Information processing device and information processing method

Abstract

An information processing device (10) comprises: a score acquisition unit (12) that obtains an IT literacy proficiency score of a child on the basis of at least one of the child's IT literacy learning history information, viewing / operation history information, and attribute information of the child, and also obtains a risk score for desired content using a generative AI (40A) that has been trained on information regarding content risks; a generation unit (13) that generates a requested item to be generated (at least one of the result of determining whether or not to permit the viewing of the desired content and advice regarding the viewing) on the basis of at least the child's IT literacy proficiency score and the risk score for the desired content; and an output unit (14) that outputs the generated requested item.

Description

Information processing device and information processing method

[0001] This disclosure relates to an information processing device and an information processing method. In this case, "child" refers to minors in general, and specifically to those for whom it is desirable to control (restrict, etc.) access to web content in order to promote healthy personality development.

[0002] With the spread of the internet, not only adults but also children can freely search for and access a vast amount of content (broadly including various websites, music content, video content, game content, etc.) that is accessible via the internet, and then refer to, view, and use the content they desire. However, it is known that a large amount of content provided on the network is considered harmful to children. For this reason, technologies have been proposed to restrict children's access to harmful content and to provide them with safe content (see Patent Document 1).

[0003] Traditionally, filtering has been widely used as a technology to restrict children's access to harmful content. One common example of filtering is the creation of blacklists of websites containing harmful content for each website category, and the subsequent prohibition of children from accessing websites on these blacklists (filtering based on uniform category classification).

[0004] Japanese Patent Publication No. 2008-059445

[0005] However, considering the reality that, for example, websites categorized as "bulletin boards" should be prohibited for children because they may come into contact with a large number of strangers, but bulletin board sites operated by schools should not be prohibited for children, it can be argued that filtering based on uniform category classification is not always appropriate.

[0006] Furthermore, children vary individually, and the content that should be allowed to be accessed changes depending on each child's development, internet usage, and other factors. Therefore, applying a uniform filtering system based on a child's age, for example, is not always effective.

[0007] Furthermore, as a filtering method for children, there is a method in which experts (such as parents with high IT literacy) manually determine whether or not a child can access various content, taking into account the content itself and individual circumstances (such as the child's development and internet usage). However, because there is a large amount of content with complex functions and complex content, manual individual approval based on the content as described above requires a lot of effort, knowledge, and experience, making it very difficult to implement. Also, while it is desirable to use individual circumstances (such as the child's development and internet usage) rather than the child's age as a criterion, even parents who are thought to know their child's individual circumstances well find it difficult to accurately grasp their child's internet usage, making it difficult to perform accurate manual individual approval as described above.

[0008] Based on the above, this disclosure aims to realize appropriate content access control that is tailored to the content and the individual circumstances of each child.

[0009] The information processing apparatus according to the present disclosure includes a reception unit that receives a generation request for a request item that is at least one of a viewing permission determination result of desired content that a target child wishes to view or advice regarding viewing, identification information of the child, and information regarding the desired content, an IT literacy proficiency score of the child is obtained based on at least one of the IT literacy learning history information of the child, the viewing / operation history information of the child, or the attribute information of the child obtained based on the identification information of the child, and a generation AI that has learned information regarding the risk of content is used to obtain a risk score of the desired content based on the information regarding the desired content, a score acquisition unit, a generation unit that generates the request item related to the generation request based on at least the IT literacy proficiency score of the child and the risk score of the desired content, and an output unit that outputs the generated request item.

[0010] According to the present disclosure, it is possible to realize appropriate content access control according to the content of the content and the individual circumstances of the child.

[0011] It is a configuration diagram of the entire system including the information processing apparatus. It is a diagram for explaining the calculation of the learning evaluation score. It is a diagram for explaining the calculation of the risk experience score. It is a diagram for explaining the calculation of the evaluation score based on attributes. It is a diagram for explaining the acquisition of the IT literacy proficiency score. It is a diagram for explaining the acquisition of the risk score of the desired content. It is a diagram for explaining the generation / obtaining of a request item (viewing permission determination result or advice). It is a flowchart of the process executed by the information processing apparatus. It is a diagram showing a modification of the system configuration including both the protector's terminal and the child's terminal. It is a diagram showing a modification of the system configuration in which the LLM is built in the information processing apparatus. It is a diagram showing an example of the hardware configuration of the information processing apparatus.

[0012] Hereinafter, an embodiment of the information processing apparatus and the information processing method according to the present disclosure will be described with reference to the drawings. In the following embodiment, as an example of the generation AI model, a form using a large language model (LLM) mainly used for text generation will be described.

[0013] [Configuration of a System including an Information Processing Device] FIG. 1 shows a configuration diagram of a system 1 including an information processing device 10 according to the present disclosure. As shown in FIG. 1, the system 1 includes an external server 40 on which a large language model (LLM) 40A operates, an information processing device 10, and an external server 30.

[0014] The LLM 40A operating on the external server 40 is an LLM (hereinafter referred to as a "knowledge-extended LLM") that has learned information on risks including the risk levels of content determined in the past, and is involved in deriving the risk level score of the desired content described later, and generating at least one of the determination result of whether the content (desired content) that the target child wishes to view can be viewed or advice regarding viewing. In the present embodiment, an example in which both the determination result of whether the desired content can be viewed and the above advice are required to be generated as the above "requirements" will be described.

[0015] The external server 30 stores and manages various information. For example, it includes an IT literacy learning history database (DB) 30A that stores children's IT literacy learning history information, a browsing / operation history database (DB) 30B that stores children's browsing / operation history information, and an attribute information database (DB) 30C that stores children's attribute information. Among the above, the browsing / operation history information stored in the browsing / operation history DB 30B includes the content information that has been browsed / operated, and the risk level score obtained by a score acquisition unit 12 described later is assigned to the content information. Note that the content information stored in the browsing / operation history DB 30B includes information for which generation of advice or the like has not been requested in the past and for which a risk level score has not been assigned, but such content can still be browsed / operated. Also, the information stored in each of the above DBs is referred to when the score acquisition unit 12 described later acquires the IT literacy proficiency score of a child. Specific examples of this information will be described later.

[0016] The information processing device 10 shown in Figure 1 can utilize various types of hardware (smartphones, mobile phones, smartwatches, wearable devices, laptops, desktop computers, servers, etc.), but the information processing device 10 in this embodiment is designed to be a mobile device carried by the user (smartphone, mobile phone, smartwatch, wearable device, etc.).

[0017] The information processing device 10 includes a receiving unit 11, a score acquisition unit 12, a generation unit 13, and an output unit 14 in order to realize the functions related to this disclosure. The functions of each unit will be described below.

[0018] The reception unit 11 is a functional unit that receives a request from the parent's terminal 20 for the generation of a request, which is at least one of the results of a determination of whether the desired content can be viewed or advice regarding viewing the content the child wishes to view, as well as the child's identification information and information regarding the desired content. Although Figure 1 shows an example configuration in which the generation request is made from the parent's terminal 20, as a modification, a configuration in which the generation request is made from the child's terminal 20S, which is authorized to operate based on approval from the parent's terminal 20, may also be adopted as shown in Figure 9. Such modifications will be described later.

[0019] The score acquisition unit 12 is a functional unit that acquires a child's IT literacy proficiency score based on at least one of the following: the child's IT literacy learning history information, the child's browsing and operation history information, or the child's attribute information, which is acquired based on the child's identification information, and also acquires a risk score for the desired content using the knowledge-enhanced LLM 40A. Of the acquired "IT literacy proficiency score" and "risk score for the desired content," the score acquisition unit 12 acquires the "risk score for the desired content" by inputting an inquiry about the risk score for the desired content to the knowledge-enhanced LLM 40A, and then acquires the risk score for the desired content output from the knowledge-enhanced LLM 40A.

[0020] Furthermore, regarding the "IT literacy proficiency score," an example will be described in which the score acquisition unit 12 acquires the child's IT literacy proficiency score based on all of the child's IT literacy learning history information, browsing and operation history information, and child's attribute information. As will be explained in detail later, the score acquisition unit 12 calculates the child's (a) learning evaluation score based on the child's IT literacy learning history information, (b) risk experience score based on the child's browsing and operation history information, and (c) attribute-based evaluation score based on the child's attribute information, and acquires the IT literacy proficiency score based on (a) learning evaluation score, (b) risk experience score, and (c) attribute-based evaluation score. The calculation of these scores will be explained later using Figures 2 to 5.

[0021] The generation unit 13 is a functional unit that generates a request related to a generation request based on at least the child's IT literacy proficiency score and the risk score of the desired content. In this embodiment, the generation unit 13 obtains the request from the knowledge-enhanced LLM 40A by instructing the knowledge-enhanced LLM 40A to generate a request based on the child's attribute information in addition to the child's IT literacy proficiency score and the risk score of the desired content.

[0022] The output unit 14 is a functional unit that outputs the generated request, and outputs the generated request to the parent's terminal 20 (Figure 1) that created the request.

[0023] The acquisition of the IT literacy proficiency score by the score acquisition unit 12 will be explained below with reference to Figures 2 to 5. First, the score acquisition unit 12 calculates (a) the child's learning evaluation score based on the child's IT literacy learning history information, (b) the child's risk experience score based on the child's browsing and operation history information, and (c) the child's attribute evaluation score based on the child's attribute information. Then, it acquires the IT literacy proficiency score based on (a) the learning evaluation score, (b) the risk experience score, and (c) the attribute evaluation score.

[0024] Figure 2 shows an example of calculating a child's learning evaluation score based on the child's IT literacy learning history information. As shown in Figure 2, the score acquisition unit 12 obtains the "problem score" and "learning score" for the target child from the IT literacy learning history information of the target child, and calculates the learning evaluation score of the target child using the following formula (1). Specifically, the score acquisition unit 12 obtains, from the IT literacy learning history information of the target child, the total number of correct answers / total number of answers for each category as the "problem score X ｑ " for each category, and the total number of learning for each category as the "learning score X ｓ " for each category. Then, the average Ave ｑ of the problem scores of all users obtained for each category and the standard deviation Stg ｑ of the problem scores, the average Ave ｓ of the learning scores of all users and the standard deviation Stg ｓ of the learning scores. Together with these, the score acquisition unit 12 applies the problem score X ｑ and the learning score X ｓ to the following formula (1) to calculate the learning evaluation score of the target child for each category. Learning evaluation score = ((X ｑ - Ave ｑ ) / Stg ｑ + (X ｓ - Ave ｓ ) / Stg ｓ ) / 2 (1) Thus, the learning evaluation score for each category shown in the lower right part of Figure 2 is calculated.

[0025] Figure 3 shows an example of calculating a child's risk experience score based on the child's browsing / operation history information. As shown in Figure 3, the score acquisition unit 12 obtains the "total browsing time" and "total number of browsing" for the target child from the browsing / operation history information of the target child, and calculates the risk experience score of the target child using the following formula (2). Specifically, the score acquisition unit 12 obtains, from the browsing / operation history information of the target child, the "total browsing time X ｔ " and "total number of browsing X ｃ " for each category. Then, the average Ave ｔ of the total browsing time of all users obtained for each category and the standard deviation Stgｔ Average total number of views by all users ｃ and the standard deviation of the total number of views Stg ｃ In addition, along with a predetermined weighting parameter γ for each category, the score acquisition unit 12 calculates the total viewing time X in the following formula (2): ｔ and total number of views X ｃ By applying this, the risk experience score of the target child is calculated for each category. Risk experience score = (X ｔ - Ave ｔ ) / Stg ｔ + (X ｃ - Ave ｃ ) / Stg ｃ +γ((X ｔ - Ave ｔ ) / Stg ｔ × (X ｃ - Ave ｃ ) / Stg ｃ (2) This allows for the calculation of the risk experience score for each category, as shown in the lower right of Figure 3.

[0026] Figure 4 shows an example of calculating an evaluation score based on a child's attribute information. As shown in Figure 4, the score acquisition unit 12 assigns predetermined scores to each attribute value of the target child's attribute information and calculates the evaluation score based on the target child's attributes using the following formula (3): Evaluation score based on attributes = α × Age + β × Edu + γ × Dev + ... (3) (α, β, γ... represent predetermined weights for each category) This calculates the evaluation score based on attributes for each category shown in the lower right of Figure 4. The scores for each attribute value shown in the lower left of Figure 4 may be predetermined by an expert.

[0027] Figure 5 shows an example of obtaining an IT literacy proficiency score based on (a) a learning evaluation score Stu, (b) a risk experience score Exp, and (c) an attribute-based evaluation score Atri. As shown in Figure 5, the score acquisition unit 12 obtains an IT literacy proficiency score for each category using a predetermined formula for each category. In the example in Figure 5, (c) the attribute-based evaluation score is used as a threshold.

[0028] For example, for the "Security" category, based on the formula (1 else 0 if Atri > 0.2) × (Stu + Exp), if the attribute-based evaluation score Atri is greater than 0.2, the sum of the learning evaluation score Stu and the risk experience score Exp is obtained as the IT literacy proficiency score; if the attribute-based evaluation score Atri is 0.2 or less, the IT literacy proficiency score is set to 0. The same applies to the "Privacy" category.

[0029] Furthermore, for the "Communication" category, based on the formula (1 else 0 if Atri > 0.5) × (Stu + Exp), if the attribute-based evaluation score Atri is greater than 0.5, the sum of the learning evaluation score Stu and the risk experience score Exp is obtained as the IT literacy proficiency score; if the attribute-based evaluation score Atri is 0.5 or less, the IT literacy proficiency score is set to 0.

[0030] Next, referring to Figure 6, we will explain how to obtain the risk score of the desired content. The knowledge-enhanced LLM 40A is a pre-trained LLM that has been pre-learned from literature, incident information, and past data on risk scores of various content that have been determined in the past. The score acquisition unit 12 inputs a query for the risk score of the desired content, including identification information of the desired content (for example, the address information of the website to be viewed), to the knowledge-enhanced LLM 40A. The score acquisition unit 12 then obtains the risk score of the desired content by receiving the response (risk score of the desired content) output from the knowledge-enhanced LLM 40A.

[0031] As described above, the IT literacy proficiency score and the risk score of the desired content for the target child, obtained by the score acquisition unit 12, are transferred to the generation unit 13. Of the acquired information, the risk score of the desired content may be stored in the child's browsing and operation history information and used for future reference and score calculation.

[0032] As shown in Figure 7, the generation unit 13 obtains the requested items from the knowledge-enhanced LLM 40A by instructing it to generate requested items based on the child's IT literacy proficiency score, the risk score of the desired content, and the child's attribute information. For example, the generation unit 13 inputs the child's IT literacy proficiency score, the risk score of the desired content, and the child's attribute information into the knowledge-enhanced LLM 40A along with a generation request that specifies the requested items (in this case, both the result of the determination of whether the desired content can be viewed and advice on viewing). At that time, it may also include instructions to consider the attribute information, such as, "If the child's attribute 'hobby' is trains, then the determination for train-related content will be made more lenient." In this case, it is expected that more appropriate requested items that take into account the child's individual attribute information will be generated and obtained.

[0033] The generation unit 13 receives requests output from the knowledge-enhanced LLM 40A (for example, both the result of determining whether the desired content can be viewed and advice regarding viewing), and the output unit 14 outputs the requests to the parent's terminal 20. As a result, the parent can refer to the advice regarding viewing and the result of determining whether the desired content can be viewed, as illustrated in Figure 7, on the terminal 20, and based on the information obtained, can appropriately control the child's access to the desired content.

[0034] [Regarding the processing performed in the information processing device] Below, an example of the processing performed in the information processing device 10 (processing related to the information processing method of this disclosure) will be explained in accordance with the flowchart in Figure 8.

[0035] When a user specifies a request for the creation of a requested item, identification information of the target child, and information regarding the desired content, and then presses an instruction button on the web page or application page displayed on the display of the information processing device 10, the creation request is transferred to the reception unit 11, and the process shown in Figure 8 is started when the reception unit 11 receives the creation request.

[0036] First, the reception unit 11 receives a request to generate the requested items (in this case, both the result of determining whether the desired content can be viewed and advice regarding viewing), the identification information of the child in question, and information regarding the desired content (step S1). The received information is then transferred to the score acquisition unit 12.

[0037] The score acquisition unit 12 acquires an IT literacy proficiency score through steps S2 to S8 in Figure 8 below, and also acquires a risk score for the desired content through steps S9 to S10.

[0038] First, let's explain the IT literacy proficiency score. The score acquisition unit 12 uses the identification information of the target child as a key to acquire the target child's IT literacy learning history information from the IT literacy learning history DB 30A, the target child's browsing and operation history information from the browsing and operation history DB 30B, and the target child's attribute information from the attribute information DB 30C (steps S2 to S4).

[0039] Next, as shown in Figure 2, the score acquisition unit 12 obtains the "Problem Score X" for each category from the target child's IT literacy learning history information. ｑ The total number of correct answers / total responses for each category is used as the "Learning Score X" for each category. ｓ The total number of learning units for each category is calculated, and the average of the problem scores of all users, which were previously calculated for each category, is calculated. ｑ and the standard deviation of the problem score Stg ｑ Average learning score of all users (Ave) ｓ and the standard deviation of the learning score Stg ｓ In addition, the problem score X is added to the following equation (1). ｑ and learning score X ｓ By applying this, the learning assessment score of the target child is calculated for each category (Step S5 in Figure 8). Learning assessment score = ((X ｑ - Ave ｑ ) / Stg ｑ + (X ｓ - Ave ｓ ) / Stg ｓ ) / 2 (1) This calculates the learning evaluation score for each category shown in the lower right of Figure 2.

[0040] Furthermore, as shown in Figure 3, the score acquisition unit 12 obtains the "total browsing time X" for each category from the browsing and operation history information of the target child. ｔ " and "Total number of views X ｃ "Calculate the average of the total viewing time of all users, which was determined in advance for each category." ｔ and the standard deviation of total viewing time Stg ｔ Average total number of views by all users ｃ and the standard deviation of the total number of views Stg ｃ In addition, the total viewing time X is added to the following equation (2) along with a predetermined weighting parameter γ for each category. ｔ and total number of views X ｃ By applying this, the risk experience score of the target child is calculated for each category (step S6 in Figure 8). Risk experience score = (X ｔ - Ave ｔ ) / Stg ｔ + (X ｃ - Ave ｃ ) / Stg ｃ +γ((X ｔ - Ave ｔ ) / Stg ｔ × (X ｃ - Ave ｃ ) / Stg ｃ (2) This allows for the calculation of the risk experience score for each category, as shown in the lower right of Figure 3.

[0041] Furthermore, as shown in Figure 4, the score acquisition unit 12 assigns predetermined scores to each attribute value for the attribute information of the target child and calculates the evaluation score by attribute of the target child using the following formula (3) (step S7 in Figure 8). Evaluation score by attribute = α × Age + β × Edu + γ × Dev + ... (3) (Note that α, β, γ... represent predetermined weights for each category) This calculates the evaluation score by attribute for each category shown in the lower right of Figure 4.

[0042] Next, as shown in Figure 5, the score acquisition unit 12 acquires the IT literacy proficiency score for each category using a predetermined formula for each category. Taking the category "Security" shown in Figure 5 as an example, the score acquisition unit 12 uses the formula (1 else 0 if Atri > 0.2) × (Stu + Exp) to acquire the IT literacy proficiency score as the sum of the learning evaluation score Stu and the risk experience score Exp if the attribute evaluation score Atri is greater than 0.2, and sets the IT literacy proficiency score to 0 if the attribute evaluation score Atri is 0.2 or less. In this way, the IT literacy proficiency score for each category is acquired.

[0043] Next, the risk score for the desired content will be explained. As shown in Figure 6, the score acquisition unit 12 queries the knowledge-enhanced LLM 40A for the risk score of the desired content, including the identification information of the desired content (for example, the address information of the website to be viewed) (step S9 in Figure 8). Subsequently, the risk score is output from the knowledge-enhanced LLM 40A, and the score acquisition unit 12 obtains the risk score of the desired content by receiving the response (risk score of the desired content) output from the knowledge-enhanced LLM 40A (step S10 in Figure 8). In this way, the IT literacy proficiency score and the risk score of the desired content for the target child are obtained and transferred to the generation unit 13.

[0044] Next, as shown in Figure 7, the generation unit 13 issues a generation instruction (step S11 in Figure 8) by inputting the child's IT literacy proficiency score, the risk score of the desired content, and the child's attribute information into the knowledge-enhanced LLM 40A, along with a generation request that specifies the requested items (for example, both the result of determining whether the desired content can be viewed and advice regarding viewing). At this time, the instruction may include, for example, a statement that the child's attribute information should be taken into consideration, such as "if the child's 'hobby' is trains, then the judgment for train-related content should be relaxed," and it is expected that more appropriate requests that take into account the child's individual attribute information can be generated and obtained.

[0045] Subsequently, the LLM 40A with enhanced knowledge outputs the requested items (the result of the determination of whether the desired content can be viewed and advice) in accordance with the generation instruction. The score acquisition unit 12 receives the requested items (the result of the determination of whether the desired content can be viewed and advice) output from the LLM 40A with enhanced knowledge (step S12 in Figure 8), and the output unit 14 outputs the requested items (the result of the determination of whether the desired content can be viewed and advice) to the parent's terminal 20 (step S13 in Figure 8). As a result, the parent can refer to the viewing advice and the result of the determination of whether the desired content can be viewed, as illustrated in Figure 7, on the terminal 20.

[0046] According to the embodiments described above, appropriate content access control tailored to the content and the individual circumstances of the child can be achieved. In particular, the "child's IT literacy proficiency score," which is the basis for generating requests, is calculated and obtained based on all of the child's IT literacy learning history information, browsing and operation history information, and attribute information. This allows for the acquisition of a more appropriate IT literacy proficiency score based on "IT literacy learning history," "browsing and operation history," and "attribute information," which are difficult for parents to grasp accurately and in detail. Then, the parent can refer to the request (advice regarding viewing and the result of the determination of whether the desired content can be viewed) generated based on such an appropriate IT literacy proficiency score and the risk score of the desired content, while also considering the child's attribute information, on terminal 20. As a result, the parent can make an appropriate viewing determination based on the obtained information and provide appropriate advice to the child.

[0047] (Various Modifications of System 1) Figure 1 shows an example of a system configuration in which the parent's terminal 20 receives and displays generation requests and requested items. As a modification of this, as shown in Figure 9, a configuration can also be adopted in which, in addition to the parent's terminal 20, there is a child's terminal 20S that is permitted to operate based on approval from the parent's terminal 20. In this configuration of Figure 9, based on approval from the parent's terminal 20, the child's terminal 20S sends the above-mentioned request for the generation of requested items to the information processing device 10, causing the information processing device 10 to execute the process in Figure 8, and finally the parent's terminal 20 receives the requested items (the result of the determination of whether the desired content can be viewed and advice). As a result, the parent can refer to the advice regarding viewing and the result of the determination of whether the desired content can be viewed, as exemplified in Figure 7, on the terminal 20, and based on the obtained information, can appropriately control the child's terminal 20S's access to the desired content.

[0048] Furthermore, System 1 is not limited to the configuration shown in Figure 1, but may also be configured such that the knowledge-enhanced LLM 40A is implemented inside the information processing device 10, as shown in Figure 10. This configuration can be achieved by installing an application that performs the functions of the knowledge-enhanced LLM 40A into the information processing device 10.

[0049] Furthermore, Figures 1, 9, and 10 show examples in which the IT literacy learning history DB 30A, browsing / operation history DB 30B, and attribute information DB 30C are implemented outside the information processing device 10 (for example, on a network), but at least one of the above DBs may be implemented inside the information processing device 10.

[0050] The gist of this disclosure is found in the following [1] to [6].

[0051] [1] An information processing device comprising: a receiving unit that receives a request to generate an item which is at least one of a determination result regarding the viewing of desired content or advice regarding viewing, the child's identification information, and information regarding the desired content; a score acquisition unit that acquires an IT literacy proficiency score of the child based on at least one of the child's IT literacy learning history information, the child's viewing / operation history information, or the child's attribute information, which is acquired based on the child's identification information, and acquires a risk score of the desired content based on information regarding the desired content using a generation AI that has learned information regarding the risks of the content; a generation unit that generates the item related to the generation request based on at least the child's IT literacy proficiency score and the risk score of the desired content; and an output unit that outputs the generated item.

[0052] [2] The information processing apparatus according to [1], wherein the score acquisition unit calculates the child's learning evaluation score based on the child's IT literacy learning history information, the child's risk experience score based on the child's browsing and operation history information, and the child's attribute-based evaluation score based on the child's attribute information, and acquires the IT literacy proficiency score based on the learning evaluation score, the risk experience score, and the attribute-based evaluation score.

[0053] [3] The information processing device according to [1] or [2], wherein the score acquisition unit inputs an inquiry for the risk score of the desired content to the generating AI, thereby acquiring the risk score of the desired content output from the generating AI.

[0054] [4] The information processing apparatus according to any one of [1] to [3], wherein the generation unit generates the requested item based on the child's IT literacy proficiency score and the child's risk score for the desired content, as well as the child's attribute information.

[0055] [5] The information processing apparatus according to any one of [1] to [4], wherein the generation unit instructs the generation AI to generate the requested item relating to the generation request, thereby acquiring the requested item from the generation AI.

[0056] [6] An information processing method comprising: a step of an information processing device receiving a request to generate an object which is at least one of a determination result regarding the viewing of desired content or advice regarding viewing, the child's identification information, and information regarding the desired content; a step of the information processing device obtaining an IT literacy proficiency score of the child based on at least one of the child's IT literacy learning history information, the child's viewing / operation history information, or the child's attribute information, which is obtained based on the child's identification information, and obtaining a risk score of the desired content based on information regarding the desired content using a generating AI that has learned information regarding the risks of the content; a step of the information processing device generating the request object related to the generation request based on at least the child's IT literacy proficiency score and the risk score of the desired content; and a step of the information processing device outputting the generated request object.

[0057] [Explanation of terms, explanation of hardware configuration (Figure 11), etc.] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired, wireless, etc.). A functional block may be realized by combining the above one device or the above multiple devices with software.

[0058] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0059] For example, the information processing device in one embodiment of the present disclosure may function as a computer that performs processing of the information processing method of the present disclosure. Figure 11 is a diagram showing an example of the hardware configuration of an information processing device 10 according to one embodiment of the present disclosure. The above-described information processing device 10 may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0060] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the information processing device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0061] Each function in the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0062] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may consist of a central processing unit (CPU) that includes interfaces with peripheral devices, control units, arithmetic units, registers, and so on.

[0063] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the score acquisition unit 12 of the information processing device 10 may be implemented by a control program stored in the memory 1002 and operated on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0064] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0065] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of the memory 1002 and the storage 1003.

[0066] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0067] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0068] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0069] Furthermore, the information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0070] The notification of information is not limited to the embodiments described herein and may be carried out by other means. For example, the notification of information may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0071] Each aspect / embodiment described in this disclosure refers to LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (where x is, for example, an integer or decimal)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20 may apply to at least one system utilizing UWB (Ultra-WideBand), Bluetooth®, or other appropriate systems, and to next-generation systems extended, modified, created, or defined based thereon. Alternatively, multiple systems may be applied in combination (e.g., a combination of at least one of LTE and LTE-A with 5G).

[0072] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0073] The specific operations described in this disclosure as being performed by a base station may, in some cases, be performed by its upper node. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal can be performed by the base station and at least one other network node (for example, an MME or S-GW, but not limited to these). Although the above example illustrates the case where there is one other network node besides the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

[0074] Information can be output from a higher layer (or lower layer) to a lower layer (or higher layer). Input and output may also occur via multiple network nodes.

[0075] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0076] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, by comparing with a predetermined value).

[0077] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0078] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0079] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0080] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0081] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0082] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0083] The terms “system” and “network” as used in this disclosure are interchangeable.

[0084] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0085] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0086] In this disclosure, terms such as “Base Station (BS),” “wireless base station,” “fixed station,” “NodeB,” “eNodeB (eNB),” “gNodeB (gNB),” “access point,” “transmission point,” “reception point,” “transmission / reception point,” “cell,” “sector,” “cell group,” “carrier,” and “component carrier” may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0087] A base station can accommodate one or more (e.g., three) cells. If a base station accommodates multiple cells, the entire coverage area of ​​the base station can be divided into multiple smaller areas, each of which may also be provided with communication services by a base station subsystem (e.g., a Remote Radio Head (RRH)). The terms “cell” or “sector” refer to part or all of the coverage area of ​​at least one of the base station and / or base station subsystems that provide communication services in that coverage.

[0088] In this disclosure, the transmission of information by a base station to a terminal may be interpreted as the base station instructing the terminal to perform control or operation based on the information.

[0089] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0090] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or several other appropriate terms.

[0091] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a communication device, etc. At least one of the base station and the mobile station may also be a device mounted on a mobile unit, the mobile unit itself, etc.

[0092] The term "moving object" refers to any object that can move, regardless of its speed. This also includes cases where the moving object is stationary. The term "moving object" includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademarks), multicopters, quadcopters, balloons, and anything carried on them.

[0093] Furthermore, the mobile entity may be one that autonomously drives based on operational commands. It may be a vehicle (e.g., a car, an airplane), an unmanned mobile entity (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may be a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an IoT (Internet of Things) device such as a sensor.

[0094] Furthermore, the term "base station" in this disclosure may be interpreted as "user terminal." For example, the various aspects / embodiments of this disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, D2D (Device-to-Device), V2X (Vehicle-to-Everything), etc.). Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (for example, "side"). For example, uplink channel, downlink channel, etc., may be interpreted as side channel.

[0095] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0096] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0097] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0098] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0099] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0100] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0101] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0102] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0103] 1...System, 10...Information processing device, 11...Reception unit, 12...Score acquisition unit, 13...Generation unit, 14...Output unit, 20...Parent's terminal, 20S...Child's terminal, 30, 40...External server, 30A...IT literacy learning history DB, 30B...Browsing / operation history DB, 30C...Attribute information DB, 40A...Knowledge-enhanced LLM, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. An information processing device comprising: a receiving unit that receives a request to generate an item which is at least one of a determination result regarding the accessibility of the desired content or advice regarding access to the content that the target child wishes to view, the child's identification information, and information regarding the desired content; a score acquisition unit that acquires an IT literacy proficiency score of the child based on at least one of the child's IT literacy learning history information, the child's browsing / operation history information, or the child's attribute information, which is acquired based on the child's identification information, and acquires a risk score of the desired content based on information regarding the desired content using a generation AI that has learned information regarding the risks of the content; a generation unit that generates the item related to the generation request based on at least the child's IT literacy proficiency score and the risk score of the desired content; and an output unit that outputs the generated item.

2. The information processing apparatus according to claim 1, wherein the score acquisition unit calculates the child's learning evaluation score based on the child's IT literacy learning history information, the child's risk experience score based on the child's browsing and operation history information, and the child's attribute-based evaluation score based on the child's attribute information, and acquires the IT literacy proficiency score based on the learning evaluation score, the risk experience score, and the attribute-based evaluation score.

3. The information processing apparatus according to claim 1, wherein the score acquisition unit inputs an inquiry for the risk score of the desired content to the generating AI, thereby acquiring the risk score of the desired content output from the generating AI.

4. The information processing apparatus according to claim 1, wherein the generation unit generates the requested item based on the child's attribute information, in addition to the child's IT literacy proficiency score and the risk score of the desired content.

5. The information processing apparatus according to claim 1, wherein the generation unit instructs the generation AI to generate the requested item relating to the generation request, thereby obtaining the requested item from the generation AI.

6. An information processing method comprising: a step of an information processing device receiving a request to generate a request that is at least one of a determination result regarding the viewing of desired content or advice regarding viewing, the child's identification information, and information regarding the desired content; a step of the information processing device obtaining an IT literacy proficiency score of the child based on at least one of the child's IT literacy learning history information, the child's viewing / operation history information, or the child's attribute information, which is obtained based on the child's identification information, and obtaining a risk score of the desired content based on information regarding the desired content using a generating AI that has learned information regarding the risks of the content; a step of the information processing device generating the request related to the generation request based on at least the child's IT literacy proficiency score and the risk score of the desired content; and a step of the information processing device outputting the generated request.

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