System

Abstract

A system is provided.SOLUTION: A communication terminal used by a minor, comprising: means for collecting data on a social networking service; means for using a generation algorithm for analyzing the collected data; means for detecting a risk factor based on a result of the analysis; means for generating a warning when the risk factor is detected; and means for transmitting the warning to a guardian.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, there has been an increase in cases of minors becoming involved in various crimes through social networking services (SNS). Specifically, problems such as slander, bullying, leaks of personal information, solicitation, and "illegal part-time jobs" have arisen, and these problems can have a significant impact on the mind and body of minors. However, it is not realistic for parents to monitor all SNS interactions in real time, and as a result, problems are often only addressed after they occur. Therefore, there is a need for a method that can detect potential risks early and prevent accidents when minors use SNS, without direct parental intervention. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes a means for collecting data on social networking services from communication terminals used by minors, a means for analyzing the collected data using a generation algorithm, a means for detecting risk factors based on the analysis results, a means for generating a warning for the detected risk factors, and a means for sending a warning to a guardian.

[0006] Specifically, communication devices collect data from social media in real time, and a generation algorithm preprocesses the data and then uses a natural language processing algorithm to analyze it. This analysis detects risk factors such as slander, bullying, leaking of personal information, luring, and "illegal part-time jobs" in real time, and warns parents as necessary, eliminating the need for real-time parental monitoring and effectively protecting minors from risk.

[0007] Of course. Now, let's create definitions for each of the important words included in the claims.

[0008] "Communication terminal" means

[0009] It is an electronic device that has the function of collecting data from social networking services and sending the analysis results to parents.

[0010] What is a "social networking service"?

[0011] It is a platform for users to communicate online and a service that collects and analyzes data.

[0012] "Data" means

[0013] This refers to the text information and media content that users send and receive on social networking services.

[0014] What is a "generative algorithm"?

[0015] It is an artificial intelligence technology used to analyze collected data and detect specific risk factors.

[0016] "Analysis" means

[0017] It is the process of processing collected data according to specific criteria to extract information and patterns.

[0018] "Risk factors" are

[0019] These include elements that can be harmful to minors, such as slander, bullying, leaking of personal information, luring, and "illegal part-time jobs."

[0020] "Warning" means:

[0021] Notifications generated and messages sent to parents when risk factors are detected.

[0022] "Guardian" means:

[0023] They are adults who have a duty to monitor and protect the safety of minor users and are the recipients of warnings.

[0024] What is a "natural language processing algorithm"?

[0025] It is a technology used to analyze human language and understand its meaning and structure. [Brief explanation of the drawings]

[0026] [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

[0027] 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.

[0028] First, the terms used in the following description will be explained.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] [First embodiment]

[0035] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0036] 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.

[0037] 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).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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."

[0047] This invention provides a system for detecting risks associated with the use of social networking services (SNS) at an early stage on a communication device used by minors, and for preventing minors from becoming involved in crime. Specific embodiments of this system are described below.

[0048] System configuration and operation overview

[0049] The communication devices are installed on smartphones, tablets, and other electronic devices commonly used by minors, and monitor text messages and posts in real time whenever a user uses a social networking site.

[0050] collection

[0051] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0052] Example: A user posts "I want to make new friends" in a messaging app. This post is collected by the device.

[0053] Pretreatment

[0054] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[0055] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0056] analysis

[0057] Generative algorithms analyze the collected data and detect risk factors, while natural language processing algorithms are used to understand the meaning and context of the text.

[0058] Example: If there is a message such as "You shouldn't come to school," the generation algorithm will determine this as bullying.

[0059] Risk Detection

[0060] If the generation algorithm detects a risk factor, it returns that information to the terminal.

[0061] Example: When a message containing personal information or a suspicious solicitation message is detected, risk information is returned to the device.

[0062] Alert Generation and Notification

[0063] The device generates appropriate warnings based on the detected risk information.

[0064] For example, generate a warning that reads, "Your child has received an inappropriate message."

[0065] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0066] Example: The server sends warning messages via email and SMS based on the parent's contact information.

[0067] Example of operation

[0068] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[0069] 1. The terminal collects this message.

[0070] 2. The terminal preprocesses the message and formats it into a parsable format.

[0071] 3. The generation algorithm analyzes the preprocessed messages to detect lure risks.

[0072] 4. The device generates a warning based on the detected risk information.

[0073] 5. The device sends the alert to the server.

[0074] 6. The server sends a warning message to the parent.

[0075] In this way, when a minor encounters a dangerous situation on social media, parents can be quickly warned and helped to take appropriate action.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The device collects all posts and messages made by users on social media in real time.

[0079] Example: When a user posts on a social networking site, "I'm planning to hang out with my friends tomorrow," the device collects that post.

[0080] Step 2:

[0081] The terminal pre-processes the collected data.

[0082] For example: normalization converts uppercase to lowercase, removes unnecessary symbols, and also removes extra spaces.

[0083] Step 3:

[0084] The terminal passes the preprocessed data to the generation algorithm.

[0085] Example: The preprocessed text "I'm going to hang out with my friends tomorrow" is sent to the generative algorithm.

[0086] Step 4:

[0087] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[0088] Example: The text "You shouldn't come to school" is collected and a generative algorithm identifies this message as a bullying risk.

[0089] Step 5:

[0090] If the generation algorithm detects a risk factor, it generates risk information and returns it to the terminal.

[0091] Example: A generative algorithm identifies the text "You shouldn't come to school" and creates risk information based on it.

[0092] Step 6:

[0093] The terminal generates a warning based on the risk information.

[0094] Example: If a bullying risk is detected, generate a warning saying "Your child has received a bullying message."

[0095] Step 7:

[0096] The terminal sends the generated alert to the server.

[0097] Example: An alert is sent to the server saying "Your child has received a bullying message."

[0098] Step 8:

[0099] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[0100] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message."

[0101] The above is a detailed processing flow of the system that detects risks early on when minors interact with social media and notifies parents.

[0102] Example 1

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

[0104] In today's world, the risk of minors becoming involved in crimes or inappropriate behavior when using social networking services (SNS) is extremely high. There are many potential dangers, particularly bullying, leaks of personal information, and even kidnapping and fraud. While a system to protect minors from these risks is necessary, the current situation is one in which the safety of minors cannot be ensured due to a lack of adequate systems for real-time monitoring, early detection of risks, and prompt notification to parents.

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

[0106] In this invention, the server

[0107] A means for the communication terminal to collect data on social networking services in real time;

[0108] means for pre-processing the collected data;

[0109] means for using a generative AI model to analyze the preprocessed data;

[0110] A means for detecting risk factors based on analysis results using the generative AI model;

[0111] means for generating an alert upon detection of said risk factor;

[0112] means for using a server to send said alert to the parent;

[0113] means for the server to send a warning message based on the parent's contact information;

[0114] This will enable early detection of risks when minors use SNS and prompt notification to parents.

[0115] 1. "Communication devices" are electronic devices such as smartphones and tablets used by minors.

[0116] 2. "Social Networking Service" means a web service that enables users to interact and share information with other users online.

[0117] 3. "Means of collection" refers to the function of a communication device to obtain data in real time on a social networking service.

[0118] 4. "Preprocessing means" refers to functions that normalize collected data, remove unnecessary symbols, and prepare it for analysis.

[0119] 5. "Generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze collected data and detect risks.

[0120] 6. "Natural language processing algorithm" is an analytical technology used to understand the meaning and context of collected text data.

[0121] 7. "Risk factors" refer to factors that may pose risks such as bullying, leakage of personal information, kidnapping, and fraud.

[0122] 8. "Means for generating warnings" refers to a function that generates a warning message indicating that a risk factor has been detected.

[0123] 9. "Server" means a computer system for sending warning messages to parents.

[0124] 10. "Means of sending" refers to the function of sending the generated alert via email or SMS based on the parent's contact information.

[0125] This invention provides a system for detecting risks associated with the use of social networking services (SNS) through communication terminals used by minors at an early stage and preventing minors from becoming involved in crime. Specific embodiments will be described below.

[0126] Hardware and software used

[0127] 1. Hardware

[0128] Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors

[0129] Server: A computer system for sending warning messages to parents.

[0130] 2. Software

[0131] Generative AI model: Natural language processing algorithms using machine learning algorithms

[0132] Data processing and calculation

[0133] 1. Data collection

[0134] The device collects social networking data in real time, including sent and received messages, posts, and comments.

[0135] Example: A user posts on a social networking site, "I want to make new friends." This post is collected by the device.

[0136] 2. Pretreatment

[0137] The terminal preprocesses the collected data, which includes normalizing the text and removing unnecessary symbols.

[0138] Example: If a user receives "See you tomorrow!", remove any extra spaces and special characters such as exclamation points.

[0139] 3. Data Analysis

[0140] The generative AI model analyzes the pre-processed data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[0141] Example: If a user receives a message that says "You shouldn't come to school," it is determined that this contains bullying content.

[0142] 4. Risk factor detection

[0143] When the generative AI model detects risk factors, it returns that information to the device.

[0144] Example: If a message containing personal information or a suspicious solicitation message is detected, that information will be sent back to the device.

[0145] 5. Alert Generation and Notification

[0146] The device generates a warning based on the detected risk information.

[0147] Example: Generating a warning that says "Your child has received an inappropriate message."

[0148] The terminal transmits the generated warning to the server, and the server notifies the parent of the warning message.

[0149] Example: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[0150] Specific examples and prompts

[0151] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[0152] 1. The device collects this message in real time.

[0153] 2. The terminal preprocesses the message, removing any extra spaces or symbols.

[0154] 3. The generative AI model analyzes the preprocessed message and determines that "Shall we meet?" poses a risk of solicitation.

[0155] 4. The device generates a warning stating "An enticement message has been detected" based on the risk information detected.

[0156] 5. The device sends a warning to the server, and the server sends a warning message to the parent via email or SMS stating that "your child has received an invitation message."

[0157] Prompt Sentence Examples

[0158] Input: A user receives a message on social media saying, "Shall we meet tomorrow?"

[0159] Task: Understand the meaning and context of this message and determine whether it contains any risk elements.

[0160] Expected Output: This message contains an exfiltration risk and should be flagged with a warning.

[0161] This will enable early detection of risks when minors use social networking sites and prompt notification to parents, helping to ensure safety.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1: Collect data

[0164] The device collects data on social networking services in real time, including sent and received messages, posts, and comments.

[0165] Input: Text data from a social networking service

[0166] Output: Collected text data

[0167] Specific operation: When a user posts on a social networking site that they want to make new friends, this post is collected in real time.

[0168] Step 2: Preprocessing the data

[0169] The terminal preprocesses the collected text data, which includes normalizing the text and removing unnecessary symbols.

[0170] Input: Collected text data

[0171] Output: Preprocessed text data

[0172] Specific behavior: If a user receives "See you tomorrow!", remove extra spaces and special characters such as exclamation marks from the text.

[0173] Step 3: Analyze the data

[0174] The device uses a generative AI model to analyze the pre-processed text data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[0175] Input: Preprocessed text data

[0176] Output: Analysis results (risk assessment)

[0177] Specific behavior: Analyzes messages such as "You shouldn't come to school" and determines that they contain bullying content.

[0178] Step 4: Identifying risk factors

[0179] The generative AI model detects risk factors based on the analysis results and returns the detection results to the device.

[0180] Input: Analysis results

[0181] Output: Risk factor information

[0182] Specific operation: Detects messages containing personal information or suspicious solicitation messages and sends that information back to the device.

[0183] Step 5: Generate warnings

[0184] The device generates a warning based on the detected risk information.

[0185] Input: Risk factor information

[0186] Output: Warning message

[0187] What it does: Generates the warning "Your child received an inappropriate message."

[0188] Step 6: Warning Notification

[0189] The terminal sends the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0190] Input: warning message

[0191] Output: Parental notification

[0192] What it does: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[0193] (Application example 1)

[0194] 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."

[0195] Currently, when minors use social networking services (SNS), they are likely to be exposed to risks such as bullying and solicitation. However, many parents do not have the means to grasp these risks in real time. This increases the risk of minors becoming involved in crime. The object of this invention is to provide a system that uses communication devices used by minors to quickly detect risks on SNS and issue prompt and accurate warnings to parents.

[0196] 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.

[0197] In this invention, the server includes means for collecting data on social networking services from communication terminals used by minors, means for preprocessing the collected data, means for using a natural language processing algorithm to analyze the collected data, means for detecting risk factors based on the analysis results, means for generating a warning when the risk factor is detected, and means for sending the warning to a guardian. This makes it possible to quickly notify a guardian when a minor encounters a dangerous situation on a social networking service and take appropriate action.

[0198] "Minors" refers to young people who have not yet reached legal adulthood.

[0199] "Communication terminal" is a general term for devices capable of voice and data communication, including smartphones, tablets, and other electronic devices that can connect to the Internet.

[0200] A "social networking service" is a platform for users to communicate with each other over the Internet, where information such as messages, posts, and comments is shared.

[0201] "Data" is a general term for information collected by a communication device, including text messages, sent and received messages, posts, comments, etc.

[0202] "Preprocessing" is the process of converting collected data into an analyzable format, and refers to operations such as normalizing text and removing unnecessary symbols.

[0203] "Natural language processing algorithms" are algorithms that allow computers to understand and analyze human language, and are a technology used to understand the meaning and context of text.

[0204] "Risk factors" refer to elements on social media that could lead minors to become involved in crimes or trouble, including bullying, luring, and leaking of personal information.

[0205] A "warning" is a notification message that is generated when a risk factor is detected, and is intended to inform parents and other relevant parties of the risk.

[0206] "Guardian" refers to a parent or someone in a similar position who has custody of a minor.

[0207] "Server" refers to a computer system for storing, processing, and managing data over a network.

[0208] The present invention provides a system for enabling minors to safely use social networking services (hereinafter referred to as SNS) on communication terminals. Detailed embodiments of this system will be described below.

[0209] System Overview

[0210] The system aims to detect risk factors early on when minors use social networking sites and send warnings to parents. The system mainly consists of the following components:

[0211] 1. Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors.

[0212] 2. Server: A computer system for managing data storage, processing, and sending alert messages.

[0213] Hardware and Software Configuration

[0214] Hardware:

[0215] Communication devices: smartphones, tablets

[0216] Server: A general internet-connected computer system

[0217] software:

[0218] Natural language processing library: TextBlob (for text analysis)

[0219] Email sending library: smtplib (for sending warning messages)

[0220] Data collection and preprocessing

[0221] The device collects data such as messages, posts, and comments sent and received by users on social media in real time. The collected data is then preprocessed to make it analyzable. Specifically, text is normalized and unnecessary symbols are removed.

[0222] Data analysis and risk detection

[0223] The preprocessed data is then analyzed using natural language processing (NLP) algorithms. Using a NLP library such as TextBlob, the text is subjected to sentiment analysis. Messages with strong negative sentiment (e.g., "Don't come to my school") are then detected as risk factors.

[0224] Alert generation and notification

[0225] If a risk factor is detected, the device generates a warning message and sends it to the server. The server then sends this warning to the parent's contact information (e.g., email address), allowing parents to respond quickly when their minor encounters a dangerous situation on social media.

[0226] Examples of specific examples and prompts

[0227] Specific examples

[0228] Example: If a user receives the message "Don't come to school," the following happens:

[0229] The device collects this message

[0230] Preprocessing to remove extra spaces and special characters

[0231] Natural language processing algorithms analyze messages and detect negative sentiment

[0232] When a risk factor is detected, a warning message is generated and sent to the server.

[0233] The server sends a warning email to the parent.

[0234] Prompt sentence for generative AI model

[0235] Evaluate the following message and determine if it poses a risk: "You shouldn't come to school."

[0236] Negative Emotion: True

[0237] Risky message: Don't come to school

[0238] In this way, a system is provided that can quickly notify parents and guardians when a minor encounters a dangerous situation on an SNS and take appropriate action.

[0239] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0240] Step 1: The device collects messages on the social networking site.

[0241] Input: Messages, comments, and posts that users send or receive on social media.

[0242] How it works: The device uses the API of the social networking application to collect this data in real time.

[0243] Output: Raw data collected (messages, comments, posts)

[0244] Step 2: Preprocess the data collected by the device

[0245] Input: Raw data collected

[0246] What it does: The terminal preprocesses and normalizes the text data, specifically removing extra spaces and special characters.

[0247] Output: Preprocessed text data

[0248] Step 3: The device analyzes the preprocessed data using natural language processing algorithms.

[0249] Input: Preprocessed text data

[0250] How it works: The device uses a natural language processing library such as TextBlob to perform sentiment analysis on the text data, calculating a sentiment score for each message.

[0251] Output: Analysis results (text data with sentiment scores)

[0252] Step 4: The device detects risk factors based on the analysis results

[0253] Input: Text data with sentiment scores

[0254] Operation: The device evaluates the analysis results and determines messages with negative emotions above a certain threshold as risk factors. For example, if the emotion score is below -0.5, it is a risk factor.

[0255] Output: Messages determined to be risk factors

[0256] Step 5: Generate a warning message when the device detects a risk factor

[0257] Input: Message determined to be a risk factor

[0258] Operation: The device generates a warning message to send to the parent. Specifically, it constructs the warning message based on the content of the message that was determined to be a risk factor.

[0259] Output: Warning message

[0260] Step 6: Server sends alert to parent

[0261] Input: warning message

[0262] How it works: When a warning message is sent from the device to the server, the server will send a warning message via email or SMS based on the parent's contact information.

[0263] Output: Notification of completion of sending warning message to parents

[0264] 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.

[0265] This invention is a system for early detection of risks associated with the use of social networking sites on communication devices used by minors, preventing them from becoming involved in crimes, and further improves the accuracy of risk detection by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0266] System configuration and operation overview

[0267] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0268] collection

[0269] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0270] Example: A user posts in a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by the device.

[0271] Pretreatment

[0272] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[0273] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0274] analysis

[0275] The generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[0276] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[0277] Risk Detection

[0278] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[0279] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[0280] Alert Generation and Notification

[0281] The device generates a warning based on the generated risk information and the results of emotion analysis.

[0282] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0283] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0284] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0285] Example of operation

[0286] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0287] 1. The terminal collects this message.

[0288] 2. The terminal preprocesses the message and formats it into a parsable format.

[0289] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0290] 4. The emotion engine recognizes the user's negative emotional state.

[0291] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0292] 6. The device sends the alert to the server.

[0293] 7. The server sends a warning message to the parent.

[0294] In this way, when a minor encounters a dangerous situation or negative emotions on social media, the system can quickly warn parents and help them take appropriate action.

[0295] The processing flow will be explained below.

[0296] Step 1:

[0297] The device collects all posts and messages made by users on social media in real time.

[0298] Example: A user sends a message to a friend on a social networking site saying, "I want a new game." This message is collected by the device.

[0299] Step 2:

[0300] The terminal pre-processes the collected data.

[0301] Example: Converting messages to uppercase and lowercase, removing unnecessary symbols, and arranging them into a unified format.

[0302] Step 3:

[0303] The terminal sends the pre-processed data to the generating algorithm.

[0304] Example: The preprocessed text "I want a new game" is sent to a generative algorithm.

[0305] Step 4:

[0306] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[0307] Example: A generative algorithm analyzes the message "I wish you were gone" and determines that it is potentially defamatory.

[0308] Step 5:

[0309] The generative algorithm detects risk factors and sends the data to the emotion engine.

[0310] Example: The message "I wish you'd just disappear" detected by the generative algorithm is passed to the emotion engine.

[0311] Step 6:

[0312] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0313] Example: The emotion engine determines that the sender's emotion in response to the message "I wish you were gone" is negative.

[0314] Step 7:

[0315] The emotion engine returns the recognized emotion information to the terminal.

[0316] Example: Returning a negative sentiment result to the terminal.

[0317] Step 8:

[0318] The terminal generates a warning based on the risk information of the generation algorithm and the analysis results of the emotion engine.

[0319] Example: Generate a warning message saying "Your child has received a bullying message."

[0320] Step 9:

[0321] The terminal sends the generated alert to the server.

[0322] Example: Sending a warning message to the server: "Your child has received a bullying message."

[0323] Step 10:

[0324] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[0325] Example: The server sends a warning email or SMS to a parent's contacts stating that "your child has received a bullying message."

[0326] This is the specific processing flow of the system that combines the emotion engine. This makes it possible to detect risks and issue warnings that take into account the emotional state of minors when they encounter dangerous situations on social media.

[0327] Example 2

[0328] 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."

[0329] When minors use social networking services (SNS), they are likely to encounter inappropriate content and harmful messages, which can have serious psychological effects. Furthermore, conventional systems have had difficulty accurately and quickly analyzing SNS data and detecting and notifying users of risks at an early stage. To address this issue, it is urgent to develop an efficient system that collects SNS data in real time, analyzes it, detects risks, and generates warnings. Furthermore, there is a need to improve the accuracy of risk detection by accurately understanding users' emotional states.

[0330] 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.

[0331] In this invention, the server includes a means for a communication terminal to collect data on a social networking service, a means for preprocessing the collected data by normalizing and removing unnecessary symbols, and a means for analyzing the preprocessed data using a generative algorithm and analyzing the emotional state using an emotion engine. This makes it possible to quickly and accurately detect risks and notify appropriate individuals of warnings when a minor encounters inappropriate content on an SNS. The emotion engine also allows for a more accurate understanding of the user's psychological state, enabling more accurate risk assessment.

[0332] Definitions of important words

[0333] "Minor" means a person who has not reached the legal age of majority.

[0334] "Communication terminal" refers to any electronic device that has communication capabilities via an Internet connection, including smartphones and tablets.

[0335] "Social networking services (SNS)" refer to platforms that allow people to interact with each other online. Examples include messaging apps and community sites.

[0336] "Means of collecting data" refers to technology for obtaining messages, posts, comments, etc. sent and received on social media in real time.

[0337] "Normalization" refers to the process of formatting collected text data into a unified format.

[0338] "Unnecessary symbol removal" refers to the process of removing special characters, spaces, etc. that are not required for data analysis.

[0339] "Preprocessing means" refers to a series of processes for formatting collected data into an analyzable format.

[0340] "Generative algorithm" refers to an algorithm that analyzes data and generates a result for a specific purpose, and specifically includes natural language processing algorithms.

[0341] "Emotion engine" refers to technology for analyzing text data to identify a user's emotional state.

[0342] "Means for detecting risk factors" refers to technology that identifies data containing inappropriate content or negative emotions based on analysis results.

[0343] "Means for generating risk information" refers to technology that generates appropriate warnings and notifications based on detected risk factors.

[0344] "Means for generating warnings" refers to techniques for creating appropriate warning messages when a risk is detected.

[0345] "Means of sending to parents" refers to technology that notifies parents of the generated warning message via email, SMS, etc.

[0346] MODE FOR CARRYING OUT THE INVENTION

[0347] This invention is a system for early detection of risks associated with the use of social networking services (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the accuracy of risk detection is improved. Specific embodiments of this system are described below.

[0348] System configuration and operation overview

[0349] The communication terminal is installed on digital devices (smartphones, tablets, etc.) that minors use on a daily basis. This terminal monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0350] collection

[0351] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0352] Example: A user posts, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[0353] Pretreatment

[0354] The terminal preprocesses the collected data by normalizing it and removing unnecessary symbols.

[0355] Example: If the message "I don't want to go to school anymore!!!" is collected, remove the extra spaces and exclamation marks and format it as "I don't want to go to school anymore".

[0356] analysis

[0357] The generative algorithm analyzes the pre-processed data to detect risk factors, while also analyzing the emotional state of the text using an emotion engine.

[0358] Example: If the message "I wish you'd just disappear" is collected, the generative algorithm will determine this as slander and the emotion engine will detect a negative emotional state.

[0359] Risk Detection

[0360] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[0361] Example: Identified messages are identified as having negative sentiment by the sentiment engine, generating risk information.

[0362] Alert Generation and Notification

[0363] The device generates a warning based on the generated risk information and the results of emotion analysis.

[0364] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0365] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0366] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0367] Example of operation

[0368] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0369] 1. The terminal collects this message.

[0370] 2. The terminal preprocesses the message and formats it into a parsable format.

[0371] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0372] 4. The emotion engine recognizes the user's negative emotional state.

[0373] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0374] 6. The device sends the alert to the server.

[0375] 7. The server sends a warning message to the parent.

[0376] Prompt Sentence Examples

[0377] When a user sends "I hate you" on social media, the system works as follows:

[0378] 1. The device collects the message.

[0379] 2. Pre-treat.

[0380] 3. Analyze with a generative algorithm.

[0381] 4. Recognize emotional states with the Emotion Engine.

[0382] 5. Generate risk information.

[0383] 6. Generate an alert and send it to the server.

[0384] 7. The server notifies the parent.

[0385] This system will enable parents to quickly receive a warning when a minor encounters a dangerous situation on social media, allowing them to take appropriate action.

[0386] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0387] Program processing steps

[0388] Step 1: Collect data

[0389] Processing flow: The device collects messages and comments sent, received, and posted by users on the social networking site in real time.

[0390] Input: User messages and posts on social media.

[0391] Data processing: Collected messages and posts are stored on the communication device.

[0392] Output: Raw data saved to the device.

[0393] Specific operation: A user posts on social media, "I don't want to go to school anymore." The device immediately collects and saves this post.

[0394] Step 2: Preprocessing the data

[0395] Processing flow: The terminal performs preprocessing on the collected data to make it easier to analyze, including text normalization and removal of unnecessary symbols.

[0396] Input: Collected raw data on social media.

[0397] Data processing: Text normalization (combining multiple whitespaces, removing special characters, etc.).

[0398] Output: Preprocessed and clean data.

[0399] Specific behavior: The device normalizes the post "I don't want to go to school anymore!!!" to "I don't want to go to school anymore" and removes unnecessary symbols.

[0400] Step 3: Analyze the data

[0401] Processing flow: The generative algorithm analyzes the preprocessed data and evaluates the user's emotional state using the emotion engine.

[0402] Input: Preprocessed clean data.

[0403] Data processing / data calculation: Generative algorithms analyze data using natural language processing techniques to assess risk factors. Emotion engines analyze the emotional state of the data.

[0404] Output: Analysis results (risk factors such as slander, stress, etc.).

[0405] How it works: The generative algorithm analyzes posts like "I don't want to go to school anymore" to detect negative emotional states, while the emotion engine evaluates the stress level felt by the user.

[0406] Step 4: Risk detection

[0407] Process flow: The generation algorithm detects risk factors from user posts and messages, and integrates them with the analysis results of the emotion engine to generate risk information.

[0408] Input: Analysis results of the generative algorithm and emotion engine.

[0409] Data processing: Integrate risk factors and emotional states to generate risk information.

[0410] Output: The generated risk information.

[0411] How it works: The generation algorithm identifies posts such as "I don't want to go to school anymore" as a sign of bullying, and the emotion engine detects high stress levels. These are then combined to generate "high risk" information.

[0412] Step 5: Alert Generation and Notification

[0413] Process flow: The device generates a warning based on the generated risk information and the results of emotion analysis, and sends it to the server. The server then sends a warning message to the parent.

[0414] Input: Generated risk information.

[0415] Data processing: Create warning messages based on risk information.

[0416] Output: Warning message sent to parents.

[0417] Specific operation: The device generates a warning saying "Your child has received a bullying message and is feeling very stressed" and sends it to the server. The server then sends the warning to the parent via email or SMS to notify them promptly.

[0418] This system makes it possible to quickly send a warning to parents when a user encounters a dangerous situation on social media, urging them to take appropriate action.

[0419] (Application example 2)

[0420] 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."

[0421] In addition to the challenge of early detection of risks (especially slander and bullying) associated with the use of social networking sites by minors and preventing them from becoming involved in crime, it is also necessary to further improve safety in self-driving vehicles. It is also necessary to recognize user emotions and achieve more accurate risk detection and warning generation.

[0422] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0423] In this invention, the server includes means for the communication terminal to collect data on social networking services, means for using a generation algorithm to analyze the collected data, means for recognizing the emotions of passengers using an emotion engine, means for detecting risk factors based on the analysis results and the emotional state, means for generating a warning based on the risk factors and the emotional state, and means for sending the generated warning to a guardian. This makes it possible to detect risks with high accuracy even when a minor uses SNS in an autonomous vehicle, and to quickly send a warning to the guardian.

[0424] "Communication devices" are electronic devices such as smartphones and tablets that are used on a daily basis by minors.

[0425] A "social networking service" is an Internet service that enables users to connect with other users online and share messages and content.

[0426] A "generative algorithm" is an algorithm used to analyze collected data and detect risk factors.

[0427] An "emotion engine" is a technology that recognizes a user's emotional state from the text and actions they enter.

[0428] "Risk factors" are elements that could potentially cause dangerous situations for users, such as defamation or bullying.

[0429] An "alert" is a warning message that is generated when a risk factor is detected.

[0430] A "guardian" is a supervisor appointed to protect the safety of minor users.

[0431] "Real-time" refers to the ability to process and analyze data as soon as it is generated.

[0432] "Preprocessing" is the process of formatting collected data into a format that is easy to analyze.

[0433] "Natural language processing algorithms" are computational techniques used to understand and analyze human language.

[0434] "Passenger vehicle" refers to an autonomous vehicle for passengers to ride in.

[0435] This invention is a system for early detection of risks associated with the use of social networking sites (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. It also aims to improve the safety of minors when riding in self-driving vehicles. Specific embodiments of this invention are described below.

[0436] System configuration and operation overview

[0437] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0438] collection

[0439] The server collects data from the communication device on the SNS in real time, including sent messages, received messages, posts, comments, etc.

[0440] Example: A user posts on a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[0441] Pretreatment

[0442] The collected data is preprocessed on the communication terminal, which is a process that normalizes the text, removes unnecessary symbols, etc., to prepare it for analysis.

[0443] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0444] analysis

[0445] A generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[0446] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[0447] Risk Detection

[0448] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the communication terminal.

[0449] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[0450] Alert Generation and Notification

[0451] The server generates a warning based on the generated risk information and the results of sentiment analysis.

[0452] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0453] The server notifies the parent of the generated alert.

[0454] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0455] Hardware and software used

[0456] The system uses the following hardware and software:

[0457] Hardware: Smartphones, tablets, and in-vehicle systems for autonomous vehicles

[0458] Software: generative algorithms (e.g., natural language processing algorithms), emotion engines (e.g., EmotionEngine)

[0459] Specific examples

[0460] When a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0461] 1. The communication terminal collects this message.

[0462] 2. The communication device preprocesses the message and formats it into a parsable format.

[0463] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0464] 4. The emotion engine recognizes the user's negative emotional state.

[0465] 5. The server integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0466] 6. The server sends the generated alert to the parent.

[0467] Example prompts to be input to the generative AI model

[0468] Analyze users' SNS messages and detect safety risks. For the following messages, determine whether they are high-risk and have negative sentiment and generate a warning message.

[0469] Message: I don't want to go to school anymore

[0470] In this way, if a minor encounters a dangerous situation or negative emotions on social media while in an autonomous vehicle, parents can be quickly warned and supported in taking appropriate action.

[0471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0472] Step 1:

[0473] The device collects data on social media.

[0474] Input: Messages and posts sent or received by users on social media.

[0475] Processing: The device monitors these messages and posts in real time and collects data.

[0476] Output: Collected social media data.

[0477] Operation: For example, if a user posts, "I'm looking forward to making plans to hang out with my friends," the device collects that posting data.

[0478] Step 2:

[0479] The terminal preprocesses the collected data.

[0480] Input: Social media data collected in step 1.

[0481] Processing: Normalize the collected data and remove unnecessary symbols and extra spaces.

[0482] Output: Normalized text data.

[0483] Action: For example, format a post like "I'm looking forward to hanging out with my friends!!!" into "I'm looking forward to hanging out with my friends."

[0484] Step 3:

[0485] The terminal analyzes the preprocessed data using the generation algorithm.

[0486] Input: The text data preprocessed in step 2.

[0487] Processing: Natural language processing algorithms are used to detect risk factors in the text.

[0488] Output: Risk factor analysis results.

[0489] Action: For example, the message "I wish you'd just disappear" is considered defamatory.

[0490] Step 4:

[0491] The terminal uses an emotion engine to analyze the user's emotional state.

[0492] Input: The text data preprocessed in step 2.

[0493] Processing: The text data is analyzed by the emotion engine to recognize the user's emotional state.

[0494] Output: Emotional state analysis result.

[0495] How it works: For example, detecting a negative emotional state from a message like "I don't want to go to school anymore."

[0496] Step 5:

[0497] The server integrates the results of the generation algorithm and the emotion engine to generate risk information.

[0498] Input: Analysis results from Step 3 and Step 4.

[0499] Processing: Integrate risk factors and emotional states to generate risk information.

[0500] Output: Risk information.

[0501] Behavior: For example, the message "I wish you'd just disappear" combined with a negative emotional state could be considered "high risk."

[0502] Step 6:

[0503] The server generates a warning based on the risk information.

[0504] Input: Risk information generated in Step 5.

[0505] Action: Generate a warning message based on the risk information.

[0506] Output: A warning message.

[0507] What it does: For example, generate a warning that says, "Your child has received bullying messages and is feeling very stressed."

[0508] Step 7:

[0509] The server generates and sends the alert to the parent.

[0510] Input: The warning message generated in step 6.

[0511] Action: Send a warning message to parents via email and / or SMS.

[0512] Output: The warning message sent.

[0513] What it does: For example, an email is sent to a parent's email address stating, "Your child has received a bullying message and is feeling very stressed."

[0514] By following these steps, even if a minor encounters a dangerous situation on social media, parents can be quickly notified and prompted to take appropriate action.

[0515] 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.

[0516] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0517] 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.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] In the smart glasses 214, 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.

[0530] 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."

[0531] This invention provides a system for detecting risks associated with the use of social networking services (SNS) at an early stage on a communication device used by minors, and for preventing minors from becoming involved in crime. Specific embodiments of this system are described below.

[0532] System configuration and operation overview

[0533] The communication devices are installed on smartphones, tablets, and other electronic devices commonly used by minors, and monitor text messages and posts in real time whenever a user uses a social networking site.

[0534] collection

[0535] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0536] Example: A user posts "I want to make new friends" in a messaging app. This post is collected by the device.

[0537] Pretreatment

[0538] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[0539] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0540] analysis

[0541] Generative algorithms analyze the collected data and detect risk factors, while natural language processing algorithms are used to understand the meaning and context of the text.

[0542] Example: If there is a message such as "You shouldn't come to school," the generation algorithm will determine this as bullying.

[0543] Risk Detection

[0544] If the generation algorithm detects a risk factor, it returns that information to the terminal.

[0545] Example: When a message containing personal information or a suspicious solicitation message is detected, risk information is returned to the device.

[0546] Alert Generation and Notification

[0547] The device generates appropriate warnings based on the detected risk information.

[0548] For example, generate a warning that reads, "Your child has received an inappropriate message."

[0549] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0550] Example: The server sends warning messages via email and SMS based on the parent's contact information.

[0551] Example of operation

[0552] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[0553] 1. The terminal collects this message.

[0554] 2. The terminal preprocesses the message and formats it into a parsable format.

[0555] 3. The generation algorithm analyzes the preprocessed messages to detect lure risks.

[0556] 4. The device generates a warning based on the detected risk information.

[0557] 5. The device sends the alert to the server.

[0558] 6. The server sends a warning message to the parent.

[0559] In this way, when a minor encounters a dangerous situation on social media, parents can be quickly warned and helped to take appropriate action.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] The device collects all posts and messages made by users on social media in real time.

[0563] Example: When a user posts on a social networking site, "I'm planning to hang out with my friends tomorrow," the device collects that post.

[0564] Step 2:

[0565] The terminal pre-processes the collected data.

[0566] For example: normalization converts uppercase to lowercase, removes unnecessary symbols, and also removes extra spaces.

[0567] Step 3:

[0568] The terminal passes the preprocessed data to the generation algorithm.

[0569] Example: The preprocessed text "I'm going to hang out with my friends tomorrow" is sent to the generative algorithm.

[0570] Step 4:

[0571] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[0572] Example: The text "You shouldn't come to school" is collected and a generative algorithm identifies this message as a bullying risk.

[0573] Step 5:

[0574] If the generation algorithm detects a risk factor, it generates risk information and returns it to the terminal.

[0575] Example: A generative algorithm identifies the text "You shouldn't come to school" and creates risk information based on it.

[0576] Step 6:

[0577] The terminal generates a warning based on the risk information.

[0578] Example: If a bullying risk is detected, generate a warning saying "Your child has received a bullying message."

[0579] Step 7:

[0580] The terminal sends the generated alert to the server.

[0581] Example: An alert is sent to the server saying "Your child has received a bullying message."

[0582] Step 8:

[0583] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[0584] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message."

[0585] The above is a detailed processing flow of the system that detects risks early on when minors interact with social media and notifies parents.

[0586] Example 1

[0587] 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."

[0588] In today's world, the risk of minors becoming involved in crimes or inappropriate behavior when using social networking services (SNS) is extremely high. There are many potential dangers, particularly bullying, leaks of personal information, and even kidnapping and fraud. While a system to protect minors from these risks is necessary, the current situation is one in which the safety of minors cannot be ensured due to a lack of adequate systems for real-time monitoring, early detection of risks, and prompt notification to parents.

[0589] 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.

[0590] In this invention, the server

[0591] A means for the communication terminal to collect data on social networking services in real time;

[0592] means for pre-processing the collected data;

[0593] means for using a generative AI model to analyze the preprocessed data;

[0594] A means for detecting risk factors based on analysis results using the generative AI model;

[0595] means for generating an alert upon detection of said risk factor;

[0596] means for using a server to send said alert to the parent;

[0597] means for the server to send a warning message based on the parent's contact information;

[0598] This will enable early detection of risks when minors use SNS and prompt notification to parents.

[0599] 1. "Communication devices" are electronic devices such as smartphones and tablets used by minors.

[0600] 2. "Social Networking Service" means a web service that enables users to interact and share information with other users online.

[0601] 3. "Means of collection" refers to the function of a communication device to obtain data in real time on a social networking service.

[0602] 4. "Preprocessing means" refers to functions that normalize collected data, remove unnecessary symbols, and prepare it for analysis.

[0603] 5. "Generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze collected data and detect risks.

[0604] 6. "Natural language processing algorithm" is an analytical technology used to understand the meaning and context of collected text data.

[0605] 7. "Risk factors" refer to factors that may pose risks such as bullying, leakage of personal information, kidnapping, and fraud.

[0606] 8. "Means for generating warnings" refers to a function that generates a warning message indicating that a risk factor has been detected.

[0607] 9. "Server" means a computer system for sending warning messages to parents.

[0608] 10. "Means of sending" refers to the function of sending the generated alert via email or SMS based on the parent's contact information.

[0609] This invention provides a system for detecting risks associated with the use of social networking services (SNS) through communication terminals used by minors at an early stage and preventing minors from becoming involved in crime. Specific embodiments will be described below.

[0610] Hardware and software used

[0611] 1. Hardware

[0612] Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors

[0613] Server: A computer system for sending warning messages to parents.

[0614] 2. Software

[0615] Generative AI model: Natural language processing algorithms using machine learning algorithms

[0616] Data processing and calculation

[0617] 1. Data collection

[0618] The device collects social networking data in real time, including sent and received messages, posts, and comments.

[0619] Example: A user posts on a social networking site, "I want to make new friends." This post is collected by the device.

[0620] 2. Pretreatment

[0621] The terminal preprocesses the collected data, which includes normalizing the text and removing unnecessary symbols.

[0622] Example: If a user receives "See you tomorrow!", remove any extra spaces and special characters such as exclamation points.

[0623] 3. Data Analysis

[0624] The generative AI model analyzes the pre-processed data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[0625] Example: If a user receives a message that says "You shouldn't come to school," it is determined that this contains bullying content.

[0626] 4. Risk factor detection

[0627] When the generative AI model detects risk factors, it returns that information to the device.

[0628] Example: If a message containing personal information or a suspicious solicitation message is detected, that information will be sent back to the device.

[0629] 5. Alert Generation and Notification

[0630] The device generates a warning based on the detected risk information.

[0631] Example: Generating a warning that says "Your child has received an inappropriate message."

[0632] The terminal transmits the generated warning to the server, and the server notifies the parent of the warning message.

[0633] Example: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[0634] Specific examples and prompts

[0635] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[0636] 1. The device collects this message in real time.

[0637] 2. The terminal preprocesses the message, removing any extra spaces or symbols.

[0638] 3. The generative AI model analyzes the preprocessed message and determines that "Shall we meet?" poses a risk of solicitation.

[0639] 4. The device generates a warning stating "An enticement message has been detected" based on the risk information detected.

[0640] 5. The device sends a warning to the server, and the server sends a warning message to the parent via email or SMS stating that "your child has received an invitation message."

[0641] Prompt Sentence Examples

[0642] Input: A user receives a message on social media saying, "Shall we meet tomorrow?"

[0643] Task: Understand the meaning and context of this message and determine whether it contains any risk elements.

[0644] Expected Output: This message contains an exfiltration risk and should be flagged with a warning.

[0645] This will enable early detection of risks when minors use social networking sites and prompt notification to parents, helping to ensure safety.

[0646] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0647] Step 1: Collect data

[0648] The device collects data on social networking services in real time, including sent and received messages, posts, and comments.

[0649] Input: Text data from a social networking service

[0650] Output: Collected text data

[0651] Specific operation: When a user posts on a social networking site that they want to make new friends, this post is collected in real time.

[0652] Step 2: Preprocessing the data

[0653] The terminal preprocesses the collected text data, which includes normalizing the text and removing unnecessary symbols.

[0654] Input: Collected text data

[0655] Output: Preprocessed text data

[0656] Specific behavior: If a user receives "See you tomorrow!", remove extra spaces and special characters such as exclamation marks from the text.

[0657] Step 3: Analyze the data

[0658] The device uses a generative AI model to analyze the pre-processed text data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[0659] Input: Preprocessed text data

[0660] Output: Analysis results (risk assessment)

[0661] Specific behavior: Analyzes messages such as "You shouldn't come to school" and determines that they contain bullying content.

[0662] Step 4: Identifying risk factors

[0663] The generative AI model detects risk factors based on the analysis results and returns the detection results to the device.

[0664] Input: Analysis results

[0665] Output: Risk factor information

[0666] Specific operation: Detects messages containing personal information or suspicious solicitation messages and sends that information back to the device.

[0667] Step 5: Generate warnings

[0668] The device generates a warning based on the detected risk information.

[0669] Input: Risk factor information

[0670] Output: Warning message

[0671] What it does: Generates the warning "Your child received an inappropriate message."

[0672] Step 6: Warning Notification

[0673] The terminal sends the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0674] Input: warning message

[0675] Output: Parental notification

[0676] What it does: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[0677] (Application example 1)

[0678] 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."

[0679] Currently, when minors use social networking services (SNS), they are likely to be exposed to risks such as bullying and solicitation. However, many parents do not have the means to grasp these risks in real time. This increases the risk of minors becoming involved in crime. The object of this invention is to provide a system that uses communication devices used by minors to quickly detect risks on SNS and issue prompt and accurate warnings to parents.

[0680] 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.

[0681] In this invention, the server includes means for collecting data on social networking services from communication terminals used by minors, means for preprocessing the collected data, means for using a natural language processing algorithm to analyze the collected data, means for detecting risk factors based on the analysis results, means for generating a warning when the risk factor is detected, and means for sending the warning to a guardian. This makes it possible to quickly notify a guardian when a minor encounters a dangerous situation on a social networking service and take appropriate action.

[0682] "Minors" refers to young people who have not yet reached legal adulthood.

[0683] "Communication terminal" is a general term for devices capable of voice and data communication, including smartphones, tablets, and other electronic devices that can connect to the Internet.

[0684] A "social networking service" is a platform for users to communicate with each other over the Internet, where information such as messages, posts, and comments is shared.

[0685] "Data" is a general term for information collected by a communication device, including text messages, sent and received messages, posts, comments, etc.

[0686] "Preprocessing" is the process of converting collected data into an analyzable format, and refers to operations such as normalizing text and removing unnecessary symbols.

[0687] "Natural language processing algorithms" are algorithms that allow computers to understand and analyze human language, and are a technology used to understand the meaning and context of text.

[0688] "Risk factors" refer to elements on social media that could lead minors to become involved in crimes or trouble, including bullying, luring, and leaking of personal information.

[0689] A "warning" is a notification message that is generated when a risk factor is detected, and is intended to inform parents and other relevant parties of the risk.

[0690] "Guardian" refers to a parent or someone in a similar position who has custody of a minor.

[0691] "Server" refers to a computer system for storing, processing, and managing data over a network.

[0692] The present invention provides a system for enabling minors to safely use social networking services (hereinafter referred to as SNS) on communication terminals. Detailed embodiments of this system will be described below.

[0693] System Overview

[0694] The system aims to detect risk factors early on when minors use social networking sites and send warnings to parents. The system mainly consists of the following components:

[0695] 1. Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors.

[0696] 2. Server: A computer system for managing data storage, processing, and sending alert messages.

[0697] Hardware and Software Configuration

[0698] Hardware:

[0699] Communication devices: smartphones, tablets

[0700] Server: A general internet-connected computer system

[0701] software:

[0702] Natural language processing library: TextBlob (for text analysis)

[0703] Email sending library: smtplib (for sending warning messages)

[0704] Data collection and preprocessing

[0705] The device collects data such as messages, posts, and comments sent and received by users on social media in real time. The collected data is then preprocessed to make it analyzable. Specifically, text is normalized and unnecessary symbols are removed.

[0706] Data analysis and risk detection

[0707] The preprocessed data is then analyzed using natural language processing (NLP) algorithms. Using a NLP library such as TextBlob, the text is subjected to sentiment analysis. Messages with strong negative sentiment (e.g., "Don't come to my school") are then detected as risk factors.

[0708] Alert generation and notification

[0709] If a risk factor is detected, the device generates a warning message and sends it to the server. The server then sends this warning to the parent's contact information (e.g., email address), allowing parents to respond quickly when their minor encounters a dangerous situation on social media.

[0710] Examples of specific examples and prompts

[0711] Specific examples

[0712] Example: If a user receives the message "Don't come to school," the following happens:

[0713] The device collects this message

[0714] Preprocessing to remove extra spaces and special characters

[0715] Natural language processing algorithms analyze messages and detect negative sentiment

[0716] When a risk factor is detected, a warning message is generated and sent to the server.

[0717] The server sends a warning email to the parent.

[0718] Prompt sentence for generative AI model

[0719] Evaluate the following message and determine if it poses a risk: "You shouldn't come to school."

[0720] Negative Emotion: True

[0721] Risky message: Don't come to school

[0722] In this way, a system is provided that can quickly notify parents and guardians when a minor encounters a dangerous situation on an SNS and take appropriate action.

[0723] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0724] Step 1: The device collects messages on the social networking site.

[0725] Input: Messages, comments, and posts that users send or receive on social media.

[0726] How it works: The device uses the API of the social networking application to collect this data in real time.

[0727] Output: Raw data collected (messages, comments, posts)

[0728] Step 2: Preprocess the data collected by the device

[0729] Input: Raw data collected

[0730] What it does: The terminal preprocesses and normalizes the text data, specifically removing extra spaces and special characters.

[0731] Output: Preprocessed text data

[0732] Step 3: The device analyzes the preprocessed data using natural language processing algorithms.

[0733] Input: Preprocessed text data

[0734] How it works: The device uses a natural language processing library such as TextBlob to perform sentiment analysis on the text data, calculating a sentiment score for each message.

[0735] Output: Analysis results (text data with sentiment scores)

[0736] Step 4: The device detects risk factors based on the analysis results

[0737] Input: Text data with sentiment scores

[0738] Operation: The device evaluates the analysis results and determines messages with negative emotions above a certain threshold as risk factors. For example, if the emotion score is below -0.5, it is a risk factor.

[0739] Output: Messages determined to be risk factors

[0740] Step 5: Generate a warning message when the device detects a risk factor

[0741] Input: Message determined to be a risk factor

[0742] Operation: The device generates a warning message to send to the parent. Specifically, it constructs the warning message based on the content of the message that was determined to be a risk factor.

[0743] Output: Warning message

[0744] Step 6: Server sends alert to parent

[0745] Input: warning message

[0746] How it works: When a warning message is sent from the device to the server, the server will send a warning message via email or SMS based on the parent's contact information.

[0747] Output: Notification of completion of sending warning message to parents

[0748] 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.

[0749] This invention is a system for early detection of risks associated with the use of social networking sites on communication devices used by minors, preventing them from becoming involved in crimes, and further improves the accuracy of risk detection by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0750] System configuration and operation overview

[0751] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0752] collection

[0753] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0754] Example: A user posts in a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by the device.

[0755] Pretreatment

[0756] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[0757] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0758] analysis

[0759] The generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[0760] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[0761] Risk Detection

[0762] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[0763] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[0764] Alert Generation and Notification

[0765] The device generates a warning based on the generated risk information and the results of emotion analysis.

[0766] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0767] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0768] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0769] Example of operation

[0770] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0771] 1. The terminal collects this message.

[0772] 2. The terminal preprocesses the message and formats it into a parsable format.

[0773] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0774] 4. The emotion engine recognizes the user's negative emotional state.

[0775] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0776] 6. The device sends the alert to the server.

[0777] 7. The server sends a warning message to the parent.

[0778] In this way, when a minor encounters a dangerous situation or negative emotions on social media, the system can quickly warn parents and help them take appropriate action.

[0779] The processing flow will be explained below.

[0780] Step 1:

[0781] The device collects all posts and messages made by users on social media in real time.

[0782] Example: A user sends a message to a friend on a social networking site saying, "I want a new game." This message is collected by the device.

[0783] Step 2:

[0784] The terminal pre-processes the collected data.

[0785] Example: Converting messages to uppercase and lowercase, removing unnecessary symbols, and arranging them into a unified format.

[0786] Step 3:

[0787] The terminal sends the pre-processed data to the generating algorithm.

[0788] Example: The preprocessed text "I want a new game" is sent to a generative algorithm.

[0789] Step 4:

[0790] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[0791] Example: A generative algorithm analyzes the message "I wish you were gone" and determines that it is potentially defamatory.

[0792] Step 5:

[0793] The generative algorithm detects risk factors and sends the data to the emotion engine.

[0794] Example: The message "I wish you'd just disappear" detected by the generative algorithm is passed to the emotion engine.

[0795] Step 6:

[0796] The emotion engine analyzes the received data and recognizes the user's emotional state.

[0797] Example: The emotion engine determines that the sender's emotion in response to the message "I wish you were gone" is negative.

[0798] Step 7:

[0799] The emotion engine returns the recognized emotion information to the terminal.

[0800] Example: Returning a negative sentiment result to the terminal.

[0801] Step 8:

[0802] The terminal generates a warning based on the risk information of the generation algorithm and the analysis results of the emotion engine.

[0803] Example: Generate a warning message saying "Your child has received a bullying message."

[0804] Step 9:

[0805] The terminal sends the generated alert to the server.

[0806] Example: Sending a warning message to the server: "Your child has received a bullying message."

[0807] Step 10:

[0808] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[0809] Example: The server sends a warning email or SMS to a parent's contacts stating that "your child has received a bullying message."

[0810] This is the specific processing flow of the system that combines the emotion engine. This makes it possible to detect risks and issue warnings that take into account the emotional state of minors when they encounter dangerous situations on social media.

[0811] Example 2

[0812] 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."

[0813] When minors use social networking services (SNS), they are likely to encounter inappropriate content and harmful messages, which can have serious psychological effects. Furthermore, conventional systems have had difficulty accurately and quickly analyzing SNS data and detecting and notifying users of risks at an early stage. To address this issue, it is urgent to develop an efficient system that collects SNS data in real time, analyzes it, detects risks, and generates warnings. Furthermore, there is a need to improve the accuracy of risk detection by accurately understanding users' emotional states.

[0814] 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.

[0815] In this invention, the server includes a means for a communication terminal to collect data on a social networking service, a means for preprocessing the collected data by normalizing and removing unnecessary symbols, and a means for analyzing the preprocessed data using a generative algorithm and analyzing the emotional state using an emotion engine. This makes it possible to quickly and accurately detect risks and notify appropriate individuals of warnings when a minor encounters inappropriate content on an SNS. The emotion engine also allows for a more accurate understanding of the user's psychological state, enabling more accurate risk assessment.

[0816] Definitions of important words

[0817] "Minor" means a person who has not reached the legal age of majority.

[0818] "Communication terminal" refers to any electronic device that has communication capabilities via an Internet connection, including smartphones and tablets.

[0819] "Social networking services (SNS)" refer to platforms that allow people to interact with each other online. Examples include messaging apps and community sites.

[0820] "Means of collecting data" refers to technology for obtaining messages, posts, comments, etc. sent and received on social media in real time.

[0821] "Normalization" refers to the process of formatting collected text data into a unified format.

[0822] "Unnecessary symbol removal" refers to the process of removing special characters, spaces, etc. that are not required for data analysis.

[0823] "Preprocessing means" refers to a series of processes for formatting collected data into an analyzable format.

[0824] "Generative algorithm" refers to an algorithm that analyzes data and generates a result for a specific purpose, and specifically includes natural language processing algorithms.

[0825] "Emotion engine" refers to technology for analyzing text data to identify a user's emotional state.

[0826] "Means for detecting risk factors" refers to technology that identifies data containing inappropriate content or negative emotions based on analysis results.

[0827] "Means for generating risk information" refers to technology that generates appropriate warnings and notifications based on detected risk factors.

[0828] "Means for generating warnings" refers to techniques for creating appropriate warning messages when a risk is detected.

[0829] "Means of sending to parents" refers to technology that notifies parents of the generated warning message via email, SMS, etc.

[0830] MODE FOR CARRYING OUT THE INVENTION

[0831] This invention is a system for early detection of risks associated with the use of social networking services (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the accuracy of risk detection is improved. Specific embodiments of this system are described below.

[0832] System configuration and operation overview

[0833] The communication terminal is installed on digital devices (smartphones, tablets, etc.) that minors use on a daily basis. This terminal monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0834] collection

[0835] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[0836] Example: A user posts, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[0837] Pretreatment

[0838] The terminal preprocesses the collected data by normalizing it and removing unnecessary symbols.

[0839] Example: If the message "I don't want to go to school anymore!!!" is collected, remove the extra spaces and exclamation marks and format it as "I don't want to go to school anymore".

[0840] analysis

[0841] The generative algorithm analyzes the pre-processed data to detect risk factors, while also analyzing the emotional state of the text using an emotion engine.

[0842] Example: If the message "I wish you'd just disappear" is collected, the generative algorithm will determine this as slander and the emotion engine will detect a negative emotional state.

[0843] Risk Detection

[0844] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[0845] Example: Identified messages are identified as having negative sentiment by the sentiment engine, generating risk information.

[0846] Alert Generation and Notification

[0847] The device generates a warning based on the generated risk information and the results of emotion analysis.

[0848] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0849] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[0850] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0851] Example of operation

[0852] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0853] 1. The terminal collects this message.

[0854] 2. The terminal preprocesses the message and formats it into a parsable format.

[0855] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0856] 4. The emotion engine recognizes the user's negative emotional state.

[0857] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0858] 6. The device sends the alert to the server.

[0859] 7. The server sends a warning message to the parent.

[0860] Prompt Sentence Examples

[0861] When a user sends "I hate you" on social media, the system works as follows:

[0862] 1. The device collects the message.

[0863] 2. Pre-treat.

[0864] 3. Analyze with a generative algorithm.

[0865] 4. Recognize emotional states with the Emotion Engine.

[0866] 5. Generate risk information.

[0867] 6. Generate an alert and send it to the server.

[0868] 7. The server notifies the parent.

[0869] This system will enable parents to quickly receive a warning when a minor encounters a dangerous situation on social media, allowing them to take appropriate action.

[0870] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0871] Program processing steps

[0872] Step 1: Collect data

[0873] Processing flow: The device collects messages and comments sent, received, and posted by users on the social networking site in real time.

[0874] Input: User messages and posts on social media.

[0875] Data processing: Collected messages and posts are stored on the communication device.

[0876] Output: Raw data saved to the device.

[0877] Specific operation: A user posts on social media, "I don't want to go to school anymore." The device immediately collects and saves this post.

[0878] Step 2: Preprocessing the data

[0879] Processing flow: The terminal performs preprocessing on the collected data to make it easier to analyze, including text normalization and removal of unnecessary symbols.

[0880] Input: Collected raw data on social media.

[0881] Data processing: Text normalization (combining multiple whitespaces, removing special characters, etc.).

[0882] Output: Preprocessed and clean data.

[0883] Specific behavior: The device normalizes the post "I don't want to go to school anymore!!!" to "I don't want to go to school anymore" and removes unnecessary symbols.

[0884] Step 3: Analyze the data

[0885] Processing flow: The generative algorithm analyzes the preprocessed data and evaluates the user's emotional state using the emotion engine.

[0886] Input: Preprocessed clean data.

[0887] Data processing / data calculation: Generative algorithms analyze data using natural language processing techniques to assess risk factors. Emotion engines analyze the emotional state of the data.

[0888] Output: Analysis results (risk factors such as slander, stress, etc.).

[0889] How it works: The generative algorithm analyzes posts like "I don't want to go to school anymore" to detect negative emotional states, while the emotion engine evaluates the stress level felt by the user.

[0890] Step 4: Risk detection

[0891] Process flow: The generation algorithm detects risk factors from user posts and messages, and integrates them with the analysis results of the emotion engine to generate risk information.

[0892] Input: Analysis results of the generative algorithm and emotion engine.

[0893] Data processing: Integrate risk factors and emotional states to generate risk information.

[0894] Output: The generated risk information.

[0895] How it works: The generation algorithm identifies posts such as "I don't want to go to school anymore" as a sign of bullying, and the emotion engine detects high stress levels. These are then combined to generate "high risk" information.

[0896] Step 5: Alert Generation and Notification

[0897] Process flow: The device generates a warning based on the generated risk information and the results of emotion analysis, and sends it to the server. The server then sends a warning message to the parent.

[0898] Input: Generated risk information.

[0899] Data processing: Create warning messages based on risk information.

[0900] Output: Warning message sent to parents.

[0901] Specific operation: The device generates a warning saying "Your child has received a bullying message and is feeling very stressed" and sends it to the server. The server then sends the warning to the parent via email or SMS to notify them promptly.

[0902] This system makes it possible to quickly send a warning to parents when a user encounters a dangerous situation on social media, urging them to take appropriate action.

[0903] (Application example 2)

[0904] 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."

[0905] In addition to the challenge of early detection of risks (especially slander and bullying) associated with the use of social networking sites by minors and preventing them from becoming involved in crime, it is also necessary to further improve safety in self-driving vehicles. It is also necessary to recognize user emotions and achieve more accurate risk detection and warning generation.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes means for the communication terminal to collect data on social networking services, means for using a generation algorithm to analyze the collected data, means for recognizing the emotions of passengers using an emotion engine, means for detecting risk factors based on the analysis results and the emotional state, means for generating a warning based on the risk factors and the emotional state, and means for sending the generated warning to a guardian. This makes it possible to detect risks with high accuracy even when a minor uses SNS in an autonomous vehicle, and to quickly send a warning to the guardian.

[0908] "Communication devices" are electronic devices such as smartphones and tablets that are used on a daily basis by minors.

[0909] A "social networking service" is an Internet service that enables users to connect with other users online and share messages and content.

[0910] A "generative algorithm" is an algorithm used to analyze collected data and detect risk factors.

[0911] An "emotion engine" is a technology that recognizes a user's emotional state from the text and actions they enter.

[0912] "Risk factors" are elements that could potentially cause dangerous situations for users, such as defamation or bullying.

[0913] An "alert" is a warning message that is generated when a risk factor is detected.

[0914] A "guardian" is a supervisor appointed to protect the safety of minor users.

[0915] "Real-time" refers to the ability to process and analyze data as soon as it is generated.

[0916] "Preprocessing" is the process of formatting collected data into a format that is easy to analyze.

[0917] "Natural language processing algorithms" are computational techniques used to understand and analyze human language.

[0918] "Passenger vehicle" refers to an autonomous vehicle for passengers to ride in.

[0919] This invention is a system for early detection of risks associated with the use of social networking sites (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. It also aims to improve the safety of minors when riding in self-driving vehicles. Specific embodiments of this invention are described below.

[0920] System configuration and operation overview

[0921] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[0922] collection

[0923] The server collects data from the communication device on the SNS in real time, including sent messages, received messages, posts, comments, etc.

[0924] Example: A user posts on a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[0925] Pretreatment

[0926] The collected data is preprocessed on the communication terminal, which is a process that normalizes the text, removes unnecessary symbols, etc., to prepare it for analysis.

[0927] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[0928] analysis

[0929] A generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[0930] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[0931] Risk Detection

[0932] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the communication terminal.

[0933] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[0934] Alert Generation and Notification

[0935] The server generates a warning based on the generated risk information and the results of sentiment analysis.

[0936] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[0937] The server notifies the parent of the generated alert.

[0938] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[0939] Hardware and software used

[0940] The system uses the following hardware and software:

[0941] Hardware: Smartphones, tablets, and in-vehicle systems for autonomous vehicles

[0942] Software: generative algorithms (e.g., natural language processing algorithms), emotion engines (e.g., EmotionEngine)

[0943] Specific examples

[0944] When a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[0945] 1. The communication terminal collects this message.

[0946] 2. The communication device preprocesses the message and formats it into a parsable format.

[0947] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[0948] 4. The emotion engine recognizes the user's negative emotional state.

[0949] 5. The server integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[0950] 6. The server sends the generated alert to the parent.

[0951] Example prompts to be input to the generative AI model

[0952] Analyze users' SNS messages and detect safety risks. For the following messages, determine whether they are high-risk and have negative sentiment and generate a warning message.

[0953] Message: I don't want to go to school anymore

[0954] In this way, if a minor encounters a dangerous situation or negative emotions on social media while in an autonomous vehicle, parents can be quickly warned and supported in taking appropriate action.

[0955] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0956] Step 1:

[0957] The device collects data on social media.

[0958] Input: Messages and posts sent or received by users on social media.

[0959] Processing: The device monitors these messages and posts in real time and collects data.

[0960] Output: Collected social media data.

[0961] Operation: For example, if a user posts, "I'm looking forward to making plans to hang out with my friends," the device collects that posting data.

[0962] Step 2:

[0963] The terminal preprocesses the collected data.

[0964] Input: Social media data collected in step 1.

[0965] Processing: Normalize the collected data and remove unnecessary symbols and extra spaces.

[0966] Output: Normalized text data.

[0967] Action: For example, format a post like "I'm looking forward to hanging out with my friends!!!" into "I'm looking forward to hanging out with my friends."

[0968] Step 3:

[0969] The terminal analyzes the preprocessed data using the generation algorithm.

[0970] Input: The text data preprocessed in step 2.

[0971] Processing: Natural language processing algorithms are used to detect risk factors in the text.

[0972] Output: Risk factor analysis results.

[0973] Action: For example, the message "I wish you'd just disappear" is considered defamatory.

[0974] Step 4:

[0975] The terminal uses an emotion engine to analyze the user's emotional state.

[0976] Input: The text data preprocessed in step 2.

[0977] Processing: The text data is analyzed by the emotion engine to recognize the user's emotional state.

[0978] Output: Emotional state analysis result.

[0979] How it works: For example, detecting a negative emotional state from a message like "I don't want to go to school anymore."

[0980] Step 5:

[0981] The server integrates the results of the generation algorithm and the emotion engine to generate risk information.

[0982] Input: Analysis results from Step 3 and Step 4.

[0983] Processing: Integrate risk factors and emotional states to generate risk information.

[0984] Output: Risk information.

[0985] Behavior: For example, the message "I wish you'd just disappear" combined with a negative emotional state could be considered "high risk."

[0986] Step 6:

[0987] The server generates a warning based on the risk information.

[0988] Input: Risk information generated in Step 5.

[0989] Action: Generate a warning message based on the risk information.

[0990] Output: A warning message.

[0991] What it does: For example, generate a warning that says, "Your child has received bullying messages and is feeling very stressed."

[0992] Step 7:

[0993] The server generates and sends the alert to the parent.

[0994] Input: The warning message generated in step 6.

[0995] Action: Send a warning message to parents via email and / or SMS.

[0996] Output: The warning message sent.

[0997] What it does: For example, an email is sent to a parent's email address stating, "Your child has received a bullying message and is feeling very stressed."

[0998] By following these steps, even if a minor encounters a dangerous situation on social media, parents can be quickly notified and prompted to take appropriate action.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] [Third embodiment]

[1003] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1004] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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).

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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."

[1015] This invention provides a system for detecting risks associated with the use of social networking services (SNS) at an early stage on a communication device used by minors, and for preventing minors from becoming involved in crime. Specific embodiments of this system are described below.

[1016] System configuration and operation overview

[1017] The communication devices are installed on smartphones, tablets, and other electronic devices commonly used by minors, and monitor text messages and posts in real time whenever a user uses a social networking site.

[1018] collection

[1019] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1020] Example: A user posts "I want to make new friends" in a messaging app. This post is collected by the device.

[1021] Pretreatment

[1022] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[1023] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1024] analysis

[1025] Generative algorithms analyze the collected data and detect risk factors, while natural language processing algorithms are used to understand the meaning and context of the text.

[1026] Example: If there is a message such as "You shouldn't come to school," the generation algorithm will determine this as bullying.

[1027] Risk Detection

[1028] If the generation algorithm detects a risk factor, it returns that information to the terminal.

[1029] Example: When a message containing personal information or a suspicious solicitation message is detected, risk information is returned to the device.

[1030] Alert Generation and Notification

[1031] The device generates appropriate warnings based on the detected risk information.

[1032] For example, generate a warning that reads, "Your child has received an inappropriate message."

[1033] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1034] Example: The server sends warning messages via email and SMS based on the parent's contact information.

[1035] Example of operation

[1036] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[1037] 1. The terminal collects this message.

[1038] 2. The terminal preprocesses the message and formats it into a parsable format.

[1039] 3. The generation algorithm analyzes the preprocessed messages to detect lure risks.

[1040] 4. The device generates a warning based on the detected risk information.

[1041] 5. The device sends the alert to the server.

[1042] 6. The server sends a warning message to the parent.

[1043] In this way, when a minor encounters a dangerous situation on social media, parents can be quickly warned and helped to take appropriate action.

[1044] The processing flow will be explained below.

[1045] Step 1:

[1046] The device collects all posts and messages made by users on social media in real time.

[1047] Example: When a user posts on a social networking site, "I'm planning to hang out with my friends tomorrow," the device collects that post.

[1048] Step 2:

[1049] The terminal pre-processes the collected data.

[1050] For example: normalization converts uppercase to lowercase, removes unnecessary symbols, and also removes extra spaces.

[1051] Step 3:

[1052] The terminal passes the preprocessed data to the generation algorithm.

[1053] Example: The preprocessed text "I'm going to hang out with my friends tomorrow" is sent to the generative algorithm.

[1054] Step 4:

[1055] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[1056] Example: The text "You shouldn't come to school" is collected and a generative algorithm identifies this message as a bullying risk.

[1057] Step 5:

[1058] If the generation algorithm detects a risk factor, it generates risk information and returns it to the terminal.

[1059] Example: A generative algorithm identifies the text "You shouldn't come to school" and creates risk information based on it.

[1060] Step 6:

[1061] The terminal generates a warning based on the risk information.

[1062] Example: If a bullying risk is detected, generate a warning saying "Your child has received a bullying message."

[1063] Step 7:

[1064] The terminal sends the generated alert to the server.

[1065] Example: An alert is sent to the server saying "Your child has received a bullying message."

[1066] Step 8:

[1067] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[1068] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message."

[1069] The above is a detailed processing flow of the system that detects risks early on when minors interact with social media and notifies parents.

[1070] Example 1

[1071] 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."

[1072] In today's world, the risk of minors becoming involved in crimes or inappropriate behavior when using social networking services (SNS) is extremely high. There are many potential dangers, particularly bullying, leaks of personal information, and even kidnapping and fraud. While a system to protect minors from these risks is necessary, the current situation is one in which the safety of minors cannot be ensured due to a lack of adequate systems for real-time monitoring, early detection of risks, and prompt notification to parents.

[1073] 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.

[1074] In this invention, the server

[1075] A means for the communication terminal to collect data on social networking services in real time;

[1076] means for pre-processing the collected data;

[1077] means for using a generative AI model to analyze the preprocessed data;

[1078] A means for detecting risk factors based on analysis results using the generative AI model;

[1079] means for generating an alert upon detection of said risk factor;

[1080] means for using a server to send said alert to the parent;

[1081] means for the server to send a warning message based on the parent's contact information;

[1082] This will enable early detection of risks when minors use SNS and prompt notification to parents.

[1083] 1. "Communication devices" are electronic devices such as smartphones and tablets used by minors.

[1084] 2. "Social Networking Service" means a web service that enables users to interact and share information with other users online.

[1085] 3. "Means of collection" refers to the function of a communication device to obtain data in real time on a social networking service.

[1086] 4. "Preprocessing means" refers to functions that normalize collected data, remove unnecessary symbols, and prepare it for analysis.

[1087] 5. "Generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze collected data and detect risks.

[1088] 6. "Natural language processing algorithm" is an analytical technology used to understand the meaning and context of collected text data.

[1089] 7. "Risk factors" refer to factors that may pose risks such as bullying, leakage of personal information, kidnapping, and fraud.

[1090] 8. "Means for generating warnings" refers to a function that generates a warning message indicating that a risk factor has been detected.

[1091] 9. "Server" means a computer system for sending warning messages to parents.

[1092] 10. "Means of sending" refers to the function of sending the generated alert via email or SMS based on the parent's contact information.

[1093] This invention provides a system for detecting risks associated with the use of social networking services (SNS) through communication terminals used by minors at an early stage and preventing minors from becoming involved in crime. Specific embodiments will be described below.

[1094] Hardware and software used

[1095] 1. Hardware

[1096] Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors

[1097] Server: A computer system for sending warning messages to parents.

[1098] 2. Software

[1099] Generative AI model: Natural language processing algorithms using machine learning algorithms

[1100] Data processing and calculation

[1101] 1. Data collection

[1102] The device collects social networking data in real time, including sent and received messages, posts, and comments.

[1103] Example: A user posts on a social networking site, "I want to make new friends." This post is collected by the device.

[1104] 2. Pretreatment

[1105] The terminal preprocesses the collected data, which includes normalizing the text and removing unnecessary symbols.

[1106] Example: If a user receives "See you tomorrow!", remove any extra spaces and special characters such as exclamation points.

[1107] 3. Data Analysis

[1108] The generative AI model analyzes the pre-processed data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[1109] Example: If a user receives a message that says "You shouldn't come to school," it is determined that this contains bullying content.

[1110] 4. Risk factor detection

[1111] When the generative AI model detects risk factors, it returns that information to the device.

[1112] Example: If a message containing personal information or a suspicious solicitation message is detected, that information will be sent back to the device.

[1113] 5. Alert Generation and Notification

[1114] The device generates a warning based on the detected risk information.

[1115] Example: Generating a warning that says "Your child has received an inappropriate message."

[1116] The terminal transmits the generated warning to the server, and the server notifies the parent of the warning message.

[1117] Example: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[1118] Specific examples and prompts

[1119] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[1120] 1. The device collects this message in real time.

[1121] 2. The terminal preprocesses the message, removing any extra spaces or symbols.

[1122] 3. The generative AI model analyzes the preprocessed message and determines that "Shall we meet?" poses a risk of solicitation.

[1123] 4. The device generates a warning stating "An enticement message has been detected" based on the risk information detected.

[1124] 5. The device sends a warning to the server, and the server sends a warning message to the parent via email or SMS stating that "your child has received an invitation message."

[1125] Prompt Sentence Examples

[1126] Input: A user receives a message on social media saying, "Shall we meet tomorrow?"

[1127] Task: Understand the meaning and context of this message and determine whether it contains any risk elements.

[1128] Expected Output: This message contains an exfiltration risk and should be flagged with a warning.

[1129] This will enable early detection of risks when minors use social networking sites and prompt notification to parents, helping to ensure safety.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1: Collect data

[1132] The device collects data on social networking services in real time, including sent and received messages, posts, and comments.

[1133] Input: Text data from a social networking service

[1134] Output: Collected text data

[1135] Specific operation: When a user posts on a social networking site that they want to make new friends, this post is collected in real time.

[1136] Step 2: Preprocessing the data

[1137] The terminal preprocesses the collected text data, which includes normalizing the text and removing unnecessary symbols.

[1138] Input: Collected text data

[1139] Output: Preprocessed text data

[1140] Specific behavior: If a user receives "See you tomorrow!", remove extra spaces and special characters such as exclamation marks from the text.

[1141] Step 3: Analyze the data

[1142] The device uses a generative AI model to analyze the pre-processed text data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[1143] Input: Preprocessed text data

[1144] Output: Analysis results (risk assessment)

[1145] Specific behavior: Analyzes messages such as "You shouldn't come to school" and determines that they contain bullying content.

[1146] Step 4: Identifying risk factors

[1147] The generative AI model detects risk factors based on the analysis results and returns the detection results to the device.

[1148] Input: Analysis results

[1149] Output: Risk factor information

[1150] Specific operation: Detects messages containing personal information or suspicious solicitation messages and sends that information back to the device.

[1151] Step 5: Generate warnings

[1152] The device generates a warning based on the detected risk information.

[1153] Input: Risk factor information

[1154] Output: Warning message

[1155] What it does: Generates the warning "Your child received an inappropriate message."

[1156] Step 6: Warning Notification

[1157] The terminal sends the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1158] Input: warning message

[1159] Output: Parental notification

[1160] What it does: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[1161] (Application example 1)

[1162] 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."

[1163] Currently, when minors use social networking services (SNS), they are likely to be exposed to risks such as bullying and solicitation. However, many parents do not have the means to grasp these risks in real time. This increases the risk of minors becoming involved in crime. The object of this invention is to provide a system that uses communication devices used by minors to quickly detect risks on SNS and issue prompt and accurate warnings to parents.

[1164] 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.

[1165] In this invention, the server includes means for collecting data on social networking services from communication terminals used by minors, means for preprocessing the collected data, means for using a natural language processing algorithm to analyze the collected data, means for detecting risk factors based on the analysis results, means for generating a warning when the risk factor is detected, and means for sending the warning to a guardian. This makes it possible to quickly notify a guardian when a minor encounters a dangerous situation on a social networking service and take appropriate action.

[1166] "Minors" refers to young people who have not yet reached legal adulthood.

[1167] "Communication terminal" is a general term for devices capable of voice and data communication, including smartphones, tablets, and other electronic devices that can connect to the Internet.

[1168] A "social networking service" is a platform for users to communicate with each other over the Internet, where information such as messages, posts, and comments is shared.

[1169] "Data" is a general term for information collected by a communication device, including text messages, sent and received messages, posts, comments, etc.

[1170] "Preprocessing" is the process of converting collected data into an analyzable format, and refers to operations such as normalizing text and removing unnecessary symbols.

[1171] "Natural language processing algorithms" are algorithms that allow computers to understand and analyze human language, and are a technology used to understand the meaning and context of text.

[1172] "Risk factors" refer to elements on social media that could lead minors to become involved in crimes or trouble, including bullying, luring, and leaking of personal information.

[1173] A "warning" is a notification message that is generated when a risk factor is detected, and is intended to inform parents and other relevant parties of the risk.

[1174] "Guardian" refers to a parent or someone in a similar position who has custody of a minor.

[1175] "Server" refers to a computer system for storing, processing, and managing data over a network.

[1176] The present invention provides a system for enabling minors to safely use social networking services (hereinafter referred to as SNS) on communication terminals. Detailed embodiments of this system will be described below.

[1177] System Overview

[1178] The system aims to detect risk factors early on when minors use social networking sites and send warnings to parents. The system mainly consists of the following components:

[1179] 1. Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors.

[1180] 2. Server: A computer system for managing data storage, processing, and sending alert messages.

[1181] Hardware and Software Configuration

[1182] Hardware:

[1183] Communication devices: smartphones, tablets

[1184] Server: A general internet-connected computer system

[1185] software:

[1186] Natural language processing library: TextBlob (for text analysis)

[1187] Email sending library: smtplib (for sending warning messages)

[1188] Data collection and preprocessing

[1189] The device collects data such as messages, posts, and comments sent and received by users on social media in real time. The collected data is then preprocessed to make it analyzable. Specifically, text is normalized and unnecessary symbols are removed.

[1190] Data analysis and risk detection

[1191] The preprocessed data is then analyzed using natural language processing (NLP) algorithms. Using a NLP library such as TextBlob, the text is subjected to sentiment analysis. Messages with strong negative sentiment (e.g., "Don't come to my school") are then detected as risk factors.

[1192] Alert generation and notification

[1193] If a risk factor is detected, the device generates a warning message and sends it to the server. The server then sends this warning to the parent's contact information (e.g., email address), allowing parents to respond quickly when their minor encounters a dangerous situation on social media.

[1194] Examples of specific examples and prompts

[1195] Specific examples

[1196] Example: If a user receives the message "Don't come to school," the following happens:

[1197] The device collects this message

[1198] Preprocessing to remove extra spaces and special characters

[1199] Natural language processing algorithms analyze messages and detect negative sentiment

[1200] When a risk factor is detected, a warning message is generated and sent to the server.

[1201] The server sends a warning email to the parent.

[1202] Prompt sentence for generative AI model

[1203] Evaluate the following message and determine if it poses a risk: "You shouldn't come to school."

[1204] Negative Emotion: True

[1205] Risky message: Don't come to school

[1206] In this way, a system is provided that can quickly notify parents and guardians when a minor encounters a dangerous situation on an SNS and take appropriate action.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1: The device collects messages on the social networking site.

[1209] Input: Messages, comments, and posts that users send or receive on social media.

[1210] How it works: The device uses the API of the social networking application to collect this data in real time.

[1211] Output: Raw data collected (messages, comments, posts)

[1212] Step 2: Preprocess the data collected by the device

[1213] Input: Raw data collected

[1214] What it does: The terminal preprocesses and normalizes the text data, specifically removing extra spaces and special characters.

[1215] Output: Preprocessed text data

[1216] Step 3: The device analyzes the preprocessed data using natural language processing algorithms.

[1217] Input: Preprocessed text data

[1218] How it works: The device uses a natural language processing library such as TextBlob to perform sentiment analysis on the text data, calculating a sentiment score for each message.

[1219] Output: Analysis results (text data with sentiment scores)

[1220] Step 4: The device detects risk factors based on the analysis results

[1221] Input: Text data with sentiment scores

[1222] Operation: The device evaluates the analysis results and determines messages with negative emotions above a certain threshold as risk factors. For example, if the emotion score is below -0.5, it is a risk factor.

[1223] Output: Messages determined to be risk factors

[1224] Step 5: Generate a warning message when the device detects a risk factor

[1225] Input: Message determined to be a risk factor

[1226] Operation: The device generates a warning message to send to the parent. Specifically, it constructs the warning message based on the content of the message that was determined to be a risk factor.

[1227] Output: Warning message

[1228] Step 6: Server sends alert to parent

[1229] Input: warning message

[1230] How it works: When a warning message is sent from the device to the server, the server will send a warning message via email or SMS based on the parent's contact information.

[1231] Output: Notification of completion of sending warning message to parents

[1232] 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.

[1233] This invention is a system for early detection of risks associated with the use of social networking sites on communication devices used by minors, preventing them from becoming involved in crimes, and further improves the accuracy of risk detection by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1234] System configuration and operation overview

[1235] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1236] collection

[1237] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1238] Example: A user posts in a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by the device.

[1239] Pretreatment

[1240] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[1241] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1242] analysis

[1243] The generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[1244] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[1245] Risk Detection

[1246] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[1247] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[1248] Alert Generation and Notification

[1249] The device generates a warning based on the generated risk information and the results of emotion analysis.

[1250] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1251] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1252] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1253] Example of operation

[1254] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1255] 1. The terminal collects this message.

[1256] 2. The terminal preprocesses the message and formats it into a parsable format.

[1257] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1258] 4. The emotion engine recognizes the user's negative emotional state.

[1259] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1260] 6. The device sends the alert to the server.

[1261] 7. The server sends a warning message to the parent.

[1262] In this way, when a minor encounters a dangerous situation or negative emotions on social media, the system can quickly warn parents and help them take appropriate action.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] The device collects all posts and messages made by users on social media in real time.

[1266] Example: A user sends a message to a friend on a social networking site saying, "I want a new game." This message is collected by the device.

[1267] Step 2:

[1268] The terminal pre-processes the collected data.

[1269] Example: Converting messages to uppercase and lowercase, removing unnecessary symbols, and arranging them into a unified format.

[1270] Step 3:

[1271] The terminal sends the pre-processed data to the generating algorithm.

[1272] Example: The preprocessed text "I want a new game" is sent to a generative algorithm.

[1273] Step 4:

[1274] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[1275] Example: A generative algorithm analyzes the message "I wish you were gone" and determines that it is potentially defamatory.

[1276] Step 5:

[1277] The generative algorithm detects risk factors and sends the data to the emotion engine.

[1278] Example: The message "I wish you'd just disappear" detected by the generative algorithm is passed to the emotion engine.

[1279] Step 6:

[1280] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1281] Example: The emotion engine determines that the sender's emotion in response to the message "I wish you were gone" is negative.

[1282] Step 7:

[1283] The emotion engine returns the recognized emotion information to the terminal.

[1284] Example: Returning a negative sentiment result to the terminal.

[1285] Step 8:

[1286] The terminal generates a warning based on the risk information of the generation algorithm and the analysis results of the emotion engine.

[1287] Example: Generate a warning message saying "Your child has received a bullying message."

[1288] Step 9:

[1289] The terminal sends the generated alert to the server.

[1290] Example: Sending a warning message to the server: "Your child has received a bullying message."

[1291] Step 10:

[1292] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[1293] Example: The server sends a warning email or SMS to a parent's contacts stating that "your child has received a bullying message."

[1294] This is the specific processing flow of the system that combines the emotion engine. This makes it possible to detect risks and issue warnings that take into account the emotional state of minors when they encounter dangerous situations on social media.

[1295] Example 2

[1296] 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."

[1297] When minors use social networking services (SNS), they are likely to encounter inappropriate content and harmful messages, which can have serious psychological effects. Furthermore, conventional systems have had difficulty accurately and quickly analyzing SNS data and detecting and notifying users of risks at an early stage. To address this issue, it is urgent to develop an efficient system that collects SNS data in real time, analyzes it, detects risks, and generates warnings. Furthermore, there is a need to improve the accuracy of risk detection by accurately understanding users' emotional states.

[1298] 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.

[1299] In this invention, the server includes a means for a communication terminal to collect data on a social networking service, a means for preprocessing the collected data by normalizing and removing unnecessary symbols, and a means for analyzing the preprocessed data using a generative algorithm and analyzing the emotional state using an emotion engine. This makes it possible to quickly and accurately detect risks and notify appropriate individuals of warnings when a minor encounters inappropriate content on an SNS. The emotion engine also allows for a more accurate understanding of the user's psychological state, enabling more accurate risk assessment.

[1300] Definitions of important words

[1301] "Minor" means a person who has not reached the legal age of majority.

[1302] "Communication terminal" refers to any electronic device that has communication capabilities via an Internet connection, including smartphones and tablets.

[1303] "Social networking services (SNS)" refer to platforms that allow people to interact with each other online. Examples include messaging apps and community sites.

[1304] "Means of collecting data" refers to technology for obtaining messages, posts, comments, etc. sent and received on social media in real time.

[1305] "Normalization" refers to the process of formatting collected text data into a unified format.

[1306] "Unnecessary symbol removal" refers to the process of removing special characters, spaces, etc. that are not required for data analysis.

[1307] "Preprocessing means" refers to a series of processes for formatting collected data into an analyzable format.

[1308] "Generative algorithm" refers to an algorithm that analyzes data and generates a result for a specific purpose, and specifically includes natural language processing algorithms.

[1309] "Emotion engine" refers to technology for analyzing text data to identify a user's emotional state.

[1310] "Means for detecting risk factors" refers to technology that identifies data containing inappropriate content or negative emotions based on analysis results.

[1311] "Means for generating risk information" refers to technology that generates appropriate warnings and notifications based on detected risk factors.

[1312] "Means for generating warnings" refers to techniques for creating appropriate warning messages when a risk is detected.

[1313] "Means of sending to parents" refers to technology that notifies parents of the generated warning message via email, SMS, etc.

[1314] MODE FOR CARRYING OUT THE INVENTION

[1315] This invention is a system for early detection of risks associated with the use of social networking services (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the accuracy of risk detection is improved. Specific embodiments of this system are described below.

[1316] System configuration and operation overview

[1317] The communication terminal is installed on digital devices (smartphones, tablets, etc.) that minors use on a daily basis. This terminal monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1318] collection

[1319] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1320] Example: A user posts, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[1321] Pretreatment

[1322] The terminal preprocesses the collected data by normalizing it and removing unnecessary symbols.

[1323] Example: If the message "I don't want to go to school anymore!!!" is collected, remove the extra spaces and exclamation marks and format it as "I don't want to go to school anymore".

[1324] analysis

[1325] The generative algorithm analyzes the pre-processed data to detect risk factors, while also analyzing the emotional state of the text using an emotion engine.

[1326] Example: If the message "I wish you'd just disappear" is collected, the generative algorithm will determine this as slander and the emotion engine will detect a negative emotional state.

[1327] Risk Detection

[1328] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[1329] Example: Identified messages are identified as having negative sentiment by the sentiment engine, generating risk information.

[1330] Alert Generation and Notification

[1331] The device generates a warning based on the generated risk information and the results of emotion analysis.

[1332] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1333] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1334] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1335] Example of operation

[1336] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1337] 1. The terminal collects this message.

[1338] 2. The terminal preprocesses the message and formats it into a parsable format.

[1339] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1340] 4. The emotion engine recognizes the user's negative emotional state.

[1341] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1342] 6. The device sends the alert to the server.

[1343] 7. The server sends a warning message to the parent.

[1344] Prompt Sentence Examples

[1345] When a user sends "I hate you" on social media, the system works as follows:

[1346] 1. The device collects the message.

[1347] 2. Pre-treat.

[1348] 3. Analyze with a generative algorithm.

[1349] 4. Recognize emotional states with the Emotion Engine.

[1350] 5. Generate risk information.

[1351] 6. Generate an alert and send it to the server.

[1352] 7. The server notifies the parent.

[1353] This system will enable parents to quickly receive a warning when a minor encounters a dangerous situation on social media, allowing them to take appropriate action.

[1354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1355] Program processing steps

[1356] Step 1: Collect data

[1357] Processing flow: The device collects messages and comments sent, received, and posted by users on the social networking site in real time.

[1358] Input: User messages and posts on social media.

[1359] Data processing: Collected messages and posts are stored on the communication device.

[1360] Output: Raw data saved to the device.

[1361] Specific operation: A user posts on social media, "I don't want to go to school anymore." The device immediately collects and saves this post.

[1362] Step 2: Preprocessing the data

[1363] Processing flow: The terminal performs preprocessing on the collected data to make it easier to analyze, including text normalization and removal of unnecessary symbols.

[1364] Input: Collected raw data on social media.

[1365] Data processing: Text normalization (combining multiple whitespaces, removing special characters, etc.).

[1366] Output: Preprocessed and clean data.

[1367] Specific behavior: The device normalizes the post "I don't want to go to school anymore!!!" to "I don't want to go to school anymore" and removes unnecessary symbols.

[1368] Step 3: Analyze the data

[1369] Processing flow: The generative algorithm analyzes the preprocessed data and evaluates the user's emotional state using the emotion engine.

[1370] Input: Preprocessed clean data.

[1371] Data processing / data calculation: Generative algorithms analyze data using natural language processing techniques to assess risk factors. Emotion engines analyze the emotional state of the data.

[1372] Output: Analysis results (risk factors such as slander, stress, etc.).

[1373] How it works: The generative algorithm analyzes posts like "I don't want to go to school anymore" to detect negative emotional states, while the emotion engine evaluates the stress level felt by the user.

[1374] Step 4: Risk detection

[1375] Process flow: The generation algorithm detects risk factors from user posts and messages, and integrates them with the analysis results of the emotion engine to generate risk information.

[1376] Input: Analysis results of the generative algorithm and emotion engine.

[1377] Data processing: Integrate risk factors and emotional states to generate risk information.

[1378] Output: The generated risk information.

[1379] How it works: The generation algorithm identifies posts such as "I don't want to go to school anymore" as a sign of bullying, and the emotion engine detects high stress levels. These are then combined to generate "high risk" information.

[1380] Step 5: Alert Generation and Notification

[1381] Process flow: The device generates a warning based on the generated risk information and the results of emotion analysis, and sends it to the server. The server then sends a warning message to the parent.

[1382] Input: Generated risk information.

[1383] Data processing: Create warning messages based on risk information.

[1384] Output: Warning message sent to parents.

[1385] Specific operation: The device generates a warning saying "Your child has received a bullying message and is feeling very stressed" and sends it to the server. The server then sends the warning to the parent via email or SMS to notify them promptly.

[1386] This system makes it possible to quickly send a warning to parents when a user encounters a dangerous situation on social media, urging them to take appropriate action.

[1387] (Application example 2)

[1388] 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."

[1389] In addition to the challenge of early detection of risks (especially slander and bullying) associated with the use of social networking sites by minors and preventing them from becoming involved in crime, it is also necessary to further improve safety in self-driving vehicles. It is also necessary to recognize user emotions and achieve more accurate risk detection and warning generation.

[1390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1391] In this invention, the server includes means for the communication terminal to collect data on social networking services, means for using a generation algorithm to analyze the collected data, means for recognizing the emotions of passengers using an emotion engine, means for detecting risk factors based on the analysis results and the emotional state, means for generating a warning based on the risk factors and the emotional state, and means for sending the generated warning to a guardian. This makes it possible to detect risks with high accuracy even when a minor uses SNS in an autonomous vehicle, and to quickly send a warning to the guardian.

[1392] "Communication devices" are electronic devices such as smartphones and tablets that are used on a daily basis by minors.

[1393] A "social networking service" is an Internet service that enables users to connect with other users online and share messages and content.

[1394] A "generative algorithm" is an algorithm used to analyze collected data and detect risk factors.

[1395] An "emotion engine" is a technology that recognizes a user's emotional state from the text and actions they enter.

[1396] "Risk factors" are elements that could potentially cause dangerous situations for users, such as defamation or bullying.

[1397] An "alert" is a warning message that is generated when a risk factor is detected.

[1398] A "guardian" is a supervisor appointed to protect the safety of minor users.

[1399] "Real-time" refers to the ability to process and analyze data as soon as it is generated.

[1400] "Preprocessing" is the process of formatting collected data into a format that is easy to analyze.

[1401] "Natural language processing algorithms" are computational techniques used to understand and analyze human language.

[1402] "Passenger vehicle" refers to an autonomous vehicle for passengers to ride in.

[1403] This invention is a system for early detection of risks associated with the use of social networking sites (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. It also aims to improve the safety of minors when riding in self-driving vehicles. Specific embodiments of this invention are described below.

[1404] System configuration and operation overview

[1405] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1406] collection

[1407] The server collects data from the communication device on the SNS in real time, including sent messages, received messages, posts, comments, etc.

[1408] Example: A user posts on a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[1409] Pretreatment

[1410] The collected data is preprocessed on the communication terminal, which is a process that normalizes the text, removes unnecessary symbols, etc., to prepare it for analysis.

[1411] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1412] analysis

[1413] A generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[1414] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[1415] Risk Detection

[1416] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the communication terminal.

[1417] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[1418] Alert Generation and Notification

[1419] The server generates a warning based on the generated risk information and the results of sentiment analysis.

[1420] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1421] The server notifies the parent of the generated alert.

[1422] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1423] Hardware and software used

[1424] The system uses the following hardware and software:

[1425] Hardware: Smartphones, tablets, and in-vehicle systems for autonomous vehicles

[1426] Software: generative algorithms (e.g., natural language processing algorithms), emotion engines (e.g., EmotionEngine)

[1427] Specific examples

[1428] When a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1429] 1. The communication terminal collects this message.

[1430] 2. The communication device preprocesses the message and formats it into a parsable format.

[1431] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1432] 4. The emotion engine recognizes the user's negative emotional state.

[1433] 5. The server integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1434] 6. The server sends the generated alert to the parent.

[1435] Example prompts to be input to the generative AI model

[1436] Analyze users' SNS messages and detect safety risks. For the following messages, determine whether they are high-risk and have negative sentiment and generate a warning message.

[1437] Message: I don't want to go to school anymore

[1438] In this way, if a minor encounters a dangerous situation or negative emotions on social media while in an autonomous vehicle, parents can be quickly warned and supported in taking appropriate action.

[1439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1440] Step 1:

[1441] The device collects data on social media.

[1442] Input: Messages and posts sent or received by users on social media.

[1443] Processing: The device monitors these messages and posts in real time and collects data.

[1444] Output: Collected social media data.

[1445] Operation: For example, if a user posts, "I'm looking forward to making plans to hang out with my friends," the device collects that posting data.

[1446] Step 2:

[1447] The terminal preprocesses the collected data.

[1448] Input: Social media data collected in step 1.

[1449] Processing: Normalize the collected data and remove unnecessary symbols and extra spaces.

[1450] Output: Normalized text data.

[1451] Action: For example, format a post like "I'm looking forward to hanging out with my friends!!!" into "I'm looking forward to hanging out with my friends."

[1452] Step 3:

[1453] The terminal analyzes the preprocessed data using the generation algorithm.

[1454] Input: The text data preprocessed in step 2.

[1455] Processing: Natural language processing algorithms are used to detect risk factors in the text.

[1456] Output: Risk factor analysis results.

[1457] Action: For example, the message "I wish you'd just disappear" is considered defamatory.

[1458] Step 4:

[1459] The terminal uses an emotion engine to analyze the user's emotional state.

[1460] Input: The text data preprocessed in step 2.

[1461] Processing: The text data is analyzed by the emotion engine to recognize the user's emotional state.

[1462] Output: Emotional state analysis result.

[1463] How it works: For example, detecting a negative emotional state from a message like "I don't want to go to school anymore."

[1464] Step 5:

[1465] The server integrates the results of the generation algorithm and the emotion engine to generate risk information.

[1466] Input: Analysis results from Step 3 and Step 4.

[1467] Processing: Integrate risk factors and emotional states to generate risk information.

[1468] Output: Risk information.

[1469] Behavior: For example, the message "I wish you'd just disappear" combined with a negative emotional state could be considered "high risk."

[1470] Step 6:

[1471] The server generates a warning based on the risk information.

[1472] Input: Risk information generated in Step 5.

[1473] Action: Generate a warning message based on the risk information.

[1474] Output: A warning message.

[1475] What it does: For example, generate a warning that says, "Your child has received bullying messages and is feeling very stressed."

[1476] Step 7:

[1477] The server generates and sends the alert to the parent.

[1478] Input: The warning message generated in step 6.

[1479] Action: Send a warning message to parents via email and / or SMS.

[1480] Output: The warning message sent.

[1481] What it does: For example, an email is sent to a parent's email address stating, "Your child has received a bullying message and is feeling very stressed."

[1482] By following these steps, even if a minor encounters a dangerous situation on social media, parents can be quickly notified and prompted to take appropriate action.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] [Fourth embodiment]

[1487] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1488] 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.

[1489] 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).

[1490] 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.

[1491] 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.

[1492] 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).

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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."

[1500] This invention provides a system for detecting risks associated with the use of social networking services (SNS) at an early stage on a communication device used by minors, and for preventing minors from becoming involved in crime. Specific embodiments of this system are described below.

[1501] System configuration and operation overview

[1502] The communication devices are installed on smartphones, tablets, and other electronic devices commonly used by minors, and monitor text messages and posts in real time whenever a user uses a social networking site.

[1503] collection

[1504] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1505] Example: A user posts "I want to make new friends" in a messaging app. This post is collected by the device.

[1506] Pretreatment

[1507] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[1508] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1509] analysis

[1510] Generative algorithms analyze the collected data and detect risk factors, while natural language processing algorithms are used to understand the meaning and context of the text.

[1511] Example: If there is a message such as "You shouldn't come to school," the generation algorithm will determine this as bullying.

[1512] Risk Detection

[1513] If the generation algorithm detects a risk factor, it returns that information to the terminal.

[1514] Example: When a message containing personal information or a suspicious solicitation message is detected, risk information is returned to the device.

[1515] Alert Generation and Notification

[1516] The device generates appropriate warnings based on the detected risk information.

[1517] For example, generate a warning that reads, "Your child has received an inappropriate message."

[1518] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1519] Example: The server sends warning messages via email and SMS based on the parent's contact information.

[1520] Example of operation

[1521] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[1522] 1. The terminal collects this message.

[1523] 2. The terminal preprocesses the message and formats it into a parsable format.

[1524] 3. The generation algorithm analyzes the preprocessed messages to detect lure risks.

[1525] 4. The device generates a warning based on the detected risk information.

[1526] 5. The device sends the alert to the server.

[1527] 6. The server sends a warning message to the parent.

[1528] In this way, when a minor encounters a dangerous situation on social media, parents can be quickly warned and helped to take appropriate action.

[1529] The processing flow will be explained below.

[1530] Step 1:

[1531] The device collects all posts and messages made by users on social media in real time.

[1532] Example: When a user posts on a social networking site, "I'm planning to hang out with my friends tomorrow," the device collects that post.

[1533] Step 2:

[1534] The terminal pre-processes the collected data.

[1535] For example: normalization converts uppercase to lowercase, removes unnecessary symbols, and also removes extra spaces.

[1536] Step 3:

[1537] The terminal passes the preprocessed data to the generation algorithm.

[1538] Example: The preprocessed text "I'm going to hang out with my friends tomorrow" is sent to the generative algorithm.

[1539] Step 4:

[1540] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[1541] Example: The text "You shouldn't come to school" is collected and a generative algorithm identifies this message as a bullying risk.

[1542] Step 5:

[1543] If the generation algorithm detects a risk factor, it generates risk information and returns it to the terminal.

[1544] Example: A generative algorithm identifies the text "You shouldn't come to school" and creates risk information based on it.

[1545] Step 6:

[1546] The terminal generates a warning based on the risk information.

[1547] Example: If a bullying risk is detected, generate a warning saying "Your child has received a bullying message."

[1548] Step 7:

[1549] The terminal sends the generated alert to the server.

[1550] Example: An alert is sent to the server saying "Your child has received a bullying message."

[1551] Step 8:

[1552] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[1553] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message."

[1554] The above is a detailed processing flow of the system that detects risks early on when minors interact with social media and notifies parents.

[1555] Example 1

[1556] 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."

[1557] In today's world, the risk of minors becoming involved in crimes or inappropriate behavior when using social networking services (SNS) is extremely high. There are many potential dangers, particularly bullying, leaks of personal information, and even kidnapping and fraud. While a system to protect minors from these risks is necessary, the current situation is one in which the safety of minors cannot be ensured due to a lack of adequate systems for real-time monitoring, early detection of risks, and prompt notification to parents.

[1558] 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.

[1559] In this invention, the server

[1560] A means for the communication terminal to collect data on social networking services in real time;

[1561] means for pre-processing the collected data;

[1562] means for using a generative AI model to analyze the preprocessed data;

[1563] A means for detecting risk factors based on analysis results using the generative AI model;

[1564] means for generating an alert upon detection of said risk factor;

[1565] means for using a server to send said alert to the parent;

[1566] means for the server to send a warning message based on the parent's contact information;

[1567] This will enable early detection of risks when minors use SNS and prompt notification to parents.

[1568] 1. "Communication devices" are electronic devices such as smartphones and tablets used by minors.

[1569] 2. "Social Networking Service" means a web service that enables users to interact and share information with other users online.

[1570] 3. "Means of collection" refers to the function of a communication device to obtain data in real time on a social networking service.

[1571] 4. "Preprocessing means" refers to functions that normalize collected data, remove unnecessary symbols, and prepare it for analysis.

[1572] 5. "Generative AI model" is an artificial intelligence model that uses machine learning algorithms to analyze collected data and detect risks.

[1573] 6. "Natural language processing algorithm" is an analytical technology used to understand the meaning and context of collected text data.

[1574] 7. "Risk factors" refer to factors that may pose risks such as bullying, leakage of personal information, kidnapping, and fraud.

[1575] 8. "Means for generating warnings" refers to a function that generates a warning message indicating that a risk factor has been detected.

[1576] 9. "Server" means a computer system for sending warning messages to parents.

[1577] 10. "Means of sending" refers to the function of sending the generated alert via email or SMS based on the parent's contact information.

[1578] This invention provides a system for detecting risks associated with the use of social networking services (SNS) through communication terminals used by minors at an early stage and preventing minors from becoming involved in crime. Specific embodiments will be described below.

[1579] Hardware and software used

[1580] 1. Hardware

[1581] Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors

[1582] Server: A computer system for sending warning messages to parents.

[1583] 2. Software

[1584] Generative AI model: Natural language processing algorithms using machine learning algorithms

[1585] Data processing and calculation

[1586] 1. Data collection

[1587] The device collects social networking data in real time, including sent and received messages, posts, and comments.

[1588] Example: A user posts on a social networking site, "I want to make new friends." This post is collected by the device.

[1589] 2. Pretreatment

[1590] The terminal preprocesses the collected data, which includes normalizing the text and removing unnecessary symbols.

[1591] Example: If a user receives "See you tomorrow!", remove any extra spaces and special characters such as exclamation points.

[1592] 3. Data Analysis

[1593] The generative AI model analyzes the pre-processed data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[1594] Example: If a user receives a message that says "You shouldn't come to school," it is determined that this contains bullying content.

[1595] 4. Risk factor detection

[1596] When the generative AI model detects risk factors, it returns that information to the device.

[1597] Example: If a message containing personal information or a suspicious solicitation message is detected, that information will be sent back to the device.

[1598] 5. Alert Generation and Notification

[1599] The device generates a warning based on the detected risk information.

[1600] Example: Generating a warning that says "Your child has received an inappropriate message."

[1601] The terminal transmits the generated warning to the server, and the server notifies the parent of the warning message.

[1602] Example: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[1603] Specific examples and prompts

[1604] When a user receives a message on SNS saying "Shall we meet tomorrow?", the system operates as follows:

[1605] 1. The device collects this message in real time.

[1606] 2. The terminal preprocesses the message, removing any extra spaces or symbols.

[1607] 3. The generative AI model analyzes the preprocessed message and determines that "Shall we meet?" poses a risk of solicitation.

[1608] 4. The device generates a warning stating "An enticement message has been detected" based on the risk information detected.

[1609] 5. The device sends a warning to the server, and the server sends a warning message to the parent via email or SMS stating that "your child has received an invitation message."

[1610] Prompt Sentence Examples

[1611] Input: A user receives a message on social media saying, "Shall we meet tomorrow?"

[1612] Task: Understand the meaning and context of this message and determine whether it contains any risk elements.

[1613] Expected Output: This message contains an exfiltration risk and should be flagged with a warning.

[1614] This will enable early detection of risks when minors use social networking sites and prompt notification to parents, helping to ensure safety.

[1615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1616] Step 1: Collect data

[1617] The device collects data on social networking services in real time, including sent and received messages, posts, and comments.

[1618] Input: Text data from a social networking service

[1619] Output: Collected text data

[1620] Specific operation: When a user posts on a social networking site that they want to make new friends, this post is collected in real time.

[1621] Step 2: Preprocessing the data

[1622] The terminal preprocesses the collected text data, which includes normalizing the text and removing unnecessary symbols.

[1623] Input: Collected text data

[1624] Output: Preprocessed text data

[1625] Specific behavior: If a user receives "See you tomorrow!", remove extra spaces and special characters such as exclamation marks from the text.

[1626] Step 3: Analyze the data

[1627] The device uses a generative AI model to analyze the pre-processed text data, using natural language processing algorithms to understand the meaning and context of the text and identify risk factors.

[1628] Input: Preprocessed text data

[1629] Output: Analysis results (risk assessment)

[1630] Specific behavior: Analyzes messages such as "You shouldn't come to school" and determines that they contain bullying content.

[1631] Step 4: Identifying risk factors

[1632] The generative AI model detects risk factors based on the analysis results and returns the detection results to the device.

[1633] Input: Analysis results

[1634] Output: Risk factor information

[1635] Specific operation: Detects messages containing personal information or suspicious solicitation messages and sends that information back to the device.

[1636] Step 5: Generate warnings

[1637] The device generates a warning based on the detected risk information.

[1638] Input: Risk factor information

[1639] Output: Warning message

[1640] What it does: Generates the warning "Your child received an inappropriate message."

[1641] Step 6: Warning Notification

[1642] The terminal sends the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1643] Input: warning message

[1644] Output: Parental notification

[1645] What it does: The server uses the parent's contact information to send a warning message via email or SMS, such as "Your child has received inappropriate content."

[1646] (Application example 1)

[1647] 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."

[1648] Currently, when minors use social networking services (SNS), they are likely to be exposed to risks such as bullying and solicitation. However, many parents do not have the means to grasp these risks in real time. This increases the risk of minors becoming involved in crime. The object of this invention is to provide a system that uses communication devices used by minors to quickly detect risks on SNS and issue prompt and accurate warnings to parents.

[1649] 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.

[1650] In this invention, the server includes means for collecting data on social networking services from communication terminals used by minors, means for preprocessing the collected data, means for using a natural language processing algorithm to analyze the collected data, means for detecting risk factors based on the analysis results, means for generating a warning when the risk factor is detected, and means for sending the warning to a guardian. This makes it possible to quickly notify a guardian when a minor encounters a dangerous situation on a social networking service and take appropriate action.

[1651] "Minors" refers to young people who have not yet reached legal adulthood.

[1652] "Communication terminal" is a general term for devices capable of voice and data communication, including smartphones, tablets, and other electronic devices that can connect to the Internet.

[1653] A "social networking service" is a platform for users to communicate with each other over the Internet, where information such as messages, posts, and comments is shared.

[1654] "Data" is a general term for information collected by a communication device, including text messages, sent and received messages, posts, comments, etc.

[1655] "Preprocessing" is the process of converting collected data into an analyzable format, and refers to operations such as normalizing text and removing unnecessary symbols.

[1656] "Natural language processing algorithms" are algorithms that allow computers to understand and analyze human language, and are a technology used to understand the meaning and context of text.

[1657] "Risk factors" refer to elements on social media that could lead minors to become involved in crimes or trouble, including bullying, luring, and leaking of personal information.

[1658] A "warning" is a notification message that is generated when a risk factor is detected, and is intended to inform parents and other relevant parties of the risk.

[1659] "Guardian" refers to a parent or someone in a similar position who has custody of a minor.

[1660] "Server" refers to a computer system for storing, processing, and managing data over a network.

[1661] The present invention provides a system for enabling minors to safely use social networking services (hereinafter referred to as SNS) on communication terminals. Detailed embodiments of this system will be described below.

[1662] System Overview

[1663] The system aims to detect risk factors early on when minors use social networking sites and send warnings to parents. The system mainly consists of the following components:

[1664] 1. Communication devices: Electronic devices such as smartphones and tablets that are used daily by minors.

[1665] 2. Server: A computer system for managing data storage, processing, and sending alert messages.

[1666] Hardware and Software Configuration

[1667] Hardware:

[1668] Communication devices: smartphones, tablets

[1669] Server: A general internet-connected computer system

[1670] software:

[1671] Natural language processing library: TextBlob (for text analysis)

[1672] Email sending library: smtplib (for sending warning messages)

[1673] Data collection and preprocessing

[1674] The device collects data such as messages, posts, and comments sent and received by users on social media in real time. The collected data is then preprocessed to make it analyzable. Specifically, text is normalized and unnecessary symbols are removed.

[1675] Data analysis and risk detection

[1676] The preprocessed data is then analyzed using natural language processing (NLP) algorithms. Using a NLP library such as TextBlob, the text is subjected to sentiment analysis. Messages with strong negative sentiment (e.g., "Don't come to my school") are then detected as risk factors.

[1677] Alert generation and notification

[1678] If a risk factor is detected, the device generates a warning message and sends it to the server. The server then sends this warning to the parent's contact information (e.g., email address), allowing parents to respond quickly when their minor encounters a dangerous situation on social media.

[1679] Examples of specific examples and prompts

[1680] Specific examples

[1681] Example: If a user receives the message "Don't come to school," the following happens:

[1682] The device collects this message

[1683] Preprocessing to remove extra spaces and special characters

[1684] Natural language processing algorithms analyze messages and detect negative sentiment

[1685] When a risk factor is detected, a warning message is generated and sent to the server.

[1686] The server sends a warning email to the parent.

[1687] Prompt sentence for generative AI model

[1688] Evaluate the following message and determine if it poses a risk: "You shouldn't come to school."

[1689] Negative Emotion: True

[1690] Risky message: Don't come to school

[1691] In this way, a system is provided that can quickly notify parents and guardians when a minor encounters a dangerous situation on an SNS and take appropriate action.

[1692] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1693] Step 1: The device collects messages on the social networking site.

[1694] Input: Messages, comments, and posts that users send or receive on social media.

[1695] How it works: The device uses the API of the social networking application to collect this data in real time.

[1696] Output: Raw data collected (messages, comments, posts)

[1697] Step 2: Preprocess the data collected by the device

[1698] Input: Raw data collected

[1699] What it does: The terminal preprocesses and normalizes the text data, specifically removing extra spaces and special characters.

[1700] Output: Preprocessed text data

[1701] Step 3: The device analyzes the preprocessed data using natural language processing algorithms.

[1702] Input: Preprocessed text data

[1703] How it works: The device uses a natural language processing library such as TextBlob to perform sentiment analysis on the text data, calculating a sentiment score for each message.

[1704] Output: Analysis results (text data with sentiment scores)

[1705] Step 4: The device detects risk factors based on the analysis results

[1706] Input: Text data with sentiment scores

[1707] Operation: The device evaluates the analysis results and determines messages with negative emotions above a certain threshold as risk factors. For example, if the emotion score is below -0.5, it is a risk factor.

[1708] Output: Messages determined to be risk factors

[1709] Step 5: Generate a warning message when the device detects a risk factor

[1710] Input: Message determined to be a risk factor

[1711] Operation: The device generates a warning message to send to the parent. Specifically, it constructs the warning message based on the content of the message that was determined to be a risk factor.

[1712] Output: Warning message

[1713] Step 6: Server sends alert to parent

[1714] Input: warning message

[1715] How it works: When a warning message is sent from the device to the server, the server will send a warning message via email or SMS based on the parent's contact information.

[1716] Output: Notification of completion of sending warning message to parents

[1717] 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.

[1718] This invention is a system for early detection of risks associated with the use of social networking sites on communication devices used by minors, preventing them from becoming involved in crimes, and further improves the accuracy of risk detection by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1719] System configuration and operation overview

[1720] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1721] collection

[1722] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1723] Example: A user posts in a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by the device.

[1724] Pretreatment

[1725] The device preprocesses the collected data, a process that involves normalizing the text, removing unnecessary symbols, and preparing it for analysis.

[1726] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1727] analysis

[1728] The generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[1729] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[1730] Risk Detection

[1731] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[1732] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[1733] Alert Generation and Notification

[1734] The device generates a warning based on the generated risk information and the results of emotion analysis.

[1735] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1736] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1737] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1738] Example of operation

[1739] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1740] 1. The terminal collects this message.

[1741] 2. The terminal preprocesses the message and formats it into a parsable format.

[1742] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1743] 4. The emotion engine recognizes the user's negative emotional state.

[1744] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1745] 6. The device sends the alert to the server.

[1746] 7. The server sends a warning message to the parent.

[1747] In this way, when a minor encounters a dangerous situation or negative emotions on social media, the system can quickly warn parents and help them take appropriate action.

[1748] The processing flow will be explained below.

[1749] Step 1:

[1750] The device collects all posts and messages made by users on social media in real time.

[1751] Example: A user sends a message to a friend on a social networking site saying, "I want a new game." This message is collected by the device.

[1752] Step 2:

[1753] The terminal pre-processes the collected data.

[1754] Example: Converting messages to uppercase and lowercase, removing unnecessary symbols, and arranging them into a unified format.

[1755] Step 3:

[1756] The terminal sends the pre-processed data to the generating algorithm.

[1757] Example: The preprocessed text "I want a new game" is sent to a generative algorithm.

[1758] Step 4:

[1759] The generating algorithm analyzes the pre-processed data and detects specific risk factors.

[1760] Example: A generative algorithm analyzes the message "I wish you were gone" and determines that it is potentially defamatory.

[1761] Step 5:

[1762] The generative algorithm detects risk factors and sends the data to the emotion engine.

[1763] Example: The message "I wish you'd just disappear" detected by the generative algorithm is passed to the emotion engine.

[1764] Step 6:

[1765] The emotion engine analyzes the received data and recognizes the user's emotional state.

[1766] Example: The emotion engine determines that the sender's emotion in response to the message "I wish you were gone" is negative.

[1767] Step 7:

[1768] The emotion engine returns the recognized emotion information to the terminal.

[1769] Example: Returning a negative sentiment result to the terminal.

[1770] Step 8:

[1771] The terminal generates a warning based on the risk information of the generation algorithm and the analysis results of the emotion engine.

[1772] Example: Generate a warning message saying "Your child has received a bullying message."

[1773] Step 9:

[1774] The terminal sends the generated alert to the server.

[1775] Example: Sending a warning message to the server: "Your child has received a bullying message."

[1776] Step 10:

[1777] The server generates a warning message for the parent and notifies the parent via a specified contact method.

[1778] Example: The server sends a warning email or SMS to a parent's contacts stating that "your child has received a bullying message."

[1779] This is the specific processing flow of the system that combines the emotion engine. This makes it possible to detect risks and issue warnings that take into account the emotional state of minors when they encounter dangerous situations on social media.

[1780] Example 2

[1781] 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."

[1782] When minors use social networking services (SNS), they are likely to encounter inappropriate content and harmful messages, which can have serious psychological effects. Furthermore, conventional systems have had difficulty accurately and quickly analyzing SNS data and detecting and notifying users of risks at an early stage. To address this issue, it is urgent to develop an efficient system that collects SNS data in real time, analyzes it, detects risks, and generates warnings. Furthermore, there is a need to improve the accuracy of risk detection by accurately understanding users' emotional states.

[1783] 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.

[1784] In this invention, the server includes a means for a communication terminal to collect data on a social networking service, a means for preprocessing the collected data by normalizing and removing unnecessary symbols, and a means for analyzing the preprocessed data using a generative algorithm and analyzing the emotional state using an emotion engine. This makes it possible to quickly and accurately detect risks and notify appropriate individuals of warnings when a minor encounters inappropriate content on an SNS. The emotion engine also allows for a more accurate understanding of the user's psychological state, enabling more accurate risk assessment.

[1785] Definitions of important words

[1786] "Minor" means a person who has not reached the legal age of majority.

[1787] "Communication terminal" refers to any electronic device that has communication capabilities via an Internet connection, including smartphones and tablets.

[1788] "Social networking services (SNS)" refer to platforms that allow people to interact with each other online. Examples include messaging apps and community sites.

[1789] "Means of collecting data" refers to technology for obtaining messages, posts, comments, etc. sent and received on social media in real time.

[1790] "Normalization" refers to the process of formatting collected text data into a unified format.

[1791] "Unnecessary symbol removal" refers to the process of removing special characters, spaces, etc. that are not required for data analysis.

[1792] "Preprocessing means" refers to a series of processes for formatting collected data into an analyzable format.

[1793] "Generative algorithm" refers to an algorithm that analyzes data and generates a result for a specific purpose, and specifically includes natural language processing algorithms.

[1794] "Emotion engine" refers to technology for analyzing text data to identify a user's emotional state.

[1795] "Means for detecting risk factors" refers to technology that identifies data containing inappropriate content or negative emotions based on analysis results.

[1796] "Means for generating risk information" refers to technology that generates appropriate warnings and notifications based on detected risk factors.

[1797] "Means for generating warnings" refers to techniques for creating appropriate warning messages when a risk is detected.

[1798] "Means of sending to parents" refers to technology that notifies parents of the generated warning message via email, SMS, etc.

[1799] MODE FOR CARRYING OUT THE INVENTION

[1800] This invention is a system for early detection of risks associated with the use of social networking services (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. Furthermore, by combining this system with an emotion engine that recognizes the user's emotions, the accuracy of risk detection is improved. Specific embodiments of this system are described below.

[1801] System configuration and operation overview

[1802] The communication terminal is installed on digital devices (smartphones, tablets, etc.) that minors use on a daily basis. This terminal monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1803] collection

[1804] The device collects data on social media in real time, including sent and received messages, posts, and comments.

[1805] Example: A user posts, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[1806] Pretreatment

[1807] The terminal preprocesses the collected data by normalizing it and removing unnecessary symbols.

[1808] Example: If the message "I don't want to go to school anymore!!!" is collected, remove the extra spaces and exclamation marks and format it as "I don't want to go to school anymore".

[1809] analysis

[1810] The generative algorithm analyzes the pre-processed data to detect risk factors, while also analyzing the emotional state of the text using an emotion engine.

[1811] Example: If the message "I wish you'd just disappear" is collected, the generative algorithm will determine this as slander and the emotion engine will detect a negative emotional state.

[1812] Risk Detection

[1813] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the terminal.

[1814] Example: Identified messages are identified as having negative sentiment by the sentiment engine, generating risk information.

[1815] Alert Generation and Notification

[1816] The device generates a warning based on the generated risk information and the results of emotion analysis.

[1817] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1818] The terminal transmits the generated warning to the server, and the server notifies the parent or guardian of the warning message.

[1819] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1820] Example of operation

[1821] If a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1822] 1. The terminal collects this message.

[1823] 2. The terminal preprocesses the message and formats it into a parsable format.

[1824] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1825] 4. The emotion engine recognizes the user's negative emotional state.

[1826] 5. The device integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1827] 6. The device sends the alert to the server.

[1828] 7. The server sends a warning message to the parent.

[1829] Prompt Sentence Examples

[1830] When a user sends "I hate you" on social media, the system works as follows:

[1831] 1. The device collects the message.

[1832] 2. Pre-treat.

[1833] 3. Analyze with a generative algorithm.

[1834] 4. Recognize emotional states with the Emotion Engine.

[1835] 5. Generate risk information.

[1836] 6. Generate an alert and send it to the server.

[1837] 7. The server notifies the parent.

[1838] This system will enable parents to quickly receive a warning when a minor encounters a dangerous situation on social media, allowing them to take appropriate action.

[1839] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1840] Program processing steps

[1841] Step 1: Collect data

[1842] Processing flow: The device collects messages and comments sent, received, and posted by users on the social networking site in real time.

[1843] Input: User messages and posts on social media.

[1844] Data processing: Collected messages and posts are stored on the communication device.

[1845] Output: Raw data saved to the device.

[1846] Specific operation: A user posts on social media, "I don't want to go to school anymore." The device immediately collects and saves this post.

[1847] Step 2: Preprocessing the data

[1848] Processing flow: The terminal performs preprocessing on the collected data to make it easier to analyze, including text normalization and removal of unnecessary symbols.

[1849] Input: Collected raw data on social media.

[1850] Data processing: Text normalization (combining multiple whitespaces, removing special characters, etc.).

[1851] Output: Preprocessed and clean data.

[1852] Specific behavior: The device normalizes the post "I don't want to go to school anymore!!!" to "I don't want to go to school anymore" and removes unnecessary symbols.

[1853] Step 3: Analyze the data

[1854] Processing flow: The generative algorithm analyzes the preprocessed data and evaluates the user's emotional state using the emotion engine.

[1855] Input: Preprocessed clean data.

[1856] Data processing / data calculation: Generative algorithms analyze data using natural language processing techniques to assess risk factors. Emotion engines analyze the emotional state of the data.

[1857] Output: Analysis results (risk factors such as slander, stress, etc.).

[1858] How it works: The generative algorithm analyzes posts like "I don't want to go to school anymore" to detect negative emotional states, while the emotion engine evaluates the stress level felt by the user.

[1859] Step 4: Risk detection

[1860] Process flow: The generation algorithm detects risk factors from user posts and messages, and integrates them with the analysis results of the emotion engine to generate risk information.

[1861] Input: Analysis results of the generative algorithm and emotion engine.

[1862] Data processing: Integrate risk factors and emotional states to generate risk information.

[1863] Output: The generated risk information.

[1864] How it works: The generation algorithm identifies posts such as "I don't want to go to school anymore" as a sign of bullying, and the emotion engine detects high stress levels. These are then combined to generate "high risk" information.

[1865] Step 5: Alert Generation and Notification

[1866] Process flow: The device generates a warning based on the generated risk information and the results of emotion analysis, and sends it to the server. The server then sends a warning message to the parent.

[1867] Input: Generated risk information.

[1868] Data processing: Create warning messages based on risk information.

[1869] Output: Warning message sent to parents.

[1870] Specific operation: The device generates a warning saying "Your child has received a bullying message and is feeling very stressed" and sends it to the server. The server then sends the warning to the parent via email or SMS to notify them promptly.

[1871] This system makes it possible to quickly send a warning to parents when a user encounters a dangerous situation on social media, urging them to take appropriate action.

[1872] (Application example 2)

[1873] 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."

[1874] In addition to the challenge of early detection of risks (especially slander and bullying) associated with the use of social networking sites by minors and preventing them from becoming involved in crime, it is also necessary to further improve safety in self-driving vehicles. It is also necessary to recognize user emotions and achieve more accurate risk detection and warning generation.

[1875] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1876] In this invention, the server includes means for the communication terminal to collect data on social networking services, means for using a generation algorithm to analyze the collected data, means for recognizing the emotions of passengers using an emotion engine, means for detecting risk factors based on the analysis results and the emotional state, means for generating a warning based on the risk factors and the emotional state, and means for sending the generated warning to a guardian. This makes it possible to detect risks with high accuracy even when a minor uses SNS in an autonomous vehicle, and to quickly send a warning to the guardian.

[1877] "Communication devices" are electronic devices such as smartphones and tablets that are used on a daily basis by minors.

[1878] A "social networking service" is an Internet service that enables users to connect with other users online and share messages and content.

[1879] A "generative algorithm" is an algorithm used to analyze collected data and detect risk factors.

[1880] An "emotion engine" is a technology that recognizes a user's emotional state from the text and actions they enter.

[1881] "Risk factors" are elements that could potentially cause dangerous situations for users, such as defamation or bullying.

[1882] An "alert" is a warning message that is generated when a risk factor is detected.

[1883] A "guardian" is a supervisor appointed to protect the safety of minor users.

[1884] "Real-time" refers to the ability to process and analyze data as soon as it is generated.

[1885] "Preprocessing" is the process of formatting collected data into a format that is easy to analyze.

[1886] "Natural language processing algorithms" are computational techniques used to understand and analyze human language.

[1887] "Passenger vehicle" refers to an autonomous vehicle for passengers to ride in.

[1888] This invention is a system for early detection of risks associated with the use of social networking sites (SNS) on communication devices used by minors, and for preventing minors from becoming involved in crime. It also aims to improve the safety of minors when riding in self-driving vehicles. Specific embodiments of this invention are described below.

[1889] System configuration and operation overview

[1890] The communication device is installed on smartphones, tablets, and other electronic devices that minors use on a daily basis. This device monitors text messages and posts in real time every time a user uses social media, and analyzes them using an emotion engine and generative algorithm.

[1891] collection

[1892] The server collects data from the communication device on the SNS in real time, including sent messages, received messages, posts, comments, etc.

[1893] Example: A user posts on a messaging app, "I'm looking forward to hanging out with my friends." This post is collected by a communication device.

[1894] Pretreatment

[1895] The collected data is preprocessed on the communication terminal, which is a process that normalizes the text, removes unnecessary symbols, etc., to prepare it for analysis.

[1896] Example: Removing extra spaces and special characters from the message to make it easier to parse.

[1897] analysis

[1898] A generative algorithm analyzes the preprocessed data to detect risk factors, while simultaneously analyzing the emotional state of the text using an emotion engine.

[1899] Example: If a message such as "I wish you would disappear" is collected, the generation algorithm will determine this as slander, and the emotion engine will detect the user's negative emotional state.

[1900] Risk Detection

[1901] If the generation algorithm detects a risk factor, it generates risk information along with the analysis results of the emotion engine and returns it to the communication terminal.

[1902] Example: Identified messages are identified as having negative sentiment by the sentiment engine, and risk information is generated based on that.

[1903] Alert Generation and Notification

[1904] The server generates a warning based on the generated risk information and the results of sentiment analysis.

[1905] For example, generate a warning that reads, "Your child has received bullying messages and is feeling very stressed."

[1906] The server notifies the parent of the generated alert.

[1907] Example: The server sends a warning to parents via email or SMS saying, "Your child has received a bullying message and is feeling very stressed."

[1908] Hardware and software used

[1909] The system uses the following hardware and software:

[1910] Hardware: Smartphones, tablets, and in-vehicle systems for autonomous vehicles

[1911] Software: generative algorithms (e.g., natural language processing algorithms), emotion engines (e.g., EmotionEngine)

[1912] Specific examples

[1913] When a user sends a negative message on social media, such as "I don't want to go to school anymore," the system operates as follows:

[1914] 1. The communication terminal collects this message.

[1915] 2. The communication device preprocesses the message and formats it into a parsable format.

[1916] 3. A generative algorithm analyzes the preprocessed messages to detect risks of defamation and bullying.

[1917] 4. The emotion engine recognizes the user's negative emotional state.

[1918] 5. The server integrates the results of the generation algorithm and the emotion engine to generate a warning based on risk information.

[1919] 6. The server sends the generated alert to the parent.

[1920] Example prompts to be input to the generative AI model

[1921] Analyze users' SNS messages and detect safety risks. For the following messages, determine whether they are high-risk and have negative sentiment and generate a warning message.

[1922] Message: I don't want to go to school anymore

[1923] In this way, if a minor encounters a dangerous situation or negative emotions on social media while in an autonomous vehicle, parents can be quickly warned and supported in taking appropriate action.

[1924] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1925] Step 1:

[1926] The device collects data on social media.

[1927] Input: Messages and posts sent or received by users on social media.

[1928] Processing: The device monitors these messages and posts in real time and collects data.

[1929] Output: Collected social media data.

[1930] Operation: For example, if a user posts, "I'm looking forward to making plans to hang out with my friends," the device collects that posting data.

[1931] Step 2:

[1932] The terminal preprocesses the collected data.

[1933] Input: Social media data collected in step 1.

[1934] Processing: Normalize the collected data and remove unnecessary symbols and extra spaces.

[1935] Output: Normalized text data.

[1936] Action: For example, format a post like "I'm looking forward to hanging out with my friends!!!" into "I'm looking forward to hanging out with my friends."

[1937] Step 3:

[1938] The terminal analyzes the preprocessed data using the generation algorithm.

[1939] Input: The text data preprocessed in step 2.

[1940] Processing: Natural language processing algorithms are used to detect risk factors in the text.

[1941] Output: Risk factor analysis results.

[1942] Action: For example, the message "I wish you'd just disappear" is considered defamatory.

[1943] Step 4:

[1944] The terminal uses an emotion engine to analyze the user's emotional state.

[1945] Input: The text data preprocessed in step 2.

[1946] Processing: The text data is analyzed by the emotion engine to recognize the user's emotional state.

[1947] Output: Emotional state analysis result.

[1948] How it works: For example, detecting a negative emotional state from a message like "I don't want to go to school anymore."

[1949] Step 5:

[1950] The server integrates the results of the generation algorithm and the emotion engine to generate risk information.

[1951] Input: Analysis results from Step 3 and Step 4.

[1952] Processing: Integrate risk factors and emotional states to generate risk information.

[1953] Output: Risk information.

[1954] Behavior: For example, the message "I wish you'd just disappear" combined with a negative emotional state could be considered "high risk."

[1955] Step 6:

[1956] The server generates a warning based on the risk information.

[1957] Input: Risk information generated in Step 5.

[1958] Action: Generate a warning message based on the risk information.

[1959] Output: A warning message.

[1960] What it does: For example, generate a warning that says, "Your child has received bullying messages and is feeling very stressed."

[1961] Step 7:

[1962] The server generates and sends the alert to the parent.

[1963] Input: The warning message generated in step 6.

[1964] Action: Send a warning message to parents via email and / or SMS.

[1965] Output: The warning message sent.

[1966] What it does: For example, an email is sent to a parent's email address stating, "Your child has received a bullying message and is feeling very stressed."

[1967] By following these steps, even if a minor encounters a dangerous situation on social media, parents can be quickly notified and prompted to take appropriate action.

[1968] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1969] 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.

[1970] 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 robot 414.

[1971] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1972] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1973] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1974] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1975] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1976] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1977] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1978] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1979] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1980] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1981] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1982] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1983] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1984] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1985] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1986] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1987] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1988] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1989] The following is further disclosed regarding the above embodiment.

[1990] (Claim 1)

[1991] In communication devices used by minors,

[1992] A means for collecting data on a social networking service in the communication terminal;

[1993] means for using a generating algorithm to analyze the collected data;

[1994] means for detecting risk factors based on the analysis results;

[1995] means for generating an alert upon detection of said risk factor;

[1996] means for transmitting said alert to a parent.

[1997] (Claim 2)

[1998] The system according to claim 1, wherein the communication terminal collects data on the social networking service in real time.

[1999] (Claim 3)

[2000] 10. The system of claim 1, wherein the generation algorithm analyzes the preprocessed data using a natural language processing algorithm.

[2001] "Example 1"

[2002] (Claim 1)

[2003] In communication devices used by minors,

[2004] A means for the communication terminal to collect data on social networking services in real time;

[2005] means for pre-processing the collected data;

[2006] means for using a generative AI model to analyze the preprocessed data;

[2007] A means for detecting risk factors based on analysis results using the generative AI model;

[2008] means for generating an alert upon detection of said risk factor;

[2009] means for using a server to send said alert to the parent;

[2010] The system further includes means for the server to send a warning message based on parental contact information.

[2011] (Claim 2)

[2012] 10. The system of claim 1, wherein the generative AI model analyzes the preprocessed data using a natural language processing algorithm.

[2013] (Claim 3)

[2014] 2. The system according to claim 1, wherein the communication terminal collects data on social networking services in real time and detects risks based on the analysis results.

[2015] "Application Example 1"

[2016] (Claim 1)

[2017] In communication devices used by minors,

[2018] A means for collecting data on a social networking service in the communication terminal;

[2019] means for pre-processing the collected data;

[2020] means for using natural language processing algorithms to analyze the collected data;

[2021] means for detecting risk factors based on the analysis results;

[2022] means for generating an alert upon detection of said risk factor;

[2023] means for sending said alert to a parent;

[2024] A system including:

[2025] (Claim 2)

[2026] The system according to claim 1, wherein the communication terminal collects data on the social networking service in real time.

[2027] (Claim 3)

[2028] 10. The system of claim 1, wherein the generation algorithm analyzes the preprocessed data using a natural language processing algorithm.

[2029] "Example 2: Combining Emotion Engines"

[2030] (Claim 1)

[2031] In communication devices used by minors,

[2032] A means for collecting data on a social networking service in the communication terminal;

[2033] means for preprocessing the collected data by normalizing and removing unwanted symbols;

[2034] means for analyzing the pre-processed data using a generative algorithm and analyzing an emotional state using an emotion engine;

[2035] means for detecting risk factors based on the analysis results;

[2036] means for generating risk information and generating a warning when the risk factor is detected;

[2037] means for sending said alert to a parent;

[2038] ...

[2039] A system including:

[2040] (Claim 2)

[2041] The system according to claim 1, wherein the communication terminal collects data on the social networking service in real time.

[2042] (Claim 3)

[2043] 10. The system of claim 1, wherein the generation algorithm analyzes the preprocessed data using a natural language processing algorithm and analyzes the emotional state using an emotion engine.

[2044] "Application example 2 when combining emotion engines"

[2045] (Claim 1)

[2046] In communication devices used by minors,

[2047] A means for collecting data on a social networking service in the communication terminal;

[2048] means for using a generating algorithm to analyze the collected data;

[2049] means for detecting risk factors based on the analysis results;

[2050] a means for the communication terminal to use an emotion engine to recognize the emotion of a user inside the vehicle;

[2051] means for generating an alert based on said risk factors and emotional state;

[2052] means for transmitting said alert to a parent.

[2053] (Claim 2)

[2054] The system according to claim 1, wherein the communication terminal collects data on the social networking service in real time.

[2055] (Claim 3)

[2056] 10. The system of claim 1, wherein the generation algorithm analyzes the preprocessed data using a natural language processing algorithm. [Explanation of symbols]

[2057] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. In communication devices used by minors, A means for collecting data on a social networking service in the communication terminal; means for using a generating algorithm to analyze the collected data; means for detecting risk factors based on the analysis results; means for generating an alert upon detection of said risk factor; means for transmitting said alert to a parent.

2. The system according to claim 1 , wherein the communication terminal collects data on the social networking service in real time.

3. The system of claim 1 , wherein the generation algorithm analyzes the preprocessed data using a natural language processing algorithm.

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