system

Abstract

We provide the system. [Solution] Natural language processing tools, A means for generating customized settings to interpret and respond to requests from parents, A control means for applying the generated settings to the child's information processing device, Monitoring means for monitoring data transmission and usage patterns on a child's information processing device, A reporting mechanism for detecting and notifying of inappropriate activity, A payment restriction mechanism that restricts the time and amount of electronic payments that can be used, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, with the increasing use of digital communication devices by children, there are diverse online risks. However, conventional parental control systems have complex settings and are difficult for guardians to manage appropriately without specialized knowledge. Also, there is a problem that flexible customization according to each family's policy and the degree of a child's growth is insufficient, and the system cannot function as intended by the guardian. Furthermore, there is also a problem that it is difficult to immediately respond to the use and transmission of inappropriate content.

Means for Solving the Problems

[0005] This invention uses natural language processing means to receive requests from parents and automatically generates flexible settings in response to those requests using a customized setting generation means. The generated settings are applied to the child's communication device through a control means. In addition, a monitoring means monitors the child's usage patterns and sends warnings to parents using an inappropriate activity detection and reporting means. In this way, parental controls tailored to the family's needs are possible with intuitive operation that does not require specialized knowledge, providing a safe online usage environment that can respond immediately to risks.

[0006] "Natural language processing tools" are technologies that have the ability to analyze requests from parents in natural language and convert them into appropriate, specific action instructions and requirements.

[0007] The "customized setting generation method" is a technology that dynamically generates appropriate settings tailored to each family and the child's developmental stage, based on analyzed requests.

[0008] "Control means" refers to a technology that has the function of applying the generated customized settings to the child's communication device and managing the device's operation.

[0009] "Monitoring methods" refer to technologies that continuously track a child's device usage and have the function of detecting inappropriate activity based on specific criteria.

[0010] A "reporting mechanism" is a technology that has the function of notifying parents of detected inappropriate activities and prompting them to take prompt action. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is an AI parental control system that allows parents to manage their children's communication devices using natural language. This system works in cooperation with the server, terminals, and users to achieve flexible and intuitive control tailored to each family's policies.

[0033] The server receives natural language requests sent from parents via smartphones or PCs. These requests may include specific instructions, such as "Please restrict internet access after 10 PM." The server analyzes these requests using natural language processing to extract their intent. Based on the extracted information, a customization settings generation system creates a policy and prepares the settings to be sent to the device.

[0034] The device applies the received policy to the device's configuration layer. For example, if it restricts internet access during a set time period, it configures the device to block communication and prevent unintended access. The device also uses monitoring mechanisms to continuously monitor how the child is using the device. If inappropriate behavior, such as an attempt to access the device during the restricted time period, is detected, the device reports this information to the server.

[0035] By receiving notifications from the server, users can monitor their child's device usage and adjust settings accordingly. For example, if a child attempts to access an app within a time limit, the server will send a warning to the user and provide an opportunity to readjust settings if necessary.

[0036] In this way, the invention provides parental controls that meet family needs through intuitive operation, supporting a safe online experience for children.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users enter a request in natural language on their smartphone or PC, stating, "I want to be unable to use the internet after 10 PM," and send it to the system.

[0040] Step 2:

[0041] The server processes natural language requests received from users using its parsing engine and interprets their meaning. Specifically, it identifies time zones and restricted activities.

[0042] Step 3:

[0043] The server generates a customized access restriction policy based on the interpretation results. This policy is configured to reflect the user's requests.

[0044] Step 4:

[0045] The server sends the generated access restriction policy to the terminal and provides instructions for applying the settings.

[0046] Step 5:

[0047] The device deploys the policy received from the server and reflects it in its settings. For example, it might configure the device to block internet access during specified time periods.

[0048] Step 6:

[0049] The device uses monitoring functions to check whether the settings are working correctly and detects actions that violate specific policies, such as access attempts during restricted time periods.

[0050] Step 7:

[0051] The device reports any inappropriate access to the server.

[0052] Step 8:

[0053] The server analyzes the information reported from the terminal and sends a notification to the user. This notification includes information about what happened and what action is required.

[0054] Step 9:

[0055] Users can receive notifications from the server and send requests to readjust their settings as needed.

[0056] Through this process, the system provides continuous and dynamic parental controls that meet the needs of the household.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] There is a need for a system that allows parents to easily and flexibly set usage restrictions on information processing devices used by children, and to effectively monitor and control inappropriate activities. Traditional manual control is time-consuming and laborious, and sometimes makes it difficult to adequately protect children's safety. Therefore, the challenge is to provide a system that parents can operate intuitively and that offers flexible control to meet diverse needs.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes a natural language analyzer, a custom setting generation device, and a warning device. This allows the parent to make a request in natural language, analyze the request using a generative AI model, automatically generate a policy suitable for the information processing device, and quickly and effectively provide warnings and notifications to the parent.

[0062] A "natural language processing device" is a device that interprets requests made in natural language by parents and processes them to accurately understand their intent.

[0063] A "customization setting generation device" is a device that automatically generates policies and settings to be applied to an information processing device based on the content of an analyzed request.

[0064] A "control device" is a device that applies a generated policy to an information processing device and enforces specified restrictions and permissions.

[0065] A "monitoring device" is a device that continuously monitors information transmission and usage patterns on an information processing device and identifies inappropriate activities.

[0066] A "reporting device" is a device that notifies parents of inappropriate activities detected by a monitoring device.

[0067] A "warning device" is a device that issues a warning to a parent if inappropriate activity occurs while using an information processing device.

[0068] A "transmitting device" is a device that has the function of sending the generated policy to an information processing device.

[0069] This invention is an AI parental control system that allows parents to manage their children's information processing devices using natural language. The invention achieves flexible and intuitive control through the collaboration of three entities: a server, a terminal, and a user.

[0070] The server receives natural language requests from parents via smartphones or personal computers. Generative AI models for natural language processing are used to analyze these requests. For example, using a natural language model like GPT-3®, the server accurately understands the request and extracts the intent behind the instruction. Based on the extracted intent, a customization settings generator creates a policy. This policy is sent to the child's device and applied by the device's built-in control unit.

[0071] The terminal reflects the received policy in its device settings. For example, it adjusts network settings to block communication in order to restrict internet access for a specific period of time. The monitoring device continuously observes the terminal's usage and reports any inappropriate activity, such as attempts to access the network during the restricted time period, to the server.

[0072] Users can monitor their child's device usage through notifications from the server. For example, if an attempt is made to access a specific app within a time limit, the user receives a warning from the server and readjusts the settings based on that information. As a concrete example of a prompt message, if the user enters the instruction, "Please restrict my child's access to YouTube® when they try to watch it at night," the system will generate and implement a policy in response to this request. In this way, the invention supports a safe online environment for children, tailored to the needs of families.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server receives natural language requests sent from parents via smartphones or personal computers. These requests include specific instructions such as, "Please restrict internet access after 10 PM." The server receives these natural language instructions as input.

[0076] Step 2:

[0077] The server analyzes the received request using a generative AI model. At this time, the server uses a natural language processing unit to syntactically analyze the input instruction and extract its intent. As a result of the analysis, a specific control objective, such as "prohibit internet access after 10 PM," is extracted. The output is the analyzed intent.

[0078] Step 3:

[0079] The server generates appropriate policies using a customization configuration generator based on the analysis results. For example, based on the extracted intent, a configuration is generated to restrict internet access during specific time periods. The input is the analyzed intent, and the output is the policy configuration information.

[0080] Step 4:

[0081] The server sends the generated policy to the terminal. The sent information includes specific details such as which applications or websites should be restricted to access during which time periods. At this stage, the policy configuration information is the input, and sending it to the terminal is the output.

[0082] Step 5:

[0083] The terminal applies the policy received from the server to the settings of the information processing device. Specifically, the network settings are adjusted based on the received policy, and communication is blocked at the specified time. The input is policy information received from the server, and the output is a change in the actual device settings.

[0084] Step 6:

[0085] The terminal monitors the child's device usage via a monitoring device. If an inappropriate access attempt is detected, it records the information and prepares to report it to the server. The input is the device usage log, and the output is a record of inappropriate behavior.

[0086] Step 7:

[0087] The server receives reports from the device and issues warnings to the parent. For example, if an attempt is made to access a website within the time limit, this information is notified to the parent. The input is the information reported from the device, and the output is the warning notification to the parent.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Traditional parental control systems focus on restricting children's use of communication devices and the internet, but have struggled to extend control to electronic payments. Therefore, there is a need for a system that proactively prevents inappropriate spending and electronic payments that disregard appropriate time zones. In particular, a system is needed that allows parents to restrict electronic payment usage more intuitively and flexibly by giving instructions in natural language.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes a natural language processing means, a customized setting generation means for interpreting and responding to requests from parents, a control means for applying the generated settings to the child's information processing device, and a payment restriction means for restricting the usage time and amount of electronic payments. This allows parents to intuitively manage their child's electronic payments using natural language and prevent inappropriate spending.

[0093] A "natural language processing device" is a device or program that has the function of analyzing requests in natural language provided by guardians and understanding their content.

[0094] A "custom setting generation means" is a device or program that has the function of generating specific settings to be applied to a child's information processing device based on an analyzed request.

[0095] "Control means" refers to a device or function that reflects the generated customized settings in the child's information processing device and performs actual operation or function restrictions.

[0096] "Monitoring means" refers to a device or program for monitoring data transmission and usage patterns in a child's information processing device.

[0097] A "reporting device" is a device or program that has the function of recording inappropriate activity detected through monitoring and notifying the guardian.

[0098] "Payment restriction measures" refer to devices or programs used to limit the time and amount of electronic payments.

[0099] The server analyzes instructions received from parents using natural language processing (NLP) and generates specific control policies. This process utilizes the Python programming language and the natural language processing library spaCy. The analyzed information is then sent to the device via Firebase. On the device, an application built with React Native controls the time and amount of electronic payments used according to the received policy. For example, if an attempt is made to exceed the limits set by the control policy, the details are reported to the server and the parent is notified. The control system also automatically adjusts the settings of the child's information processing device and applies predetermined rules. Monitoring measures track data transmission patterns and usage on the device.

[0100] As a concrete example, the server receives a prompt message from a parent such as, "Please notify me if my child's spending exceeds 1,000 yen per day." This message is parsed and applied as a policy to the child's electronic payments. If the child actually exceeds the specified amount, the device reports this information to the server, and a notification is immediately sent to the parent. In this way, the invention utilizes natural language to achieve intuitive parental controls, enabling flexible and appropriate management of a child's financial activities.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server receives natural language prompts entered by the user (parent / guardian). These prompts may include instructions such as, "Please restrict internet access after 10 PM." The server analyzes these prompts using natural language processing to extract the intent of the request.

[0104] Step 2:

[0105] The server uses a customized configuration generation mechanism based on the analysis results to generate control policies to be applied to the child's information processing device. The output for the analyzed input data includes specific configuration policies, which incorporate restrictions on the usage time and amount of electronic payments.

[0106] Step 3:

[0107] The device receives control policies sent from the server via Firebase. Based on the received policy data, the device sets restrictions on electronic payments within the React Native application. Specifically, it configures the application to block payments when certain time limits or price limits are reached.

[0108] Step 4:

[0109] The device monitors the child's usage using monitoring methods based on the configured restriction policies. In particular, it checks for recurring access patterns and data transmissions on the device to determine if there is any inappropriate activity. Based on the input monitoring data, an activity report is generated as output.

[0110] Step 5:

[0111] If inappropriate activity is detected, the device reports this information to the server. The reporting mechanism reacts, and the resulting generated report data is sent to the server. Upon receiving the report, the server uses a pre-configured notification method to send a notification to the parent or guardian. The notification includes details of the activity and information prompting further action.

[0112] Step 6:

[0113] Users (parents) receive notifications from the server and adjust control policies as needed. They can also receive real-time feedback through the system, allowing for enhanced control over their child's activities. The output of this process includes revised policies and their application to devices.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention combines an emotion engine with an AI parental control system to recognize user emotions and implement adaptive control accordingly. The system allows the server, terminal, and user to each play their respective roles, providing a more human-like interaction.

[0116] The server receives natural language requests from users and interprets their content using an analysis engine, as well as using an emotion engine to read the user's emotions. For example, if a user makes a request expressing anxiety or worry, the server adjusts the response content and the nuances of the setting rules based on that emotional information. The emotion engine also understands real-time changes in emotions through interaction with the user and provides feedback and advice.

[0117] The device receives settings and emotion-based instructions sent from the server and incorporates them into its operation. For example, it allows users to set stricter rules for their child's device use during specific times when they feel anxious. The device also has a monitoring function that tracks the child's device usage patterns and reports the information to the server.

[0118] Through this system, users can adjust device settings while expressing their emotions in natural language. They can also receive notifications and feedback about device usage generated by the system, taking their emotional information into account. In this way, the system is attuned to the user's emotions and helps them control their digital environment appropriately and flexibly.

[0119] This invention enables parents to respond to online risks in a flexible and emotionally sensitive manner, helping to provide a safer and more secure digital device experience for their children.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users enter natural language requests into the device to add or modify parental controls. The system can convey emotions based on the user's tone of voice and keywords in the text.

[0123] Step 2:

[0124] The server receives natural language requests sent from the device and interprets their content using natural language processing tools. During this process, it utilizes an emotion engine to extract the user's emotional state from the input information.

[0125] Step 3:

[0126] The server generates appropriate parental rules using a customized settings generation mechanism based on the intent of the request and the user's emotional state. For example, if the user expresses concern, a stricter access restriction policy will be set.

[0127] Step 4:

[0128] The server sends the generated access restriction settings to the terminal. This includes information about how to respond based on emotions.

[0129] Step 5:

[0130] The device expands the received configuration information and applies it to the device's control system. This includes setting restrictions on the use of specific apps and configuring communication time zones.

[0131] Step 6:

[0132] The terminal continuously monitors device usage using monitoring mechanisms. If any abnormalities or inappropriate use are detected, the terminal reports that information to the server.

[0133] Step 7:

[0134] The server combines the reported data with the sentiment engine's evaluation to send appropriate feedback or warnings to the user. For example, if a child performs a prohibited action, a warning message in a softer tone may be selected.

[0135] Step 8:

[0136] Users can receive notifications from the server and, if necessary, request readjustments to their settings by conveying new emotions. By obtaining emotion-conscious feedback, more accurate device management becomes possible.

[0137] (Example 2)

[0138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0139] In recent years, with the proliferation of digital devices, the risks to children from exposure to inappropriate content and prolonged device use have increased. Parents need to address these risks while adjusting restrictions on digital device use, taking into account their children's emotional state. However, conventional systems have made it difficult to achieve this efficiently and flexibly. In particular, controlling device use in accordance with a child's emotions has been insufficient.

[0140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0141] In this invention, the server includes means for analyzing natural language, means for extracting emotions from input linguistic information, and means for interpreting requests from parents and generating responses based on the extracted emotions. This allows parents to flexibly adjust device usage restrictions while considering their child's emotional state and receive appropriate feedback.

[0142] "Means for analyzing natural language" refers to processing devices and programs that analyze natural language text entered by a user and understand its meaning and intent.

[0143] "Means for extracting emotions" refers to functions or processing devices that identify emotional elements from user input information and detect the type and intensity of those emotions.

[0144] "Generation means" refers to a processing device or program for dynamically generating user-appropriate responses and settings based on the results of natural language analysis and extracted sentiment information.

[0145] "Means of application" refers to control devices and operating programs used to reflect the generated responses and settings in digital devices.

[0146] "Monitoring methods" refer to functions and systems for observing the usage and communication patterns of digital devices, and for recording and analyzing them.

[0147] "Means of reporting" refers to devices or programs that notify designated recipients of information when inappropriate activity is detected.

[0148] This invention combines an AI-based parental control system with emotion analysis capabilities. This system assigns specific roles to the server, terminal, and user, providing advanced interaction to fulfill user requests.

[0149] The server is the core of this system, receiving user input and parsing natural language. Natural language processing uses libraries such as NLTK and SpaCy, implemented in Python. This analysis helps to understand the intent behind the input language information. Furthermore, the server uses sentiment analysis models, such as BERT, to extract the user's emotional state. This emotional information is used to appropriately adjust responses to the user and set device usage restrictions.

[0150] The device has the ability to control the user's digital devices based on instructions from the server. Specifically, a dedicated application runs on the device to adjust device usage time and restrict accessible content. This application works in conjunction with Android® and iOS device management APIs to adjust device settings according to the parent's wishes.

[0151] Users can send requests to this system through natural language and receive responses that take their emotions into consideration. For example, if a user sends a request such as "I'm worried my child is playing too many online games," the system will receive the anxiety and send feedback such as advice on device use and setting limits. The generative AI model utilizes suggestions for appropriate device use. An example of a prompt might be, "Please explain how the emotion engine handles the user's anxiety."

[0152] This system provides users with a sense of security and proper management in their digital environment.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] Users input requests in natural language through applications on their devices. For example, they might express specific anxieties such as, "I'm worried my child is playing too many online games." The input data is textual information that includes the user's emotions and intentions.

[0156] Step 2:

[0157] The server receives requests sent by users. It performs text analysis on the input natural language data using Python's natural language processing libraries (such as NLTK and SpaCy) to extract the intent. The output is the analysis result regarding specific requests or instructions.

[0158] Step 3:

[0159] The server uses generative AI models such as BERT to perform sentiment analysis and extract emotions from user input. The analyzed emotional data is output and categorized into categories such as anxiety and worry.

[0160] Step 4:

[0161] The server generates adaptive responses based on natural language processing and sentiment analysis results. To achieve this, it utilizes a generative AI model to create suggestions and advice that take the user's emotions into account. The output is a generated text message.

[0162] Step 5:

[0163] The server sends instructions regarding device management to the terminal along with the generated response. These instructions may include adjusting device limits or changing monitoring settings. The output consists of specific control commands.

[0164] Step 6:

[0165] The device receives instructions from the server and applies the settings to the device. Specifically, applications on Android and iOS use device management APIs to perform operations such as limiting game usage time. The input is configuration information from the server, and the output is the change in the device's state.

[0166] Step 7:

[0167] The user receives feedback from the terminal based on responses and control instructions generated by the server. As output, the user gains a sense of security by obtaining device control information that addresses their concerns.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] Traditional parental control systems could only offer uniform controls based on parental policies and children's usage patterns, failing to address the emotional shifts of users in the digital environment. This resulted in insufficient emotional care and could be a source of stress for users in environments requiring flexible responses. Furthermore, they lacked the ability to recognize and appropriately address children's anxiety and stress.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0172] In this invention, the server includes natural language processing means, customized setting generation means for interpreting and responding to requests from parents, and emotion analysis means for analyzing the user's emotions. This enables device control in accordance with the user's emotions, selection of appropriate content, and promotion of parent-child communication.

[0173] "Natural language processing means" refers to technology that analyzes natural language input from users and processes it to understand its content.

[0174] The "customized settings generation method" is a function that generates settings tailored to the child based on requests from parents.

[0175] "Control means" refers to a function that applies the generated settings to the child's communication device and manages its operation.

[0176] "Monitoring means" refers to techniques for continuously observing data transmission and usage patterns on children's communication devices.

[0177] A "reporting mechanism" is a function that detects inappropriate activity and notifies parents of that information.

[0178] "Emotional analysis tools" are technologies used to analyze a user's emotions and determine the type and intensity of those emotions.

[0179] "Adaptive control means" refers to a function that adjusts content filtering settings based on the results of sentiment analysis, thereby flexibly changing the usage environment.

[0180] This system is server-centric and implemented using natural language processing, customization setting generation, and sentiment analysis. The server receives requests from users in natural language and analyzes their content using the natural language processing means. This allows the server to understand the user's intentions and requests, and then generates corresponding device settings through the customization setting generation means.

[0181] The terminal applies settings received from the server to the child's communication device using control means, and adjusts operations using adaptive control means based on the user's emotional data acquired by emotion analysis means. This allows the child's access to digital content to be flexibly controlled according to their emotions.

[0182] Furthermore, the device uses monitoring mechanisms to continuously monitor data transmission and usage patterns on the child's communication device, and if inappropriate activity is detected through reporting mechanisms, it notifies the parent or guardian. This allows the parent or guardian to immediately understand the situation and take appropriate action.

[0183] Users can adjust device settings while expressing their emotions in natural language through the device. This interaction allows for emotional support for anxieties and worries related to device use.

[0184] For example, if a child doing schoolwork shows signs of anxiety through sentiment analysis during a search, the system adjusts its filtering settings. This allows the child to access appropriate information and feel secure. Furthermore, this system can be used as a conversational agent for task management and learning. By using a generated AI model to create prompts such as, "Please advise on how to care for a child who is feeling anxious," appropriate responses can be provided.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server receives requests from users in natural language. It receives text data sent by the user through their device as input and analyzes its intent using natural language processing tools. The information obtained as a result of the analysis includes requests regarding device settings and expressions of emotion.

[0188] Step 2:

[0189] The server generates device settings through a customized settings generation mechanism based on the results of natural language processing. In this step, it processes the analysis results obtained as input and outputs the optimal device settings corresponding to the user's intent. The generated settings are then ready to be applied to the child's communication device.

[0190] Step 3:

[0191] The terminal executes device settings sent from the server using control means. The input is configuration data received from the server, and by applying this to the child's communication device, the use of the device is managed.

[0192] Step 4:

[0193] The device monitors the user's emotional state in real time using emotion analysis technology. It takes user voice and text data as input, performs emotion analysis, and generates output information on the type and intensity of emotions.

[0194] Step 5:

[0195] Based on the results of emotion analysis, the device adjusts its content filtering using adaptive control means. By inputting emotion information, the filtering strictness is dynamically changed according to the user's psychological state, providing an optimal user environment.

[0196] Step 6:

[0197] The device uses monitoring mechanisms to track the usage patterns and data transmission status of children's communication devices. It analyzes daily usage history as input data, and if inappropriate activity is detected, it notifies the parent or guardian using a reporting mechanism and outputs that information.

[0198] Step 7:

[0199] Users can provide emotionally responsive feedback about device settings and controls via their device. This feedback is used as prompts by a generative AI model, which then outputs appropriate advice and information to the user.

[0200] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0206] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0207] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0208] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0210] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0211] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0213] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0214] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0216] This invention is an AI parental control system that allows parents to manage their children's communication devices using natural language. This system works in cooperation with the server, terminals, and users to achieve flexible and intuitive control tailored to each family's policies.

[0217] The server receives natural language requests sent from parents via smartphones or PCs. These requests may include specific instructions, such as "Please restrict internet access after 10 PM." The server analyzes these requests using natural language processing to extract their intent. Based on the extracted information, a customization settings generation system creates a policy and prepares the settings to be sent to the device.

[0218] The device applies the received policy to the device's configuration layer. For example, if it restricts internet access during a set time period, it configures the device to block communication and prevent unintended access. The device also uses monitoring mechanisms to continuously monitor how the child is using the device. If inappropriate behavior, such as an attempt to access the device during the restricted time period, is detected, the device reports this information to the server.

[0219] By receiving notifications from the server, users can monitor their child's device usage and adjust settings accordingly. For example, if a child attempts to access an app within a time limit, the server will send a warning to the user and provide an opportunity to readjust settings if necessary.

[0220] In this way, the invention provides parental controls that meet family needs through intuitive operation, supporting a safe online experience for children.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users enter a request in natural language on their smartphone or PC, stating, "I want to be unable to use the internet after 10 PM," and send it to the system.

[0224] Step 2:

[0225] The server processes natural language requests received from users using its parsing engine and interprets their meaning. Specifically, it identifies time zones and restricted activities.

[0226] Step 3:

[0227] The server generates a customized access restriction policy based on the interpretation results. This policy is configured to reflect the user's requests.

[0228] Step 4:

[0229] The server sends the generated access restriction policy to the terminal and provides instructions for applying the settings.

[0230] Step 5:

[0231] The device deploys the policy received from the server and reflects it in its settings. For example, it might configure the device to block internet access during specified time periods.

[0232] Step 6:

[0233] The device uses monitoring functions to check whether the settings are working correctly and detects actions that violate specific policies, such as access attempts during restricted time periods.

[0234] Step 7:

[0235] The device reports any inappropriate access to the server.

[0236] Step 8:

[0237] The server analyzes the information reported from the terminal and sends a notification to the user. This notification includes information about what happened and what action is required.

[0238] Step 9:

[0239] Users can receive notifications from the server and send requests to readjust their settings as needed.

[0240] Through this process, the system provides continuous and dynamic parental controls that meet the needs of the household.

[0241] (Example 1)

[0242] Next, we will describe Example 1. 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".

[0243] There is a need for a system that allows parents to easily and flexibly set usage restrictions on information processing devices used by children, and to effectively monitor and control inappropriate activities. Traditional manual control is time-consuming and laborious, and sometimes makes it difficult to adequately protect children's safety. Therefore, the challenge is to provide a system that parents can operate intuitively and that offers flexible control to meet diverse needs.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes a natural language analyzer, a custom setting generation device, and a warning device. This allows the parent to make a request in natural language, analyze the request using a generative AI model, automatically generate a policy suitable for the information processing device, and quickly and effectively provide warnings and notifications to the parent.

[0246] A "natural language processing device" is a device that interprets requests made in natural language by parents and processes them to accurately understand their intent.

[0247] A "customization setting generation device" is a device that automatically generates policies and settings to be applied to an information processing device based on the content of an analyzed request.

[0248] A "control device" is a device that applies a generated policy to an information processing device and enforces specified restrictions and permissions.

[0249] A "monitoring device" is a device that continuously monitors information transmission and usage patterns on an information processing device and identifies inappropriate activities.

[0250] A "reporting device" is a device that notifies parents of inappropriate activities detected by a monitoring device.

[0251] A "warning device" is a device that issues a warning to a parent if inappropriate activity occurs while using an information processing device.

[0252] A "transmitting device" is a device that has the function of sending the generated policy to an information processing device.

[0253] This invention is an AI parental control system that allows parents to manage their children's information processing devices using natural language. The invention achieves flexible and intuitive control through the cooperation of three entities: a server, a terminal, and a user.

[0254] The server receives natural language requests from parents via smartphones or personal computers. Generative AI models for natural language processing are used to analyze these requests. For example, using a natural language model like GPT-3, the server accurately understands the request and extracts the intent behind the instruction. Based on the extracted intent, a customization settings generator creates a policy. This policy is sent to the child's device and applied by the device's built-in control unit.

[0255] The terminal reflects the received policy in its device settings. For example, it adjusts network settings to block communication in order to restrict internet access for a specific period of time. The monitoring device continuously observes the terminal's usage and reports any inappropriate activity, such as attempts to access the network during the restricted time period, to the server.

[0256] Users can monitor their child's device usage through notifications from the server. For example, if an attempt is made to access a specific app within a time limit, the user receives a warning from the server and readjusts the settings based on that information. As a concrete example of a prompt message, if the user enters a command such as, "Please restrict my child's access to YouTube when they try to watch it at night," the system will generate and implement a policy in response to this request. In this way, the invention supports a safe online environment for children, tailored to the needs of the family.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server receives natural language requests sent from parents via smartphones or personal computers. These requests include specific instructions such as, "Please restrict internet access after 10 PM." The server receives these natural language instructions as input.

[0260] Step 2:

[0261] The server analyzes the received request using a generative AI model. At this time, the server uses a natural language processing unit to syntactically analyze the input instruction and extract its intent. As a result of the analysis, a specific control objective, such as "prohibit internet access after 10 PM," is extracted. The output is the analyzed intent.

[0262] Step 3:

[0263] The server generates appropriate policies using a customization setting generator based on the analysis results. For example, based on the extracted intent, a setting is generated to restrict internet access during specific time periods. The input is the analyzed intent, and the output is the policy configuration information.

[0264] Step 4:

[0265] The server sends the generated policy to the terminal. The sent information includes specific details such as which applications or websites should be restricted to access during which time periods. At this stage, the policy configuration information is the input, and sending it to the terminal is the output.

[0266] Step 5:

[0267] The terminal applies the policy received from the server to the settings of the information processing device. Specifically, the network settings are adjusted based on the received policy, and communication is blocked at the specified time. The input is policy information received from the server, and the output is a change in the actual device settings.

[0268] Step 6:

[0269] The device monitors the child's device usage via a monitoring system. If an inappropriate access attempt is detected, the system records the information and prepares to report it to the server. The input is the device usage log, and the output is a record of inappropriate behavior.

[0270] Step 7:

[0271] The server receives reports from the device and issues warnings to the parent. For example, if an attempt is made to access a website within the time limit, this information is notified to the parent. The input is the information reported from the device, and the output is the warning notification to the parent.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] Traditional parental control systems focus on restricting children's use of communication devices and the internet, but have struggled to extend control to electronic payments. Therefore, there is a need for a system that proactively prevents inappropriate spending and electronic payments that disregard appropriate time zones. In particular, a system is needed that allows parents to restrict electronic payment usage more intuitively and flexibly by giving instructions in natural language.

[0275] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0276] In this invention, the server includes a natural language processing means, a customized setting generation means for interpreting and responding to requests from parents, a control means for applying the generated settings to the child's information processing device, and a payment restriction means for restricting the usage time and amount of electronic payments. This allows parents to intuitively manage their child's electronic payments using natural language and prevent inappropriate spending.

[0277] A "natural language processing device" is a device or program that has the function of analyzing requests in natural language provided by guardians and understanding their content.

[0278] A "custom setting generation means" is a device or program that has the function of generating specific settings to be applied to a child's information processing device based on an analyzed request.

[0279] "Control means" refers to a device or function that reflects the generated customized settings in the child's information processing device and performs actual operation or function restrictions.

[0280] The "monitoring means" is a device or program for monitoring data transmission and usage patterns in a child's information processing device.

[0281] The "reporting means" is a device or program that has the function of recording and notifying the guardian when inappropriate activities are detected through monitoring.

[0282] The "payment restriction means" is a device or program for restricting the usage time and amount in electronic payments.

[0283] The server analyzes the instructions received from the guardian using natural language analysis means and generates a specific control policy. At this time, the Python programming language and spaCy, a natural language processing library, are used. The analyzed content is then transmitted to the terminal via Firebase. On the terminal, an application built with React Native controls the usage time and amount of electronic payments according to the received policy. For example, if an attempt is made to make an expenditure exceeding the limit according to the control policy, the details are reported to the server and notified to the guardian. Also, the control means automatically adjusts the settings of the child's information processing device and applies predetermined rules. The monitoring means for this tracks data transmission patterns and usage status on the terminal.

[0284] As a specific example, the server receives a prompt sentence such as "Please notify me when the expenditure exceeds 1000 yen per day" from the guardian. This sentence is analyzed and applied as a policy to the child's electronic payment. When the child actually exceeds a specific amount, the terminal reports the information to the server and an immediate notification is sent to the guardian. In this way, the invention utilizes natural language to realize intuitive parental control and enables flexible and appropriate management of the child's economic activities.

[0285] The flow of the specific process in Application Example 1 will be explained using FIG. 12.

[0286] Step 1:

[0287] The server receives a natural language prompt sentence input by the user (guardian). The input prompt sentence includes instructions such as "I want the Internet to be restricted after 10 pm". The server analyzes this prompt sentence by natural language analysis means and extracts the intention of the request.

[0288] Step 2:

[0289] Based on the analysis result, the server uses customization setting generation means to generate a control policy applicable to the child's information processing device. The output for the input analyzed data includes specific setting policies, and restrictions on the usage time and amount of electronic payment are incorporated into the settings.

[0290] Step 3:

[0291] The terminal receives the control policy sent from the server through Firebase. Based on the received policy data, the terminal sets restrictions on electronic payment within the React Native application. Specifically, it is set to block the payment when a certain time period or the upper limit of the amount is reached.

[0292] Step 4:

[0293] Based on the set restriction policy, the terminal uses monitoring means to monitor the child's usage situation. In particular, it checks the repeated access patterns and data transmission on the device to determine whether there is any inappropriate activity. An activity report is generated as the output according to the input monitoring data.

[0294] Step 5:

[0295] If inappropriate activity is detected, the device reports this information to the server. The reporting mechanism reacts, and the resulting generated report data is sent to the server. Upon receiving the report, the server uses a pre-configured notification method to send a notification to the parent or guardian. The notification includes details of the activity and information prompting further action.

[0296] Step 6:

[0297] Users (parents) receive notifications from the server and adjust control policies as needed. They can also receive real-time feedback through the system, allowing for enhanced control over their child's activities. The output of this process includes revised policies and their application to devices.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention combines an emotion engine with an AI parental control system to recognize user emotions and implement adaptive control accordingly. The system allows the server, terminal, and user to each play their respective roles, providing a more human-like interaction.

[0300] The server receives natural language requests from users and interprets their content using an analysis engine, as well as using an emotion engine to read the user's emotions. For example, if a user makes a request expressing anxiety or worry, the server adjusts the response content and the nuances of the setting rules based on that emotional information. The emotion engine also understands real-time changes in emotions through interaction with the user and provides feedback and advice.

[0301] The terminal receives instructions based on settings and emotions sent from the server and reflects them in the operation of the device. For example, it becomes possible to set the rules for a child's device usage more strictly during specific time periods when the user feels anxious. Also, the terminal has a monitoring function, monitors the child's device usage pattern, and reports the obtained information to the server.

[0302] The user can adjust the device settings while expressing emotions in natural language through this system. Also, it is possible to receive notifications and feedback regarding device usage generated by the system considering emotion information. In this way, the system helps to adapt to the user's emotions and appropriately and flexibly control the digital environment.

[0303] According to this invention, guardians can respond to online risks in a flexible and emotion - considerate manner, which helps to provide a safer and more reassuring digital device usage experience for children.

[0304] The processing flow will be described below.

[0305] Step 1:

[0306] The user inputs a natural - language request to the device to add or change parental controls. At this time, emotions can be conveyed based on the tone of the user's voice and keywords included in the text.

[0307] Step 2:

[0308] The server receives the natural - language request sent from the device and interprets the content using natural - language analysis means. At this time, an emotion engine is utilized to extract the user's emotional state from the input information.

[0309] Step 3:

[0310] The server generates appropriate parental rules using a customized settings generation mechanism based on the intent of the request and the user's emotional state. For example, if the user expresses concern, a stricter access restriction policy will be set.

[0311] Step 4:

[0312] The server sends the generated access restriction settings to the terminal. This includes information about how to respond based on emotions.

[0313] Step 5:

[0314] The device expands the received configuration information and applies it to the device's control system. This includes setting restrictions on the use of specific apps and configuring communication time zones.

[0315] Step 6:

[0316] The terminal continuously monitors device usage using monitoring mechanisms. If any abnormalities or inappropriate use are detected, the terminal reports that information to the server.

[0317] Step 7:

[0318] The server combines the reported data with the sentiment engine's evaluation to send appropriate feedback or warnings to the user. For example, if a child performs a prohibited action, a warning message in a softer tone may be selected.

[0319] Step 8:

[0320] Users can receive notifications from the server and, if necessary, request readjustments to their settings by conveying new emotions. By obtaining emotion-conscious feedback, more accurate device management becomes possible.

[0321] (Example 2)

[0322] Next, we will describe Example 2. 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".

[0323] In recent years, with the proliferation of digital devices, the risks to children from exposure to inappropriate content and prolonged device use have increased. Parents need to address these risks while adjusting restrictions on digital device use, taking into account their children's emotional state. However, conventional systems have made it difficult to achieve this efficiently and flexibly. In particular, controlling device use in accordance with a child's emotions has been insufficient.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for analyzing natural language, means for extracting emotions from input linguistic information, and means for interpreting requests from parents and generating responses based on the extracted emotions. This allows parents to flexibly adjust device usage restrictions while considering their child's emotional state and receive appropriate feedback.

[0326] "Means for analyzing natural language" refers to processing devices and programs that analyze natural language text entered by a user and understand its meaning and intent.

[0327] "Means for extracting emotions" refers to functions or processing devices that identify emotional elements from user input information and detect the type and intensity of those emotions.

[0328] "Generation means" refers to a processing device or program for dynamically generating user-appropriate responses and settings based on the results of natural language analysis and extracted sentiment information.

[0329] "Means of application" refers to control devices and operating programs used to reflect the generated responses and settings in digital devices.

[0330] "Monitoring methods" refer to functions and systems for observing the usage and communication patterns of digital devices, and for recording and analyzing them.

[0331] "Means of reporting" refers to devices or programs that notify designated recipients of information when inappropriate activity is detected.

[0332] This invention combines an AI-based parental control system with emotion analysis capabilities. This system assigns specific roles to the server, terminal, and user, providing advanced interaction to fulfill user requests.

[0333] The server is the core of this system, receiving user input and parsing natural language. Natural language processing uses libraries such as NLTK and SpaCy, implemented in Python. This analysis helps to understand the intent behind the input language information. Furthermore, the server uses sentiment analysis models, such as BERT, to extract the user's emotional state. This emotional information is used to appropriately adjust responses to the user and set device usage restrictions.

[0334] The device has the ability to control the user's digital devices based on instructions from the server. Specifically, a dedicated application runs on the device to adjust device usage time and restrict accessible content. This application works in conjunction with Android and iOS device management APIs to adjust device settings according to the parent's wishes.

[0335] Users can send requests to this system through natural language and receive responses that take their emotions into consideration. For example, if a user sends a request such as "I'm worried my child is playing too many online games," the system will receive the anxiety and send feedback such as advice on device use and setting limits. The generative AI model utilizes suggestions for appropriate device use. An example of a prompt might be, "Please explain how the emotion engine handles the user's anxiety."

[0336] This system provides users with a sense of security and proper management in their digital environment.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] Users input requests in natural language through applications on their devices. For example, they might express specific anxieties such as, "I'm worried my child is playing too many online games." The input data is textual information that includes the user's emotions and intentions.

[0340] Step 2:

[0341] The server receives requests sent by users. It performs text analysis on the input natural language data using Python's natural language processing libraries (such as NLTK and SpaCy) to extract the intent. The output is the analysis result regarding specific requests or instructions.

[0342] Step 3:

[0343] The server uses generative AI models such as BERT to perform sentiment analysis and extract emotions from user input. The analyzed emotional data is output and categorized into categories such as anxiety and worry.

[0344] Step 4:

[0345] The server generates adaptive responses based on natural language processing and sentiment analysis results. To achieve this, it utilizes a generative AI model to create suggestions and advice that take the user's emotions into account. The output is a generated text message.

[0346] Step 5:

[0347] The server sends instructions regarding device management to the terminal along with the generated response. These instructions may include adjusting device limits or changing monitoring settings. The output consists of specific control commands.

[0348] Step 6:

[0349] The device receives instructions from the server and applies the settings to the device. Specifically, applications on Android and iOS use device management APIs to perform operations such as limiting game usage time. The input is configuration information from the server, and the output is the change in the device's state.

[0350] Step 7:

[0351] The user receives feedback from the terminal based on responses and control instructions generated by the server. As output, the user gains a sense of security by obtaining device control information that addresses their concerns.

[0352] (Application Example 2)

[0353] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0354] Traditional parental control systems could only offer uniform controls based on parental policies and children's usage patterns, failing to address the emotional shifts of users in the digital environment. This resulted in insufficient emotional care and could be a source of stress for users in environments requiring flexible responses. Furthermore, they lacked the ability to recognize and appropriately address children's anxiety and stress.

[0355] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0356] In this invention, the server includes natural language processing means, customized setting generation means for interpreting and responding to requests from parents, and emotion analysis means for analyzing the user's emotions. This enables device control in accordance with the user's emotions, selection of appropriate content, and promotion of parent-child communication.

[0357] "Natural language processing means" refers to technology that analyzes natural language input from users and processes it to understand its content.

[0358] The "customized settings generation method" is a function that generates settings tailored to the child based on requests from parents.

[0359] "Control means" refers to a function that applies the generated settings to the child's communication device and manages its operation.

[0360] "Monitoring means" refers to techniques for continuously observing data transmission and usage patterns on children's communication devices.

[0361] A "reporting mechanism" is a function that detects inappropriate activity and notifies parents of that information.

[0362] "Emotional analysis tools" are technologies used to analyze a user's emotions and determine the type and intensity of those emotions.

[0363] "Adaptive control means" refers to a function that adjusts content filtering settings based on the results of sentiment analysis, thereby flexibly changing the usage environment.

[0364] This system is server-centric and implemented using natural language processing, customization setting generation, and sentiment analysis. The server receives requests from users in natural language and analyzes their content using the natural language processing means. This allows the server to understand the user's intentions and requests, and then generates corresponding device settings through the customization setting generation means.

[0365] The terminal applies settings received from the server to the child's communication device using control means, and adjusts operations using adaptive control means based on the user's emotional data acquired by emotion analysis means. This allows the child's access to digital content to be flexibly controlled according to their emotions.

[0366] Furthermore, the device uses monitoring mechanisms to continuously monitor data transmission and usage patterns on the child's communication device, and if inappropriate activity is detected through reporting mechanisms, it notifies the parent or guardian. This allows the parent or guardian to immediately understand the situation and take appropriate action.

[0367] Users can adjust device settings while expressing their emotions in natural language through the device. This interaction allows for emotional support for anxieties and worries related to device use.

[0368] For example, if a child doing schoolwork shows signs of anxiety through sentiment analysis during a search, the system adjusts its filtering settings. This allows the child to access appropriate information and feel secure. Furthermore, this system can be used as a conversational agent for task management and learning. By using a generated AI model to create prompts such as, "Please advise on how to care for a child who is feeling anxious," appropriate responses can be provided.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The server receives requests from users in natural language. It receives text data sent by the user through their device as input and analyzes its intent using natural language processing tools. The information obtained as a result of the analysis includes requests regarding device settings and expressions of emotion.

[0372] Step 2:

[0373] The server generates device settings through a customized settings generation mechanism based on the results of natural language processing. In this step, it processes the analysis results obtained as input and outputs the optimal device settings corresponding to the user's intent. The generated settings are then ready to be applied to the child's communication device.

[0374] Step 3:

[0375] The terminal executes device settings sent from the server using control means. The input is configuration data received from the server, and by applying this to the child's communication device, the use of the device is managed.

[0376] Step 4:

[0377] The device monitors the user's emotional state in real time using emotion analysis technology. It takes user voice and text data as input, performs emotion analysis, and generates output information on the type and intensity of emotions.

[0378] Step 5:

[0379] Based on the results of emotion analysis, the device adjusts its content filtering using adaptive control means. By inputting emotion information, the filtering strictness is dynamically changed according to the user's psychological state, providing an optimal user environment.

[0380] Step 6:

[0381] The device uses monitoring mechanisms to track the usage patterns and data transmission status of children's communication devices. It analyzes daily usage history as input data, and if inappropriate activity is detected, it notifies the parent or guardian using a reporting mechanism and outputs that information.

[0382] Step 7:

[0383] Users can provide emotionally responsive feedback about device settings and controls via their device. This feedback is used as prompts by a generative AI model, which then outputs appropriate advice and information to the user.

[0384] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention is an AI parental control system that allows parents to manage their children's communication devices using natural language. This system works in cooperation with the server, terminals, and users to achieve flexible and intuitive control tailored to each family's policies.

[0401] The server receives natural language requests sent from parents via smartphones or PCs. These requests may include specific instructions, such as "Please restrict internet access after 10 PM." The server analyzes these requests using natural language processing to extract their intent. Based on the extracted information, a customization settings generation system creates a policy and prepares the settings to be sent to the device.

[0402] The device applies the received policy to the device's configuration layer. For example, if it restricts internet access during a set time period, it configures the device to block communication and prevent unintended access. The device also uses monitoring mechanisms to continuously monitor how the child is using the device. If inappropriate behavior, such as an attempt to access the device during the restricted time period, is detected, the device reports this information to the server.

[0403] By receiving notifications from the server, users can monitor their child's device usage and adjust settings accordingly. For example, if a child attempts to access an app within a time limit, the server will send a warning to the user and provide an opportunity to readjust settings if necessary.

[0404] In this way, the invention provides parental controls that meet family needs through intuitive operation, supporting a safe online experience for children.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] Users enter a request in natural language on their smartphone or PC, stating, "I want to be unable to use the internet after 10 PM," and send it to the system.

[0408] Step 2:

[0409] The server processes natural language requests received from users using its parsing engine and interprets their meaning. Specifically, it identifies time zones and restricted activities.

[0410] Step 3:

[0411] The server generates a customized access restriction policy based on the interpretation results. This policy is configured to reflect the user's requests.

[0412] Step 4:

[0413] The server sends the generated access restriction policy to the terminal and provides instructions for applying the settings.

[0414] Step 5:

[0415] The device deploys the policy received from the server and reflects it in its settings. For example, it might configure the device to block internet access during specified time periods.

[0416] Step 6:

[0417] The device uses monitoring functions to check whether the settings are working correctly and detects actions that violate specific policies, such as access attempts during restricted time periods.

[0418] Step 7:

[0419] The device reports any inappropriate access to the server.

[0420] Step 8:

[0421] The server analyzes the information reported from the terminal and sends a notification to the user. This notification includes information about what happened and what action is required.

[0422] Step 9:

[0423] Users can receive notifications from the server and send requests to readjust their settings as needed.

[0424] Through this process, the system provides continuous and dynamic parental controls that meet the needs of the household.

[0425] (Example 1)

[0426] Next, we will describe Example 1. 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."

[0427] There is a need for a system that allows parents to easily and flexibly set usage restrictions on information processing devices used by children, and to effectively monitor and control inappropriate activities. Traditional manual control is time-consuming and laborious, and sometimes makes it difficult to adequately protect children's safety. Therefore, the challenge is to provide a system that parents can operate intuitively and that offers flexible control to meet diverse needs.

[0428] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0429] In this invention, the server includes a natural language analyzer, a custom setting generation device, and a warning device. This allows the parent to make a request in natural language, analyze the request using a generative AI model, automatically generate a policy suitable for the information processing device, and quickly and effectively provide warnings and notifications to the parent.

[0430] A "natural language processing device" is a device that interprets requests made in natural language by parents and processes them to accurately understand their intent.

[0431] A "customization setting generation device" is a device that automatically generates policies and settings to be applied to an information processing device based on the content of an analyzed request.

[0432] A "control device" is a device that applies a generated policy to an information processing device and enforces specified restrictions and permissions.

[0433] A "monitoring device" is a device that continuously monitors information transmission and usage patterns on an information processing device and identifies inappropriate activities.

[0434] A "reporting device" is a device that notifies parents of inappropriate activities detected by a monitoring device.

[0435] A "warning device" is a device that issues a warning to a parent if inappropriate activity occurs while using an information processing device.

[0436] A "transmitting device" is a device that has the function of sending the generated policy to an information processing device.

[0437] This invention is an AI parental control system that allows parents to manage their children's information processing devices using natural language. The invention achieves flexible and intuitive control through the cooperation of three entities: a server, a terminal, and a user.

[0438] The server receives natural language requests from parents via smartphones or personal computers. Generative AI models for natural language processing are used to analyze these requests. For example, using a natural language model like GPT-3, the server accurately understands the request and extracts the intent behind the instruction. Based on the extracted intent, a customization settings generator creates a policy. This policy is sent to the child's device and applied by the device's built-in control unit.

[0439] The terminal reflects the received policy in its device settings. For example, it adjusts network settings to block communication in order to restrict internet access for a specific period of time. The monitoring device continuously observes the terminal's usage and reports any inappropriate activity, such as attempts to access the network during the restricted time period, to the server.

[0440] Users can monitor their child's device usage through notifications from the server. For example, if an attempt is made to access a specific app within a time limit, the user receives a warning from the server and readjusts the settings based on that information. As a concrete example of a prompt message, if the user enters a command such as, "Please restrict my child's access to YouTube when they try to watch it at night," the system will generate and implement a policy in response to this request. In this way, the invention supports a safe online environment for children, tailored to the needs of the family.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server receives natural language requests sent from parents via smartphones or personal computers. These requests include specific instructions such as, "Please restrict internet access after 10 PM." The server receives these natural language instructions as input.

[0444] Step 2:

[0445] The server analyzes the received request using a generative AI model. At this time, the server uses a natural language processing unit to syntactically analyze the input instruction and extract its intent. As a result of the analysis, a specific control objective, such as "prohibit internet access after 10 PM," is extracted. The output is the analyzed intent.

[0446] Step 3:

[0447] The server generates appropriate policies using a customization setting generator based on the analysis results. For example, based on the extracted intent, a setting is generated to restrict internet access during specific time periods. The input is the analyzed intent, and the output is the policy configuration information.

[0448] Step 4:

[0449] The server sends the generated policy to the terminal. The sent information includes specific details such as which applications or websites should be restricted to access during which time periods. At this stage, the policy configuration information is the input, and sending it to the terminal is the output.

[0450] Step 5:

[0451] The terminal applies the policy received from the server to the settings of the information processing device. Specifically, the network settings are adjusted based on the received policy, and communication is blocked at the specified time. The input is policy information received from the server, and the output is a change in the actual device settings.

[0452] Step 6:

[0453] The device monitors the child's device usage via a monitoring system. If an inappropriate access attempt is detected, the system records the information and prepares to report it to the server. The input is the device usage log, and the output is a record of inappropriate behavior.

[0454] Step 7:

[0455] The server receives reports from the device and issues warnings to the parent. For example, if an attempt is made to access a website within the time limit, this information is notified to the parent. The input is the information reported from the device, and the output is the warning notification to the parent.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, 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."

[0458] Traditional parental control systems focus on restricting children's use of communication devices and the internet, but have struggled to extend control to electronic payments. Therefore, there is a need for a system that proactively prevents inappropriate spending and electronic payments that disregard appropriate time zones. In particular, a system is needed that allows parents to restrict electronic payment usage more intuitively and flexibly by giving instructions in natural language.

[0459] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0460] In this invention, the server includes a natural language processing means, a customized setting generation means for interpreting and responding to requests from parents, a control means for applying the generated settings to the child's information processing device, and a payment restriction means for restricting the usage time and amount of electronic payments. This allows parents to intuitively manage their child's electronic payments using natural language and prevent inappropriate spending.

[0461] A "natural language processing device" is a device or program that has the function of analyzing requests in natural language provided by guardians and understanding their content.

[0462] A "custom setting generation means" is a device or program that has the function of generating specific settings to be applied to a child's information processing device based on an analyzed request.

[0463] "Control means" refers to a device or function that reflects the generated customized settings in the child's information processing device and performs actual operation or function restrictions.

[0464] "Monitoring means" refers to a device or program for monitoring data transmission and usage patterns in a child's information processing device.

[0465] A "reporting device" is a device or program that has the function of recording inappropriate activity detected through monitoring and notifying the guardian.

[0466] "Payment restriction measures" refer to devices or programs used to limit the time and amount of electronic payments.

[0467] The server analyzes instructions received from parents using natural language processing (NLP) and generates specific control policies. This process utilizes the Python programming language and the natural language processing library spaCy. The analyzed information is then sent to the device via Firebase. On the device, an application built with React Native controls the time and amount of electronic payments used according to the received policy. For example, if an attempt is made to exceed the limits set by the control policy, the details are reported to the server and the parent is notified. The control system also automatically adjusts the settings of the child's information processing device and applies predetermined rules. Monitoring measures track data transmission patterns and usage on the device.

[0468] As a concrete example, the server receives a prompt message from a parent such as, "Please notify me if my child's spending exceeds 1,000 yen per day." This message is parsed and applied as a policy to the child's electronic payments. If the child actually exceeds the specified amount, the device reports this information to the server, and a notification is immediately sent to the parent. In this way, the invention utilizes natural language to achieve intuitive parental controls, enabling flexible and appropriate management of a child's financial activities.

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The server receives natural language prompts entered by the user (parent / guardian). These prompts may include instructions such as, "Please restrict internet access after 10 PM." The server analyzes these prompts using natural language processing to extract the intent of the request.

[0472] Step 2:

[0473] The server uses a customized configuration generation mechanism based on the analysis results to generate control policies to be applied to the child's information processing device. The output for the analyzed input data includes specific configuration policies, which incorporate restrictions on the usage time and amount of electronic payments.

[0474] Step 3:

[0475] The device receives control policies sent from the server via Firebase. Based on the received policy data, the device sets restrictions on electronic payments within the React Native application. Specifically, it configures the application to block payments when certain time limits or price limits are reached.

[0476] Step 4:

[0477] The device monitors the child's usage using monitoring methods based on the configured restriction policies. In particular, it checks for recurring access patterns and data transmissions on the device to determine if there is any inappropriate activity. Based on the input monitoring data, an activity report is generated as output.

[0478] Step 5:

[0479] If inappropriate activity is detected, the device reports this information to the server. The reporting mechanism reacts, and the resulting generated report data is sent to the server. Upon receiving the report, the server uses a pre-configured notification method to send a notification to the parent or guardian. The notification includes details of the activity and information prompting further action.

[0480] Step 6:

[0481] Users (parents) receive notifications from the server and adjust control policies as needed. They can also receive real-time feedback through the system, allowing for enhanced control over their child's activities. The output of this process includes revised policies and their application to devices.

[0482] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0483] This invention combines an emotion engine with an AI parental control system to recognize user emotions and implement adaptive control accordingly. The system allows the server, terminal, and user to each play their respective roles, providing a more human-like interaction.

[0484] The server receives natural language requests from users and interprets their content using an analysis engine, as well as using an emotion engine to read the user's emotions. For example, if a user makes a request expressing anxiety or worry, the server adjusts the response content and the nuances of the setting rules based on that emotional information. The emotion engine also understands real-time changes in emotions through interaction with the user and provides feedback and advice.

[0485] The device receives settings and emotion-based instructions sent from the server and incorporates them into its operation. For example, it allows users to set stricter rules for their child's device use during specific times when they feel anxious. The device also has a monitoring function that tracks the child's device usage patterns and reports the information to the server.

[0486] Through this system, users can adjust device settings while expressing their emotions in natural language. They can also receive notifications and feedback about device usage generated by the system, taking their emotional information into account. In this way, the system is attuned to the user's emotions and helps them control their digital environment appropriately and flexibly.

[0487] This invention enables parents to respond to online risks in a flexible and emotionally sensitive manner, helping to provide a safer and more secure digital device experience for their children.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] Users enter natural language requests into the device to add or modify parental controls. The system can convey emotions based on the user's tone of voice and keywords in the text.

[0491] Step 2:

[0492] The server receives natural language requests sent from the device and interprets their content using natural language processing tools. During this process, it utilizes an emotion engine to extract the user's emotional state from the input information.

[0493] Step 3:

[0494] The server generates appropriate parental rules using a customized settings generation mechanism based on the intent of the request and the user's emotional state. For example, if the user expresses concern, a stricter access restriction policy will be set.

[0495] Step 4:

[0496] The server sends the generated access restriction settings to the terminal. This includes information about how to respond based on emotions.

[0497] Step 5:

[0498] The device expands the received configuration information and applies it to the device's control system. This includes setting restrictions on the use of specific apps and configuring communication time zones.

[0499] Step 6:

[0500] The terminal continuously monitors device usage using monitoring mechanisms. If any abnormalities or inappropriate use are detected, the terminal reports that information to the server.

[0501] Step 7:

[0502] The server combines the reported data with the sentiment engine's evaluation to send appropriate feedback or warnings to the user. For example, if a child performs a prohibited action, a warning message in a softer tone may be selected.

[0503] Step 8:

[0504] Users can receive notifications from the server and, if necessary, request adjustments to their settings by conveying new emotions. By obtaining emotion-conscious feedback, more accurate device management becomes possible.

[0505] (Example 2)

[0506] Next, we will describe Example 2. 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."

[0507] In recent years, with the proliferation of digital devices, the risks to children from exposure to inappropriate content and prolonged device use have increased. Parents need to address these risks while adjusting restrictions on digital device use, taking into account their children's emotional state. However, conventional systems have made it difficult to achieve this efficiently and flexibly. In particular, controlling device use in accordance with a child's emotions has been insufficient.

[0508] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0509] In this invention, the server includes means for analyzing natural language, means for extracting emotions from input linguistic information, and means for interpreting requests from parents and generating responses based on the extracted emotions. This allows parents to flexibly adjust device usage restrictions while considering their child's emotional state and receive appropriate feedback.

[0510] "Means for analyzing natural language" refers to processing devices and programs that analyze natural language text entered by a user and understand its meaning and intent.

[0511] "Means for extracting emotions" refers to functions or processing devices that identify emotional elements from user input information and detect the type and intensity of those emotions.

[0512] "Generation means" refers to a processing device or program for dynamically generating user-appropriate responses and settings based on the results of natural language analysis and extracted sentiment information.

[0513] "Means of application" refers to control devices and operating programs used to reflect the generated responses and settings in digital devices.

[0514] "Monitoring methods" refer to functions and systems for observing the usage and communication patterns of digital devices, and for recording and analyzing them.

[0515] "Means of reporting" refers to devices or programs that notify designated recipients of information when inappropriate activity is detected.

[0516] This invention combines an AI-based parental control system with emotion analysis capabilities. This system assigns specific roles to the server, terminal, and user, providing advanced interaction to fulfill user requests.

[0517] The server is the core of this system, receiving user input and parsing natural language. Natural language processing uses libraries such as NLTK and SpaCy, implemented in Python. This analysis helps to understand the intent behind the input language information. Furthermore, the server uses sentiment analysis models, such as BERT, to extract the user's emotional state. This emotional information is used to appropriately adjust responses to the user and set device usage restrictions.

[0518] The device has the ability to control the user's digital devices based on instructions from the server. Specifically, a dedicated application runs on the device to adjust device usage time and restrict accessible content. This application works in conjunction with Android and iOS device management APIs to adjust device settings according to the parent's wishes.

[0519] Users can send requests to this system through natural language and receive responses that take their emotions into consideration. For example, if a user sends a request such as "I'm worried my child is playing too many online games," the system will receive the anxiety and send feedback such as advice on device use and setting limits. The generative AI model utilizes suggestions for appropriate device use. An example of a prompt might be, "Please explain how the emotion engine handles the user's anxiety."

[0520] This system provides users with a sense of security and proper management in their digital environment.

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] Users input requests in natural language through applications on their devices. For example, they might express specific anxieties such as, "I'm worried my child is playing too many online games." The input data is textual information that includes the user's emotions and intentions.

[0524] Step 2:

[0525] The server receives requests sent by users. It performs text analysis on the input natural language data using Python's natural language processing libraries (such as NLTK and SpaCy) to extract the intent. The output is the analysis result regarding specific requests or instructions.

[0526] Step 3:

[0527] The server uses generative AI models such as BERT to perform sentiment analysis and extract emotions from user input. The analyzed emotional data is output and categorized into categories such as anxiety and worry.

[0528] Step 4:

[0529] The server generates adaptive responses based on natural language processing and sentiment analysis results. To achieve this, it utilizes a generative AI model to create suggestions and advice that take the user's emotions into account. The output is a generated text message.

[0530] Step 5:

[0531] The server sends instructions regarding device management to the terminal along with the generated response. These instructions may include adjusting device limits or changing monitoring settings. The output consists of specific control commands.

[0532] Step 6:

[0533] The device receives instructions from the server and applies the settings to the device. Specifically, applications on Android and iOS use device management APIs to perform operations such as limiting game usage time. The input is configuration information from the server, and the output is the change in the device's state.

[0534] Step 7:

[0535] The user receives feedback from the terminal based on responses and control instructions generated by the server. As output, the user gains a sense of security by obtaining device control information that addresses their concerns.

[0536] (Application Example 2)

[0537] Next, we will explain application example 2. In the following explanation, 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."

[0538] Traditional parental control systems could only offer uniform controls based on parental policies and children's usage patterns, failing to address the emotional shifts of users in the digital environment. This resulted in insufficient emotional care and could be a source of stress for users in environments requiring flexible responses. Furthermore, they lacked the ability to recognize and appropriately address children's anxiety and stress.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0540] In this invention, the server includes natural language processing means, customized setting generation means for interpreting and responding to requests from parents, and emotion analysis means for analyzing the user's emotions. This enables device control in accordance with the user's emotions, selection of appropriate content, and promotion of parent-child communication.

[0541] "Natural language processing means" refers to technology that analyzes natural language input from users and processes it to understand its content.

[0542] The "customized settings generation method" is a function that generates settings tailored to the child based on requests from parents.

[0543] "Control means" refers to a function that applies the generated settings to the child's communication device and manages its operation.

[0544] "Monitoring means" refers to techniques for continuously observing data transmission and usage patterns on children's communication devices.

[0545] A "reporting mechanism" is a function that detects inappropriate activity and notifies parents of that information.

[0546] "Emotional analysis tools" are technologies used to analyze a user's emotions and determine the type and intensity of those emotions.

[0547] "Adaptive control means" refers to a function that adjusts content filtering settings based on the results of sentiment analysis, flexibly changing the usage environment.

[0548] This system is server-centric and implemented using natural language processing, customization setting generation, and sentiment analysis. The server receives requests from users in natural language and analyzes their content using the natural language processing means. This allows the server to understand the user's intentions and requests, and then generates corresponding device settings through the customization setting generation means.

[0549] The terminal applies settings received from the server to the child's communication device using control means, and adjusts operations using adaptive control means based on the user's emotional data acquired by emotion analysis means. This allows the child's access to digital content to be flexibly controlled according to their emotions.

[0550] Furthermore, the device uses monitoring mechanisms to continuously monitor data transmission and usage patterns on the child's communication device, and if inappropriate activity is detected through reporting mechanisms, it notifies the parent or guardian. This allows the parent or guardian to immediately understand the situation and take appropriate action.

[0551] Users can adjust device settings while expressing their emotions in natural language through the device. This interaction allows for emotional support for anxieties and worries related to device use.

[0552] For example, if a child doing schoolwork shows signs of anxiety through sentiment analysis during a search, the system adjusts its filtering settings. This allows the child to access appropriate information and feel secure. Furthermore, this system can be used as a conversational agent for task management and learning. By using a generated AI model to create prompts such as, "Please advise on how to care for a child who is feeling anxious," appropriate responses can be provided.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The server receives requests from users in natural language. It receives text data sent by the user through their device as input and analyzes its intent using natural language processing tools. The information obtained as a result of the analysis includes requests regarding device settings and expressions of emotion.

[0556] Step 2:

[0557] The server generates device settings through a customized settings generation mechanism based on the results of natural language processing. In this step, it processes the analysis results obtained as input and outputs the optimal device settings corresponding to the user's intent. The generated settings are then ready to be applied to the child's communication device.

[0558] Step 3:

[0559] The terminal executes device settings sent from the server using control means. The input is the configuration data received from the server, and by applying this to the child's communication device, the use of the device is managed.

[0560] Step 4:

[0561] The device monitors the user's emotional state in real time using emotion analysis technology. It takes user voice and text data as input, performs emotion analysis, and generates information on the type and intensity of emotions as output.

[0562] Step 5:

[0563] Based on the results of emotion analysis, the device adjusts its content filtering using adaptive control means. By inputting emotion information, the filtering strictness is dynamically changed according to the user's psychological state, providing an optimal user environment.

[0564] Step 6:

[0565] The device uses monitoring mechanisms to track the usage patterns and data transmission status of children's communication devices. It analyzes daily usage history as input data, and if inappropriate activity is detected, it notifies the parent or guardian using a reporting mechanism and outputs that information.

[0566] Step 7:

[0567] Users can provide emotionally reflective feedback about device settings and controls via their device. This feedback is used as prompts by a generative AI model, which then provides the user with appropriate advice and information as output.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the 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.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] This invention is an AI parental control system that allows parents to manage their children's communication devices using natural language. This system works in cooperation with the server, terminals, and users to achieve flexible and intuitive control tailored to each family's policies.

[0586] The server receives natural language requests sent from parents via smartphones or PCs. These requests may include specific instructions, such as "Please restrict internet access after 10 PM." The server analyzes these requests using natural language processing to extract their intent. Based on the extracted information, a customization settings generation system creates a policy and prepares the settings to be sent to the device.

[0587] The device applies the received policy to the device's configuration layer. For example, if it restricts internet access during a set time period, it configures the device to block communication and prevent unintended access. The device also uses monitoring mechanisms to continuously monitor how the child is using the device. If inappropriate behavior, such as an attempt to access the device during the restricted time period, is detected, the device reports this information to the server.

[0588] By receiving notifications from the server, users can monitor their child's device usage and adjust settings accordingly. For example, if a child attempts to access an app within a time limit, the server will send a warning to the user and provide an opportunity to readjust settings if necessary.

[0589] In this way, the invention provides parental controls that meet family needs through intuitive operation, supporting a safe online experience for children.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] Users enter a request in natural language on their smartphone or PC, stating, "I want to be unable to use the internet after 10 PM," and send it to the system.

[0593] Step 2:

[0594] The server processes natural language requests received from users using its parsing engine and interprets their meaning. Specifically, it identifies time zones and restricted activities.

[0595] Step 3:

[0596] The server generates a customized access restriction policy based on the interpretation results. This policy is configured to reflect the user's requests.

[0597] Step 4:

[0598] The server sends the generated access restriction policy to the terminal and provides instructions for applying the settings.

[0599] Step 5:

[0600] The device deploys the policy received from the server and reflects it in its settings. For example, it might configure the device to block internet access during specified time periods.

[0601] Step 6:

[0602] The device uses monitoring functions to check whether the settings are working correctly and detects actions that violate specific policies, such as access attempts during restricted time periods.

[0603] Step 7:

[0604] The device reports any inappropriate access to the server.

[0605] Step 8:

[0606] The server analyzes the information reported from the terminal and sends a notification to the user. This notification includes information about what happened and what action is required.

[0607] Step 9:

[0608] Users can receive notifications from the server and send requests to readjust their settings as needed.

[0609] Through this process, the system provides continuous and dynamic parental controls that meet the needs of the household.

[0610] (Example 1)

[0611] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] There is a need for a system that allows parents to easily and flexibly set usage restrictions on information processing devices used by children, and to effectively monitor and control inappropriate activities. Traditional manual control is time-consuming and laborious, and sometimes makes it difficult to adequately protect children's safety. Therefore, the challenge is to provide a system that parents can operate intuitively and that offers flexible control to meet diverse needs.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0614] In this invention, the server includes a natural language analyzer, a custom setting generation device, and a warning device. This allows the parent to make a request in natural language, analyze the request using a generative AI model, automatically generate a policy suitable for the information processing device, and quickly and effectively provide warnings and notifications to the parent.

[0615] A "natural language processing device" is a device that interprets requests made in natural language by parents and processes them to accurately understand their intent.

[0616] A "customization setting generation device" is a device that automatically generates policies and settings to be applied to an information processing device based on the content of an analyzed request.

[0617] A "control device" is a device that applies a generated policy to an information processing device and enforces specified restrictions and permissions.

[0618] A "monitoring device" is a device that continuously monitors information transmission and usage patterns on an information processing device and identifies inappropriate activities.

[0619] A "reporting device" is a device that notifies parents of inappropriate activities detected by a monitoring device.

[0620] A "warning device" is a device that issues a warning to a parent if inappropriate activity occurs while using an information processing device.

[0621] A "transmitting device" is a device that has the function of sending the generated policy to an information processing device.

[0622] This invention is an AI parental control system that allows parents to manage their children's information processing devices using natural language. The invention achieves flexible and intuitive control through the cooperation of three entities: a server, a terminal, and a user.

[0623] The server receives natural language requests from parents via smartphones or personal computers. Generative AI models for natural language processing are used to analyze these requests. For example, using a natural language model like GPT-3, the server accurately understands the request and extracts the intent behind the instruction. Based on the extracted intent, a customization settings generator creates a policy. This policy is sent to the child's device and applied by the device's built-in control unit.

[0624] The terminal reflects the received policy in its device settings. For example, it adjusts network settings to block communication in order to restrict internet access for a specific period of time. The monitoring device continuously observes the terminal's usage and reports any inappropriate activity, such as attempts to access the network during the restricted time period, to the server.

[0625] Users can monitor their child's device usage through notifications from the server. For example, if an attempt is made to access a specific app within a time limit, the user receives a warning from the server and readjusts the settings based on that information. As a concrete example of a prompt message, if the user enters a command such as, "Please restrict my child's access to YouTube when they try to watch it at night," the system will generate and implement a policy in response to this request. In this way, the invention supports a safe online environment for children, tailored to the needs of the family.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The server receives natural language requests sent from parents via smartphones or personal computers. These requests include specific instructions such as, "Please restrict internet access after 10 PM." The server receives these natural language instructions as input.

[0629] Step 2:

[0630] The server analyzes the received request using a generative AI model. At this time, the server uses a natural language processing unit to syntactically analyze the input instruction and extract its intent. As a result of the analysis, a specific control objective, such as "prohibit internet access after 10 PM," is extracted. The output is the analyzed intent.

[0631] Step 3:

[0632] The server generates appropriate policies using a customization setting generator based on the analysis results. For example, based on the extracted intent, a setting is generated to restrict internet access during specific time periods. The input is the analyzed intent, and the output is the policy configuration information.

[0633] Step 4:

[0634] The server sends the generated policy to the terminal. The sent information includes specific details such as which applications or websites should be restricted to access during which time periods. At this stage, the policy configuration information is the input, and sending it to the terminal is the output.

[0635] Step 5:

[0636] The terminal applies the policy received from the server to the settings of the information processing device. Specifically, the network settings are adjusted based on the received policy, and communication is blocked at the specified time. The input is policy information received from the server, and the output is a change in the actual device settings.

[0637] Step 6:

[0638] The device monitors the child's device usage via a monitoring system. If an inappropriate access attempt is detected, the system records the information and prepares to report it to the server. The input is the device usage log, and the output is a record of inappropriate behavior.

[0639] Step 7:

[0640] The server receives reports from the device and issues warnings to the parent. For example, if an attempt is made to access a website within the time limit, this information is notified to the parent. The input is the information reported from the device, and the output is the warning notification to the parent.

[0641] (Application Example 1)

[0642] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] Traditional parental control systems focus on restricting children's use of communication devices and the internet, but have struggled to extend control to electronic payments. Therefore, there is a need for a system that proactively prevents inappropriate spending and electronic payments that disregard appropriate time zones. In particular, a system is needed that allows parents to restrict electronic payment usage more intuitively and flexibly by giving instructions in natural language.

[0644] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0645] In this invention, the server includes a natural language processing means, a customized setting generation means for interpreting and responding to requests from parents, a control means for applying the generated settings to the child's information processing device, and a payment restriction means for restricting the usage time and amount of electronic payments. This allows parents to intuitively manage their child's electronic payments using natural language and prevent inappropriate spending.

[0646] A "natural language processing device" is a device or program that has the function of analyzing requests in natural language provided by guardians and understanding their content.

[0647] A "custom setting generation means" is a device or program that has the function of generating specific settings to be applied to a child's information processing device based on an analyzed request.

[0648] "Control means" refers to a device or function that reflects the generated customized settings in the child's information processing device and performs actual operation or function restrictions.

[0649] "Monitoring means" refers to a device or program for monitoring data transmission and usage patterns in a child's information processing device.

[0650] A "reporting device" is a device or program that has the function of recording inappropriate activity detected through monitoring and notifying the guardian.

[0651] "Payment restriction measures" refer to devices or programs used to limit the time and amount of electronic payments.

[0652] The server analyzes instructions received from parents using natural language processing (NLP) and generates specific control policies. This process utilizes the Python programming language and the natural language processing library spaCy. The analyzed information is then sent to the device via Firebase. On the device, an application built with React Native controls the time and amount of electronic payments used according to the received policy. For example, if an attempt is made to exceed the limits set by the control policy, the details are reported to the server and the parent is notified. The control system also automatically adjusts the settings of the child's information processing device and applies predetermined rules. Monitoring measures track data transmission patterns and usage on the device.

[0653] As a concrete example, the server receives a prompt message from a parent such as, "Please notify me if my child's spending exceeds 1,000 yen per day." This message is parsed and applied as a policy to the child's electronic payments. If the child actually exceeds the specified amount, the device reports this information to the server, and a notification is immediately sent to the parent. In this way, the invention utilizes natural language to achieve intuitive parental controls, enabling flexible and appropriate management of a child's financial activities.

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1:

[0656] The server receives natural language prompts entered by the user (parent / guardian). These prompts may include instructions such as, "Please restrict internet access after 10 PM." The server analyzes these prompts using natural language processing to extract the intent of the request.

[0657] Step 2:

[0658] The server uses a customized configuration generation mechanism based on the analysis results to generate control policies to be applied to the child's information processing device. The output for the analyzed input data includes specific configuration policies, which incorporate restrictions on the usage time and amount of electronic payments.

[0659] Step 3:

[0660] The device receives control policies sent from the server via Firebase. Based on the received policy data, the device sets restrictions on electronic payments within the React Native application. Specifically, it configures the application to block payments when certain time limits or price limits are reached.

[0661] Step 4:

[0662] The device monitors the child's usage using monitoring methods based on the configured restriction policies. In particular, it checks for recurring access patterns and data transmissions on the device to determine if there is any inappropriate activity. Based on the input monitoring data, an activity report is generated as output.

[0663] Step 5:

[0664] If inappropriate activity is detected, the device reports this information to the server. The reporting mechanism reacts, and the resulting generated report data is sent to the server. Upon receiving the report, the server uses a pre-configured notification method to send a notification to the parent or guardian. The notification includes details of the activity and information prompting further action.

[0665] Step 6:

[0666] Users (parents) receive notifications from the server and adjust control policies as needed. They can also receive real-time feedback through the system, allowing for enhanced control over their child's activities. The output of this process includes revised policies and their application to devices.

[0667] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0668] This invention combines an emotion engine with an AI parental control system to recognize user emotions and implement adaptive control accordingly. The system allows the server, terminal, and user to each play their respective roles, providing a more human-like interaction.

[0669] The server receives natural language requests from users and interprets their content using an analysis engine, as well as using an emotion engine to read the user's emotions. For example, if a user makes a request expressing anxiety or worry, the server adjusts the response content and the nuances of the setting rules based on that emotional information. The emotion engine also understands real-time changes in emotions through interaction with the user and provides feedback and advice.

[0670] The device receives settings and emotion-based instructions sent from the server and incorporates them into its operation. For example, it allows users to set stricter rules for their child's device use during specific times when they feel anxious. The device also has a monitoring function that tracks the child's device usage patterns and reports the information to the server.

[0671] Through this system, users can adjust device settings while expressing their emotions in natural language. They can also receive notifications and feedback about device usage generated by the system, taking their emotional information into account. In this way, the system is attuned to the user's emotions and helps them control their digital environment appropriately and flexibly.

[0672] This invention enables parents to respond to online risks in a flexible and emotionally sensitive manner, helping to provide a safer and more secure digital device experience for their children.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] Users enter natural language requests into the device to add or modify parental controls. The system can convey emotions based on the user's tone of voice and keywords in the text.

[0676] Step 2:

[0677] The server receives natural language requests sent from the device and interprets their content using natural language processing tools. During this process, it utilizes an emotion engine to extract the user's emotional state from the input information.

[0678] Step 3:

[0679] The server generates appropriate parental rules using a customized settings generation mechanism based on the intent of the request and the user's emotional state. For example, if the user expresses concern, a stricter access restriction policy will be set.

[0680] Step 4:

[0681] The server sends the generated access restriction settings to the terminal. This includes information about how to respond based on emotions.

[0682] Step 5:

[0683] The device expands the received configuration information and applies it to the device's control system. This includes setting restrictions on the use of specific apps and configuring communication time zones.

[0684] Step 6:

[0685] The terminal continuously monitors device usage using monitoring mechanisms. If any abnormalities or inappropriate use are detected, the terminal reports that information to the server.

[0686] Step 7:

[0687] The server combines the reported data with the sentiment engine's evaluation to send appropriate feedback or warnings to the user. For example, if a child performs a prohibited action, a warning message in a softer tone may be selected.

[0688] Step 8:

[0689] Users can receive notifications from the server and, if necessary, request adjustments to their settings by conveying new emotions. By obtaining emotion-conscious feedback, more accurate device management becomes possible.

[0690] (Example 2)

[0691] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] In recent years, with the proliferation of digital devices, the risks to children from exposure to inappropriate content and prolonged device use have increased. Parents need to address these risks while adjusting restrictions on digital device use, taking into account their children's emotional state. However, conventional systems have made it difficult to achieve this efficiently and flexibly. In particular, controlling device use in accordance with a child's emotions has been insufficient.

[0693] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0694] In this invention, the server includes means for analyzing natural language, means for extracting emotions from input linguistic information, and means for interpreting requests from parents and generating responses based on the extracted emotions. This allows parents to flexibly adjust device usage restrictions while considering their child's emotional state and receive appropriate feedback.

[0695] "Means for analyzing natural language" refers to processing devices and programs that analyze natural language text entered by a user and understand its meaning and intent.

[0696] "Means for extracting emotions" refers to functions or processing devices that identify emotional elements from user input information and detect the type and intensity of those emotions.

[0697] "Generation means" refers to a processing device or program for dynamically generating user-appropriate responses and settings based on the results of natural language analysis and extracted sentiment information.

[0698] "Means of application" refers to control devices and operating programs used to reflect the generated responses and settings in digital devices.

[0699] "Monitoring methods" refer to functions and systems for observing the usage and communication patterns of digital devices, and for recording and analyzing them.

[0700] "Means of reporting" refers to devices or programs that notify designated recipients of information when inappropriate activity is detected.

[0701] This invention combines an AI-based parental control system with emotion analysis capabilities. This system assigns specific roles to the server, terminal, and user, providing advanced interaction to fulfill user requests.

[0702] The server is the core of this system, receiving user input and parsing natural language. Natural language processing uses libraries such as NLTK and SpaCy, implemented in Python. This analysis helps to understand the intent behind the input language information. Furthermore, the server uses sentiment analysis models, such as BERT, to extract the user's emotional state. This emotional information is used to appropriately adjust responses to the user and set device usage restrictions.

[0703] The device has the ability to control the user's digital devices based on instructions from the server. Specifically, a dedicated application runs on the device to adjust device usage time and restrict accessible content. This application works in conjunction with Android and iOS device management APIs to adjust device settings according to the parent's wishes.

[0704] Users can send requests to this system through natural language and receive responses that take their emotions into consideration. For example, if a user sends a request such as "I'm worried my child is playing too many online games," the system will receive the anxiety and send feedback such as advice on device use and setting limits. The generative AI model utilizes suggestions for appropriate device use. An example of a prompt might be, "Please explain how the emotion engine handles the user's anxiety."

[0705] This system provides users with a sense of security and proper management in their digital environment.

[0706] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0707] Step 1:

[0708] Users input requests in natural language through applications on their devices. For example, they might express specific anxieties such as, "I'm worried my child is playing too many online games." The input data is textual information that includes the user's emotions and intentions.

[0709] Step 2:

[0710] The server receives requests sent by users. It performs text analysis on the input natural language data using Python's natural language processing libraries (such as NLTK and SpaCy) to extract the intent. The output is the analysis result regarding specific requests or instructions.

[0711] Step 3:

[0712] The server uses generative AI models such as BERT to perform sentiment analysis and extract emotions from user input. The analyzed emotional data is output and categorized into categories such as anxiety and worry.

[0713] Step 4:

[0714] The server generates adaptive responses based on natural language processing and sentiment analysis results. To achieve this, it utilizes a generative AI model to create suggestions and advice that take the user's emotions into account. The output is a generated text message.

[0715] Step 5:

[0716] The server sends instructions regarding device management to the terminal along with the generated response. These instructions may include adjusting device limits or changing monitoring settings. The output consists of specific control commands.

[0717] Step 6:

[0718] The device receives instructions from the server and applies the settings to the device. Specifically, applications on Android and iOS use device management APIs to perform operations such as limiting game usage time. The input is configuration information from the server, and the output is the change in the device's state.

[0719] Step 7:

[0720] The user receives feedback from the terminal based on responses and control instructions generated by the server. As output, the user gains a sense of security by obtaining device control information that addresses their concerns.

[0721] (Application Example 2)

[0722] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] Traditional parental control systems could only offer uniform controls based on parental policies and children's usage patterns, failing to address the emotional shifts of users in the digital environment. This resulted in insufficient emotional care and could be a source of stress for users in environments requiring flexible responses. Furthermore, they lacked the ability to recognize and appropriately address children's anxiety and stress.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0725] In this invention, the server includes natural language processing means, customized setting generation means for interpreting and responding to requests from parents, and emotion analysis means for analyzing the user's emotions. This enables device control in accordance with the user's emotions, selection of appropriate content, and promotion of parent-child communication.

[0726] "Natural language processing means" refers to technology that analyzes natural language input from users and processes it to understand its content.

[0727] The "customized settings generation method" is a function that generates settings tailored to the child based on requests from parents.

[0728] "Control means" refers to a function that applies the generated settings to the child's communication device and manages its operation.

[0729] "Monitoring means" refers to techniques for continuously observing data transmission and usage patterns on children's communication devices.

[0730] A "reporting mechanism" is a function that detects inappropriate activity and notifies parents of that information.

[0731] "Emotional analysis tools" are technologies used to analyze a user's emotions and determine the type and intensity of those emotions.

[0732] "Adaptive control means" refers to a function that adjusts content filtering settings based on the results of sentiment analysis, flexibly changing the usage environment.

[0733] This system is server-centric and implemented using natural language processing, customization setting generation, and sentiment analysis. The server receives requests from users in natural language and analyzes their content using the natural language processing means. This allows the server to understand the user's intentions and requests, and then generates corresponding device settings through the customization setting generation means.

[0734] The terminal applies settings received from the server to the child's communication device using control means, and adjusts operations using adaptive control means based on the user's emotional data acquired by emotion analysis means. This allows the child's access to digital content to be flexibly controlled according to their emotions.

[0735] Furthermore, the device uses monitoring mechanisms to continuously monitor data transmission and usage patterns on the child's communication device, and if inappropriate activity is detected through reporting mechanisms, it notifies the parent or guardian. This allows the parent or guardian to immediately understand the situation and take appropriate action.

[0736] Users can adjust device settings while expressing their emotions in natural language through the device. This interaction allows for emotional support for anxieties and worries related to device use.

[0737] For example, if a child doing schoolwork shows signs of anxiety through sentiment analysis during a search, the system adjusts its filtering settings. This allows the child to access appropriate information and feel secure. Furthermore, this system can be used as a conversational agent for task management and learning. By using a generated AI model to create prompts such as, "Please advise on how to care for a child who is feeling anxious," appropriate responses can be provided.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server receives requests from users in natural language. It receives text data sent by the user through their device as input and analyzes its intent using natural language processing tools. The information obtained as a result of the analysis includes requests regarding device settings and expressions of emotion.

[0741] Step 2:

[0742] The server generates device settings through a customized settings generation mechanism based on the results of natural language processing. In this step, it processes the analysis results obtained as input and outputs the optimal device settings corresponding to the user's intent. The generated settings are then ready to be applied to the child's communication device.

[0743] Step 3:

[0744] The terminal executes device settings sent from the server using control means. The input is the configuration data received from the server, and by applying this to the child's communication device, the use of the device is managed.

[0745] Step 4:

[0746] The device monitors the user's emotional state in real time using emotion analysis technology. It takes user voice and text data as input, performs emotion analysis, and generates information on the type and intensity of emotions as output.

[0747] Step 5:

[0748] Based on the results of emotion analysis, the device adjusts its content filtering using adaptive control means. By inputting emotion information, the filtering strictness is dynamically changed according to the user's psychological state, providing an optimal user environment.

[0749] Step 6:

[0750] The device uses monitoring mechanisms to track the usage patterns and data transmission status of children's communication devices. It analyzes daily usage history as input data, and if inappropriate activity is detected, it notifies the parent or guardian using a reporting mechanism and outputs that information.

[0751] Step 7:

[0752] Users can provide emotionally reflective feedback about device settings and controls via their device. This feedback is used as prompts by a generative AI model, which then provides the user with appropriate advice and information as output.

[0753] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0756] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0757] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0758] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0759] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0760] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0761] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0763] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0764] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0765] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0766] 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.

[0767] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0768] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0769] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0770] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0771] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0772] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] Natural language processing tools,

[0777] A means for generating customized settings to interpret and respond to requests from parents,

[0778] A control means for applying the generated settings to the child's communication device,

[0779] Monitoring means for monitoring data transmission and usage patterns on a child's communication device,

[0780] A reporting mechanism for detecting and notifying of inappropriate activity,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising a customization function that adjusts settings according to the parents' policies and the child's developmental stage.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising a notification means for sending a warning to a parent or guardian if the use or transmission of inappropriate content is detected.

[0786] "Example 1"

[0787] (Claim 1)

[0788] Natural language processing device,

[0789] A device for generating customized settings to interpret and respond to requests from parents,

[0790] A control device that applies the generated settings to an information processing device,

[0791] A monitoring device that monitors information transmission and usage patterns on an information processing device,

[0792] A reporting device that detects and notifies of inappropriate activities,

[0793] A warning device that provides a warning to the parent,

[0794] A transmitting device that sends the generated policy to an information processing device,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, comprising a customization function that adjusts settings according to the parents' policies and the child's developmental stage.

[0798] (Claim 3)

[0799] The system according to claim 1, further comprising a notification device that sends a warning to a parent when the use or transmission of inappropriate content is detected.

[0800] "Application Example 1"

[0801] (Claim 1)

[0802] Natural language processing tools,

[0803] A means for generating customized settings to interpret and respond to requests from parents,

[0804] A control means for applying the generated settings to the child's information processing device,

[0805] Monitoring means for monitoring data transmission and usage patterns on a child's information processing device,

[0806] A reporting mechanism for detecting and notifying of inappropriate activity,

[0807] A payment restriction mechanism that restricts the time and amount of electronic payments that can be used,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, comprising a customization function that adjusts settings according to the parents' policies and the child's developmental stage.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising a notification means for sending a warning to a parent or guardian when inappropriate payment activity is detected.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] Means for analyzing natural language,

[0816] A means of extracting emotions from input linguistic information,

[0817] A generation means for interpreting requests from guardians and generating responses based on extracted emotions,

[0818] Means for applying the generated response to parental policies or the control of digital devices,

[0819] Means for monitoring the usage patterns of digital devices,

[0820] Means for detecting and reporting inappropriate activities,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, comprising an adaptive function that adjusts settings according to the parent's policy and the child's emotional state.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising a notification means for sending a warning to a guardian if the use or transmission of inappropriate information is detected.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] Natural language processing tools,

[0829] A means for generating customized settings to interpret and respond to requests from parents,

[0830] A control means for applying the generated settings to the child's communication device,

[0831] Monitoring means for monitoring data transmission and usage patterns on a child's communication device,

[0832] A reporting mechanism for detecting and notifying of inappropriate activity,

[0833] A means of analyzing user emotions,

[0834] Adaptive control means that adjusts content filters based on emotions,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, further comprising a customization function that adjusts settings according to parental policies, the child's developmental stage, and the user's emotions.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising a notification means for sending a warning to a parent or guardian and providing emotionally responsive dialogue support when the use or transmission of inappropriate content is detected. [Explanation of Symbols]

[0840] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Natural language processing tools, A means for generating customized settings to interpret and respond to requests from parents, A control means for applying the generated settings to the child's information processing device, Monitoring means for monitoring data transmission and usage patterns on a child's information processing device, A reporting mechanism for detecting and notifying of inappropriate activity, A payment restriction mechanism that restricts the time and amount of electronic payments that can be used, A system that includes this.

2. The system according to claim 1, further comprising a customization function that adjusts settings according to the parents' policies and the child's developmental stage.

3. The system according to claim 1, further comprising a notification means for sending a warning to a parent or guardian when inappropriate payment activity is detected.

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