system

Abstract

We provide the system. [Solution] A means for sending content access requests from a user's information device, A data processing device receives the request and evaluates the safety of the content via an automated intelligent agent, A means of controlling users' access to information devices and restricting access to inappropriate content based on evaluation results, Means for notifying the administrator of the evaluation results via a control device, A means of providing feedback on evaluation results to home appliances using voice and display devices, A system that includes means for evaluating the safety level of internet usage and providing safety guidance.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] For modern children, the use of smartphones is common, but behind its convenience, there are risks such as access to harmful content, excessive in-app purchases, and troubles in SNS. However, in the current technology, there are no effective means other than simply physically interfering with access to content, and a method for comprehensively and flexibly solving these problems is required.

Means for Solving the Problems

[0005] This invention provides a system that receives content access requests transmitted from a user's terminal via a server and evaluates and classifies the safety of the content through an artificial intelligence agent. This system has the function of actively controlling access from the user's terminal based on the evaluation results, thereby restricting access to inappropriate content. Furthermore, the artificial intelligence agent utilizes reinforcement learning algorithms to continuously evolve and improve evaluation accuracy. It also records the user's content usage history and notifies administrators of usage reports, enabling more effective management.

[0006] A "user terminal" is a communication device used by a user to access content, and includes mobile phones, smartphones, tablets, and other similar devices.

[0007] A "content access request" refers to request information sent from a user's device to display or use specific content.

[0008] A "server" is a computer device that receives content access requests and processes them.

[0009] An "artificial intelligence agent" is a program that operates autonomously in a digital environment, performing information analysis and decision-making.

[0010] "Content safety" refers to the integrity and appropriateness of information and data, which are evaluated to prevent users from being negatively affected.

[0011] A "reinforcement learning algorithm" is a machine learning technique that allows a specific computer program to optimize its own actions through trial and error.

[0012] An "administrator" is a person or organization that has the authority to monitor and manage users' content usage and issue instructions as needed.

[0013] A "usage report" is a document or collection of data that includes user content usage history and analytical information, and is presented to the administrator. [Brief explanation of the drawing]

[0014] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled 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, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system of the present invention consists of a user's terminal, a server, and an artificial intelligence agent deployed on the server. The user's terminal is a device for accessing content, such as a smartphone or tablet. The terminal is responsible for monitoring the user's content access requests and transmitting that information to the server.

[0036] The server has the functionality to receive content access requests sent from user terminals. The received requests are passed to an artificial intelligence agent on the server, where the process of determining the safety of the content begins. The AI ​​agent uses a reinforcement learning algorithm and constantly improves its evaluation accuracy based on past data and accumulated knowledge. Specifically, it evaluates whether the content is harmful, whether the charges are excessive, and whether there is a risk of trouble on social media.

[0037] The server feeds the evaluation results back to the user's device and restricts access to content as needed. Furthermore, the evaluation results are notified to the parent via the device, and a report is sent detailing the child's smartphone usage. This report reflects the user's content usage history and provides useful information for administrators.

[0038] As a concrete example, consider a scenario where a user's child attempts to access a new video streaming service. The device detects the access request and sends it to the server. An artificial intelligence agent on the server evaluates the video content and determines whether it contains inappropriate elements. If it is deemed inappropriate, the result is fed back to the device, and access to the video is restricted. The parent is notified of this situation through the app management dashboard.

[0039] In this way, the present invention safely manages children's smartphone use and helps parents protect their children from harmful content and risks.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] When a user attempts to access content on their smartphone, the device detects the request. It collects and logs information about the URL and application included in the request. The log also includes the date and time of access and basic user information.

[0043] Step 2:

[0044] The terminal sends the collected request information to the server. Since the data is transmitted asynchronously over the network, it does not affect the user experience.

[0045] Step 3:

[0046] The server analyzes content access requests received from terminals. The analyzed information is quickly input into an artificial intelligence agent.

[0047] Step 4:

[0048] An artificial intelligence agent begins evaluating the content. Based on past data and algorithms, it makes real-time safety assessments. Specifically, it checks whether the content contains harmful elements, whether it might lead to excessive purchasing behavior, and whether there are any signs of trouble.

[0049] Step 5:

[0050] The server receives evaluation results from the artificial intelligence agent. Based on these results, the server generates instructions for the user's device. If the content is evaluated as inappropriate, the instructions will include a directive to prohibit access to the content.

[0051] Step 6:

[0052] The device receives instructions from the server and notifies the user as needed. The notification may include the reason for the restriction and, if necessary, contact information for parents.

[0053] Step 7:

[0054] The server accumulates users' content usage history and periodically generates usage reports for administrators (parents). This helps parents appropriately monitor their children's digital activities. These reports are accessible via smartphones and computers.

[0055] (Example 1)

[0056] 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."

[0057] The amount of digital content available on the internet is vast, and some of it contains information inappropriate for minors or involves excessive charges. Therefore, it is becoming increasingly difficult for parents to provide their children with a safe environment for using digital content. Furthermore, constantly monitoring usage is time-consuming and laborious, so there is a need for more efficient methods to do so.

[0058] 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.

[0059] In this invention, the server includes means for transmitting requests for digital content from a user device, means for receiving the requests at the information server and analyzing the safety of the digital content via a machine learning agent, and means for adjusting access to the user device and restricting access to inappropriate content based on the analysis results. This makes it easy to manage the safe and appropriate use of digital content for users, and allows parents to confidently let their children use digital devices.

[0060] "User device" refers to an electronic device used by a user to access digital content, and includes portable devices such as smartphones and tablets.

[0061] An "information server" is a central processing unit that receives requests from user devices on a network and performs analysis and evaluation of digital content.

[0062] A "machine learning agent" is a program that uses machine learning algorithms to analyze data in order to determine the safety of received digital content.

[0063] "Digital content" refers to data such as videos, music, games, and ebooks that are distributed over the internet.

[0064] "Analysis results" refer to safety evaluation data obtained when a machine learning agent evaluates digital content.

[0065] A "generative AI model" is a type of artificial intelligence used to evaluate digital content, and it is a model that sets specific evaluation criteria based on the provided prompt text.

[0066] The system of this invention mainly consists of a user device, an information server, and a machine learning agent located on the server. The user device refers to an electronic device for accessing digital content, and generally includes portable devices such as smartphones and tablets. This device is responsible for detecting user requests to access digital content and transmitting those requests to the information server.

[0067] The server has the function of receiving access requests sent from user devices, and a machine learning agent residing on the server processes the request. This agent analyzes the security of digital content using a generative AI model. The generative AI model evaluates the content based on specific prompt sentences and returns the analysis results to the server.

[0068] Specifically, consider a scenario where a user's child attempts to access a new video streaming service. The user's device immediately detects the access request and sends it to an information server. A machine learning agent on the server then evaluates the digital content in question. The prompts used might include specific questions such as, "Is this content safe for my child?", "Is the charge appropriate?", and "Is there a possibility of problems occurring on social media?".

[0069] The server sends feedback to the user's device based on the acquired analysis results. Based on this feedback, access to inappropriate content is restricted. In addition, parents are notified of their child's digital content usage via the information processing device. This allows parents to take measures to ensure appropriate and safe use of digital content. This invention is a system that safely manages children's use of digital devices and helps parents protect their children from harmful content.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user generates a request to access digital content. The user's device detects this request and collects information such as the current time, device ID, and the URL being accessed. This serves as input for the next step. At this stage, the user's device generates an access log and prepares to send this data to the information server.

[0073] Step 2:

[0074] The terminal sends the collected access request data to the information server. The server securely receives the data via the HTTPS protocol. The input to the server is the request data sent from the user device. Using this, the server sets triggers for analyzing the data. In the data analysis, the accessed URL is compared with a known safe list. This allows for a primary safety assessment.

[0075] Step 3:

[0076] The server passes the received data to the machine learning agent. The machine learning agent uses a generative AI model to analyze the digital content. This agent scrutinizes the content of the input URL and evaluates its safety, whether it charges fees, and the risk of problems on social media. The prompt "Is this content safe for my child?" is used in the analysis process. The output generates an evaluation result regarding the safety of the content.

[0077] Step 4:

[0078] The server analyzes the evaluation results obtained from the machine learning agent and generates feedback for the user's device. This feedback determines access restrictions to the content based on the evaluation results. A notification indicating that access restrictions have been applied is generated as output.

[0079] Step 5:

[0080] The device receives feedback from the server and notifies the user. Simultaneously, a report is generated for administrators (e.g., parents) containing the child's digital content usage history and evaluation results. This report is available on the parent's dashboard app and can be used as part of ensuring children's safe digital content use.

[0081] (Application Example 1)

[0082] 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."

[0083] In modern households, the risk of children accessing inappropriate content while using the internet is increasing. Furthermore, the burden on parents and administrators to manage these risks is also growing. Current measures lack real-time monitoring and guidance on appropriate content use, making it difficult to achieve safe and healthy internet use.

[0084] 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.

[0085] In this invention, the server includes means for transmitting content access requests from a user's information device, means for receiving the requests in a data processing device and evaluating the security of the content via an automated intelligent agent, and means for feeding back the evaluation results to a home device using voice and a display device. This makes it possible to ensure the security of internet use within the home in real time and to allow parents to easily manage their children's content access.

[0086] "User information devices" refer to devices such as smartphones, tablets, and personal computers used at home or by individuals, which can connect to the internet and access content.

[0087] A "data processing device" refers to a system that includes hardware and software for receiving information transmitted from a user's information device and processing and analyzing it through an artificial intelligence agent.

[0088] An "automated intelligent agent" is a program that uses reinforcement learning algorithms to evaluate the safety of digital content and continuously improves the accuracy of that evaluation.

[0089] "Household appliances" refer to robots and smart devices used in the home that provide feedback to the user through voice or a display, based on the results of processing from an information processing device.

[0090] "Evaluation results" refer to information regarding the safety assessment made by an automated intelligent agent for content that a user attempts to access, as well as access control measures based on that assessment.

[0091] "Sound and display devices" refer to devices and functions that provide audio output or visual display to convey information such as evaluation results to the user.

[0092] In this invention, content access requests transmitted from information devices within the home are received and processed by a server. The server is composed of multiple information processing devices and runs an automated intelligent agent program. Specifically, it is envisioned as a hardware platform such as Raspberry Pi or NVIDIA Jetson Nano, and reinforcement learning algorithms are operated using libraries such as TENSORFLOW® and PyTorch.

[0093] When the server receives a request from an information device, it begins a data processing process to analyze its content. During the content safety assessment phase, an artificial intelligence agent retrieves relevant information and evaluates it against previously accumulated data. As a result, it determines whether the content is inappropriate or safe. This result is then fed back to the user via home devices, specifically terminals equipped with voice and display devices.

[0094] This system significantly improves the security of internet use within the home. Users, especially parents, can instantly understand their children's internet usage through voice notifications from their devices. For example, a robot can notify them, "A risk was detected in today's internet use, but it has all been blocked."

[0095] When using a generative AI model, a possible prompt might be: "Design a system that monitors what content children at home are watching on YouTube (registered trademark) and has a robot warn them if there are any dangerous videos." By utilizing this invention, it is possible to provide a safe internet connection within the home and support an environment that gives parents peace of mind.

[0096] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0097] Step 1:

[0098] The device detects user content access requests in real time and sends those requests to the server. The device has scripts implemented to monitor calls during web browsing and app use. Input includes data such as the user's accessed URL and access time, which is sent to the server. Output is the sent access request data.

[0099] Step 2:

[0100] The server records content access requests received from terminals in a database. The input data is user request information, which the server organizes and stores in the database chronologically. Data processing includes duplicate checking and format normalization, and the output is a structured database entry.

[0101] Step 3:

[0102] An automated intelligent agent on the server evaluates the safety of the content. It uses recorded content access information as input and a reinforcement learning algorithm to determine its safety. The data calculations here involve a risk assessment by the model and the calculation of a safety score. The output is the risk assessment result for the content.

[0103] Step 4:

[0104] The server provides real-time feedback to the terminal based on the evaluation results. The server compares and analyzes the access control policy with the evaluation results and implements processes to restrict access to inappropriate content. The risk evaluation results are used as input, and feedback information is generated as output.

[0105] Step 5:

[0106] The terminal receives feedback sent from the server and notifies the user of its contents. Functions such as alerts and warnings are activated via voice and display devices. The input is feedback information from the server, and the output is a notification message that the user can see or hear.

[0107] Step 6:

[0108] Users receive advice on internet usage based on evaluation results through home devices. Robots and smart devices installed in the home provide safety evaluation results via voice and display. The input is evaluation results from the server, and the output is specific advice that leads to improvements in the user's behavior.

[0109] 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.

[0110] This invention relates to a system that combines a user's emotions with an emotion engine that recognizes user emotions in real time and adjusts access control to content based on that information. The system consists of a user's terminal, a server, an artificial intelligence agent, and the emotion engine.

[0111] The user's device collects emotions from the user's facial expressions and voice through its camera and sensors. This allows the system to determine the user's emotional state and send that data to a server.

[0112] The server simultaneously analyzes sentiment data and content access requests received from user terminals. An artificial intelligence agent is deployed within the server, responsible for evaluating content safety. This agent uses reinforcement learning algorithms and continuously improves accuracy based on a constantly updated dataset. The sentiment engine analyzes the user's sentiment data and provides this information to the artificial intelligence agent.

[0113] This means that content evaluation is not only based on information safety, but also dynamically adjusted according to the user's emotional state. If an inappropriate emotional state is detected, access to the content will be restricted. For example, if negative emotions are detected, the emotion engine will tighten the content evaluation criteria, allowing only relaxing content.

[0114] The server reports the evaluation results and the user's emotional state to the administrator, and uses this information to help parents understand their child's state. The usage report also records changes in emotional state along with the content access history, making it easy for administrators to review.

[0115] For example, if the system detects that a user is experiencing stress while watching a video, it re-analyzes the content's evaluation and performs a more rigorous check. As a result, access to the content may be temporarily restricted, and alternative, relaxing content may be recommended. This process improves the user experience and protects users from the effects of harmful content.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] A user operates their smartphone and attempts to access content. At this time, the device uses sensors to read the user's facial expressions and voice, collecting emotional data.

[0119] Step 2:

[0120] The device sends collected sentiment data and content access requests to the server. This includes the URL of the webpage being accessed and app information.

[0121] Step 3:

[0122] The server begins analyzing the received emotion data and content access requests. The emotion engine within the server determines the user's emotional state in real time.

[0123] Step 4:

[0124] The server sends the emotion engine's judgment results to an artificial intelligence agent, which then incorporates them into the content safety assessment. The agent uses a reinforcement learning algorithm to perform the assessment, taking emotional states into account.

[0125] Step 5:

[0126] Based on the evaluation results and emotional state obtained, the server generates access instructions for the user's device. Content deemed inappropriate includes instructions to restrict access, and more appropriate content based on the emotional state is presented instead.

[0127] Step 6:

[0128] The device, based on instructions from the server, notifies the user that access has been restricted and recommends content. The notification also includes an explanation of the reason and the user's emotional state.

[0129] Step 7:

[0130] The server records users' content usage history and changes in their emotional state, and periodically generates usage reports for administrators. These reports are designed to help administrators understand the impact of content and changes in users' emotional states.

[0131] (Example 2)

[0132] 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 will be referred to as the "terminal."

[0133] When users access online content, failing to consider how well that content aligns with their emotional state can lead to them being exposed to inappropriate content. Furthermore, a lack of dynamic content control that takes users' emotional states into account can compromise the quality and safety of the user experience. This highlights the challenge of insufficient user protection, particularly in terms of emotional well-being.

[0134] 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.

[0135] In this invention, the server includes means for collecting data to detect the user's emotional state, means for transmitting the data to the server, means for simultaneously analyzing the emotional data and content access requests on the server, means for evaluating the emotional suitability of content via an emotional engine, means for dynamically controlling access to the user terminal based on the evaluation results and recommending alternative content when the emotional state is inappropriate, and means for notifying the administrator of the evaluation results and the user's emotional state via an information processing device. This enables appropriate control of access to content according to the user's emotional state, allowing for safe and comfortable use.

[0136] "User emotional state" refers to the user's mental or emotional state, determined based on information obtained from the user's facial expressions and voice.

[0137] "Data collection means" refers to a function or device that uses sensors, cameras, microphones, etc., built into the user's terminal to acquire the user's emotional state in real time.

[0138] A "server" is a computer system that receives and processes user sentiment data and content access requests within a network.

[0139] An "emotion engine" refers to a system or software that analyzes acquired user emotion data and uses that information to evaluate the emotional relevance of content.

[0140] "Evaluation results" refer to conclusions or judgments regarding the safety and emotional appropriateness of content, which are analyzed by the server using an emotion engine or artificial intelligence agent.

[0141] "Alternative content" refers to alternative content that is recommended to maintain the user's safety and comfort when an inappropriate emotional state is detected.

[0142] An "information processing device" primarily refers to a computer or digital device that has the ability to manage collected data and evaluation results, and to notify administrators of necessary information.

[0143] An "administrator" is an individual or organization responsible for monitoring system operations, receiving reports on user content usage and emotional state, and intervening or adjusting as necessary.

[0144] This system is implemented as a complex configuration including user terminals, servers, an emotion engine, and an artificial intelligence agent.

[0145] First, the user's device utilizes built-in sensors such as cameras and microphones to collect the user's facial expressions and voice in real time. This provides basic data for inferring the user's emotional state. For example, facial recognition software analyzes elements such as the user's smile, serious expression, and frown lines. Voice recognition software determines emotions by capturing changes in voice tone, volume, and speed.

[0146] Next, the device sends the acquired sentiment data to the server using a secure protocol. The server receives the user's content access request along with this sentiment data, and simultaneously analyzes the sentiment data and evaluates the suitability of the requested content.

[0147] The server incorporates an emotion engine and an artificial intelligence agent, which are used to process data. The emotion engine works in conjunction with the AI ​​agent, which uses reinforcement learning algorithms, to continuously improve its accuracy based on the dataset. This allows it to determine whether content is appropriate for the user's current emotional state and dynamically adjust access control if it is inappropriate. For example, if the engine detects that the user is stressed, it will recommend relaxing educational content or positive videos.

[0148] Furthermore, the server notifies the administrator of these evaluation results and the user's emotional state through an information processing device. This report includes detailed emotional transitions and access history, enabling the administrator to provide appropriate support based on this information.

[0149] For example, if data is recorded showing a user exhibiting stress over a long period, the system will automatically learn and optimize content selection to prevent similar trends from becoming prominent in the future.

[0150] An example prompt is, "If the user is expressing sadness, evaluate what content would be appropriate and generate a list." This allows the generative AI model to provide information that helps in specific content recommendations.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The user's device begins collecting data to detect their emotional state. Inputs include the user's facial expressions and voice data obtained through the camera and microphone. The device uses facial recognition software to analyze emotions from facial expressions. Voice data is also processed by voice recognition software to analyze emotions from voice tone and speed. This process outputs data indicating the user's emotional state.

[0154] Step 2:

[0155] The user's device sends the collected emotion data to the server. The input for this step is the emotion state data output in step 1. The device uses a secure protocol to encrypt and transmit the data. The output is the secure receipt of the data to the server.

[0156] Step 3:

[0157] The server analyzes the received sentiment data and the user's content access request. The input for this step is the sentiment data and content access request sent from the terminal. The server searches its database and evaluates the relevance of the sentiment data to the requested content. The output is the content access decision based on sentiment relevance.

[0158] Step 4:

[0159] The server evaluates emotional suitability via an emotional engine. Inputs include emotional data and analysis results. The emotional engine uses artificial intelligence algorithms to dynamically process the data and select content appropriate for the user. The output is a list of content recommended for safe access.

[0160] Step 5:

[0161] The server controls access to the user's terminal based on the evaluation results. The input is a list of evaluated content. The server sets access permissions or restrictions according to the user's emotional state and recommends alternative content if inappropriate. The output is new content access information for the user's terminal.

[0162] Step 6:

[0163] The server notifies the administrator of the evaluation results and the user's emotional state via an information processing device. Inputs include access history and evaluated emotional data. The server generates a report, providing the administrator with information on user emotional changes and content usage. Output is the report data received by the administrator.

[0164] (Application Example 2)

[0165] 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".

[0166] In today's world, where many users utilize various content distribution services, providing appropriate content based on a user's temporary emotional state is challenging. Furthermore, minimizing the impact of inappropriate content on users is also necessary. Traditional systems control content based on fixed evaluation criteria without considering user emotions, potentially leading to a degraded user experience.

[0167] 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.

[0168] In this invention, the server includes means for analyzing user emotion data and making content recommendations based thereon; means for dynamically adjusting access to the user terminal based on evaluation results and the user's emotional state, and restricting access to inappropriate content; and means for recording the user's content usage history and changes in emotional state, and notifying the administrator of usage status reports based thereon. This enables the provision of optimal content tailored to the user's emotions and safe and personalized content use.

[0169] A "user terminal" is an electronic device that users directly operate to exchange information, and includes smartphones and tablets.

[0170] A "server" is a central computer system that processes information and stores data on a network.

[0171] "Artificial intelligence" refers to the technology and systems that enable machines to learn and reason like humans and automatically provide optimal output.

[0172] "Emotional data" refers to a collection of information about a user's emotions, gathered from their facial expressions and voice.

[0173] "Content recommendation" refers to suggesting the most suitable digital information and services to users based on analyzed data.

[0174] "Dynamic adjustment" means changing controls and settings in real time according to the situation.

[0175] "Inappropriate content" refers to digital materials or information that may be harmful or offensive to users.

[0176] "Restricting access" refers to features or measures that prevent users from accessing specific digital information or services under certain conditions.

[0177] "Content usage history" refers to a record of digital information and services that a user has accessed in the past.

[0178] "Changes in emotional state" refers to the flow of a user's mental state as it changes over time.

[0179] An "information processing device" refers to a collection of hardware and software for receiving, analyzing, transforming, and transmitting data.

[0180] An "administrator" is a person or organization responsible for overseeing and managing systems and information, and providing support to users.

[0181] To realize this invention, the program will run on the user's device, such as a smartphone or tablet. Specifically, it will use the user's camera and microphone to acquire emotional data from the user's facial expressions and voice. This will utilize software frameworks such as OpenCV and TensorFlow. These software components will perform facial recognition and voice analysis of the user, enabling the prediction of emotions.

[0182] The server analyzes this emotional data in real time and recommends content that matches the user's current psychological state. The AI ​​on the server runs a reinforcement learning algorithm to evaluate the user's emotions and past content usage history. The Recommendation Engine selects content suitable for the user and makes it available for delivery. Furthermore, if inappropriate content is detected, access is dynamically restricted.

[0183] Furthermore, the server sends usage reports to administrators, including the user's content consumption history and changes in their emotional state. This helps parents and educators understand the user's behavior.

[0184] For example, when a user needs to relax after work, the system captures the user's emotional state and selects and recommends relaxing music or videos. By sending a prompt message such as, "When the user is feeling stressed, recommend relaxing content. Emotional data will be collected from camera footage and microphone audio," to the AI ​​model, optimal content recommendations are made.

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

[0186] Step 1:

[0187] The user's device activates its camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotion data from the user. This data is input to the device as image and audio data.

[0188] Step 2:

[0189] The user terminal uses OpenCV to analyze facial expressions and TensorFlow to infer emotions from speech. This outputs emotion data as numerical values ​​(e.g., stress level, happiness level). Once this emotion data is generated, the process proceeds to the next step.

[0190] Step 3:

[0191] The user's terminal sends the acquired sentiment data to the server. The server receives this data and compares it with past usage history stored in the database. The server takes the sentiment data as input and uses it as a criterion for making appropriate content recommendations.

[0192] Step 4:

[0193] The server uses artificial intelligence to select the most suitable content based on emotional data and usage history data. It utilizes a generative AI model to output a list of recommended content. A prompt statement is then inserted as a variable, and data calculations are performed under the command "Recommend relaxing content."

[0194] Step 5:

[0195] The server sends selected content to the user's terminal, displaying relaxing videos and music. By displaying this content, it becomes possible to provide an optimal user experience tailored to the user's mood and state.

[0196] Step 6:

[0197] The server generates and periodically sends reports to administrators that include users' content usage history and changes in their emotional state. This allows for an understanding of users' content consumption patterns and psychological states, providing useful information for parents and educators.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] The system of the present invention consists of a user's terminal, a server, and an artificial intelligence agent deployed on the server. The user's terminal is a device for accessing content, such as a smartphone or tablet. The terminal is responsible for monitoring the user's content access requests and transmitting that information to the server.

[0215] The server has the functionality to receive content access requests sent from user terminals. The received requests are passed to an artificial intelligence agent on the server, where the process of determining the safety of the content begins. The AI ​​agent uses a reinforcement learning algorithm and constantly improves its evaluation accuracy based on past data and accumulated knowledge. Specifically, it evaluates whether the content is harmful, whether the charges are excessive, and whether there is a risk of trouble on social media.

[0216] The server feeds the evaluation results back to the user's device and restricts access to content as needed. Furthermore, the evaluation results are notified to the parent via the device, and a report is sent detailing the child's smartphone usage. This report reflects the user's content usage history and provides useful information for administrators.

[0217] As a concrete example, consider a scenario where a user's child attempts to access a new video streaming service. The device detects the access request and sends it to the server. An artificial intelligence agent on the server evaluates the video content and determines whether it contains inappropriate elements. If it is deemed inappropriate, the result is fed back to the device, and access to the video is restricted. The parent is notified of this situation through the app management dashboard.

[0218] In this way, the present invention safely manages children's smartphone use and helps parents protect their children from harmful content and risks.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] When a user attempts to access content on their smartphone, the device detects the request. It collects and logs information about the URL and application included in the request. The log also includes the date and time of access and basic user information.

[0222] Step 2:

[0223] The terminal sends the collected request information to the server. Since the data is transmitted asynchronously over the network, it does not affect the user experience.

[0224] Step 3:

[0225] The server analyzes content access requests received from terminals. The analyzed information is quickly input into an artificial intelligence agent.

[0226] Step 4:

[0227] An artificial intelligence agent begins evaluating the content. Based on past data and algorithms, it makes real-time safety assessments. Specifically, it checks whether the content contains harmful elements, whether it might lead to excessive purchasing behavior, and whether there are any signs of trouble.

[0228] Step 5:

[0229] The server receives evaluation results from the artificial intelligence agent. Based on these results, the server generates instructions for the user's device. If the content is evaluated as inappropriate, the instructions will include a directive to prohibit access to the content.

[0230] Step 6:

[0231] The device receives instructions from the server and notifies the user as needed. The notification may include the reason for the restriction and, if necessary, contact information for parents.

[0232] Step 7:

[0233] The server accumulates users' content usage history and periodically generates usage reports for administrators (parents). This helps parents appropriately monitor their children's digital activities. These reports are accessible via smartphones and computers.

[0234] (Example 1)

[0235] 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."

[0236] The amount of digital content available on the internet is vast, and some of it contains information inappropriate for minors or involves excessive charges. Therefore, it is becoming increasingly difficult for parents to provide their children with a safe environment for using digital content. Furthermore, constantly monitoring usage is time-consuming and laborious, so there is a need for more efficient methods to do so.

[0237] 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.

[0238] In this invention, the server includes means for transmitting requests for digital content from a user device, means for receiving the requests at the information server and analyzing the safety of the digital content via a machine learning agent, and means for adjusting access to the user device and restricting access to inappropriate content based on the analysis results. This makes it easy to manage the safe and appropriate use of digital content for users, and allows parents to confidently let their children use digital devices.

[0239] "User device" refers to an electronic device used by a user to access digital content, and includes portable devices such as smartphones and tablets.

[0240] An "information server" is a central processing unit that receives requests from user devices on a network and performs analysis and evaluation of digital content.

[0241] A "machine learning agent" is a program that uses machine learning algorithms to analyze data in order to determine the safety of received digital content.

[0242] "Digital content" refers to data such as videos, music, games, and ebooks that are distributed over the internet.

[0243] "Analysis results" refer to safety evaluation data obtained when a machine learning agent evaluates digital content.

[0244] A "generative AI model" is a type of artificial intelligence used to evaluate digital content, and it is a model that sets specific evaluation criteria based on the provided prompt text.

[0245] The system of this invention mainly consists of a user device, an information server, and a machine learning agent located on the server. The user device refers to an electronic device for accessing digital content, and generally includes portable devices such as smartphones and tablets. This device is responsible for detecting user requests to access digital content and transmitting those requests to the information server.

[0246] The server has the function of receiving access requests sent from user devices, and a machine learning agent residing on the server processes the request. This agent analyzes the security of digital content using a generative AI model. The generative AI model evaluates the content based on specific prompt sentences and returns the analysis results to the server.

[0247] Specifically, consider a scenario where a user's child attempts to access a new video streaming service. The user's device immediately detects the access request and sends it to an information server. A machine learning agent on the server then evaluates the digital content in question. The prompts used might include specific questions such as, "Is this content safe for my child?", "Is the charge appropriate?", and "Is there a possibility of problems occurring on social media?".

[0248] The server sends feedback to the user's device based on the acquired analysis results. Based on this feedback, access to inappropriate content is restricted. In addition, parents are notified of their child's digital content usage via the information processing device. This allows parents to take measures to ensure appropriate and safe use of digital content. This invention is a system that safely manages children's use of digital devices and helps parents protect their children from harmful content.

[0249] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0250] Step 1:

[0251] The user generates a request to access digital content. The user's device detects this request and collects information such as the current time, device ID, and the URL being accessed. This serves as input for the next step. At this stage, the user's device generates an access log and prepares to send this data to the information server.

[0252] Step 2:

[0253] The terminal sends the collected access request data to the information server. The server securely receives the data via the HTTPS protocol. The input to the server is the request data sent from the user device. Using this, the server sets triggers for analyzing the data. In the data analysis, the accessed URL is compared with a known safe list. This allows for a primary safety assessment.

[0254] Step 3:

[0255] The server passes the received data to the machine learning agent. The machine learning agent uses a generative AI model to analyze the digital content. This agent scrutinizes the content of the input URL and evaluates its safety, whether it charges fees, and the risk of problems on social media. The prompt "Is this content safe for my child?" is used in the analysis process. The output generates an evaluation result regarding the safety of the content.

[0256] Step 4:

[0257] The server analyzes the evaluation results obtained from the machine learning agent and generates feedback for the user's device. This feedback determines access restrictions to the content based on the evaluation results. A notification indicating that access restrictions have been applied is generated as output.

[0258] Step 5:

[0259] The device receives feedback from the server and notifies the user. Simultaneously, a report is generated for administrators (e.g., parents) containing the child's digital content usage history and evaluation results. This report is available on the parent's dashboard app and can be used as part of ensuring children's safe digital content use.

[0260] (Application Example 1)

[0261] 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."

[0262] In modern households, the risk of children accessing inappropriate content while using the internet is increasing. Furthermore, the burden on parents and administrators to manage these risks is also growing. Current measures lack real-time monitoring and guidance on appropriate content use, making it difficult to achieve safe and healthy internet use.

[0263] 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.

[0264] In this invention, the server includes means for transmitting content access requests from a user's information device, means for receiving the requests in a data processing device and evaluating the security of the content via an automated intelligent agent, and means for feeding back the evaluation results to a home device using voice and a display device. This makes it possible to ensure the security of internet use within the home in real time and to allow parents to easily manage their children's content access.

[0265] "User information devices" refer to devices such as smartphones, tablets, and personal computers used at home or by individuals, which can connect to the internet and access content.

[0266] A "data processing device" refers to a system that includes hardware and software for receiving information transmitted from a user's information device and processing and analyzing it through an artificial intelligence agent.

[0267] An "automated intelligent agent" is a program that uses reinforcement learning algorithms to evaluate the safety of digital content and continuously improves the accuracy of that evaluation.

[0268] "Household appliances" refer to robots and smart devices used in the home that provide feedback to the user through voice or a display, based on the results of processing from an information processing device.

[0269] "Evaluation results" refer to information regarding the safety assessment made by an automated intelligent agent for content that a user attempts to access, as well as access control measures based on that assessment.

[0270] "Sound and display devices" refer to devices and functions that provide audio output or visual display to convey information such as evaluation results to the user.

[0271] In this invention, content access requests sent from information devices within the home are received and processed by a server. The server is composed of multiple information processing devices and runs an automated intelligent agent program. Specifically, it is envisioned as a hardware platform such as Raspberry Pi or NVIDIA Jetson Nano, and reinforcement learning algorithms are operated using libraries such as TensorFlow and PyTorch.

[0272] When the server receives a request from an information device, it begins a data processing process to analyze its content. During the content safety assessment phase, an artificial intelligence agent retrieves relevant information and evaluates it against previously accumulated data. As a result, it determines whether the content is inappropriate or safe. This result is then fed back to the user via home devices, specifically terminals equipped with voice and display devices.

[0273] This system significantly improves the security of internet use within the home. Users, especially parents, can instantly understand their children's internet usage through voice notifications from their devices. For example, a robot can notify them, "A risk was detected in today's internet use, but it has all been blocked."

[0274] When using a generative AI model, a possible prompt might be: "Design a system that monitors what content children at home are watching on YouTube and has a robot warn them if there are dangerous videos." By utilizing this invention, it is possible to provide a safe internet connection within the home and support an environment that gives parents peace of mind.

[0275] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0276] Step 1:

[0277] The device detects user content access requests in real time and sends those requests to the server. The device has scripts implemented to monitor calls during web browsing and app use. Input includes data such as the user's accessed URL and access time, which is sent to the server. Output is the sent access request data.

[0278] Step 2:

[0279] The server records content access requests received from terminals in a database. The input data is user request information, which the server organizes and stores in the database chronologically. Data processing includes duplicate checking and format normalization, and the output is a structured database entry.

[0280] Step 3:

[0281] An automated intelligent agent on the server evaluates the safety of the content. Referring to the recorded content access information as input, it determines its safety using a reinforcement learning algorithm. The data operations here are risk assessment by the model and calculation of the safety score. The output is the risk assessment result of the content.

[0282] Step 4:

[0283] Based on the evaluation result, the server performs real-time feedback to the terminal. The server compares and analyzes the access control policy and the evaluation result, and implements a process to restrict access to inappropriate content. Using the risk assessment result as input, feedback information is generated as output.

[0284] Step 5:

[0285] The terminal receives the feedback sent from the server and notifies the user of its content. Functions such as alerting and advising are activated via the audio and display devices. The input is the feedback information from the server, and the output is a notification message that the user can confirm visually or aurally.

[0286] Step 6:

[0287] The user receives advice on Internet usage based on the evaluation result through household devices. Robots and smart devices installed in the home provide the evaluation result of safety via audio and display. The input is the evaluation result from the server, and the output is specific advice leading to improvement of the user's behavior.

[0288] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0289] This invention relates to a system that combines a user's emotions with an emotion engine that recognizes user emotions in real time and adjusts access control to content based on that information. The system consists of a user's terminal, a server, an artificial intelligence agent, and the emotion engine.

[0290] The user's device collects emotions from the user's facial expressions and voice through its camera and sensors. This allows the system to determine the user's emotional state and send that data to a server.

[0291] The server simultaneously analyzes sentiment data and content access requests received from user terminals. An artificial intelligence agent is deployed within the server, responsible for evaluating content safety. This agent uses reinforcement learning algorithms and continuously improves accuracy based on a constantly updated dataset. The sentiment engine analyzes the user's sentiment data and provides this information to the artificial intelligence agent.

[0292] This means that content evaluation is not only based on information safety, but also dynamically adjusted according to the user's emotional state. If an inappropriate emotional state is detected, access to the content will be restricted. For example, if negative emotions are detected, the emotion engine will tighten the content evaluation criteria, allowing only relaxing content.

[0293] The server reports the evaluation results and the user's emotional state to the administrator, and uses this information to help parents understand their child's state. The usage report also records changes in emotional state along with the content access history, making it easy for administrators to review.

[0294] For example, if the system detects that a user is experiencing stress while watching a video, it re-analyzes the content's evaluation and performs a more rigorous check. As a result, access to the content may be temporarily restricted, and alternative, relaxing content may be recommended. This process improves the user experience and protects users from the effects of harmful content.

[0295] The following describes the processing flow.

[0296] Step 1:

[0297] A user operates their smartphone and attempts to access content. At this time, the device uses sensors to read the user's facial expressions and voice, collecting emotional data.

[0298] Step 2:

[0299] The device sends collected sentiment data and content access requests to the server. This includes the URL of the webpage being accessed and app information.

[0300] Step 3:

[0301] The server begins analyzing the received emotion data and content access requests. The emotion engine within the server determines the user's emotional state in real time.

[0302] Step 4:

[0303] The server sends the emotion engine's judgment results to an artificial intelligence agent, which then incorporates them into the content safety assessment. The agent uses a reinforcement learning algorithm to perform the assessment, taking emotional states into account.

[0304] Step 5:

[0305] Based on the evaluation results and emotional states obtained by the server, an access instruction to the user's terminal is generated. For content evaluated as inappropriate, an instruction to impose access restrictions is included, and the optimal content according to the emotional state is presented instead.

[0306] Step 6:

[0307] Based on the instruction from the server, the terminal notifies the user that access restrictions have been imposed and provides recommended content. The notification also includes an explanation of the reason and the emotional state.

[0308] Step 7:

[0309] The server records the user's content usage history and changes in emotional states, and periodically generates a usage report for the administrator. This report is designed to enable the administrator to understand the impact of the content and the changes in the user's emotions.

[0310] (Example 2)

[0311] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0312] When the user accesses online content, if the response does not consider the degree to which the content matches the user's emotional state, the user may be exposed to inappropriate content. Also, if dynamic content control considering the user's emotional state is not performed, the quality and safety of the user experience may decline. As a result, there is a problem that user protection, especially in terms of emotions, is not sufficient.

[0313] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0314] In this invention, the server includes means for collecting data to detect the user's emotional state, means for transmitting the data to the server, means for simultaneously analyzing the emotional data and content access requests on the server, means for evaluating the emotional suitability of content via an emotional engine, means for dynamically controlling access to the user terminal based on the evaluation results and recommending alternative content when the emotional state is inappropriate, and means for notifying the administrator of the evaluation results and the user's emotional state via an information processing device. This enables appropriate control of access to content according to the user's emotional state, allowing for safe and comfortable use.

[0315] "User emotional state" refers to the user's mental or emotional state, determined based on information obtained from the user's facial expressions and voice.

[0316] "Data collection means" refers to a function or device that uses sensors, cameras, microphones, etc., built into the user's terminal to acquire the user's emotional state in real time.

[0317] A "server" is a computer system that receives and processes user sentiment data and content access requests within a network.

[0318] An "emotion engine" refers to a system or software that analyzes acquired user emotion data and uses that information to evaluate the emotional relevance of content.

[0319] "Evaluation results" refer to conclusions or judgments regarding the safety and emotional appropriateness of content, which are analyzed by the server using an emotion engine or artificial intelligence agent.

[0320] "Alternative content" refers to alternative content that is recommended to maintain the user's safety and comfort when an inappropriate emotional state is detected.

[0321] An "information processing device" primarily refers to a computer or digital device that has the ability to manage collected data and evaluation results, and to notify administrators of necessary information.

[0322] An "administrator" is an individual or organization responsible for monitoring system operations, receiving reports on user content usage and emotional state, and intervening or adjusting as necessary.

[0323] This system is implemented as a complex configuration including user terminals, servers, an emotion engine, and an artificial intelligence agent.

[0324] First, the user's device utilizes built-in sensors such as cameras and microphones to collect the user's facial expressions and voice in real time. This provides basic data for inferring the user's emotional state. For example, facial recognition software analyzes elements such as the user's smile, serious expression, and frown lines. Voice recognition software determines emotions by capturing changes in voice tone, volume, and speed.

[0325] Next, the device sends the acquired sentiment data to the server using a secure protocol. The server receives the user's content access request along with this sentiment data, and simultaneously analyzes the sentiment data and evaluates the suitability of the requested content.

[0326] The server incorporates an emotion engine and an artificial intelligence agent, which are used to process data. The emotion engine works in conjunction with the AI ​​agent, which uses reinforcement learning algorithms, to continuously improve its accuracy based on the dataset. This allows it to determine whether content is appropriate for the user's current emotional state and dynamically adjust access control if it is inappropriate. For example, if the engine detects that the user is stressed, it will recommend relaxing educational content or positive videos.

[0327] Furthermore, the server notifies the administrator of these evaluation results and the user's emotional state through an information processing device. This report includes detailed emotional transitions and access history, enabling the administrator to provide appropriate support based on this information.

[0328] For example, if data is recorded showing a user exhibiting stress over a long period, the system will automatically learn and optimize content selection to prevent similar trends from becoming prominent in the future.

[0329] An example prompt is, "If the user is expressing sadness, evaluate what content would be appropriate and generate a list." This allows the generative AI model to provide information that helps in specific content recommendations.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The user's device begins collecting data to detect their emotional state. Inputs include the user's facial expressions and voice data obtained through the camera and microphone. The device uses facial recognition software to analyze emotions from facial expressions. Voice data is also processed by voice recognition software to analyze emotions from voice tone and speed. This process outputs data indicating the user's emotional state.

[0333] Step 2:

[0334] The user's device sends the collected emotion data to the server. The input for this step is the emotion state data output in step 1. The device uses a secure protocol to encrypt and transmit the data. The output is the secure receipt of the data to the server.

[0335] Step 3:

[0336] The server analyzes the received sentiment data and the user's content access request. The input for this step is the sentiment data and content access request sent from the terminal. The server searches its database and evaluates the relevance of the sentiment data to the requested content. The output is the content access decision based on sentiment relevance.

[0337] Step 4:

[0338] The server evaluates emotional suitability via an emotional engine. Inputs include emotional data and analysis results. The emotional engine uses artificial intelligence algorithms to dynamically process the data and select content appropriate for the user. The output is a list of content recommended for safe access.

[0339] Step 5:

[0340] The server controls access to the user's terminal based on the evaluation results. The input is a list of evaluated content. The server sets access permissions or restrictions according to the user's emotional state and recommends alternative content if inappropriate. The output is new content access information for the user's terminal.

[0341] Step 6:

[0342] The server notifies the administrator of the evaluation results and the user's emotional state via an information processing device. Inputs include access history and evaluated emotional data. The server generates a report, providing the administrator with information on user emotional changes and content usage. Output is the report data received by the administrator.

[0343] (Application Example 2)

[0344] 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."

[0345] In today's world, where many users utilize various content distribution services, providing appropriate content based on a user's temporary emotional state is challenging. Furthermore, minimizing the impact of inappropriate content on users is also necessary. Traditional systems control content based on fixed evaluation criteria without considering user emotions, potentially leading to a degraded user experience.

[0346] 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.

[0347] In this invention, the server includes means for analyzing user emotion data and making content recommendations based thereon; means for dynamically adjusting access to the user terminal based on evaluation results and the user's emotional state, and restricting access to inappropriate content; and means for recording the user's content usage history and changes in emotional state, and notifying the administrator of usage status reports based thereon. This enables the provision of optimal content tailored to the user's emotions and safe and personalized content use.

[0348] A "user terminal" is an electronic device that users directly operate to exchange information, and includes smartphones and tablets.

[0349] A "server" is a central computer system that processes information and stores data on a network.

[0350] "Artificial intelligence" refers to the technology and systems that enable machines to learn and reason like humans and automatically provide optimal output.

[0351] "Emotional data" refers to a collection of information about a user's emotions, gathered from their facial expressions and voice.

[0352] "Content recommendation" refers to suggesting the most suitable digital information and services to users based on analyzed data.

[0353] "Dynamic adjustment" means changing controls and settings in real time according to the situation.

[0354] "Inappropriate content" refers to digital materials or information that may be harmful or offensive to users.

[0355] "Restricting access" refers to features or measures that prevent users from accessing specific digital information or services under certain conditions.

[0356] "Content usage history" refers to a record of digital information and services that a user has accessed in the past.

[0357] "Changes in emotional state" refers to the flow of a user's mental state as it changes over time.

[0358] An "information processing device" refers to a collection of hardware and software for receiving, analyzing, transforming, and transmitting data.

[0359] An "administrator" is a person or organization responsible for overseeing and managing systems and information, and providing support to users.

[0360] To realize this invention, the program will run on the user's device, such as a smartphone or tablet. Specifically, it will use the user's camera and microphone to acquire emotional data from the user's facial expressions and voice. This will utilize software frameworks such as OpenCV and TensorFlow. These software components will perform facial recognition and voice analysis of the user, enabling the prediction of emotions.

[0361] The server analyzes this emotional data in real time and recommends content that matches the user's current psychological state. The AI ​​on the server runs a reinforcement learning algorithm to evaluate the user's emotions and past content usage history. The Recommendation Engine selects content suitable for the user and makes it available for delivery. Furthermore, if inappropriate content is detected, access is dynamically restricted.

[0362] Furthermore, the server sends usage reports to administrators, including the user's content consumption history and changes in their emotional state. This helps parents and educators understand the user's behavior.

[0363] For example, when a user needs to relax after work, the system captures the user's emotional state and selects and recommends relaxing music or videos. By sending a prompt message such as, "When the user is feeling stressed, recommend relaxing content. Emotional data will be collected from camera footage and microphone audio," to the AI ​​model, optimal content recommendations are made.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The user's device activates its camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotion data from the user. This data is input to the device as image and audio data.

[0367] Step 2:

[0368] The user terminal uses OpenCV to analyze facial expressions and TensorFlow to infer emotions from speech. This outputs emotion data as numerical values ​​(e.g., stress level, happiness level). Once this emotion data is generated, the process proceeds to the next step.

[0369] Step 3:

[0370] The user's terminal sends the acquired sentiment data to the server. The server receives this data and compares it with past usage history stored in the database. The server takes the sentiment data as input and uses it as a criterion for making appropriate content recommendations.

[0371] Step 4:

[0372] The server uses artificial intelligence to select the most suitable content based on emotional data and usage history data. It utilizes a generative AI model to output a list of recommended content. A prompt statement is then inserted as a variable, and data calculations are performed under the command "Recommend relaxing content."

[0373] Step 5:

[0374] The server sends selected content to the user's terminal, displaying relaxing videos and music. By displaying this content, it becomes possible to provide an optimal user experience tailored to the user's mood and state.

[0375] Step 6:

[0376] The server generates and periodically sends reports to administrators that include users' content usage history and changes in their emotional state. This allows for an understanding of users' content consumption patterns and psychological states, providing useful information for parents and educators.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] The system of the present invention consists of a user's terminal, a server, and an artificial intelligence agent deployed on the server. The user's terminal is a device for accessing content, such as a smartphone or tablet. The terminal is responsible for monitoring the user's content access requests and transmitting that information to the server.

[0394] The server has the functionality to receive content access requests sent from user terminals. The received requests are passed to an artificial intelligence agent on the server, where the process of determining the safety of the content begins. The AI ​​agent uses a reinforcement learning algorithm and constantly improves its evaluation accuracy based on past data and accumulated knowledge. Specifically, it evaluates whether the content is harmful, whether the charges are excessive, and whether there is a risk of trouble on social media.

[0395] The server feeds the evaluation results back to the user's device and restricts access to content as needed. Furthermore, the evaluation results are notified to the parent via the device, and a report is sent detailing the child's smartphone usage. This report reflects the user's content usage history and provides useful information for administrators.

[0396] As a concrete example, consider a scenario where a user's child attempts to access a new video streaming service. The device detects the access request and sends it to the server. An artificial intelligence agent on the server evaluates the video content and determines whether it contains inappropriate elements. If it is deemed inappropriate, the result is fed back to the device, and access to the video is restricted. The parent is notified of this situation through the app management dashboard.

[0397] In this way, the present invention safely manages children's smartphone use and helps parents protect their children from harmful content and risks.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] When a user attempts to access content on their smartphone, the device detects the request. It collects and logs information about the URL and application included in the request. The log also includes the date and time of access and basic user information.

[0401] Step 2:

[0402] The terminal sends the collected request information to the server. Since the data is transmitted asynchronously over the network, it does not affect the user experience.

[0403] Step 3:

[0404] The server analyzes content access requests received from terminals. The analyzed information is quickly input into an artificial intelligence agent.

[0405] Step 4:

[0406] An artificial intelligence agent begins evaluating the content. Based on past data and algorithms, it makes real-time safety assessments. Specifically, it checks whether the content contains harmful elements, whether it might lead to excessive purchasing behavior, and whether there are any signs of trouble.

[0407] Step 5:

[0408] The server receives evaluation results from the artificial intelligence agent. Based on these results, the server generates instructions for the user's device. If the content is evaluated as inappropriate, the instructions will include a directive to prohibit access to the content.

[0409] Step 6:

[0410] The device receives instructions from the server and notifies the user as needed. The notification may include the reason for the restriction and, if necessary, contact information for parents.

[0411] Step 7:

[0412] The server accumulates users' content usage history and periodically generates usage reports for administrators (parents). This helps parents appropriately monitor their children's digital activities. These reports are accessible via smartphones and computers.

[0413] (Example 1)

[0414] 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."

[0415] The amount of digital content available on the internet is vast, and some of it contains information inappropriate for minors or involves excessive charges. Therefore, it is becoming increasingly difficult for parents to provide their children with a safe environment for using digital content. Furthermore, constantly monitoring usage is time-consuming and laborious, so there is a need for more efficient methods to do so.

[0416] 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.

[0417] In this invention, the server includes means for transmitting requests for digital content from a user device, means for receiving the requests at the information server and analyzing the safety of the digital content via a machine learning agent, and means for adjusting access to the user device and restricting access to inappropriate content based on the analysis results. This makes it easy to manage the safe and appropriate use of digital content for users, and allows parents to confidently let their children use digital devices.

[0418] "User device" refers to an electronic device used by a user to access digital content, and includes portable devices such as smartphones and tablets.

[0419] An "information server" is a central processing unit that receives requests from user devices on a network and performs analysis and evaluation of digital content.

[0420] A "machine learning agent" is a program that uses machine learning algorithms to analyze data in order to determine the safety of received digital content.

[0421] "Digital content" refers to data such as videos, music, games, and ebooks that are distributed over the internet.

[0422] "Analysis results" refer to safety evaluation data obtained when a machine learning agent evaluates digital content.

[0423] A "generative AI model" is a type of artificial intelligence used to evaluate digital content, and it is a model that sets specific evaluation criteria based on the provided prompt text.

[0424] The system of this invention mainly consists of a user device, an information server, and a machine learning agent located on the server. The user device refers to an electronic device for accessing digital content, and generally includes portable devices such as smartphones and tablets. This device is responsible for detecting user requests to access digital content and transmitting those requests to the information server.

[0425] The server has the function of receiving access requests sent from user devices, and a machine learning agent residing on the server processes the request. This agent analyzes the security of digital content using a generative AI model. The generative AI model evaluates the content based on specific prompt sentences and returns the analysis results to the server.

[0426] Specifically, consider a scenario where a user's child attempts to access a new video streaming service. The user's device immediately detects the access request and sends it to an information server. A machine learning agent on the server then evaluates the digital content in question. The prompts used might include specific questions such as, "Is this content safe for my child?", "Is the charge appropriate?", and "Is there a possibility of problems occurring on social media?".

[0427] The server sends feedback to the user's device based on the acquired analysis results. Based on this feedback, access to inappropriate content is restricted. In addition, parents are notified of their child's digital content usage via the information processing device. This allows parents to take measures to ensure appropriate and safe use of digital content. This invention is a system that safely manages children's use of digital devices and helps parents protect their children from harmful content.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The user generates a request to access digital content. The user's device detects this request and collects information such as the current time, device ID, and the URL being accessed. This serves as input for the next step. At this stage, the user's device generates an access log and prepares to send this data to the information server.

[0431] Step 2:

[0432] The terminal sends the collected access request data to the information server. The server securely receives the data via the HTTPS protocol. The input to the server is the request data sent from the user device. Using this, the server sets triggers for analyzing the data. In the data analysis, the accessed URL is compared with a known safe list. This allows for a primary safety assessment.

[0433] Step 3:

[0434] The server passes the received data to the machine learning agent. The machine learning agent uses a generative AI model to analyze the digital content. This agent scrutinizes the content of the input URL and evaluates its safety, whether it charges fees, and the risk of problems on social media. The prompt "Is this content safe for my child?" is used in the analysis process. The output generates an evaluation result regarding the safety of the content.

[0435] Step 4:

[0436] The server analyzes the evaluation results obtained from the machine learning agent and generates feedback for the user's device. This feedback determines access restrictions to the content based on the evaluation results. A notification indicating that access restrictions have been applied is generated as output.

[0437] Step 5:

[0438] The device receives feedback from the server and notifies the user. Simultaneously, a report is generated for administrators (e.g., parents) containing the child's digital content usage history and evaluation results. This report is available on the parent's dashboard app and can be used as part of ensuring children's safe digital content use.

[0439] (Application Example 1)

[0440] 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."

[0441] In modern households, the risk of children accessing inappropriate content while using the internet is increasing. Furthermore, the burden on parents and administrators to manage these risks is also growing. Current measures lack real-time monitoring and guidance on appropriate content use, making it difficult to achieve safe and healthy internet use.

[0442] 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.

[0443] In this invention, the server includes means for transmitting content access requests from a user's information device, means for receiving the requests in a data processing device and evaluating the security of the content via an automated intelligent agent, and means for feeding back the evaluation results to a home device using voice and a display device. This makes it possible to ensure the security of internet use within the home in real time and to allow parents to easily manage their children's content access.

[0444] "User information devices" refer to devices such as smartphones, tablets, and personal computers used at home or by individuals, which can connect to the internet and access content.

[0445] A "data processing device" refers to a system that includes hardware and software for receiving information transmitted from a user's information device and processing and analyzing it through an artificial intelligence agent.

[0446] An "automated intelligent agent" is a program that uses reinforcement learning algorithms to evaluate the safety of digital content and continuously improves the accuracy of that evaluation.

[0447] "Household appliances" refer to robots and smart devices used in the home that provide feedback to the user through voice or a display, based on the results of processing from an information processing device.

[0448] "Evaluation results" refer to information regarding the safety assessment made by an automated intelligent agent for content that a user attempts to access, as well as access control measures based on that assessment.

[0449] "Sound and display devices" refer to devices and functions that provide audio output or visual display to convey information such as evaluation results to the user.

[0450] In this invention, content access requests sent from information devices within the home are received and processed by a server. The server is composed of multiple information processing devices and runs an automated intelligent agent program. Specifically, it is envisioned as a hardware platform such as Raspberry Pi or NVIDIA Jetson Nano, and reinforcement learning algorithms are operated using libraries such as TensorFlow and PyTorch.

[0451] When the server receives a request from an information device, it begins a data processing process to analyze its content. During the content safety assessment phase, an artificial intelligence agent retrieves relevant information and evaluates it against previously accumulated data. As a result, it determines whether the content is inappropriate or safe. This result is then fed back to the user via home devices, specifically terminals equipped with voice and display devices.

[0452] This system significantly improves the security of internet use within the home. Users, especially parents, can instantly understand their children's internet usage through voice notifications from their devices. For example, a robot can notify them, "A risk was detected in today's internet use, but it has all been blocked."

[0453] When using a generative AI model, a possible prompt might be: "Design a system that monitors what content children at home are watching on YouTube and has a robot warn them if there are dangerous videos." By utilizing this invention, it is possible to provide a safe internet connection within the home and support an environment that gives parents peace of mind.

[0454] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0455] Step 1:

[0456] The device detects user content access requests in real time and sends those requests to the server. The device has scripts implemented to monitor calls during web browsing and app use. Input includes data such as the user's accessed URL and access time, which is sent to the server. Output is the sent access request data.

[0457] Step 2:

[0458] The server records content access requests received from terminals in a database. The input data is user request information, which the server organizes and stores in the database chronologically. Data processing includes duplicate checking and format normalization, and the output is a structured database entry.

[0459] Step 3:

[0460] An automated intelligent agent on the server evaluates the safety of the content. It uses recorded content access information as input and a reinforcement learning algorithm to determine its safety. The data calculations here involve a risk assessment by the model and the calculation of a safety score. The output is the risk assessment result for the content.

[0461] Step 4:

[0462] The server provides real-time feedback to the terminal based on the evaluation results. The server compares and analyzes the access control policy with the evaluation results and implements processes to restrict access to inappropriate content. The risk evaluation results are used as input, and feedback information is generated as output.

[0463] Step 5:

[0464] The terminal receives feedback sent from the server and notifies the user of its contents. Functions such as alerts and warnings are activated via voice and display devices. The input is feedback information from the server, and the output is a notification message that the user can see or hear.

[0465] Step 6:

[0466] Users receive advice on internet usage based on evaluation results through home devices. Robots and smart devices installed in the home provide safety evaluation results via voice and display. The input is evaluation results from the server, and the output is specific advice that leads to improvements in the user's behavior.

[0467] 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.

[0468] This invention relates to a system that combines a user's emotions with an emotion engine that recognizes user emotions in real time and adjusts access control to content based on that information. The system consists of a user's terminal, a server, an artificial intelligence agent, and the emotion engine.

[0469] The user's device collects emotions from the user's facial expressions and voice through its camera and sensors. This allows the system to determine the user's emotional state and send that data to a server.

[0470] The server simultaneously analyzes sentiment data and content access requests received from user terminals. An artificial intelligence agent is deployed within the server, responsible for evaluating content safety. This agent uses reinforcement learning algorithms and continuously improves accuracy based on a constantly updated dataset. The sentiment engine analyzes the user's sentiment data and provides this information to the artificial intelligence agent.

[0471] This means that content evaluation is not only based on information safety, but also dynamically adjusted according to the user's emotional state. If an inappropriate emotional state is detected, access to the content will be restricted. For example, if negative emotions are detected, the emotion engine will tighten the content evaluation criteria, allowing only relaxing content.

[0472] The server reports the evaluation results and the user's emotional state to the administrator, and uses this information to help parents understand their child's state. The usage report also records changes in emotional state along with the content access history, making it easy for administrators to review.

[0473] For example, if the system detects that a user is experiencing stress while watching a video, it re-analyzes the content's evaluation and performs a more rigorous check. As a result, access to the content may be temporarily restricted, and alternative, relaxing content may be recommended. This process improves the user experience and protects users from the effects of harmful content.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] A user operates their smartphone and attempts to access content. At this time, the device uses sensors to read the user's facial expressions and voice, collecting emotional data.

[0477] Step 2:

[0478] The device sends collected sentiment data and content access requests to the server. This includes the URL of the webpage being accessed and app information.

[0479] Step 3:

[0480] The server begins analyzing the received emotion data and content access requests. The emotion engine within the server determines the user's emotional state in real time.

[0481] Step 4:

[0482] The server sends the emotion engine's judgment results to an artificial intelligence agent, which then incorporates them into the content safety assessment. The agent uses a reinforcement learning algorithm to perform the assessment, taking emotional states into account.

[0483] Step 5:

[0484] Based on the evaluation results and emotional state obtained, the server generates access instructions for the user's device. Content deemed inappropriate includes instructions to restrict access, and more appropriate content based on the emotional state is presented instead.

[0485] Step 6:

[0486] The device, based on instructions from the server, notifies the user that access has been restricted and recommends content. The notification also includes an explanation of the reason and the user's emotional state.

[0487] Step 7:

[0488] The server records users' content usage history and changes in their emotional state, and periodically generates usage reports for administrators. These reports are designed to help administrators understand the impact of content and changes in users' emotional states.

[0489] (Example 2)

[0490] 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."

[0491] When users access online content, failing to consider how well that content aligns with their emotional state can lead to them being exposed to inappropriate content. Furthermore, a lack of dynamic content control that takes users' emotional states into account can compromise the quality and safety of the user experience. This highlights the challenge of insufficient user protection, particularly in terms of emotional well-being.

[0492] 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.

[0493] In this invention, the server includes means for collecting data to detect the user's emotional state, means for transmitting the data to the server, means for simultaneously analyzing the emotional data and content access requests on the server, means for evaluating the emotional suitability of content via an emotional engine, means for dynamically controlling access to the user terminal based on the evaluation results and recommending alternative content when the emotional state is inappropriate, and means for notifying the administrator of the evaluation results and the user's emotional state via an information processing device. This enables appropriate control of access to content according to the user's emotional state, allowing for safe and comfortable use.

[0494] "User emotional state" refers to the user's mental or emotional state, determined based on information obtained from the user's facial expressions and voice.

[0495] "Data collection means" refers to a function or device that uses sensors, cameras, microphones, etc., built into the user's terminal to acquire the user's emotional state in real time.

[0496] A "server" is a computer system that receives and processes user sentiment data and content access requests within a network.

[0497] An "emotion engine" refers to a system or software that analyzes acquired user emotion data and uses that information to evaluate the emotional relevance of content.

[0498] "Evaluation results" refer to conclusions or judgments regarding the safety and emotional appropriateness of content, which are analyzed by the server using an emotion engine or artificial intelligence agent.

[0499] "Alternative content" refers to alternative content that is recommended to maintain the user's safety and comfort when an inappropriate emotional state is detected.

[0500] An "information processing device" primarily refers to a computer or digital device that has the ability to manage collected data and evaluation results, and to notify administrators of necessary information.

[0501] An "administrator" is an individual or organization responsible for monitoring system operations, receiving reports on user content usage and emotional state, and intervening or adjusting as necessary.

[0502] This system is implemented as a complex configuration including user terminals, servers, an emotion engine, and an artificial intelligence agent.

[0503] First, the user's device utilizes built-in sensors such as cameras and microphones to collect the user's facial expressions and voice in real time. This provides basic data for inferring the user's emotional state. For example, facial recognition software analyzes elements such as the user's smile, serious expression, and frown lines. Voice recognition software determines emotions by capturing changes in voice tone, volume, and speed.

[0504] Next, the device sends the acquired sentiment data to the server using a secure protocol. The server receives the user's content access request along with this sentiment data, and simultaneously analyzes the sentiment data and evaluates the suitability of the requested content.

[0505] The server incorporates an emotion engine and an artificial intelligence agent, which are used to process data. The emotion engine works in conjunction with the AI ​​agent, which uses reinforcement learning algorithms, to continuously improve its accuracy based on the dataset. This allows it to determine whether content is appropriate for the user's current emotional state and dynamically adjust access control if it is inappropriate. For example, if the engine detects that the user is stressed, it will recommend relaxing educational content or positive videos.

[0506] Furthermore, the server notifies the administrator of these evaluation results and the user's emotional state through an information processing device. This report includes detailed emotional transitions and access history, enabling the administrator to provide appropriate support based on this information.

[0507] For example, if data is recorded showing a user exhibiting stress over a long period, the system will automatically learn and optimize content selection to prevent similar trends from becoming prominent in the future.

[0508] An example prompt is, "If the user is expressing sadness, evaluate what content would be appropriate and generate a list." This allows the generative AI model to provide information that helps in specific content recommendations.

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] The user's device begins collecting data to detect their emotional state. Inputs include the user's facial expressions and voice data obtained through the camera and microphone. The device uses facial recognition software to analyze emotions from facial expressions. Voice data is also processed by voice recognition software to analyze emotions from voice tone and speed. This process outputs data indicating the user's emotional state.

[0512] Step 2:

[0513] The user's device sends the collected emotion data to the server. The input for this step is the emotion state data output in step 1. The device uses a secure protocol to encrypt and transmit the data. The output is the secure receipt of the data to the server.

[0514] Step 3:

[0515] The server analyzes the received sentiment data and the user's content access request. The input for this step is the sentiment data and content access request sent from the terminal. The server searches its database and evaluates the relevance of the sentiment data to the requested content. The output is the content access decision based on sentiment relevance.

[0516] Step 4:

[0517] The server evaluates emotional suitability via an emotional engine. Inputs include emotional data and analysis results. The emotional engine uses artificial intelligence algorithms to dynamically process the data and select content appropriate for the user. The output is a list of content recommended for safe access.

[0518] Step 5:

[0519] The server controls access to the user's terminal based on the evaluation results. The input is a list of evaluated content. The server sets access permissions or restrictions according to the user's emotional state and recommends alternative content if inappropriate. The output is new content access information for the user's terminal.

[0520] Step 6:

[0521] The server notifies the administrator of the evaluation results and the user's emotional state via an information processing device. Inputs include access history and evaluated emotional data. The server generates a report, providing the administrator with information on user emotional changes and content usage. Output is the report data received by the administrator.

[0522] (Application Example 2)

[0523] 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."

[0524] In today's world, where many users utilize various content distribution services, providing appropriate content based on a user's temporary emotional state is challenging. Furthermore, minimizing the impact of inappropriate content on users is also necessary. Traditional systems control content based on fixed evaluation criteria without considering user emotions, potentially leading to a degraded user experience.

[0525] 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.

[0526] In this invention, the server includes means for analyzing user emotion data and making content recommendations based thereon; means for dynamically adjusting access to the user terminal based on evaluation results and the user's emotional state, and restricting access to inappropriate content; and means for recording the user's content usage history and changes in emotional state, and notifying the administrator of usage status reports based thereon. This enables the provision of optimal content tailored to the user's emotions and safe and personalized content use.

[0527] A "user terminal" is an electronic device that users directly operate to exchange information, and includes smartphones and tablets.

[0528] A "server" is a central computer system that processes information and stores data on a network.

[0529] "Artificial intelligence" refers to the technology and systems that enable machines to learn and reason like humans and automatically provide optimal output.

[0530] "Emotional data" refers to a collection of information about a user's emotions, gathered from their facial expressions and voice.

[0531] "Content recommendation" refers to suggesting the most suitable digital information and services to users based on analyzed data.

[0532] "Dynamic adjustment" means changing controls and settings in real time according to the situation.

[0533] "Inappropriate content" refers to digital materials or information that may be harmful or offensive to users.

[0534] "Restricting access" refers to features or measures that prevent users from accessing specific digital information or services under certain conditions.

[0535] "Content usage history" refers to a record of digital information and services that a user has accessed in the past.

[0536] "Changes in emotional state" refers to the flow of a user's mental state as it changes over time.

[0537] An "information processing device" refers to a collection of hardware and software for receiving, analyzing, transforming, and transmitting data.

[0538] An "administrator" is a person or organization responsible for overseeing and managing systems and information, and providing support to users.

[0539] To realize this invention, the program will run on the user's device, such as a smartphone or tablet. Specifically, it will use the user's camera and microphone to acquire emotional data from the user's facial expressions and voice. This will utilize software frameworks such as OpenCV and TensorFlow. These software components will perform facial recognition and voice analysis of the user, enabling the prediction of emotions.

[0540] The server analyzes this emotional data in real time and recommends content that matches the user's current psychological state. The AI ​​on the server runs a reinforcement learning algorithm to evaluate the user's emotions and past content usage history. The Recommendation Engine selects content suitable for the user and makes it available for delivery. Furthermore, if inappropriate content is detected, access is dynamically restricted.

[0541] Furthermore, the server sends usage reports to administrators, including the user's content consumption history and changes in their emotional state. This helps parents and educators understand the user's behavior.

[0542] For example, when a user needs to relax after work, the system captures the user's emotional state and selects and recommends relaxing music or videos. By sending a prompt message such as, "When the user is feeling stressed, recommend relaxing content. Emotional data will be collected from camera footage and microphone audio," to the AI ​​model, optimal content recommendations are made.

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The user's device activates its camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotion data from the user. This data is input to the device as image and audio data.

[0546] Step 2:

[0547] The user terminal uses OpenCV to analyze facial expressions and TensorFlow to infer emotions from speech. This outputs emotion data as numerical values ​​(e.g., stress level, happiness level). Once this emotion data is generated, the process proceeds to the next step.

[0548] Step 3:

[0549] The user's terminal sends the acquired sentiment data to the server. The server receives this data and compares it with past usage history stored in the database. The server takes the sentiment data as input and uses it as a criterion for making appropriate content recommendations.

[0550] Step 4:

[0551] The server uses artificial intelligence to select the most suitable content based on emotional data and usage history data. It utilizes a generative AI model to output a list of recommended content. A prompt statement is then inserted as a variable, and data calculations are performed under the command "Recommend relaxing content."

[0552] Step 5:

[0553] The server sends selected content to the user's terminal, displaying relaxing videos and music. By displaying this content, it becomes possible to provide an optimal user experience tailored to the user's mood and state.

[0554] Step 6:

[0555] The server generates and periodically sends reports to administrators that include users' content usage history and changes in their emotional state. This allows for an understanding of users' content consumption patterns and psychological states, providing useful information for parents and educators.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] [Fourth Embodiment]

[0560] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0561] 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.

[0562] 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).

[0563] 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.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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".

[0573] The system of the present invention consists of a user's terminal, a server, and an artificial intelligence agent deployed on the server. The user's terminal is a device for accessing content, such as a smartphone or tablet. The terminal is responsible for monitoring the user's content access requests and transmitting that information to the server.

[0574] The server has the functionality to receive content access requests sent from user terminals. The received requests are passed to an artificial intelligence agent on the server, where the process of determining the safety of the content begins. The AI ​​agent uses a reinforcement learning algorithm and constantly improves its evaluation accuracy based on past data and accumulated knowledge. Specifically, it evaluates whether the content is harmful, whether the charges are excessive, and whether there is a risk of trouble on social media.

[0575] The server feeds the evaluation results back to the user's device and restricts access to content as needed. Furthermore, the evaluation results are notified to the parent via the device, and a report is sent detailing the child's smartphone usage. This report reflects the user's content usage history and provides useful information for administrators.

[0576] As a concrete example, consider a scenario where a user's child attempts to access a new video streaming service. The device detects the access request and sends it to the server. An artificial intelligence agent on the server evaluates the video content and determines whether it contains inappropriate elements. If it is deemed inappropriate, the result is fed back to the device, and access to the video is restricted. The parent is notified of this situation through the app management dashboard.

[0577] In this way, the present invention safely manages children's smartphone use and helps parents protect their children from harmful content and risks.

[0578] The following describes the processing flow.

[0579] Step 1:

[0580] When a user attempts to access content on their smartphone, the device detects the request. It collects and logs information about the URL and application included in the request. The log also includes the date and time of access and basic user information.

[0581] Step 2:

[0582] The terminal sends the collected request information to the server. Since the data is transmitted asynchronously over the network, it does not affect the user experience.

[0583] Step 3:

[0584] The server analyzes content access requests received from terminals. The analyzed information is quickly input into an artificial intelligence agent.

[0585] Step 4:

[0586] An artificial intelligence agent begins evaluating the content. Based on past data and algorithms, it makes real-time safety assessments. Specifically, it checks whether the content contains harmful elements, whether it might lead to excessive purchasing behavior, and whether there are any signs of trouble.

[0587] Step 5:

[0588] The server receives evaluation results from the artificial intelligence agent. Based on these results, the server generates instructions for the user's device. If the content is evaluated as inappropriate, the instructions will include a directive to prohibit access to the content.

[0589] Step 6:

[0590] The device receives instructions from the server and notifies the user as needed. The notification may include the reason for the restriction and, if necessary, contact information for parents.

[0591] Step 7:

[0592] The server accumulates users' content usage history and periodically generates usage reports for administrators (parents). This helps parents appropriately monitor their children's digital activities. These reports are accessible via smartphones and computers.

[0593] (Example 1)

[0594] 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".

[0595] The amount of digital content available on the internet is vast, and some of it contains information inappropriate for minors or involves excessive charges. Therefore, it is becoming increasingly difficult for parents to provide their children with a safe environment for using digital content. Furthermore, constantly monitoring usage is time-consuming and laborious, so there is a need for more efficient methods to do so.

[0596] 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.

[0597] In this invention, the server includes means for transmitting requests for digital content from a user device, means for receiving the requests at the information server and analyzing the safety of the digital content via a machine learning agent, and means for adjusting access to the user device and restricting access to inappropriate content based on the analysis results. This makes it easy to manage the safe and appropriate use of digital content for users, and allows parents to confidently let their children use digital devices.

[0598] "User device" refers to an electronic device used by a user to access digital content, and includes portable devices such as smartphones and tablets.

[0599] An "information server" is a central processing unit that receives requests from user devices on a network and performs analysis and evaluation of digital content.

[0600] A "machine learning agent" is a program that uses machine learning algorithms to analyze data in order to determine the safety of received digital content.

[0601] "Digital content" refers to data such as videos, music, games, and ebooks that are distributed over the internet.

[0602] "Analysis results" refer to safety evaluation data obtained when a machine learning agent evaluates digital content.

[0603] A "generative AI model" is a type of artificial intelligence used to evaluate digital content, and it is a model that sets specific evaluation criteria based on the provided prompt text.

[0604] The system of this invention mainly consists of a user device, an information server, and a machine learning agent located on the server. The user device refers to an electronic device for accessing digital content, and generally includes portable devices such as smartphones and tablets. This device is responsible for detecting user requests to access digital content and transmitting those requests to the information server.

[0605] The server has the function of receiving access requests sent from user devices, and a machine learning agent residing on the server processes the request. This agent analyzes the security of digital content using a generative AI model. The generative AI model evaluates the content based on specific prompt sentences and returns the analysis results to the server.

[0606] Specifically, consider a scenario where a user's child attempts to access a new video streaming service. The user's device immediately detects the access request and sends it to an information server. A machine learning agent on the server then evaluates the digital content in question. The prompts used might include specific questions such as, "Is this content safe for my child?", "Is the charge appropriate?", and "Is there a possibility of problems occurring on social media?".

[0607] The server sends feedback to the user's device based on the acquired analysis results. Based on this feedback, access to inappropriate content is restricted. In addition, parents are notified of their child's digital content usage via the information processing device. This allows parents to take measures to ensure appropriate and safe use of digital content. This invention is a system that safely manages children's use of digital devices and helps parents protect their children from harmful content.

[0608] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0609] Step 1:

[0610] The user generates a request to access digital content. The user's device detects this request and collects information such as the current time, device ID, and the URL being accessed. This serves as input for the next step. At this stage, the user's device generates an access log and prepares to send this data to the information server.

[0611] Step 2:

[0612] The terminal sends the collected access request data to the information server. The server securely receives the data via the HTTPS protocol. The input to the server is the request data sent from the user device. Using this, the server sets triggers for analyzing the data. In the data analysis, the accessed URL is compared with a known safe list. This allows for a primary safety assessment.

[0613] Step 3:

[0614] The server passes the received data to the machine learning agent. The machine learning agent uses a generative AI model to analyze the digital content. This agent scrutinizes the content of the input URL and evaluates its safety, whether it charges fees, and the risk of problems on social media. The prompt "Is this content safe for my child?" is used in the analysis process. The output generates an evaluation result regarding the safety of the content.

[0615] Step 4:

[0616] The server analyzes the evaluation results obtained from the machine learning agent and generates feedback for the user's device. This feedback determines access restrictions to the content based on the evaluation results. A notification indicating that access restrictions have been applied is generated as output.

[0617] Step 5:

[0618] The device receives feedback from the server and notifies the user. Simultaneously, a report is generated for administrators (e.g., parents) containing the child's digital content usage history and evaluation results. This report is available on the parent's dashboard app and can be used as part of ensuring children's safe digital content use.

[0619] (Application Example 1)

[0620] 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".

[0621] In modern households, the risk of children accessing inappropriate content while using the internet is increasing. Furthermore, the burden on parents and administrators to manage these risks is also growing. Current measures lack real-time monitoring and guidance on appropriate content use, making it difficult to achieve safe and healthy internet use.

[0622] 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.

[0623] In this invention, the server includes means for transmitting content access requests from a user's information device, means for receiving the requests in a data processing device and evaluating the security of the content via an automated intelligent agent, and means for feeding back the evaluation results to a home device using voice and a display device. This makes it possible to ensure the security of internet use within the home in real time and to allow parents to easily manage their children's content access.

[0624] "User information devices" refer to devices such as smartphones, tablets, and personal computers used at home or by individuals, which can connect to the internet and access content.

[0625] A "data processing device" refers to a system that includes hardware and software for receiving information transmitted from a user's information device and processing and analyzing it through an artificial intelligence agent.

[0626] An "automated intelligent agent" is a program that uses reinforcement learning algorithms to evaluate the safety of digital content and continuously improves the accuracy of that evaluation.

[0627] "Household appliances" refer to robots and smart devices used in the home that provide feedback to the user through voice or a display, based on the results of processing from an information processing device.

[0628] "Evaluation results" refer to information regarding the safety assessment made by an automated intelligent agent for content that a user attempts to access, as well as access control measures based on that assessment.

[0629] "Sound and display devices" refer to devices and functions that provide audio output or visual display to convey information such as evaluation results to the user.

[0630] In this invention, content access requests sent from information devices within the home are received and processed by a server. The server is composed of multiple information processing devices and runs an automated intelligent agent program. Specifically, it is envisioned as a hardware platform such as Raspberry Pi or NVIDIA Jetson Nano, and reinforcement learning algorithms are operated using libraries such as TensorFlow and PyTorch.

[0631] When the server receives a request from an information device, it begins a data processing process to analyze its content. During the content safety assessment phase, an artificial intelligence agent retrieves relevant information and evaluates it against previously accumulated data. As a result, it determines whether the content is inappropriate or safe. This result is then fed back to the user via home devices, specifically terminals equipped with voice and display devices.

[0632] This system significantly improves the security of internet use within the home. Users, especially parents, can instantly understand their children's internet usage through voice notifications from their devices. For example, a robot can notify them, "A risk was detected in today's internet use, but it has all been blocked."

[0633] When using a generative AI model, a possible prompt might be: "Design a system that monitors what content children at home are watching on YouTube and has a robot warn them if there are dangerous videos." By utilizing this invention, it is possible to provide a safe internet connection within the home and support an environment that gives parents peace of mind.

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The device detects user content access requests in real time and sends those requests to the server. The device has scripts implemented to monitor calls during web browsing and app use. Input includes data such as the user's accessed URL and access time, which is sent to the server. Output is the sent access request data.

[0637] Step 2:

[0638] The server records content access requests received from terminals in a database. The input data is user request information, which the server organizes and stores in the database chronologically. Data processing includes duplicate checking and format normalization, and the output is a structured database entry.

[0639] Step 3:

[0640] An automated intelligent agent on the server evaluates the safety of the content. It uses recorded content access information as input and a reinforcement learning algorithm to determine its safety. The data calculations here involve a risk assessment by the model and the calculation of a safety score. The output is the risk assessment result for the content.

[0641] Step 4:

[0642] The server provides real-time feedback to the terminal based on the evaluation results. The server compares and analyzes the access control policy with the evaluation results and implements processes to restrict access to inappropriate content. The risk evaluation results are used as input, and feedback information is generated as output.

[0643] Step 5:

[0644] The terminal receives feedback sent from the server and notifies the user of its contents. Functions such as alerts and warnings are activated via voice and display devices. The input is feedback information from the server, and the output is a notification message that the user can see or hear.

[0645] Step 6:

[0646] Users receive advice on internet usage based on evaluation results through home devices. Robots and smart devices installed in the home provide safety evaluation results via voice and display. The input is evaluation results from the server, and the output is specific advice that leads to improvements in the user's behavior.

[0647] 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.

[0648] This invention relates to a system that combines a user's emotions with an emotion engine that recognizes user emotions in real time and adjusts access control to content based on that information. The system consists of a user's terminal, a server, an artificial intelligence agent, and the emotion engine.

[0649] The user's device collects emotions from the user's facial expressions and voice through its camera and sensors. This allows the system to determine the user's emotional state and send that data to a server.

[0650] The server simultaneously analyzes sentiment data and content access requests received from user terminals. An artificial intelligence agent is deployed within the server, responsible for evaluating content safety. This agent uses reinforcement learning algorithms and continuously improves accuracy based on a constantly updated dataset. The sentiment engine analyzes the user's sentiment data and provides this information to the artificial intelligence agent.

[0651] This means that content evaluation is not only based on information safety, but also dynamically adjusted according to the user's emotional state. If an inappropriate emotional state is detected, access to the content will be restricted. For example, if negative emotions are detected, the emotion engine will tighten the content evaluation criteria, allowing only relaxing content.

[0652] The server reports the evaluation results and the user's emotional state to the administrator, and uses this information to help parents understand their child's state. The usage report also records changes in emotional state along with the content access history, making it easy for administrators to review.

[0653] For example, if the system detects that a user is experiencing stress while watching a video, it re-analyzes the content's evaluation and performs a more rigorous check. As a result, access to the content may be temporarily restricted, and alternative, relaxing content may be recommended. This process improves the user experience and protects users from the effects of harmful content.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] A user operates their smartphone and attempts to access content. At this time, the device uses sensors to read the user's facial expressions and voice, collecting emotional data.

[0657] Step 2:

[0658] The device sends collected sentiment data and content access requests to the server. This includes the URL of the webpage being accessed and app information.

[0659] Step 3:

[0660] The server begins analyzing the received emotion data and content access requests. The emotion engine within the server determines the user's emotional state in real time.

[0661] Step 4:

[0662] The server sends the emotion engine's judgment results to an artificial intelligence agent, which then incorporates them into the content safety assessment. The agent uses a reinforcement learning algorithm to perform the assessment, taking emotional states into account.

[0663] Step 5:

[0664] Based on the evaluation results and emotional state obtained, the server generates access instructions for the user's device. Content deemed inappropriate includes instructions to restrict access, and more appropriate content based on the emotional state is presented instead.

[0665] Step 6:

[0666] The device, based on instructions from the server, notifies the user that access has been restricted and recommends content. The notification also includes an explanation of the reason and the user's emotional state.

[0667] Step 7:

[0668] The server records users' content usage history and changes in their emotional state, and periodically generates usage reports for administrators. These reports are designed to help administrators understand the impact of content and changes in users' emotional states.

[0669] (Example 2)

[0670] 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".

[0671] When users access online content, failing to consider how well that content aligns with their emotional state can lead to them being exposed to inappropriate content. Furthermore, a lack of dynamic content control that takes users' emotional states into account can compromise the quality and safety of the user experience. This highlights the challenge of insufficient user protection, particularly in terms of emotional well-being.

[0672] 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.

[0673] In this invention, the server includes means for collecting data to detect the user's emotional state, means for transmitting the data to the server, means for simultaneously analyzing the emotional data and content access requests on the server, means for evaluating the emotional suitability of content via an emotional engine, means for dynamically controlling access to the user terminal based on the evaluation results and recommending alternative content when the emotional state is inappropriate, and means for notifying the administrator of the evaluation results and the user's emotional state via an information processing device. This enables appropriate control of access to content according to the user's emotional state, allowing for safe and comfortable use.

[0674] "User emotional state" refers to the user's mental or emotional state, determined based on information obtained from the user's facial expressions and voice.

[0675] "Data collection means" refers to a function or device that uses sensors, cameras, microphones, etc., built into the user's terminal to acquire the user's emotional state in real time.

[0676] A "server" is a computer system that receives and processes user sentiment data and content access requests within a network.

[0677] An "emotion engine" refers to a system or software that analyzes acquired user emotion data and uses that information to evaluate the emotional relevance of content.

[0678] "Evaluation results" refer to conclusions or judgments regarding the safety and emotional appropriateness of content, which are analyzed by the server using an emotion engine or artificial intelligence agent.

[0679] "Alternative content" refers to alternative content that is recommended to maintain the user's safety and comfort when an inappropriate emotional state is detected.

[0680] An "information processing device" primarily refers to a computer or digital device that has the ability to manage collected data and evaluation results, and to notify administrators of necessary information.

[0681] An "administrator" is an individual or organization responsible for monitoring system operations, receiving reports on user content usage and emotional state, and intervening or adjusting as necessary.

[0682] This system is implemented as a complex configuration including user terminals, servers, an emotion engine, and an artificial intelligence agent.

[0683] First, the user's device utilizes built-in sensors such as cameras and microphones to collect the user's facial expressions and voice in real time. This provides basic data for inferring the user's emotional state. For example, facial recognition software analyzes elements such as the user's smile, serious expression, and frown lines. Voice recognition software determines emotions by capturing changes in voice tone, volume, and speed.

[0684] Next, the device sends the acquired sentiment data to the server using a secure protocol. The server receives the user's content access request along with this sentiment data, and simultaneously analyzes the sentiment data and evaluates the suitability of the requested content.

[0685] The server incorporates an emotion engine and an artificial intelligence agent, which are used to process data. The emotion engine works in conjunction with the AI ​​agent, which uses reinforcement learning algorithms, to continuously improve its accuracy based on the dataset. This allows it to determine whether content is appropriate for the user's current emotional state and dynamically adjust access control if it is inappropriate. For example, if the engine detects that the user is stressed, it will recommend relaxing educational content or positive videos.

[0686] Furthermore, the server notifies the administrator of these evaluation results and the user's emotional state through an information processing device. This report includes detailed emotional transitions and access history, enabling the administrator to provide appropriate support based on this information.

[0687] For example, if data is recorded showing a user exhibiting stress over a long period, the system will automatically learn and optimize content selection to prevent similar trends from becoming prominent in the future.

[0688] An example prompt is, "If the user is expressing sadness, evaluate what content would be appropriate and generate a list." This allows the generative AI model to provide information that helps in specific content recommendations.

[0689] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0690] Step 1:

[0691] The user's device begins collecting data to detect their emotional state. Inputs include the user's facial expressions and voice data obtained through the camera and microphone. The device uses facial recognition software to analyze emotions from facial expressions. Voice data is also processed by voice recognition software to analyze emotions from voice tone and speed. This process outputs data indicating the user's emotional state.

[0692] Step 2:

[0693] The user's device sends the collected emotion data to the server. The input for this step is the emotion state data output in step 1. The device uses a secure protocol to encrypt and transmit the data. The output is the secure receipt of the data to the server.

[0694] Step 3:

[0695] The server analyzes the received sentiment data and the user's content access request. The input for this step is the sentiment data and content access request sent from the terminal. The server searches its database and evaluates the relevance of the sentiment data to the requested content. The output is the content access decision based on sentiment relevance.

[0696] Step 4:

[0697] The server evaluates emotional suitability via an emotional engine. Inputs include emotional data and analysis results. The emotional engine uses artificial intelligence algorithms to dynamically process the data and select content appropriate for the user. The output is a list of content recommended for safe access.

[0698] Step 5:

[0699] The server controls access to the user's terminal based on the evaluation results. The input is a list of evaluated content. The server sets access permissions or restrictions according to the user's emotional state and recommends alternative content if inappropriate. The output is new content access information for the user's terminal.

[0700] Step 6:

[0701] The server notifies the administrator of the evaluation results and the user's emotional state via an information processing device. Inputs include access history and evaluated emotional data. The server generates a report, providing the administrator with information on user emotional changes and content usage. Output is the report data received by the administrator.

[0702] (Application Example 2)

[0703] 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".

[0704] In today's world, where many users utilize various content distribution services, providing appropriate content based on a user's temporary emotional state is challenging. Furthermore, minimizing the impact of inappropriate content on users is also necessary. Traditional systems control content based on fixed evaluation criteria without considering user emotions, potentially leading to a degraded user experience.

[0705] 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.

[0706] In this invention, the server includes means for analyzing user emotion data and making content recommendations based thereon; means for dynamically adjusting access to the user terminal based on evaluation results and the user's emotional state, and restricting access to inappropriate content; and means for recording the user's content usage history and changes in emotional state, and notifying the administrator of usage status reports based thereon. This enables the provision of optimal content tailored to the user's emotions and safe and personalized content use.

[0707] A "user terminal" is an electronic device that users directly operate to exchange information, and includes smartphones and tablets.

[0708] A "server" is a central computer system that processes information and stores data on a network.

[0709] "Artificial intelligence" refers to the technology and systems that enable machines to learn and reason like humans and automatically provide optimal output.

[0710] "Emotional data" refers to a collection of information about a user's emotions, gathered from their facial expressions and voice.

[0711] "Content recommendation" refers to suggesting the most suitable digital information and services to users based on analyzed data.

[0712] "Dynamic adjustment" means changing controls and settings in real time according to the situation.

[0713] "Inappropriate content" refers to digital materials or information that may be harmful or offensive to users.

[0714] "Restricting access" refers to features or measures that prevent users from accessing specific digital information or services under certain conditions.

[0715] "Content usage history" refers to a record of digital information and services that a user has accessed in the past.

[0716] "Changes in emotional state" refers to the flow of a user's mental state as it changes over time.

[0717] An "information processing device" refers to a collection of hardware and software for receiving, analyzing, transforming, and transmitting data.

[0718] An "administrator" is a person or organization responsible for overseeing and managing systems and information, and providing support to users.

[0719] To realize this invention, the program will run on the user's device, such as a smartphone or tablet. Specifically, it will use the user's camera and microphone to acquire emotional data from the user's facial expressions and voice. This will utilize software frameworks such as OpenCV and TensorFlow. These software components will perform facial recognition and voice analysis of the user, enabling the prediction of emotions.

[0720] The server analyzes this emotional data in real time and recommends content that matches the user's current psychological state. The AI ​​on the server runs a reinforcement learning algorithm to evaluate the user's emotions and past content usage history. The Recommendation Engine selects content suitable for the user and makes it available for delivery. Furthermore, if inappropriate content is detected, access is dynamically restricted.

[0721] Furthermore, the server sends usage reports to administrators, including the user's content consumption history and changes in their emotional state. This helps parents and educators understand the user's behavior.

[0722] For example, when a user needs to relax after work, the system captures the user's emotional state and selects and recommends relaxing music or videos. By sending a prompt message such as, "When the user is feeling stressed, recommend relaxing content. Emotional data will be collected from camera footage and microphone audio," to the AI ​​model, optimal content recommendations are made.

[0723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0724] Step 1:

[0725] The user's device activates its camera and microphone to capture the user's facial expressions and voice. This allows for the acquisition of real-time emotion data from the user. This data is input to the device as image and audio data.

[0726] Step 2:

[0727] The user terminal uses OpenCV to analyze facial expressions and TensorFlow to infer emotions from speech. This outputs emotion data as numerical values ​​(e.g., stress level, happiness level). Once this emotion data is generated, the process proceeds to the next step.

[0728] Step 3:

[0729] The user's terminal sends the acquired sentiment data to the server. The server receives this data and compares it with past usage history stored in the database. The server takes the sentiment data as input and uses it as a criterion for making appropriate content recommendations.

[0730] Step 4:

[0731] The server uses artificial intelligence to select the most suitable content based on emotional data and usage history data. It utilizes a generative AI model to output a list of recommended content. A prompt statement is then inserted as a variable, and data calculations are performed under the command "Recommend relaxing content."

[0732] Step 5:

[0733] The server sends selected content to the user's terminal, displaying relaxing videos and music. By displaying this content, it becomes possible to provide an optimal user experience tailored to the user's mood and state.

[0734] Step 6:

[0735] The server generates and periodically sends reports to administrators that include users' content usage history and changes in their emotional state. This allows for an understanding of users' content consumption patterns and psychological states, providing useful information for parents and educators.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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."

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] The following is further disclosed regarding the embodiments described above.

[0758] (Claim 1)

[0759] A means of sending a content access request from a user terminal,

[0760] A server receives the aforementioned request and evaluates the safety of the content via an artificial intelligence agent;

[0761] A means of controlling access to user terminals based on evaluation results and restricting access to inappropriate content,

[0762] A system including means for notifying an administrator of the evaluation results via an information processing device.

[0763] (Claim 2)

[0764] The system according to claim 1, comprising means for an artificial intelligence agent to constantly evolve using a reinforcement learning algorithm to improve the accuracy of content evaluation.

[0765] (Claim 3)

[0766] The system according to claim 1, comprising means for recording a user's content usage history and notifying an administrator of usage status reports based on that history.

[0767] "Example 1"

[0768] (Claim 1)

[0769] Means for transmitting requests for digital content from user equipment,

[0770] A means for receiving the request on an information server and analyzing the safety of digital content via a machine learning agent,

[0771] A means for adjusting access to user devices based on analysis results and restricting access to inappropriate content,

[0772] Means for notifying the administrator of the analysis results via an information processing device,

[0773] A method for evaluating digital content using a generative AI model,

[0774] A means of notifying parents or guardians of the evaluation results,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, comprising means for a machine learning agent to continuously evolve using a reinforcement learning algorithm to improve the accuracy of digital content evaluation.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising means for recording a user's digital content usage history and notifying an administrator of usage status reports based on that history.

[0780] "Application Example 1"

[0781] (Claim 1)

[0782] A means for sending content access requests from a user's information device,

[0783] A data processing device receives the request and evaluates the safety of the content via an automated intelligent agent,

[0784] A means of controlling users' access to information devices and restricting access to inappropriate content based on evaluation results,

[0785] Means for notifying the administrator of the evaluation results via a control device,

[0786] A means of providing feedback on evaluation results to home appliances using voice and display devices,

[0787] A system that includes means for evaluating the safety level of internet usage and providing safety guidance.

[0788] (Claim 2)

[0789] The system according to claim 1, comprising means for an automated intelligent agent to continuously evolve using a reinforcement learning algorithm to improve the accuracy of content evaluation.

[0790] (Claim 3)

[0791] The system according to claim 1, comprising means for recording a user's content usage history and notifying an administrator of usage status reports based on that history.

[0792] "Example 2 of combining an emotion engine"

[0793] (Claim 1)

[0794] A means for collecting data to detect the emotional state of a user,

[0795] Means for transmitting the aforementioned data to a server,

[0796] A means for simultaneously analyzing the aforementioned sentiment data and content access requests on the server,

[0797] A means of evaluating the emotional relevance of content via an emotion engine,

[0798] A means of dynamically controlling access to user terminals based on evaluation results and recommending alternative content when the user is in an inappropriate emotional state,

[0799] A system including means for notifying an administrator of the aforementioned evaluation results and the user's emotional state through an information processing device.

[0800] (Claim 2)

[0801] The system according to claim 1, comprising means for an artificial intelligence agent to continuously evolve using a reinforcement learning algorithm to improve the accuracy of its emotional compatibility assessment.

[0802] (Claim 3)

[0803] The system according to claim 1, comprising means for recording a history of content usage, including changes in the user's emotional state, and for notifying an administrator of a usage status report based on that history.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A means of sending a content access request from a user terminal,

[0807] A server receives the aforementioned request and evaluates the safety of the content using artificial intelligence,

[0808] A means of dynamically adjusting access to user devices based on evaluation results and the user's emotional state, and restricting access to inappropriate content,

[0809] A means of analyzing user sentiment data and recommending content based on that data,

[0810] A system including means for notifying an administrator of the evaluation results via an information processing device.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising means for artificial intelligence to constantly evolve using reinforcement learning algorithms to improve the accuracy of content evaluation.

[0813] (Claim 3)

[0814] The system according to claim 1, comprising means for recording a user's content usage history and changes in their emotional state, and for notifying an administrator of usage status reports based on this information. [Explanation of Symbols]

[0815] 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. A means for sending content access requests from a user's information device, A data processing device receives the request and evaluates the safety of the content via an automated intelligent agent, A means of controlling users' access to information devices and restricting access to inappropriate content based on evaluation results, Means for notifying the administrator of the evaluation results via a control device, A means of providing feedback on evaluation results to home appliances using voice and display devices, A system that includes means for evaluating the safety level of internet usage and providing safety guidance.

2. The system according to claim 1, comprising means for an automated intelligent agent to continuously evolve using a reinforcement learning algorithm to improve the accuracy of content evaluation.

3. The system according to claim 1, comprising means for recording a user's content usage history and notifying an administrator of usage status reports based on that history.

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