System

Abstract

An object of a system according to an embodiment is to provide an environment in which a child can safely use the Internet and to provide information according to personality and matters of interest.SOLUTION: A system includes a collection unit, an identification unit, a block unit, a second collection unit, an analysis unit, and a provision unit. The collection unit collects information on a website accessed by the child. The specification unit specifies an educationally inappropriate site based on the information collected by the collection unit. The blocking unit blocks access to the inappropriate site specified by the specifying unit. The second collection unit collects a use history of the smartphone. The analysis unit analyzes the use history collected by the second collection unit, and analyzes the individuality and matters of interest of the child. The providing unit provides information based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately ensured the safety of websites accessed by children while providing information tailored to their personalities and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an environment in which children can safely use the Internet and to provide information that suits their personalities and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an identification unit, a blocking unit, a second collection unit, an analysis unit, and a providing unit. The collection unit collects information on websites accessed by children. The identification unit identifies educationally inappropriate sites based on the information collected by the collection unit. The blocking unit blocks access to inappropriate sites identified by the identification unit. The second collection unit collects smartphone usage history. The analysis unit analyzes the usage history collected by the second collection unit and analyzes the child's personality and interests. The providing unit provides information based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which children can safely use the Internet and can provide information according to their personalities and interests. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention protects children from educationally inappropriate websites and supports their development by analyzing their personalities and interests based on their smartphone usage history. The system monitors websites accessed by children and blocks access to educationally inappropriate websites. Next, the system collects smartphone usage history and analyzes their personalities and interests using AI. Based on the analysis results, the system provides parents and children with information to support their child's development. For example, the system monitors websites accessed by children by analyzing the website's URL and content to identify educationally inappropriate websites. For example, websites containing violent or adult content can be blocked. Next, the system collects smartphone usage history, such as data on which apps the child used and which websites the child visited. This data is input into AI. The AI ​​analyzes the collected data and analyzes the child's personality and interests. For example, if a child likes a particular genre of game, the system can provide information related to that genre. Similarly, if a child frequently uses learning apps, the system can provide learning-related information. This system supports children's safety and development. The system can understand children's smartphone usage and provide appropriate advice. In addition, children can obtain information based on their own interests, which can promote their development. For example, if a child is interested in science, providing information on science-related events and books can further deepen the child's interest.

[0029] A growth support system according to an embodiment includes a collection unit, an identification unit, a blocking unit, a second collection unit, an analysis unit, and a provision unit. The collection unit collects information about websites accessed by a child. For example, the collection unit collects URLs and content of websites accessed by a child. The collection unit can also collect webpage titles, metadata, and text. The identification unit identifies educationally inappropriate websites based on the information collected by the collection unit. For example, the identification unit identifies websites containing violent content or adult content. The identification unit can also analyze the collected information and identify inappropriate websites based on specific keywords. The blocking unit blocks access to inappropriate websites identified by the identification unit. For example, the blocking unit blocks access to inappropriate websites using IP blocking or content filtering. The blocking unit can also add the URL of the identified website to a blacklist to restrict access. The second collection unit collects smartphone usage history. For example, the second collection unit collects usage history such as which apps the child used and which websites they visited. The second collection unit can also collect app usage time and frequency, website browsing history, etc. The analysis unit analyzes the usage history collected by the second collection unit and analyzes the child's personality and interests. The analysis unit, for example, identifies the child's hobbies and topics of interest based on the collected usage history. The analysis unit can also use AI to analyze patterns of the usage history and analyze the child's interests. The provision unit provides information based on the analysis results obtained by the analysis unit. The provision unit can provide parents and children with information to help with the child's growth, for example. The provision unit can also provide information on books and events related to the child's interests based on the analysis results. In this way, the growth support system according to the embodiment can help children grow.

[0030] The collection unit can collect URLs or content of websites accessed by children. For example, the collection unit collects URLs of websites accessed by children. The collection unit can also collect titles and metadata of web pages. The collection unit can also analyze the text of web pages and collect content. By collecting URLs and content of websites accessed by children, information for identifying educationally inappropriate sites can be obtained. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input URLs and metadata of web pages into AI, which then performs the analysis.

[0031] The identification unit can identify sites containing violent content or adult content based on the collected information. For example, the identification unit analyzes the collected information to identify sites containing violent content. The identification unit can also identify sites containing adult content based on the collected information. The identification unit can also identify sites containing violent content or adult content based on specific keywords. In this way, by identifying sites containing violent content or adult content, it is possible to prevent children from coming into contact with inappropriate information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the collected information into AI, which can identify violent content or adult content.

[0032] The blocking unit can block access to the identified inappropriate sites. For example, the blocking unit can block access to the identified inappropriate sites using IP blocking. The blocking unit can also block access to the inappropriate sites using content filtering. The blocking unit can also add the URL of the identified site to a blacklist and restrict access. In this way, by blocking access to the identified inappropriate sites, children can be prevented from accessing inappropriate information. Some or all of the above-described processing in the blocking unit can be performed using, for example, AI, or can be performed without using AI. For example, the blocking unit can input the URL of the identified site into AI, which blocks access.

[0033] The second collection unit can collect usage history, such as which apps the child used and which websites the child visited. For example, the second collection unit collects usage history, such as which apps the child used. The second collection unit can also collect usage history, such as which websites the child visited. The second collection unit can also collect app usage time and frequency, website browsing history, and the like. By collecting usage history, such as which apps the child used and which websites the child visited, information for analyzing the child's personality and interests can be obtained. Some or all of the above-described processing in the second collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the second collection unit can input the usage history into AI, which then performs analysis.

[0034] The analysis unit can analyze the collected usage history and analyze the child's personality and interests. For example, the analysis unit can identify the child's hobbies and topics of interest based on the collected usage history. The analysis unit can also use AI to analyze the usage history patterns and analyze the child's interests. The analysis unit can also analyze the child's learning situation based on the collected usage history. In this way, by analyzing the collected usage history and analyzing the child's personality and interests, it is possible to provide information to help the child's development. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the usage history into AI, which then performs the analysis.

[0035] The providing unit can provide parents and children with information that helps the child's development based on the analysis results. The providing unit, for example, provides parents and children with information that helps the child's development. The providing unit can also provide information about books and events related to the child's interests based on the analysis results. The providing unit can also provide advice according to the child's learning situation. In this way, by providing parents and children with information that helps the child's development based on the analysis results, the child's development can be promoted. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results into AI, which then provides the information.

[0036] The collection unit can analyze the child's past access history and select an appropriate collection method. For example, the collection unit prioritizes collection of sites frequently accessed by the child. The collection unit can also analyze sites accessed by the child during specific time periods and concentrate collection during those time periods. The collection unit can also analyze the categories of sites accessed by the child in the past and collect sites in similar categories. In this way, by analyzing the child's past access history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past access history into AI, which can select the optimal collection method.

[0037] When collecting website information, the collection unit can filter the information based on the child's current interests. For example, the collection unit collects only sites related to topics that the child is currently interested in. The collection unit can also filter related sites based on keywords recently searched by the child. The collection unit can also collect information based on the topics of online communities in which the child participates. In this way, highly relevant information can be collected by filtering based on the child's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which then performs the filtering.

[0038] When collecting information from a website, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child uses voice input, the collection unit can collect information using voice recognition technology. Also, if the child uses text input, the collection unit can collect information using text analysis technology. Also, if the child uses images, the collection unit can collect related information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data or text data into AI, which can select the optimal collection means.

[0039] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the collection unit can collect educational event information related to the child's current location. The collection unit can also prioritize collecting news and topics related to the area where the child lives. If the child is traveling, the collection unit can also collect tourist information and safety information about the travel destination. This allows for more appropriate information to be provided by prioritizing the collection of highly relevant information by taking into account the child's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI, which then collects highly relevant information.

[0040] When collecting website information, the collection unit can analyze the child's social media activities and collect related information. For example, the collection unit can analyze the content posted by accounts the child follows on social media to collect related information. The collection unit can also collect information based on the topics of groups the child participates in on social media. The collection unit can also collect related information based on links the child shares on social media. In this way, highly relevant information can be collected by analyzing the child's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input social media data into AI, which then collects the related information.

[0041] When collecting website information, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting sites that the child has previously rated highly. The collection unit can also collect information by excluding sites that the child has previously rated poorly. The collection unit can also adjust the categories of information to be collected based on feedback the child has previously provided. In this way, by customizing the collection method by reflecting the child's past feedback, more appropriate information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, which then customizes the collection method.

[0042] The identification unit can improve the accuracy of identification by taking into account the interrelationships between websites during identification. The identification unit, for example, analyzes link relationships between websites to identify highly related sites. The identification unit can also refer to domain information of websites to preferentially identify highly reliable sites. The identification unit can also analyze cross-references between website content to improve the accuracy of identification. This allows the accuracy of identification to be improved by taking into account the interrelationships between websites. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input link relationships and domain information between websites into AI, which can improve the accuracy of identification.

[0043] The identification unit can perform the identification by taking into consideration attribute information of the website content. For example, the identification unit can analyze the website metadata and perform the identification based on the attribute information of the content. The identification unit can also analyze the text content of the website and perform the identification based on specific keywords. The identification unit can also analyze attribute information of images and videos on the website to identify inappropriate content. This allows for more accurate identification of inappropriate sites by taking into consideration the attribute information of the website content. Some or all of the above-described processing by the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input the website metadata and text content into AI, which can then perform the identification.

[0044] During identification, the identification unit can assign a specific weight based on the update frequency of the website. For example, the identification unit can prioritize identifying sites that are updated frequently. The identification unit can also perform identification while excluding sites that are updated infrequently. The identification unit can also assign a specific weight based on the update frequency to identify highly reliable sites. In this way, highly reliable sites can be identified by assigning a specific weight based on the update frequency of the website. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the update frequency of the website into AI, which can then assign a specific weight.

[0045] The identification unit can perform the identification by taking into consideration the geographical distribution of websites. For example, the identification unit can prioritize identifying websites in the area where a child lives. Furthermore, if a child is traveling, the identification unit can also identify websites related to the area where the child is traveling. The identification unit can also perform the identification by taking into consideration the location of the website server. In this way, by taking into consideration the geographical distribution of websites, more relevant sites can be identified. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input data regarding geographical distribution into AI, which can then perform the identification.

[0046] The identification unit can improve the accuracy of the identification by referring to related literature on the website during identification. For example, the identification unit can identify highly reliable sites by referring to related literature on the website. The identification unit can also analyze cited literature on the website to improve the accuracy of the identification. The identification unit can also perform identification by referring to research papers related to the website. In this way, the accuracy of the identification can be improved by referring to related literature on the website. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input related literature into AI, which can improve the accuracy of the identification.

[0047] The identification unit can take into consideration the market value of the website when identifying it. For example, the identification unit can prioritize identifying sites with high market value. The identification unit can also exclude sites with low market value when identifying it. The identification unit can also evaluate and identify market value based on the website's advertising revenue and traffic data. This makes it possible to identify highly reliable sites by taking the market value of the website into consideration. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input data regarding market value into AI, and the AI ​​can perform the identification.

[0048] When blocking, the blocking unit can select the optimal blocking method by referring to the website's past blocking history. For example, the blocking unit may re-block a site that was previously blocked. The blocking unit may also analyze the categories of sites that were previously blocked and block sites in similar categories. The blocking unit may also customize the blocking method based on the past blocking history. This allows the optimal blocking method to be selected by referring to the website's past blocking history, enabling efficient blocking. Some or all of the above-described processing in the blocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the blocking unit may input the past blocking history into AI, which then selects the optimal blocking method.

[0049] The blocking unit may take into consideration attribute information of the website content when blocking. For example, the blocking unit may analyze the website metadata and block sites containing inappropriate content. The blocking unit may also analyze the text content of the website and block based on specific keywords. The blocking unit may also analyze attribute information of images and videos on the website and block inappropriate content. This allows for more accurate blocking of inappropriate sites by taking into consideration the attribute information of the website content. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI. For example, the blocking unit may input the website metadata and text content into AI, which then performs the blocking.

[0050] When blocking, the blocking unit can weight the blocks based on the update frequency of the website. For example, the blocking unit prioritizes blocking of frequently updated sites. The blocking unit can also block sites that are updated infrequently, excluding those that are updated infrequently. The blocking unit can also weight the blocks based on the update frequency and block highly reliable sites. In this way, highly reliable sites can be blocked by weighting the blocks based on the update frequency of the website. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI, for example. For example, the blocking unit can input the update frequency of the website into AI, which then weights the blocks.

[0051] The blocking unit can take into account the geographical distribution of websites when blocking. For example, the blocking unit can prioritize blocking websites in areas where children live. Furthermore, if a child is traveling, the blocking unit can block websites related to the area where the child is traveling. The blocking unit can also take into account the location of the website's server when blocking. This allows more relevant sites to be blocked by taking into account the geographical distribution of websites. Some or all of the above-mentioned processing by the blocking unit can be performed using, or without, AI. For example, the blocking unit can input data regarding geographical distribution into AI, which then performs the blocking.

[0052] When blocking, the blocking unit can improve the accuracy of the blocking by referring to related literature of the website. For example, the blocking unit can block highly reliable sites by referring to related literature of the website. The blocking unit can also analyze the cited literature of the website to improve the accuracy of the blocking. The blocking unit can also block by referring to research papers related to the website. In this way, the accuracy of the blocking can be improved by referring to related literature of the website. Some or all of the above-mentioned processing in the blocking unit can be performed using, for example, AI, or can be performed without using AI. For example, the blocking unit can input related literature into AI, which can improve the accuracy of the blocking.

[0053] The blocking unit can block websites taking into account their market value. For example, the blocking unit may prioritize blocking sites with high market value. The blocking unit can also block websites excluding those with low market value. The blocking unit can also evaluate and block market value based on the website's advertising revenue and traffic data. This allows highly reliable websites to be blocked by taking their market value into consideration. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI. For example, the blocking unit can input data regarding market value into AI, which then performs the blocking.

[0054] The collection unit can analyze the child's past usage history and select the optimal collection method. For example, the collection unit prioritizes collecting the history of apps frequently used by the child. The collection unit can also analyze the history of apps used by the child during a specific time period and concentrate collection during that time period. The collection unit can also analyze the categories of apps used by the child in the past and collect history of apps in similar categories. In this way, by analyzing the child's past usage history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past usage history into AI, which can select the optimal collection method.

[0055] When collecting the usage history, the collection unit can filter the usage history based on the child's current interests. For example, the collection unit can collect only the history of apps related to topics in which the child is currently interested. The collection unit can also filter the history of related apps based on keywords recently searched by the child. The collection unit can also collect the usage history based on topics in online communities in which the child participates. This makes it possible to collect highly relevant information by filtering based on the child's current interests. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which can then perform the filtering.

[0056] When collecting the usage history, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child uses voice input, the collection unit collects the usage history using voice recognition technology. Furthermore, if the child uses text input, the collection unit can also collect the usage history using text analysis technology. Furthermore, if the child uses images, the collection unit can also collect related usage history using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data or text data into AI, which can select the optimal collection means.

[0057] When collecting usage history, the collection unit can prioritize collecting highly relevant history by taking into account the child's geographical location information. For example, the collection unit collects usage history of apps related to the child's current location. The collection unit can also prioritize collecting usage history of apps related to news and topics in the area where the child lives. If the child is traveling, the collection unit can also collect usage history of apps related to the area where the child is traveling. This makes it possible to provide more appropriate information by prioritizing collection of highly relevant history by taking into account the child's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI, which then collects highly relevant history.

[0058] When collecting the usage history, the collection unit can analyze the child's social media activities and collect related history. For example, the collection unit can analyze the content posted by accounts the child follows on social media and collect related app usage history. The collection unit can also collect app usage history based on the topics of groups the child participates in on social media. The collection unit can also collect related app usage history based on links the child shared on social media. In this way, by analyzing the child's social media activities, highly relevant history can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI, which then collects related history.

[0059] When collecting the usage history, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting the usage history of apps that the child has previously rated highly. The collection unit can also collect the usage history by excluding the usage history of apps that the child has previously rated poorly. The collection unit can also adjust the categories of usage history to be collected based on feedback provided by the child in the past. In this way, by customizing the collection method by reflecting the child's past feedback, more appropriate information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, which can customize the collection method.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the usage history. For example, the analysis unit performs a detailed analysis on usage history with a high level of importance. The analysis unit can also perform a concise analysis on usage history with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the usage history. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the usage history to AI, which can adjust the level of detail of the analysis.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the usage history. For example, the analysis unit can apply an algorithm that evaluates learning effect to the usage history of educational apps. The analysis unit can also apply an algorithm that evaluates enjoyment to the usage history of entertainment apps. The analysis unit can also apply an algorithm that evaluates the quality of communication to the usage history of social media. In this way, by applying different analysis algorithms depending on the category of the usage history, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the usage history into AI, and the AI ​​can apply different analysis algorithms.

[0062] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. For example, the analysis unit corrects the current analysis result based on the child's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the child's past analysis results. The analysis unit can also compare the child's past analysis results, grasp trends, and improve the accuracy of the analysis. In this way, by referring to the child's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI, which can improve the accuracy of the analysis.

[0063] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the usage history. For example, the analysis unit prioritizes analysis of recent usage history. The analysis unit can also analyze usage history that was submitted earlier later. The analysis unit can also dynamically adjust the priority of analysis based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission date into AI, and the AI ​​can determine the priority of analysis.

[0064] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the usage history. For example, the analysis unit prioritizes analysis of highly relevant usage history. The analysis unit can also postpone analysis of less relevant usage history. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the usage history. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the usage history into AI, which can then adjust the order of analysis.

[0065] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the child's level of expertise is low, the analysis unit can also provide analysis results in simple language. The analysis unit can also dynamically adjust the way the analysis results are presented based on the child's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the child's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the child's level of expertise into AI, which can then adjust the use of technical terms.

[0066] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for information with a high level of importance. The providing unit can also provide a concise explanation for information with a low level of importance. The providing unit can also dynamically adjust the level of detail of the information provided based on the importance of the information. This allows information to be provided efficiently by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to AI, and the AI ​​can adjust the level of detail of the information provided.

[0067] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply an algorithm that enhances learning effectiveness to educational information. The providing unit can also apply an algorithm that emphasizes enjoyment to entertainment information. The providing unit can also apply an algorithm that enhances the quality of communication to social media-related information. In this way, by applying different providing algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI, and the AI ​​can apply different providing algorithms.

[0068] The providing unit can improve the accuracy of the provision by referring to the child's past provision results when providing the data. The providing unit, for example, corrects the current provision content based on the child's past provision results. The providing unit can also optimize the provision algorithm by referring to the child's past provision results. The providing unit can also compare the child's past provision results, understand trends, and improve the accuracy of the provision. In this way, the accuracy of the provision can be improved by referring to the child's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provision results into AI, which can improve the accuracy of the provision.

[0069] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit can provide the most recent information preferentially. The providing unit can also provide information that was submitted earlier later. The providing unit can also dynamically adjust the priority of provision based on the time of submission. This allows information to be provided efficiently by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the time of submission into AI, and the AI ​​can determine the priority of provision.

[0070] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, it is possible to provide information efficiently. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information to AI, and the AI ​​can adjust the order of provision.

[0071] The providing unit can adjust the use of technical terminology in the provided information depending on the child's level of expertise. For example, if the child's level of expertise is high, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the child's level of expertise is low, the providing unit can also provide information in simple language. The providing unit can also dynamically adjust the way the provided content is expressed based on the child's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terminology in the provided information depending on the child's level of expertise. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data regarding the child's level of expertise into AI, which can then adjust the use of technical terminology.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The collection unit can analyze the child's past usage history and select the optimal collection method. For example, the collection unit can prioritize collecting the history of apps frequently used by the child. The collection unit can also analyze the history of apps used by the child during a specific time period and concentrate collection during that time period. The collection unit can also analyze the categories of apps used by the child in the past and collect history of apps in similar categories. In this way, by analyzing the child's past usage history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past usage history into AI, which can select the optimal collection method.

[0074] The identification unit can improve the accuracy of identification by taking into account the interrelationships between websites during identification. For example, the identification unit can analyze the link relationships between websites to identify highly related sites. The identification unit can also refer to the domain information of websites to preferentially identify highly reliable sites. The identification unit can also analyze the interrelationships between website content to improve the accuracy of identification. In this way, the accuracy of identification can be improved by taking into account the interrelationships between websites. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the link relationships and domain information of websites into AI, which can improve the accuracy of identification.

[0075] When collecting usage history, the collection unit can filter the usage history based on the child's current interests. For example, the collection unit can collect only the history of apps related to topics that the child is currently interested in. The collection unit can also filter the history of related apps based on keywords recently searched by the child. The collection unit can also collect usage history based on topics of online communities in which the child participates. This makes it possible to collect highly relevant information by filtering based on the child's current interests. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which then performs the filtering.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the usage history. For example, an algorithm that evaluates learning effectiveness can be applied to the usage history of educational apps. The analysis unit can also apply an algorithm that evaluates enjoyment to the usage history of entertainment apps. The analysis unit can also apply an algorithm that evaluates the quality of communication to the usage history of social media. In this way, by applying different analysis algorithms depending on the category of the usage history, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the usage history into AI, and the AI ​​can apply different analysis algorithms.

[0077] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, highly relevant information can be provided preferentially. The providing unit can also provide less relevant information later. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, it is possible to provide information efficiently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the information to AI, and the AI ​​can adjust the order of provision.

[0078] During the identification, the identification unit can assign a specific weight based on the update frequency of the website. For example, the identification unit can prioritize the identification of frequently updated sites. The identification unit can also perform the identification by excluding sites that are updated infrequently. The identification unit can also assign a specific weight based on the update frequency to identify highly reliable sites. In this way, highly reliable sites can be identified by assigning a specific weight based on the update frequency of the website. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the update frequency of the website into AI, which can then assign a specific weight.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects information about websites accessed by children. For example, the collection unit collects URLs and content of websites accessed by children, as well as titles, metadata, and text of web pages. Step 2: The identifying unit identifies educationally inappropriate sites based on the information collected by the collecting unit. For example, the identifying unit may identify sites containing violent content or adult content, and may also identify inappropriate sites based on specific keywords. Step 3: The blocking unit blocks access to the inappropriate sites identified by the identifying unit. The blocking unit can block access to the inappropriate sites using, for example, IP blocking or content filtering, and can also restrict access by adding the URL of the identified site to a blacklist. Step 4: The second collection unit collects smartphone usage history. The second collection unit collects usage history such as which apps the child used, which websites they visited, the duration and frequency of app use, and website browsing history. Step 5: The analysis unit analyzes the usage history collected by the second collection unit and analyzes the child's personality and interests. For example, the analysis unit can identify the child's hobbies and topics of interest based on the collected usage history and analyze usage history patterns using AI. Step 6: The providing unit provides information based on the analysis results obtained by the analyzing unit. For example, the providing unit can provide parents and children with information to help with the child's development, and can also provide information on books and events related to the child's interests.

[0081] (Example 2) A system according to an embodiment of the present invention protects children from educationally inappropriate websites and supports their development by analyzing their personalities and interests based on their smartphone usage history. The system monitors websites accessed by children and blocks access to educationally inappropriate websites. Next, the system collects smartphone usage history and analyzes their personalities and interests using AI. Based on the analysis results, the system provides parents and children with information to support their child's development. For example, the system monitors websites accessed by children by analyzing the website's URL and content to identify educationally inappropriate websites. For example, websites containing violent or adult content can be blocked. Next, the system collects smartphone usage history, such as data on which apps the child used and which websites the child visited. This data is input into AI. The AI ​​analyzes the collected data and analyzes the child's personality and interests. For example, if a child likes a particular genre of game, the system can provide information related to that genre. Similarly, if a child frequently uses learning apps, the system can provide learning-related information. This system supports children's safety and development. The system can understand children's smartphone usage and provide appropriate advice. In addition, children can obtain information based on their own interests, which can promote their development. For example, if a child is interested in science, providing information on science-related events and books can further deepen the child's interest.

[0082] A growth support system according to an embodiment includes a collection unit, an identification unit, a blocking unit, a second collection unit, an analysis unit, and a provision unit. The collection unit collects information about websites accessed by a child. For example, the collection unit collects URLs and content of websites accessed by a child. The collection unit can also collect webpage titles, metadata, and text. The identification unit identifies educationally inappropriate websites based on the information collected by the collection unit. For example, the identification unit identifies websites containing violent content or adult content. The identification unit can also analyze the collected information and identify inappropriate websites based on specific keywords. The blocking unit blocks access to inappropriate websites identified by the identification unit. For example, the blocking unit blocks access to inappropriate websites using IP blocking or content filtering. The blocking unit can also add the URL of the identified website to a blacklist to restrict access. The second collection unit collects smartphone usage history. For example, the second collection unit collects usage history such as which apps the child used and which websites they visited. The second collection unit can also collect app usage time and frequency, website browsing history, etc. The analysis unit analyzes the usage history collected by the second collection unit and analyzes the child's personality and interests. The analysis unit, for example, identifies the child's hobbies and topics of interest based on the collected usage history. The analysis unit can also use AI to analyze patterns of the usage history and analyze the child's interests. The provision unit provides information based on the analysis results obtained by the analysis unit. The provision unit can provide parents and children with information to help with the child's growth, for example. The provision unit can also provide information on books and events related to the child's interests based on the analysis results. In this way, the growth support system according to the embodiment can help children grow.

[0083] The collection unit can collect URLs or content of websites accessed by children. For example, the collection unit collects URLs of websites accessed by children. The collection unit can also collect titles and metadata of web pages. The collection unit can also analyze the text of web pages and collect content. By collecting URLs and content of websites accessed by children, information for identifying educationally inappropriate sites can be obtained. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input URLs and metadata of web pages into AI, which then performs the analysis.

[0084] The identification unit can identify sites containing violent content or adult content based on the collected information. For example, the identification unit analyzes the collected information to identify sites containing violent content. The identification unit can also identify sites containing adult content based on the collected information. The identification unit can also identify sites containing violent content or adult content based on specific keywords. In this way, by identifying sites containing violent content or adult content, it is possible to prevent children from coming into contact with inappropriate information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the collected information into AI, which can identify violent content or adult content.

[0085] The blocking unit can block access to the identified inappropriate sites. For example, the blocking unit can block access to the identified inappropriate sites using IP blocking. The blocking unit can also block access to the inappropriate sites using content filtering. The blocking unit can also add the URL of the identified site to a blacklist and restrict access. In this way, by blocking access to the identified inappropriate sites, children can be prevented from accessing inappropriate information. Some or all of the above-described processing in the blocking unit can be performed using, for example, AI, or can be performed without using AI. For example, the blocking unit can input the URL of the identified site into AI, which blocks access.

[0086] The second collection unit can collect usage history, such as which apps the child used and which websites the child visited. For example, the second collection unit collects usage history, such as which apps the child used. The second collection unit can also collect usage history, such as which websites the child visited. The second collection unit can also collect app usage time and frequency, website browsing history, and the like. By collecting usage history, such as which apps the child used and which websites the child visited, information for analyzing the child's personality and interests can be obtained. Some or all of the above-described processing in the second collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the second collection unit can input the usage history into AI, which then performs analysis.

[0087] The analysis unit can analyze the collected usage history and analyze the child's personality and interests. For example, the analysis unit can identify the child's hobbies and topics of interest based on the collected usage history. The analysis unit can also use AI to analyze the usage history patterns and analyze the child's interests. The analysis unit can also analyze the child's learning situation based on the collected usage history. In this way, by analyzing the collected usage history and analyzing the child's personality and interests, it is possible to provide information to help the child's development. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the usage history into AI, which then performs the analysis.

[0088] The providing unit can provide parents and children with information that helps the child's development based on the analysis results. The providing unit, for example, provides parents and children with information that helps the child's development. The providing unit can also provide information about books and events related to the child's interests based on the analysis results. The providing unit can also provide advice according to the child's learning situation. In this way, by providing parents and children with information that helps the child's development based on the analysis results, the child's development can be promoted. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the analysis results into AI, which then provides the information.

[0089] The collection unit can estimate the child's emotions and specifically adjust the timing of website information collection based on the estimated child's emotions. For example, if the child is stressed, the collection unit can reduce the frequency of information collection and collect information when the child is relaxed. Furthermore, if the child is excited, the collection unit can collect information in real time and perform immediate analysis. Furthermore, if the child is tired, the collection unit can temporarily stop information collection and resume it after the child has rested. This allows the timing of website information collection to be adjusted according to the child's emotions, thereby collecting information at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into an AI, which can then adjust the timing of information collection.

[0090] The collection unit can analyze the child's past access history and select an appropriate collection method. For example, the collection unit prioritizes collection of sites frequently accessed by the child. The collection unit can also analyze sites accessed by the child during specific time periods and concentrate collection during those time periods. The collection unit can also analyze the categories of sites accessed by the child in the past and collect sites in similar categories. In this way, by analyzing the child's past access history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past access history into AI, which can select the optimal collection method.

[0091] When collecting website information, the collection unit can filter the information based on the child's current interests. For example, the collection unit collects only sites related to topics that the child is currently interested in. The collection unit can also filter related sites based on keywords recently searched by the child. The collection unit can also collect information based on the topics of online communities in which the child participates. In this way, highly relevant information can be collected by filtering based on the child's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which then performs the filtering.

[0092] When collecting information from a website, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child uses voice input, the collection unit can collect information using voice recognition technology. Also, if the child uses text input, the collection unit can collect information using text analysis technology. Also, if the child uses images, the collection unit can collect related information using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data or text data into AI, which can select the optimal collection means.

[0093] The collection unit can estimate the child's emotions and prioritize websites to collect based on the estimated child's emotions. For example, if the child is relaxed, the collection unit can prioritize collecting educational websites. Also, if the child is excited, the collection unit can prioritize collecting entertainment websites. Also, if the child is stressed, the collection unit can prioritize collecting websites containing relaxing content. This allows for more appropriate information to be collected by prioritizing websites to collect based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into an AI and determine the priority of websites to be collected by the AI.

[0094] When collecting website information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. For example, the collection unit can collect educational event information related to the child's current location. The collection unit can also prioritize collecting news and topics related to the area where the child lives. If the child is traveling, the collection unit can also collect tourist information and safety information about the travel destination. This allows for more appropriate information to be provided by prioritizing the collection of highly relevant information by taking into account the child's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI, which then collects highly relevant information.

[0095] When collecting website information, the collection unit can analyze the child's social media activities and collect related information. For example, the collection unit can analyze the content posted by accounts the child follows on social media to collect related information. The collection unit can also collect information based on the topics of groups the child participates in on social media. The collection unit can also collect related information based on links the child shares on social media. In this way, highly relevant information can be collected by analyzing the child's social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input social media data into AI, which then collects the related information.

[0096] When collecting website information, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting sites that the child has previously rated highly. The collection unit can also collect information by excluding sites that the child has previously rated poorly. The collection unit can also adjust the categories of information to be collected based on feedback the child has previously provided. In this way, by customizing the collection method by reflecting the child's past feedback, more appropriate information can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, which then customizes the collection method.

[0097] The identification unit can estimate the child's emotions and adjust the criteria for identifying inappropriate sites based on the estimated child's emotions. For example, if the child is stressed, the identification unit can identify inappropriate sites using stricter criteria. Furthermore, if the child is relaxed, the identification unit can identify inappropriate sites using normal criteria. Furthermore, if the child is excited, the identification unit can relax the criteria and allow entertainment sites. Thus, by adjusting the criteria for identifying inappropriate sites according to the child's emotions, sites can be identified using more appropriate criteria. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can input the child's emotion data into an AI, which can then adjust the criteria.

[0098] The identification unit can improve the accuracy of identification by taking into account the interrelationships between websites during identification. The identification unit, for example, analyzes link relationships between websites to identify highly related sites. The identification unit can also refer to domain information of websites to preferentially identify highly reliable sites. The identification unit can also analyze cross-references between website content to improve the accuracy of identification. This allows the accuracy of identification to be improved by taking into account the interrelationships between websites. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input link relationships and domain information between websites into AI, which can improve the accuracy of identification.

[0099] The identification unit can perform the identification by taking into consideration attribute information of the website content. For example, the identification unit can analyze the website metadata and perform the identification based on the attribute information of the content. The identification unit can also analyze the text content of the website and perform the identification based on specific keywords. The identification unit can also analyze attribute information of images and videos on the website to identify inappropriate content. This allows for more accurate identification of inappropriate sites by taking into consideration the attribute information of the website content. Some or all of the above-described processing by the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input the website metadata and text content into AI, which can then perform the identification.

[0100] During identification, the identification unit can assign a specific weight based on the update frequency of the website. For example, the identification unit can prioritize identifying sites that are updated frequently. The identification unit can also perform identification while excluding sites that are updated infrequently. The identification unit can also assign a specific weight based on the update frequency to identify highly reliable sites. In this way, highly reliable sites can be identified by assigning a specific weight based on the update frequency of the website. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the update frequency of the website into AI, which can then assign a specific weight.

[0101] The identification unit can estimate the child's emotions and adjust the display order of the identified sites based on the estimated child's emotions. For example, if the child is relaxed, the identification unit can display educational sites at the top. Furthermore, if the child is excited, the identification unit can display entertainment sites at the top. Furthermore, if the child is stressed, the identification unit can display sites containing relaxing content at the top. By adjusting the display order of the identified sites according to the child's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can input the child's emotion data into an AI, which can then adjust the display order.

[0102] The identification unit can perform the identification by taking into consideration the geographical distribution of websites. For example, the identification unit can prioritize identifying websites in the area where a child lives. Furthermore, if a child is traveling, the identification unit can also identify websites related to the area where the child is traveling. The identification unit can also perform the identification by taking into consideration the location of the website server. In this way, by taking into consideration the geographical distribution of websites, more relevant sites can be identified. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input data regarding geographical distribution into AI, which can then perform the identification.

[0103] The identification unit can improve the accuracy of the identification by referring to related literature on the website during identification. For example, the identification unit can identify highly reliable sites by referring to related literature on the website. The identification unit can also analyze cited literature on the website to improve the accuracy of the identification. The identification unit can also perform identification by referring to research papers related to the website. In this way, the accuracy of the identification can be improved by referring to related literature on the website. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input related literature into AI, which can improve the accuracy of the identification.

[0104] The identification unit can take into consideration the market value of the website when identifying it. For example, the identification unit can prioritize identifying sites with high market value. The identification unit can also exclude sites with low market value when identifying it. The identification unit can also evaluate and identify market value based on the website's advertising revenue and traffic data. This makes it possible to identify highly reliable sites by taking the market value of the website into consideration. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input data regarding market value into AI, and the AI ​​can perform the identification.

[0105] The blocking unit can estimate the child's emotions and adjust the blocking method based on the estimated child's emotions. For example, the blocking unit can apply a strict blocking method when the child is stressed. The blocking unit can also apply a normal blocking method when the child is relaxed. The blocking unit can also allow entertainment sites when the child is excited. This allows for more appropriate blocking by adjusting the blocking method according to the child's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the blocking unit can be performed using, for example, an AI, or without an AI. For example, the blocking unit can input the child's emotion data into an AI, which can then adjust the blocking method.

[0106] When blocking, the blocking unit can select the optimal blocking method by referring to the website's past blocking history. For example, the blocking unit may re-block a site that was previously blocked. The blocking unit may also analyze the categories of sites that were previously blocked and block sites in similar categories. The blocking unit may also customize the blocking method based on the past blocking history. This allows the optimal blocking method to be selected by referring to the website's past blocking history, enabling efficient blocking. Some or all of the above-described processing in the blocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the blocking unit may input the past blocking history into AI, which then selects the optimal blocking method.

[0107] The blocking unit may take into consideration attribute information of the website content when blocking. For example, the blocking unit may analyze the website metadata and block sites containing inappropriate content. The blocking unit may also analyze the text content of the website and block based on specific keywords. The blocking unit may also analyze attribute information of images and videos on the website and block inappropriate content. This allows for more accurate blocking of inappropriate sites by taking into consideration the attribute information of the website content. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI. For example, the blocking unit may input the website metadata and text content into AI, which then performs the blocking.

[0108] When blocking, the blocking unit can weight the blocks based on the update frequency of the website. For example, the blocking unit prioritizes blocking of frequently updated sites. The blocking unit can also block sites that are updated infrequently, excluding those that are updated infrequently. The blocking unit can also weight the blocks based on the update frequency and block highly reliable sites. In this way, highly reliable sites can be blocked by weighting the blocks based on the update frequency of the website. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI, for example. For example, the blocking unit can input the update frequency of the website into AI, which then weights the blocks.

[0109] The blocking unit can estimate a child's emotions and determine the priority of sites to block based on the estimated child's emotions. For example, if a child is stressed, the blocking unit can prioritize blocking sites containing violent content. Furthermore, if a child is relaxed, the blocking unit can block sites with normal priority. Furthermore, if a child is excited, the blocking unit can block entertainment sites later. This allows for more appropriate blocking by determining the priority of sites to block based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the blocking unit can be performed using, for example, an AI, or without an AI. For example, the blocking unit can input the child's emotion data into an AI, which can then determine the priority of sites to block.

[0110] The blocking unit can take into account the geographical distribution of websites when blocking. For example, the blocking unit can prioritize blocking websites in areas where children live. Furthermore, if a child is traveling, the blocking unit can block websites related to the area where the child is traveling. The blocking unit can also take into account the location of the website's server when blocking. This allows more relevant sites to be blocked by taking into account the geographical distribution of websites. Some or all of the above-mentioned processing by the blocking unit can be performed using, or without, AI. For example, the blocking unit can input data regarding geographical distribution into AI, which then performs the blocking.

[0111] When blocking, the blocking unit can improve the accuracy of the blocking by referring to related literature of the website. For example, the blocking unit can block highly reliable sites by referring to related literature of the website. The blocking unit can also analyze the cited literature of the website to improve the accuracy of the blocking. The blocking unit can also block by referring to research papers related to the website. In this way, the accuracy of the blocking can be improved by referring to related literature of the website. Some or all of the above-mentioned processing in the blocking unit can be performed using, for example, AI, or can be performed without using AI. For example, the blocking unit can input related literature into AI, which can improve the accuracy of the blocking.

[0112] The blocking unit can block websites taking into account their market value. For example, the blocking unit may prioritize blocking sites with high market value. The blocking unit can also block websites excluding those with low market value. The blocking unit can also evaluate and block market value based on the website's advertising revenue and traffic data. This allows highly reliable websites to be blocked by taking their market value into consideration. Some or all of the above-described processing by the blocking unit may be performed using, or without, AI. For example, the blocking unit can input data regarding market value into AI, which then performs the blocking.

[0113] The collection unit can estimate the child's emotions and adjust the timing of collecting the usage history based on the estimated child's emotions. For example, if the child is stressed, the collection unit can reduce the frequency of collecting the usage history and collect it when the child is relaxed. Furthermore, if the child is excited, the collection unit can collect the usage history in real time and perform immediate analysis. Furthermore, if the child is tired, the collection unit can temporarily stop collecting the usage history and resume it after the child has rested. This allows information to be collected at a more appropriate time by adjusting the timing of collecting the usage history according to the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into an AI, which can then adjust the collection timing.

[0114] The collection unit can analyze the child's past usage history and select the optimal collection method. For example, the collection unit prioritizes collecting the history of apps frequently used by the child. The collection unit can also analyze the history of apps used by the child during a specific time period and concentrate collection during that time period. The collection unit can also analyze the categories of apps used by the child in the past and collect history of apps in similar categories. In this way, by analyzing the child's past usage history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past usage history into AI, which can select the optimal collection method.

[0115] When collecting the usage history, the collection unit can filter the usage history based on the child's current interests. For example, the collection unit can collect only the history of apps related to topics in which the child is currently interested. The collection unit can also filter the history of related apps based on keywords recently searched by the child. The collection unit can also collect the usage history based on topics in online communities in which the child participates. This makes it possible to collect highly relevant information by filtering based on the child's current interests. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which can then perform the filtering.

[0116] When collecting the usage history, the collection unit can select the optimal collection means depending on the child's input method. For example, if the child uses voice input, the collection unit collects the usage history using voice recognition technology. Furthermore, if the child uses text input, the collection unit can also collect the usage history using text analysis technology. Furthermore, if the child uses images, the collection unit can also collect related usage history using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data or text data into AI, which can select the optimal collection means.

[0117] The collection unit can estimate the child's emotions and determine the priority of the usage history to be collected based on the estimated child's emotions. For example, if the child is relaxed, the collection unit can prioritize collecting the usage history of educational apps. Furthermore, if the child is excited, the collection unit can prioritize collecting the usage history of entertainment apps. Furthermore, if the child is stressed, the collection unit can prioritize collecting the usage history of apps containing relaxing content. This allows for more appropriate information to be collected by determining the priority of the usage history to be collected according to the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into an AI and determine the priority of the usage history to be collected by the AI.

[0118] When collecting usage history, the collection unit can prioritize collecting highly relevant history by taking into account the child's geographical location information. For example, the collection unit collects usage history of apps related to the child's current location. The collection unit can also prioritize collecting usage history of apps related to news and topics in the area where the child lives. If the child is traveling, the collection unit can also collect usage history of apps related to the area where the child is traveling. This makes it possible to provide more appropriate information by prioritizing collection of highly relevant history by taking into account the child's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into AI, which then collects highly relevant history.

[0119] When collecting the usage history, the collection unit can analyze the child's social media activities and collect related history. For example, the collection unit can analyze the content posted by accounts the child follows on social media and collect related app usage history. The collection unit can also collect app usage history based on the topics of groups the child participates in on social media. The collection unit can also collect related app usage history based on links the child shared on social media. In this way, by analyzing the child's social media activities, highly relevant history can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into AI, which then collects related history.

[0120] When collecting the usage history, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes collecting the usage history of apps that the child has previously rated highly. The collection unit can also collect the usage history by excluding the usage history of apps that the child has previously rated poorly. The collection unit can also adjust the categories of usage history to be collected based on feedback provided by the child in the past. In this way, by customizing the collection method by reflecting the child's past feedback, more appropriate information can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback into AI, which can customize the collection method.

[0121] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. If the child is stressed, the analysis unit can also provide concise and concise analysis results. If the child is excited, the analysis unit can also provide analysis results with visually stimulating effects. By adjusting the way the analysis is presented based on the child's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into an AI, which can then adjust the way the analysis is presented.

[0122] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the usage history. For example, the analysis unit performs a detailed analysis on usage history with a high level of importance. The analysis unit can also perform a concise analysis on usage history with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the usage history. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the usage history to AI, which can adjust the level of detail of the analysis.

[0123] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the usage history. For example, the analysis unit can apply an algorithm that evaluates learning effect to the usage history of educational apps. The analysis unit can also apply an algorithm that evaluates enjoyment to the usage history of entertainment apps. The analysis unit can also apply an algorithm that evaluates the quality of communication to the usage history of social media. In this way, by applying different analysis algorithms depending on the category of the usage history, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the usage history into AI, and the AI ​​can apply different analysis algorithms.

[0124] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. For example, the analysis unit corrects the current analysis result based on the child's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the child's past analysis results. The analysis unit can also compare the child's past analysis results, grasp trends, and improve the accuracy of the analysis. In this way, by referring to the child's past analysis results, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into AI, which can improve the accuracy of the analysis.

[0125] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated child's emotions. For example, if the child is in a hurry, the analysis unit can provide a short, to-the-point analysis result. Alternatively, if the child is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Alternatively, if the child is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into an AI, which can then adjust the length of the analysis.

[0126] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the usage history. For example, the analysis unit prioritizes analysis of recent usage history. The analysis unit can also analyze usage history that was submitted earlier later. The analysis unit can also dynamically adjust the priority of analysis based on the submission date. This allows for efficient analysis by determining the priority of analysis based on the submission date of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the submission date into AI, and the AI ​​can determine the priority of analysis.

[0127] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the usage history. For example, the analysis unit prioritizes analysis of highly relevant usage history. The analysis unit can also postpone analysis of less relevant usage history. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the usage history. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the usage history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the usage history into AI, which can then adjust the order of analysis.

[0128] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the child's level of expertise is low, the analysis unit can also provide analysis results in simple language. The analysis unit can also dynamically adjust the way the analysis results are presented based on the child's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the child's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the child's level of expertise into AI, which can then adjust the use of technical terms.

[0129] The providing unit can estimate the child's emotions and adjust the way the information is presented based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can provide detailed information. If the child is stressed, the providing unit can provide concise, to-the-point information. If the child is excited, the providing unit can provide information with visually stimulating effects. This allows for adjusting the way the information is presented based on the child's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the child's emotion data into an AI, which can then adjust the way the information is presented.

[0130] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit provides a detailed explanation for information with a high level of importance. The providing unit can also provide a concise explanation for information with a low level of importance. The providing unit can also dynamically adjust the level of detail of the information provided based on the importance of the information. This allows information to be provided efficiently by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the information to AI, and the AI ​​can adjust the level of detail of the information provided.

[0131] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can apply an algorithm that enhances learning effectiveness to educational information. The providing unit can also apply an algorithm that emphasizes enjoyment to entertainment information. The providing unit can also apply an algorithm that enhances the quality of communication to social media-related information. In this way, by applying different providing algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the category of information into AI, and the AI ​​can apply different providing algorithms.

[0132] The providing unit can improve the accuracy of the provision by referring to the child's past provision results when providing the data. The providing unit, for example, corrects the current provision content based on the child's past provision results. The providing unit can also optimize the provision algorithm by referring to the child's past provision results. The providing unit can also compare the child's past provision results, understand trends, and improve the accuracy of the provision. In this way, the accuracy of the provision can be improved by referring to the child's past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provision results into AI, which can improve the accuracy of the provision.

[0133] The providing unit can estimate the child's emotions and adjust the length of the information to be provided based on the estimated child's emotions. For example, if the child is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the child is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the child is excited, the providing unit can provide information with visually stimulating effects. By adjusting the length of the information to be provided according to the child's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the child's emotion data into an AI, which can then adjust the length of the information.

[0134] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. For example, the providing unit can provide the most recent information preferentially. The providing unit can also provide information that was submitted earlier later. The providing unit can also dynamically adjust the priority of provision based on the time of submission. This allows information to be provided efficiently by determining the priority of provision based on the time of submission of information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data regarding the time of submission into AI, and the AI ​​can determine the priority of provision.

[0135] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, the providing unit can provide highly relevant information preferentially. The providing unit can also provide less relevant information later. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, it is possible to provide information efficiently. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the information to AI, and the AI ​​can adjust the order of provision.

[0136] The providing unit can adjust the use of technical terminology in the provided information depending on the child's level of expertise. For example, if the child's level of expertise is high, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the child's level of expertise is low, the providing unit can also provide information in simple language. The providing unit can also dynamically adjust the way the provided content is expressed based on the child's level of expertise. This makes it possible to provide more appropriate information by adjusting the use of technical terminology in the provided information depending on the child's level of expertise. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data regarding the child's level of expertise into AI, which can then adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, identification unit, blocking unit, second collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can estimate a child's emotions using the camera 42 and microphone 38B of the smart device 14 and adjust the timing of information collection by the identification processing unit 290 of the data processing device 12. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies educationally inappropriate websites based on information obtained from the collection unit. The blocking unit is realized by the identification processing unit 290 of the data processing device 12 and blocks access to the identified inappropriate websites. The second collection unit is realized by the control unit 46A of the smart device 14 and collects smartphone usage history. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected usage history and analyzes the child's personality and interests. The provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides information based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, identification unit, blocking unit, second collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can estimate a child's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjust the timing of information collection by the identification processing unit 290 of the data processing device 12. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies educationally inappropriate websites based on information obtained from the collection unit. The blocking unit is realized by the identification processing unit 290 of the data processing device 12 and blocks access to the identified inappropriate websites. The second collection unit is realized by the control unit 46A of the smart glasses 214 and collects smartphone usage history. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected usage history and analyzes the child's personality and interests. The provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides information based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, identification unit, blocking unit, second collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can estimate a child's emotions using the camera 42 and microphone 238 of the headset-type terminal 314 and adjust the timing of information collection by the identification processing unit 290 of the data processing device 12. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies educationally inappropriate websites based on information obtained from the collection unit. The blocking unit is realized by the identification processing unit 290 of the data processing device 12 and blocks access to the identified inappropriate websites. The second collection unit is realized by the control unit 46A of the headset-type terminal 314 and collects smartphone usage history. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected usage history and analyzes the child's personality and interests. The provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides information based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, identification unit, blocking unit, second collection unit, analysis unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can estimate a child's emotions using the camera 42 and microphone 238 of the robot 414 and adjust the timing of information collection by the identification processing unit 290 of the data processing device 12. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies educationally inappropriate websites based on information obtained from the collection unit. The blocking unit is realized by the identification processing unit 290 of the data processing device 12 and blocks access to the identified inappropriate websites. The second collection unit is realized by the control unit 46A of the robot 414 and collects smartphone usage history. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected usage history and analyzes the child's personality and interests. The provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides information based on the analysis results.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The providing unit can estimate the child's emotions and adjust the way information is presented based on the estimated child's emotions. For example, if the child is relaxed, detailed information can be provided. If the child is stressed, concise, to-the-point information can be provided. If the child is excited, information with visually stimulating effects can be provided. This allows for more appropriate information to be provided by adjusting the way information is presented based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the child's emotion data into an AI, which can then adjust the way information is presented.

[0139] The collection unit can analyze the child's past usage history and select the optimal collection method. For example, the collection unit can prioritize collecting the history of apps frequently used by the child. The collection unit can also analyze the history of apps used by the child during a specific time period and concentrate collection during that time period. The collection unit can also analyze the categories of apps used by the child in the past and collect history of apps in similar categories. In this way, by analyzing the child's past usage history, the optimal collection method can be selected and information can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past usage history into AI, which can select the optimal collection method.

[0140] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, if the child is relaxed, a detailed analysis result can be provided. If the child is stressed, a concise and to-the-point analysis result can be provided. If the child is excited, a visually stimulating analysis result can be provided. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the child's emotion data into an AI, which can then adjust the way the analysis is presented.

[0141] The identification unit can improve the accuracy of identification by taking into account the interrelationships between websites during identification. For example, the identification unit can analyze the link relationships between websites to identify highly related sites. The identification unit can also refer to the domain information of websites to preferentially identify highly reliable sites. The identification unit can also analyze the interrelationships between website content to improve the accuracy of identification. In this way, the accuracy of identification can be improved by taking into account the interrelationships between websites. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the link relationships and domain information of websites into AI, which can improve the accuracy of identification.

[0142] The blocking unit can estimate the child's emotions and adjust the blocking method based on the estimated child's emotions. For example, if the child is stressed, a strict blocking method can be applied. If the child is relaxed, a normal blocking method can be applied. If the child is excited, entertainment sites can be allowed. By adjusting the blocking method according to the child's emotions, more appropriate blocking can be performed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the blocking unit can be performed using an AI, for example, or without an AI. For example, the blocking unit can input the child's emotion data into an AI, which can then adjust the blocking method.

[0143] When collecting usage history, the collection unit can filter the usage history based on the child's current interests. For example, the collection unit can collect only the history of apps related to topics that the child is currently interested in. The collection unit can also filter the history of related apps based on keywords recently searched by the child. The collection unit can also collect usage history based on topics of online communities in which the child participates. This makes it possible to collect highly relevant information by filtering based on the child's current interests. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests into AI, which then performs the filtering.

[0144] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the usage history. For example, an algorithm that evaluates learning effectiveness can be applied to the usage history of educational apps. The analysis unit can also apply an algorithm that evaluates enjoyment to the usage history of entertainment apps. The analysis unit can also apply an algorithm that evaluates the quality of communication to the usage history of social media. In this way, by applying different analysis algorithms depending on the category of the usage history, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the usage history into AI, and the AI ​​can apply different analysis algorithms.

[0145] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. For example, highly relevant information can be provided preferentially. The providing unit can also provide less relevant information later. The providing unit can also dynamically adjust the order of provision based on the relevance of the information. In this way, by adjusting the order of provision based on the relevance of the information, it is possible to provide information efficiently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the information to AI, and the AI ​​can adjust the order of provision.

[0146] The collection unit can estimate the child's emotions and prioritize the usage history to be collected based on the estimated child's emotions. For example, if the child is relaxed, the collection unit can prioritize collecting the usage history of educational apps. Furthermore, if the child is excited, the collection unit can prioritize collecting the usage history of entertainment apps. Furthermore, if the child is stressed, the collection unit can prioritize collecting the usage history of apps containing relaxing content. By prioritizing the usage history to be collected based on the child's emotions, more appropriate information can be collected. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the child's emotion data into an AI and prioritize the usage history to be collected by the AI.

[0147] During the identification, the identification unit can assign a specific weight based on the update frequency of the website. For example, the identification unit can prioritize the identification of frequently updated sites. The identification unit can also perform the identification by excluding sites that are updated infrequently. The identification unit can also assign a specific weight based on the update frequency to identify highly reliable sites. In this way, highly reliable sites can be identified by assigning a specific weight based on the update frequency of the website. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the update frequency of the website into AI, which can then assign a specific weight.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The collection unit collects information about websites accessed by children. For example, the collection unit collects URLs and content of websites accessed by children, as well as titles, metadata, and text of web pages. Step 2: The identifying unit identifies educationally inappropriate sites based on the information collected by the collecting unit. For example, the identifying unit may identify sites containing violent content or adult content, and may also identify inappropriate sites based on specific keywords. Step 3: The blocking unit blocks access to the inappropriate sites identified by the identifying unit. The blocking unit can block access to the inappropriate sites using, for example, IP blocking or content filtering, and can also restrict access by adding the URL of the identified site to a blacklist. Step 4: The second collection unit collects smartphone usage history. The second collection unit collects usage history such as which apps the child used, which websites they visited, the duration and frequency of app use, and website browsing history. Step 5: The analysis unit analyzes the usage history collected by the second collection unit and analyzes the child's personality and interests. For example, the analysis unit can identify the child's hobbies and topics of interest based on the collected usage history and analyze usage history patterns using AI. Step 6: The providing unit provides information based on the analysis results obtained by the analyzing unit. For example, the providing unit can provide parents and children with information to help with the child's development, and can also provide information on books and events related to the child's interests.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] 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.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0199] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0201] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] 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.

[0213] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0215] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0219] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0221] [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information about websites visited by children; an identifying unit that identifies educationally inappropriate sites based on the information collected by the collecting unit; a blocking unit that blocks access to the inappropriate site identified by the identifying unit; a second collection unit that collects smartphone usage history; an analysis unit that analyzes the usage history collected by the second collection unit and analyzes the personality and interests of the child; a providing unit that provides information based on the analysis result obtained by the analyzing unit. A system characterized by:

2. The collecting unit Collecting URLs or content of websites visited by children 2. The system of claim 1.

3. The identification unit Identify sites containing violent or adult content based on the information collected 2. The system of claim 1.

4. The block portion is Block access to identified inappropriate sites 2. The system of claim 1.

5. The collecting unit Collect usage history of which apps your child uses and which websites they visit 2. The system of claim 1.

6. The analysis unit Analyze collected usage history and analyze children's personalities and interests 2. The system of claim 1.

7. The providing unit Based on the analysis results, provide parents and children with information to help them grow 2. The system of claim 1.

8. The collecting unit Inferring a child's emotions and specifically tailoring the timing of website information collection based on the child's inferred emotions 2. The system of claim 1.

9. The collecting unit Analyze children's past access history and select appropriate collection methods 2. The system of claim 1.

10. The collecting unit Filter websites based on your child's current interests and concerns 2. The system of claim 1.

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