methods" refers to the technology or interface that notifies or presents the user with the assistance methods derived by the system.

[1260] "Means for assessing a user's emotional state using an emotion engine" refers to an engine and its process that automatically determines a user's emotional state using technologies such as text analysis and speech analysis.

[1261] "Means for adjusting the information provided or the support method based on the evaluation" refers to the technology or process for appropriately changing or adjusting the information provided or the support method based on the evaluation results of the emotion engine.

[1262] "Means for suggesting psychological support according to the user's emotional state" refers to a technology or process that suggests psychological support, such as stress reduction or motivation improvement, based on an emotional assessment of the user.

[1263] This invention is a system for improving work efficiency and providing psychological support to staff at a logistics center. This system is composed of a server, terminals, users, and an emotion engine.

[1264] System Configuration

[1265] server

[1266] The server automatically collects data from multiple sources on the Internet and analyzes it using a natural language processing model. The collected data is assessed for reliability and classified as "high reliability," "medium reliability," or "low reliability." Information assessed as "high reliability" is then stored in a database. The server then receives requests from users, searches the database for the latest information, and provides it.

[1267] This allows logistics center staff to quickly and accurately obtain the information they need.

[1268] Terminal

[1269] The device is the interface operated by the user, such as a smartphone or PC. The device sends requests and inputs from the user to the server and displays information provided by the server to the user. The device also acquires information about the user's characteristics and emotional state and sends it to the server.

[1270] User

[1271] The users are staff at a logistics center who use the system to improve work efficiency. They request work information through their terminals and carry out their work based on the information provided. The system also evaluates their emotional state and suggests appropriate support methods.

[1272] Emotion Engine

[1273] The emotion engine analyzes the user's input text and voice data to assess the user's emotional state, for example by using TextBlob or other natural language processing models to detect positive and negative emotions from the input text.

[1274] System Features

[1275] Data collection

[1276] The server collects data in real time from the Internet, including social media, news sites, government and local government pages, etc. This collection is done automatically using the Requests library.

[1277] Data analysis

[1278] The server analyzes the collected data using natural language processing models (e.g., TextBlob, scikit-learn) and evaluates its reliability. Highly reliable information is stored in a database and used in response to future user requests.

[1279] Processing user requests

[1280] When a user sends a request via a terminal saying, "Tell me the latest situation at the logistics center," the server receives the request and searches for and provides reliable information. For example, it might display information from the database such as, "The logistics center's operations are currently at their peak."

[1281] Characteristics information and support methods

[1282] When a user asks, "How can I make my work more efficient?", the device obtains characteristic information from the user (e.g., current work content, areas of expertise, working hours, etc.) and sends it to the server. Based on this information, the server proposes the most suitable support method for the user. For example, it might say, "Since you're good at driving, why don't you take charge of operating logistics vehicles?"

[1283] Sentiment analysis and assistance adjustment

[1284] If a user types, "I'm stressed because my work just isn't going well," the emotion engine analyzes this text and evaluates that the user is feeling stressed. Based on this evaluation, the system will suggest psychological support, such as, "I recommend you take a break to relax."

[1285] Specific examples

[1286] As a specific example of use, if logistics center staff member A enters into the app, "I'm tired because work has been so busy recently," the emotion engine evaluates that emotional state, and the server suggests psychological support. It also provides reliable work information, such as "The logistics center is currently at its peak," and suggests appropriate support methods, such as "Take a break and refresh yourself."

[1287] Example prompt sentence:

[1288] "I've been feeling tired lately because of the heavy workload. How can I improve this?"

[1289] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1290] Step 1:

[1291] The server collects data from sources on the Internet, specifically from social media sites, news sites, government and local government pages, etc., using the Requests library. The input is the URL of each website or API, and the output is the collected raw data.

[1292] Step 2:

[1293] The server analyzes the collected data using a natural language processing model (e.g., TextBlob, scikit-learn). The input is the collected raw data, and the output is the text data as the analysis result and its reliability evaluation. During this process, semantic analysis and sentiment evaluation of the data are performed.

[1294] Step 3:

[1295] The server evaluates the reliability of the information based on the analysis results and classifies the information as "high reliability," "medium reliability," or "low reliability." The input is the analyzed data, and the output is the reliability evaluation and its classification result.

[1296] Step 4:

[1297] The server stores only information that is evaluated as highly reliable in the database. The input is the highly reliable information from the classification results, and the output is the highly reliable data stored in the database.

[1298] Step 5:

[1299] A user sends an information request through a terminal. Specifically, the user types something like "Tell me about the latest situation at the logistics center" into the chat interface. The input is the user's request, and the output is the transmission of the request content to the server.

[1300] Step 6:

[1301] The server receives the request and searches the database for the relevant, up-to-date, and reliable information. The input is the user request, and the output is the search results for the relevant information.

[1302] Step 7:

[1303] The server sends the search result information to the terminal, which then displays it to the user. The input is the search result information, and the output is the information provided to the user.

[1304] Step 8:

[1305] The user types "How can we make our work more efficient?" and the question is sent from the terminal to the server. The input is the user's question, and the output is the transmission of the question to the server.

[1306] Step 9:

[1307] The server acquires characteristic information about the user (e.g., current job content, areas of expertise, working hours, etc.). This information is sent from the terminal to the server. The input is the user's characteristic information, and the output is the transmission of the characteristic information to the server.

[1308] Step 10:

[1309] The server generates the optimal support method for the user based on the acquired characteristic information and reliable information. The input is the characteristic information and information from the database, and the output is the proposed support method.

[1310] Step 11:

[1311] The server sends the generated assistance method to the terminal, which then displays it to the user. The input is the assistance method, and the output is the display of the assistance method to the user.

[1312] Step 12:

[1313] The user inputs emotional content (e.g., "I'm stressed because my work just isn't going well"), and the emotion engine analyzes this text. The input is the emotional text, and the output is the emotion evaluation result.

[1314] Step 13:

[1315] The server generates support methods for psychological support and work adjustment based on the emotion evaluation results. The input is the emotion evaluation results, and the output is proposals for psychological support and work adjustment.

[1316] Step 14:

[1317] The server sends the proposed assistance method to the terminal, which then displays it to the user. The input is the proposed assistance method, and the output is the display to the user.

[1318] In this way, the system allows logistics center staff to quickly and effectively obtain information and receive appropriate assistance depending on their emotional state.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] [Fourth embodiment]

[1323] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1324] 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.

[1325] 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).

[1326] 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.

[1327] 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.

[1328] 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).

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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."

[1336] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[1337] System Configuration

[1338] server

[1339] The server has the function of collecting and analyzing data from multiple sources on the Internet. The server evaluates the reliability of the collected data and categorizes the information. It also stores highly reliable information in a database and provides the necessary information in response to user requests.

[1340] Terminal

[1341] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also acquires user characteristic information and sends it to the server.

[1342] User

[1343] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system via their terminals and receive the necessary information and support methods.

[1344] System Features

[1345] Data collection

[1346] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[1347] Data analysis

[1348] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[1349] Accumulation of information

[1350] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[1351] Receiving requests and providing information

[1352] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[1353] Acquisition of characteristic information and proposal of support methods

[1354] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages specific action.

[1355] Specific examples

[1356] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[1357] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server proposes a support method: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[1358] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and propose appropriate support methods that suit the characteristics of each individual.

[1359] The processing flow will be explained below.

[1360] Step 1:

[1361] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[1362] Step 2:

[1363] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[1364] Step 3:

[1365] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[1366] Step 4:

[1367] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[1368] Step 5:

[1369] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[1370] Step 6:

[1371] The terminal displays the highly reliable information received from the server to the user, allowing the user to check the latest information on the disaster area.

[1372] Step 7:

[1373] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[1374] Step 8:

[1375] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[1376] Step 9:

[1377] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[1378] Step 10:

[1379] The server sends the generated support method to the terminal, which displays the support method to the user, enabling the user to provide accurate and effective support activities.

[1380] Example 1

[1381] 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."

[1382] During disasters, there is a need for systems that can quickly collect and analyze reliable information and propose appropriate support methods to users. However, current systems have low information reliability and lack the functionality to propose appropriate support methods based on user characteristics. In addition, it is difficult to collect information in real time, and they are unable to respond promptly to user needs. Therefore, the challenge is to create a system that can efficiently collect and analyze reliable information and quickly provide users with the optimal support methods.

[1383] 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.

[1384] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a natural language processing model, evaluating its reliability and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and providing the latest highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, and means for providing the user with the proposed support method. This makes it possible to collect and analyze highly reliable information in real time and quickly provide an appropriate support method based on the user's characteristics.

[1385] "Multiple sources on the Internet" refers to multiple data sources on the Internet, such as websites, social media, news sites, and official government and local government pages.

[1386] "Means of collecting data" refers to methods or technologies for obtaining data from the Internet, such as web scraping, API access, RSS feeds, etc.

[1387] "Natural language processing model" refers to machine learning algorithms and software tools used to analyze collected text data and evaluate its content and reliability.

[1388] "Means for assessing reliability and classifying information" refers to a method or technology that uses an algorithm to score the reliability of text data and classify the data as "high reliability," "medium reliability," or "low reliability."

[1389] "Means for storing highly reliable information in a database" refers to a method or technology for saving and storing information that has received a high score in the reliability evaluation in a database.

[1390] "Means for receiving requests from users and providing up-to-date, reliable information" refers to a method or technology for receiving information requests entered by users, searching for up-to-date, reliable information from a database according to the content of the requests, and providing the information.

[1391] "Means for acquiring user characteristic information and proposing appropriate support methods based on those characteristics" refers to algorithms and processes that collect information about the user (e.g., available time, available materials, special skills, etc.) and propose the most appropriate support method based on that information.

[1392] The "means for providing the user with the proposed assistance method" refers to a method or technology for notifying and displaying the assistance method generated by the server on the user's terminal.

[1393] "Chat Interface" means an interactive communication interface that enables a User to submit requests or questions to a System in text form and receive responses from the System.

[1394] MODE FOR CARRYING OUT THE INVENTION

[1395] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is mainly composed of a server, terminals, and users.

[1396] Server Roles

[1397] The server is a high-performance server machine that uses Python and natural language processing libraries (e.g., NLTK, SpaCy) to collect data from multiple sources on the Internet. Web scraping tools (e.g., BeautifulSoup, Scrapy) and API access are used to obtain data from social media, news sites, and government and local government pages. This data is stored in temporary data storage (e.g., Redis, MongoDB).

[1398] The server then applies a natural language processing model to the collected data to analyze its content and reliability. The reliability assessment uses an algorithm that compares it with past data and calculates a reliability score. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data that is assessed as highly reliable is then stored in a database (e.g., MySQL, PostgreSQL).

[1399] Device Role

[1400] A terminal is a device used by a user, such as a smartphone or PC, and operates by combining a front-end framework (e.g., React, Vue.js) and a back-end framework (e.g., Flask, Django). The terminal sends requests from the user to the server, receives responses from the server, and displays them.

[1401] For example, if a user requests "Please tell me the latest information on disaster areas," the device will send this request to the server as an API request. The server will search the database for the latest and most reliable information on disaster areas and return it to the device. The device will then display the retrieved information to the user.

[1402] User Roles

[1403] Users are individuals who use the system and wish to provide support to disaster-stricken areas. They send requests to the system through their terminals and receive information and suggestions. For example, if a user asks "What can I do?" through the chat interface, the terminal will ask the user about "what supplies can be provided" and "when you are available."

[1404] Specific examples

[1405] Prompt Sentence Examples

[1406] "Please tell me the latest information on the disaster-stricken areas"

[1407] "What can I do?"

[1408] As a concrete example, let's say a citizen wants to help a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to the server. The server searches its database for reliable information, such as "There is currently a food shortage in a specific area," and provides it. The device then displays this information to the citizen.

[1409] Next, when the citizen asks, "What can I do?", the device asks the citizen about the supplies they can provide, the time they are available, their areas of expertise, etc. If the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this to the server. Based on this characteristic information, the server proposes a method of assistance: "I can deliver batteries and other supplies to the support center," and the device displays this to the citizen.

[1410] The above is a specific embodiment of the present invention. This system can provide highly reliable information in the event of a disaster and quickly propose appropriate support methods tailored to individual characteristics.

[1411] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1412] Step 1:

[1413] Data collection

[1414] Server Role:

[1415] The server uses web scraping tools (e.g., BeautifulSoup, Scrapy) or API access to collect data from social media sites, news sites, government and local government pages on the Internet. Specifically, the server periodically executes data collection tasks using a job scheduler (e.g., Celery, Cron). For example, the server scrapes news sites every hour to obtain new articles.

[1416] Input: Internet URL

[1417] Output: raw collected text data

[1418] Step 2:

[1419] Data analysis

[1420] Server Role:

[1421] The server analyzes the collected data using natural language processing libraries (e.g., NLTK, SpaCy). The server cleans the collected text data, tokenizes it, and tags it with parts of speech. To analyze reliability, an algorithm is used to compare it with past data and calculate a reliability score.

[1422] Input: Raw collected text data

[1423] Output: Parsed text data (with confidence scores)

[1424] Step 3:

[1425] Classification and storage of information

[1426] Server Role:

[1427] The server classifies the data into "high reliability," "medium reliability," and "low reliability" based on the analysis results. Of the classified data, data evaluated as high reliability is stored in a database (e.g., MySQL, PostgreSQL). During this process, the server generates SQL queries and inserts them into the database.

[1428] Input: Parsed text data (with confidence scores)

[1429] Output: Reliable data stored in a database

[1430] Step 4:

[1431] Receiving a user request

[1432] User role:

[1433] The user uses a terminal to send a request (e.g., "Please tell me the latest information on the disaster area") to the system.

[1434] Device role:

[1435] The device sends the user's request to the server as an API request. When the user enters a request into the chat interface and presses the send button, the device sends the input request to the server.

[1436] Input: User request (prompt)

[1437] Output: API request sent to the server

[1438] Step 5:

[1439] Providing information

[1440] Server Role:

[1441] The server receives the user's request, searches the database for the latest and most reliable information, and provides it to the device. The server generates an SQL query to retrieve the required information from the database and returns it in JSON format to the device.

[1442] Input: User request (prompt)

[1443] Output: Latest reliable information (JSON format)

[1444] Step 6:

[1445] Acquisition of characteristic information and proposal of support methods

[1446] User role:

[1447] The user provides characteristic information (e.g., "available supplies," "available time," "areas of expertise") to the system.

[1448] Device role:

[1449] The device sends the collected characteristic information in JSON format to the server. The user enters the characteristic information in the chat interface, and the device sends it to the server.

[1450] Server Role:

[1451] The server calculates the appropriate support method based on the characteristic information and returns the result to the device. This support method is generated using an algorithm that proposes the optimal support method based on the results of analyzing the user's characteristic information.

[1452] Input: User characteristics information (prompt sentence)

[1453] Output: Proposed support measures (JSON format)

[1454] Step 7:

[1455] View Suggestions

[1456] Device role:

[1457] The terminal displays the support methods sent from the server to the user, allowing the user to confirm the specific action plan.

[1458] Input: Proposed support method (JSON format)

[1459] Output: The specific assistance method displayed to the user

[1460] The above is the specific processing flow of the system program. The seamless coordination of each step allows users to provide support to disaster-stricken areas efficiently and effectively.

[1461] (Application example 1)

[1462] 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."

[1463] Conventional data analysis systems only provide the functionality to collect data from multiple sources on the Internet and evaluate its reliability. Furthermore, they have limitations in providing reliable information in response to user requests. In particular, in the advertising field, not only is data reliability required, but the generation and effective delivery of targeted advertisements based on user characteristics is also required. However, current systems do not adequately predict advertising effectiveness in real time or perform individual targeting. To address these issues, a system is needed that integrates the functions of predicting advertising effectiveness based on reliable data and generating and delivering targeted advertisements based on user characteristics.

[1464] 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.

[1465] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for storing highly reliable information, means for receiving requests from users and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for providing the user with the proposed support method, means for predicting advertising effectiveness from the analyzed data, means for generating appropriate targeted advertisements based on the user characteristic information, and means for delivering the generated advertisements to users. This makes it possible to predict advertising effectiveness based on highly reliable data and to generate and deliver targeted advertisements based on user characteristics.

[1466] "Multiple sources of information on the Internet" refers to information sources that can be accessed via the Internet, such as various websites, social media, news media, and official government and local government pages.

[1467] "Means of collecting data" refers to methods of obtaining data from sources on the Internet, such as web scraping, API usage, and RSS feeds.

[1468] "Means for analyzing data and assessing reliability" refers to methods for analyzing the content of collected data using natural language processing models or machine learning algorithms, and quantifying or classifying the reliability.

[1469] "Means for classifying information" refers to a method for categorizing data into categories such as "high reliability," "medium reliability," and "low reliability" based on the results of the reliability assessment.

[1470] "Means for storing highly reliable information" refers to a method of storing information that has been evaluated as being highly reliable in a database or the like, making it easily accessible later.

[1471] "Means for receiving requests from users" refers to an interface through which users input questions or requests for information to the system, and includes smartphone apps and web applications.

[1472] "Means for providing highly reliable information" refers to a method for searching for highly reliable information stored in response to a user's request and presenting it to the user.

[1473] The "means for acquiring user characteristic information" is a method for collecting information such as the materials that a user can provide, the time available, and areas of expertise.

[1474] The "means for proposing an appropriate support method" is a method for recommending optimal support or actions to a user based on collected characteristic information of the user.

[1475] The "means for providing the user with the proposed support method" is a method for displaying the support method proposed by the server to the user.

[1476] "Means for predicting advertising effectiveness" refers to methods that include algorithms and statistical models for predicting the influence and effectiveness of targeted advertising in advance, based on reliable data.

[1477] The "means for generating targeted advertisements" is a method for automatically creating optimized advertisement content based on user characteristic information.

[1478] The "means for delivering the generated advertisement to the user" refers to a method for displaying the generated advertisement on the user's screen or notifying the user of the advertisement.

[1479] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. This system is composed of a server, a terminal, and a user. A specific embodiment of the system is described below.

[1480] System Configuration

[1481] server

[1482] The server has the ability to collect and analyze data from multiple sources on the Internet (e.g., social media, news sites, government and local government pages). The server evaluates the reliability of the collected data and classifies it as "high reliability," "medium reliability," or "low reliability." Highly reliable information is stored in a database and provided upon user request.

[1483] Terminal

[1484] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user. It also acquires user characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server.

[1485] User

[1486] Users are individuals who use the system, and are those who wish to support disaster-stricken areas or are targets for receiving advertisements. Users send requests to the system through their terminals and receive the necessary information and support methods.

[1487] System Features

[1488] Data collection

[1489] The server uses constantly running web crawlers and APIs to collect data in real time from social media sites, news sites, government and local government pages, and other sources on the Internet.

[1490] Data analysis

[1491] The server applies natural language processing models (e.g., Hugging Face Transformers) to the collected data to analyze its content and reliability. Based on the analysis results, the data is classified and only highly reliable information is stored in the database.

[1492] Predicting advertising effectiveness

[1493] Furthermore, the server uses machine learning algorithms (e.g., scikit-learn's Linear Regression) based on reliable data to predict advertising effectiveness.

[1494] Generating and disseminating targeted advertisements

[1495] The server collects user characteristic information and uses a generative AI model to generate advertising content optimized for the user's characteristics. The generated advertisements are then delivered to the user via their device.

[1496] Feedback Loop

[1497] The server monitors the effectiveness of the delivered advertisements in real time, and AI optimizes the advertisements based on the feedback.

[1498] Specific examples

[1499] If a citizen wishes to provide support to a disaster-stricken area, they access the chat interface on their smartphone and type, "Please tell me the latest information about the disaster area." This request is sent from the device to the server, which searches for reliable information from its database and provides it. The device then displays this information to the citizen. If the citizen asks, "What can I do?", the device obtains the citizen's characteristics and sends them to the server. The server performs an analysis and generates a suggestion, such as, "We can deliver batteries and other supplies to the support center," which is displayed on the device.

[1500] Example prompt sentence:

[1501] Based on the following user information, predict what kind of ads will be effective.

[1502] Characteristics information:

[1503] Male in his 20s

[1504] Interests: Sports, technology

[1505] Residence: Tokyo

[1506] The above is a specific embodiment for carrying out the invention. This system makes it possible to predict the effectiveness of advertising based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[1507] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1508] Step 1:

[1509] Data collection

[1510] The server collects data in real time from multiple sources on the Internet using web scraping techniques or APIs (e.g., NewsAPI, Twitter API). The input is the URLs and API keys of the sources to be collected, and the output is a set of collected raw data.

[1511] Step 2:

[1512] Data analysis

[1513] The collected raw data is sent to a server and analyzed using a natural language processing model (e.g., Hugging Face Transformers). The analysis evaluates the content and reliability of the data, and classifies the data as "high reliability," "medium reliability," or "low reliability." The input is raw data, and the output is classified data.

[1514] Step 3:

[1515] Data storage

[1516] The server stores the data that is rated as reliable in a database. The input is the data that is classified as reliable, and the output is an entry stored in the database.

[1517] Step 4:

[1518] Receiving a request from a user

[1519] A user inputs a request such as "Please tell me the latest information on disaster areas" through a terminal. The input is the user's request text, and the output is a data packet containing the request content sent to the server.

[1520] Step 5:

[1521] Reliable information search

[1522] The server searches the database for reliable information and extracts the most appropriate information for the request. The input is the user's request, and the output is reliable information as a search result.

[1523] Step 6:

[1524] Information provision

[1525] The reliable information provided by the server is sent to the terminal and displayed to the user. The input is the reliable information as a search result, and the output is the information displayed on the user's terminal.

[1526] Step 7:

[1527] Collecting user characteristics information

[1528] When a user inputs a question such as "What kind of assistance do you need?" through the terminal, the terminal collects the user's characteristic information (e.g., available supplies, available time, areas of expertise) and sends it to the server. The input is the user's characteristic information, and the output is the characteristic information data sent to the server.

[1529] Step 8:

[1530] Proposal of support methods

[1531] The server analyzes the collected characteristic information and generates the optimal support method. The input is the user's characteristic information, and the output is the proposed support method. Specifically, the support method is predicted using a machine learning algorithm (e.g., Linear Regression in scikit-learn).

[1532] Step 9:

[1533] Providing support methods

[1534] The generated assistance method is presented to the user through a terminal. The input is the proposed assistance method, and the output is the assistance proposal displayed on the user's terminal.

[1535] Step 10:

[1536] Predicting advertising effectiveness

[1537] The server predicts advertising effectiveness using a generative AI model (e.g., Hugging Face Transformers) based on the collected reliable data. The input is reliable data and user characteristic information, and the output is the predicted advertising effectiveness.

[1538] Step 11:

[1539] Generate targeted ads

[1540] The server generates a targeted advertisement optimized for user characteristics based on the predicted results of the advertisement effectiveness. The input is the predicted results of the advertisement effectiveness and the user characteristic information, and the output is the generated advertisement content.

[1541] Step 12:

[1542] Ad serving

[1543] The generated targeted advertisement is delivered to the user through the terminal, where the input is the generated advertisement content and the output is the advertisement delivered to the user terminal.

[1544] The above processing steps make it possible to predict advertising effectiveness based on highly reliable data and to generate and distribute targeted advertisements based on user characteristics.

[1545] 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.

[1546] The present invention relates to a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and suggests appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to suggest support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine. A specific embodiment of the system is described below.

[1547] System Configuration

[1548] server

[1549] The server has the function of collecting and analyzing data from multiple sources on the Internet. It evaluates the reliability of the collected data and classifies the information. It also stores highly reliable information in a database and provides necessary information in response to user requests. It also obtains information about the user's characteristics and proposes appropriate support methods.

[1550] Terminal

[1551] A terminal is a device that provides an interface for users, such as a smartphone or PC. The terminal sends user requests to a server and displays information provided by the server to the user. It also has the role of acquiring user characteristic information and sending it to the server.

[1552] User

[1553] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send requests to the system through their terminals and receive the necessary information and support methods. Furthermore, the system also recognizes the user's emotional state.

[1554] Emotion Engine

[1555] The emotion engine is responsible for assessing the user's emotional state based on the user's input and analysis results. Based on this assessment, the emotion engine suggests appropriate support methods for the user and adjusts the content and method of providing information.

[1556] System Features

[1557] Data collection

[1558] The server collects data in real time from online social media sites, news sites, government and local government pages, etc. The collected data is stored in temporary data storage.

[1559] Data analysis

[1560] The server applies natural language processing models to the collected data to analyze its content and reliability, and based on the results of the analysis, classifies the data as "high," "medium," or "low reliability."

[1561] Accumulation of information

[1562] Data that is judged to be highly reliable is stored in a database, forming a stock of reliable information that can be provided to users later.

[1563] Receiving requests and providing information

[1564] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." The device then sends the request to the server, which then searches its database for the most up-to-date, reliable information and provides it. The device then displays the received information to the user.

[1565] Acquisition of characteristic information and proposal of support methods

[1566] When a user asks "What kind of support do you need?" through a chat interface, the device collects the user's characteristic information (available supplies, available time, special skills, etc.). This information is sent to the server, which analyzes it and proposes the most suitable support method for the user. The device displays the proposed support method to the user and encourages them to take specific action.

[1567] Sentiment analysis and support method suggestions

[1568] When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and evaluates the user's emotional state. Based on this evaluation, the server generates appropriate support methods. For example, if the user is feeling stressed, the emotion engine determines that psychological support is needed and suggests support methods. The device displays these support methods to the user.

[1569] Specific examples

[1570] Suppose a citizen wishes to provide support to a disaster-stricken area. The citizen accesses a chat interface on their smartphone and types, "Please tell me the latest information about the disaster-stricken area." This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in Tokyo," and provides it. The device then displays this information to the citizen.

[1571] Next, the citizen asks, "What can I do?" The device asks the citizen for information such as "what supplies can I provide," "what time I'm available," and "what areas of expertise I have." When the citizen inputs, "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the device sends this information to the server. Based on this characteristic information, the server suggests a support method: "I can deliver batteries and other supplies to the support center." The device then displays this support method to the citizen.

[1572] Furthermore, if a citizen enters a message expressing anxiety, such as "Is this amount of support enough?", the emotion engine will analyze the message and assess that the citizen is feeling anxious. Based on the emotion engine's assessment, the server will determine that psychological support or additional information is necessary, and will display a message on the device saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind."

[1573] The above is a specific embodiment of the present invention. By combining emotion engines, it is possible to provide appropriate support methods and information according to the user's emotional state, thereby promoting more effective support activities.

[1574] The processing flow will be explained below.

[1575] Step 1:

[1576] The server periodically scrapes data from social media sites, news sites, government and local government pages on the Internet, and stores the collected data in temporary data storage.

[1577] Step 2:

[1578] The server applies natural language processing models to the collected data to analyze its content and reliability, and classifies the data as "high," "medium," or "low reliability" based on the analysis results.

[1579] Step 3:

[1580] The server stores only highly reliable data in the database based on the reliability evaluation, and discards data with medium or low reliability.

[1581] Step 4:

[1582] A user accesses the chat interface using a terminal and types, "Please tell me the latest information about the disaster area." The terminal sends this request to the server.

[1583] Step 5:

[1584] The server receives a request from the user, searches the database for the most up-to-date and reliable information, and sends the information to the terminal.

[1585] Step 6:

[1586] The terminal displays the reliable information received from the server to the user, who then confirms the information.

[1587] Step 7:

[1588] The user types "What kind of help do you need?" into the chat interface. In response to this request, the device displays prompts to collect the user's characteristic information (such as available supplies, available time, and special skills).

[1589] Step 8:

[1590] The user inputs the items they can provide (e.g., batteries), the time they are available (e.g., weekends), and their special skills (e.g., driving skills). The device then sends this information to the server.

[1591] Step 9:

[1592] The server analyzes the user's characteristics and generates appropriate support methods, such as "We can deliver batteries and other supplies to the support center."

[1593] Step 10:

[1594] The server sends the generated support method to the terminal, which displays the support method to the user and provides the user with specific instructions for actions.

[1595] Step 11:

[1596] The user enters a message containing emotional content into the chat interface, such as a message expressing anxiety, such as "Is this enough support?"

[1597] Step 12:

[1598] An emotion engine analyzes the user's emotional message and evaluates the user's emotional state, for example, determining whether the user is feeling anxious.

[1599] Step 13:

[1600] Based on the evaluation results of the emotion engine, the server re-proposes appropriate support methods and information to the user. For example, it generates content such as, "Through your support activities, you can contact other volunteers. Please feel free to do so."

[1601] Step 14:

[1602] The server sends the generated re-proposal to the terminal, which displays the re-proposal to the user, giving the user a sense of security.

[1603] These are the specific processing steps of the present invention. In each step, the server, terminal, and user play their respective roles and cooperate with each other in real time, thereby providing highly reliable information and proposing individually optimized support methods. Utilizing an emotion engine also makes it possible to respond flexibly to the user's emotional state.

[1604] Example 2

[1605] 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."

[1606] In today's world, where the diversity of information on the Internet makes it difficult to evaluate its reliability, it is important for users to obtain reliable information quickly and accurately. Furthermore, in specific situations such as disaster relief, it is essential to propose prompt and appropriate support methods. Furthermore, if the user's emotional state affects the effectiveness of support, it is necessary to provide support methods that take emotions into consideration. A system that can address these challenges is needed.

[1607] 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.

[1608] In this invention, the server includes means for collecting data from multiple information sources on the Internet, analyzing the collected data using a natural language processing model, evaluating its reliability, and classifying the information, means for storing highly reliable information in a database, means for receiving requests from users and searching for and providing highly reliable information, means for acquiring user characteristic information and proposing an appropriate support method based on the characteristics, means for evaluating the user's emotional state using an emotion engine when the user inputs a message containing emotional content through a chat interface, and means for generating an appropriate support method based on the evaluation results and providing the user with the proposed support method. This enables the user to quickly obtain highly reliable information, have an appropriate support method suggested based on the user's characteristics, and is also provided with support that takes the user's emotional state into consideration.

[1609] "Multiple sources of information on the Internet" refers to multiple online information sources with different characteristics, such as social media, news sites, and government and local government websites.

[1610] "Data collection methods" refers to the technologies and processes that automatically obtain the required data from sources on the Internet.

[1611] A "natural language processing model" is an artificial intelligence technology for analyzing and understanding human language, and is used to analyze text data and evaluate its reliability.

[1612] "Reliability assessment" is the process of determining how accurate the collected data is and how trustworthy it is as information.

[1613] "Means of classifying information" refers to techniques for organizing collected information based on its reliability and content and separating it into different categories.

[1614] A "database" is a systematically organized collection of data, a system that facilitates the storage, retrieval, and management of information.

[1615] "User request" refers to a query or request sent by a user to the system.

[1616] "Characteristic information" is information about the characteristics of each user, such as the goods and services that the user can provide, the time available, and special skills.

[1617] The "means for proposing a support method" is a technology that generates and proposes a support method suited to a user based on the user's characteristic information and other data.

[1618] "Chat interface" means an interface through which a user can enter text messages and interact with the system in real time.

[1619] An "emotion engine" is a technology that analyzes text entered by a user and evaluates the user's emotional state based on the content.

[1620] "Means for generating appropriate support methods" refers to technology that automatically creates optimal support methods for users based on collected information and analysis results.

[1621] This invention is a system that collects data from multiple information sources on the Internet, analyzes it, evaluates its reliability, classifies the information, and proposes appropriate support methods to users. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to propose support methods and adjust the information provided based on the user's emotional state. This system is composed of a server, a terminal, a user, and an emotion engine.

[1622] System Configuration

[1623] server

[1624] The server collects data from multiple sources on the Internet, specifically from social media, news sites, government and local government pages, etc., using a crawler. The collected data is then stored in temporary data storage.

[1625] Next, the server analyzes the collected data using natural language processing models (e.g., BERT or GPT) and evaluates its reliability. Based on the analysis results, the data is classified as "high reliability," "medium reliability," or "low reliability." Data evaluated as "high reliability" is stored in a database.

[1626] For example, if the server inputs a news article collected from "https: / / news.example.com" into the BERT model and determines that it is "highly reliable," it stores it in the database.

[1627] Terminal

[1628] The terminal is a device that provides the interface used by the user, such as a smartphone or PC. The terminal sends the user's request to the server and displays the information provided by the server to the user.

[1629] For example, if a user types "Please tell me the latest information on disaster areas" into a smartphone app, this is sent to the server as an HTTP request. In response to the server's response, the device displays to the user the information that "Evacuation shelters have been opened in City A."

[1630] User

[1631] Users are individuals who use the system and wish to provide support to disaster-stricken areas. Users send necessary requests to the system via their terminals and receive information and support methods according to their requests.

[1632] For example, when a user asks, "What kind of support do you need?", the device collects information about the user's characteristics (such as the supplies they can provide, the time they have available, and their special skills) and sends it to the server. Based on this information, the server proposes an appropriate method of support, which the device then displays.

[1633] Emotion Engine

[1634] The emotion engine evaluates the user's emotional state based on the user's input and analysis results. When a user enters a message containing emotional content into the chat interface, the emotion engine analyzes the message and recognizes the user's emotional state (e.g., stress, anxiety, joy, etc.).

[1635] For example, if a user types, "Is this enough support?", the emotion engine will detect the emotion "anxiety." Based on this analysis, the server will determine that psychological support is needed and generate a message that reads, "You can contact other volunteers through support activities. Please proceed without worry," and display it on the device.

[1636] Specific examples

[1637] A citizen wants to help support a disaster-stricken area, so they type "Please tell me the latest information about the disaster area" into their smartphone. This request is sent from the device to a server, which searches its database for reliable information, such as "There is currently a food shortage in City A," and provides it. The device then displays this information to the citizen.

[1638] Next, the citizen asks, "What can I do?" The terminal asks the citizen about "what supplies can I provide," "what time is available," "what are my areas of expertise," etc. When the citizen enters "I can provide batteries," "I have time on the weekend," or "I'm good at driving," the terminal sends this to the server.

[1639] Based on this information, the server suggests a method of support, such as "We can deliver batteries and other supplies to the support center," and the terminal displays this method of support to the citizen.

[1640] Furthermore, if a citizen inputs a message containing emotional content such as "Is this amount of support enough?", the emotion engine will recognize the emotion as "anxiety." Based on this result, the server will generate a message saying, "You can contact other volunteers through your support activities. Please proceed with peace of mind," and the device will display this to the citizen.

[1641] Examples of prompt statements

[1642] Prompts for suggesting ways to help

[1643] Please propose the best way to provide support based on the available supplies (batteries), available times (weekends), and area of ​​expertise (driving).

[1644] Sentiment Analysis Prompt

[1645] When a user types "Is this enough support?", analyze the anxiety factor and generate an appropriate response.

[1646] In this way, the present invention enables users to quickly obtain highly reliable information and to be proposed appropriate support methods based on their own characteristics. Furthermore, by providing support methods that take into account the user's emotional state, more effective support activities can be promoted.

[1647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1648] Program processing steps

[1649] Step 1: Start collecting data

[1650] The server automatically collects data from social media sites, news sites, government and local government pages, etc. This process is carried out using a crawler. The input is the URL of each source, and the output is the collected text data.

[1651] Step 2: Save your data

[1652] The server stores the collected data in temporary data storage, where preprocessing such as data format conversion and basic filtering is performed. The input is the collected text data, and the output is the preprocessed data.

[1653] Step 3: Applying the natural language processing model

[1654] The server uses a natural language processing model (e.g., BERT or GPT) to analyze the content and reliability of the preprocessed data. In this step, text data is input to the model, and the analysis results are output. The input is the preprocessed data, and the output is the reliability rating and the analysis results.

[1655] Step 4: Classify the data

[1656] Based on the analysis results, the server classifies the data as "high confidence," "medium confidence," or "low confidence." This classification is done automatically based on an algorithm. The input is the analysis results, and the output is the classified data.

[1657] Step 5: Store reliable data

[1658] The server stores the data that is rated as "highly reliable" in a database. In this step, only data that meets certain criteria is registered in the database. The input is the classified data, and the output is a database entry.

[1659] Step 6: Receiving the request

[1660] The user sends a request through their device saying, "Please tell me the latest information about the disaster area." This request is sent from the device to the server as an HTTP request. The input is the user's request, and the output is an HTTP request.

[1661] Step 7: Submitting the request

[1662] The terminal sends the user's request to the server, where the network communication takes place and the request reaches the server. The input is the HTTP request, and the output is the data transmission to the server.

[1663] Step 8: Finding information

[1664] The server searches the database for reliable, up-to-date information. The search query is generated based on the user's request. The input is the user's request, and the output is the search results.

[1665] Step 9: Provide information

[1666] The server sends the searched information to the terminal, which displays it to the user. This is where format conversion and data transmission take place. The input is the search results, and the output is the information displayed on the user's screen.

[1667] Step 10: Obtaining User Characteristics

[1668] When a user asks "What kind of help do you need?", the device obtains the user's characteristic information (available supplies, available time, special skills, etc.). The input is the user's answer, and the output is the collected characteristic information.

[1669] Step 11: Sending characteristic information

[1670] The terminal sends the collected characteristic information to the server, which converts this information into a format required for processing. The input is the collected characteristic information, and the output is the data after format conversion.

[1671] Step 12: Propose ways to help

[1672] The server analyzes the transmitted characteristic information and proposes the optimal assistance method for the user. In this step, the proposal is generated using an AI model. The input is the characteristic information, and the output is the proposed assistance method.

[1673] Step 13: Provide a proposal

[1674] The terminal displays the proposed support methods to the user and encourages specific actions. The input is the content of the proposal, and the output is the proposed information displayed on the user's screen.

[1675] Step 14: Sentiment Analysis

[1676] When a user types a message containing emotional content into the chat interface, the emotion engine analyzes the message: the input is the user's message and the output is the analyzed emotional state.

[1677] Step 15: Generate support methods

[1678] The server generates an appropriate support method based on the evaluation of the emotion engine. In this step, a proposal is made based on the emotion analysis results. The input is the emotion analysis results, and the output is the generated support method.

[1679] Step 16: Providing psychological support

[1680] The terminal displays the generated support method and psychological support information to the user. The input is the generated support method, and the output is the support information displayed to the user.

[1681] (Application example 2)

[1682] 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."

[1683] Modern logistics centers collect large amounts of information daily, and there is a need to select and provide necessary and reliable information from that information. However, it is not easy to efficiently evaluate the reliability of information and provide optimal support methods. It is also important to provide support methods that take into account the emotional state of logistics center staff, but the technology to achieve this in real time is not yet in place. Therefore, it is necessary to develop a system that collects, analyzes, and evaluates reliable information and provides optimal support methods based on the user's emotional state.

[1684] 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 collecting data from multiple information sources on the Internet, means for analyzing the collected data, evaluating its reliability, and classifying the information, means for accumulating highly reliable information, means for receiving a request from a user and providing highly reliable information, means for acquiring the user's characteristic information and emotional state and proposing an appropriate support method based thereon, means for providing the user with the proposed support method, means for evaluating the user's emotional state using an emotion engine, means for adjusting the information provision and support method based on the evaluation, and means for proposing psychological support according to the user's emotional state. This makes it possible to efficiently collect, analyze, and evaluate highly reliable information and provide an optimal support method that takes the user's emotional state into consideration.

[1685] "Multiple sources on the internet" refers to the wide range of data sources available online, including websites, social media, news feeds, government and public agency pages, and APIs.

[1686] "Data collection methods" refers to the technologies and processes used to automatically obtain the required information from multiple sources on the Internet.

[1687] "Means of analyzing data, assessing reliability, and classifying information" refers to technology that uses natural language processing and machine learning to analyze collected data, assess its reliability, and organize the data into categories based on the assessment.

[1688] "Means for storing highly reliable information" refers to the processes and technologies for storing evaluated, highly reliable data in a storage device such as a database.

[1689] "Means for receiving requests from users and providing highly reliable information" refers to systems and technologies that receive information requests from users, extract stored, highly reliable information, and provide it.

[1690] "Means for acquiring user characteristic information and emotional state and proposing appropriate support methods based on that" refers to the technology and process for analyzing the user's abilities, state, and emotions, and then deriving and proposing the most appropriate support method based on that.

[1691] "Means for providing the user with the proposed assistance methods" refers to the technology or interface that notifies or presents the user with the assistance methods derived by the system.

[1692] "Means for assessing a user's...

Claims

1. a means for collecting data from multiple sources on the Internet; A means of analyzing the collected data, assessing its reliability and categorizing the information; A means of accumulating reliable information; A means for receiving requests from users and providing reliable information; A means for acquiring characteristic information of a user and proposing an appropriate support method based on the characteristic information; means for providing the suggested assistance methods to the user; A system including:

2. a means for collecting data in real time from multiple sources on the Internet; A means for analyzing the collected data using a natural language processing model; a means of assessing and classifying trustworthiness; A means of storing only reliable information in the database; A means for retrieving and providing up-to-date, reliable information in response to a user request; A means for generating an appropriate support method based on the content of the support available to the user; The system of claim 1 , comprising:

3. a means for providing a chat interface; A means for proposing an appropriate support method based on the content of possible support received from the user; means for displaying the suggestions to the user; The system of claim 1 , comprising:

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