System

Abstract

An object of a system according to an embodiment is to automatically generate and provide child support content in accordance with a child's stage of development.SOLUTION: A system includes an input part, a generation part, an analysis part, a recommendation part, and an online access part. The input unit allows a parent to input a profile of a child. The generation unit generates child support content from the development database on the basis of the information inputted by the inputting unit. The analysis unit analyzes the viewing history of the content generated by the generation unit. The recommendation unit recommends personalized content on the basis of the analysis result obtained by the analysis unit. The section to be accessed online is accessed online.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 technology has had the problem of making it difficult to provide appropriate childcare support content that is appropriate for a child's developmental stage.

[0005] The system according to the embodiment aims to automatically generate and provide childcare support content according to the developmental stage of a child. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, an analysis unit, a recommendation unit, and an online access unit. The input unit allows a parent to input a child's profile. The generation unit generates childcare support content from a development database based on the information input by the input unit. The analysis unit analyzes the viewing history of the content generated by the generation unit. The recommendation unit recommends personalized content based on the analysis results obtained by the analysis unit. The online access unit provides online access. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and provide childcare support content according to the developmental stage of a child. [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 childcare support system according to an embodiment of the present invention utilizes a generation AI to automatically generate and provide childcare support content tailored to a child's developmental stage. The childcare support system allows parents to input their child's profile, view the generated childcare support content, and receive personalized content recommendations, enabling them to receive optimal childcare support tailored to their child's developmental stage. For example, in the childcare support system, parents select their child's profile (age, developmental stage, and preferences). This information is input into a generation AI. The generation AI then automatically generates optimal childcare advice videos, e-picture books, and expert-supervised content from a developmental database. For example, advice videos tailored to the child's developmental stage according to the child's age and e-picture books tailored to the child's preferences are generated. Furthermore, the generation AI analyzes the child's interests based on the viewing history and recommends personalized content. For example, if a child is interested in a particular character, content featuring that character is recommended. This service is provided online, allowing parents to easily access it at any time. For example, the childcare support content can be viewed using a smartphone or tablet. This allows parents to receive optimal childcare support tailored to their child's developmental stage and support their child's growth by providing content tailored to the child's interests.

[0029] A childcare support system according to an embodiment includes an input unit, a generation unit, an analysis unit, a recommendation unit, and an access unit. A parent inputs a child's profile through the input unit. The profile includes, but is not limited to, information such as the child's name, age, gender, and interests. The input unit can input the profile using, for example, text input, voice input, or image input. The generation unit uses a generation AI to generate childcare support content from a development database based on the information input by the input unit. The generated content includes, but is not limited to, information such as parenting advice videos, digital picture books, and expert-supervised content. For example, the generation AI generates a script for a parenting advice video using a text generation AI (e.g., LLM). The generation unit can also generate content for a digital picture book using a multimodal generation AI. The generation unit can also generate expert-supervised content using the generation AI. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation AI uses keyword extraction technology to select particularly important information from the childcare support content and generates content based on that information. The analysis unit analyzes the viewing history of the content generated by the generation unit. The viewing history includes, but is not limited to, the title of the content viewed, viewing time, and viewing frequency. The analysis unit, for example, analyzes the viewing history to identify the child's interests. For example, the analysis unit determines whether the child is interested in a particular character based on the viewing frequency and viewing time. The recommendation unit recommends personalized content based on the analysis results obtained by the analysis unit. The recommended content includes, but is not limited to, individually customized videos, articles, advice, etc. For example, if the child is interested in a particular character, the recommendation unit recommends content featuring that character. The access unit accesses the childcare support content online.The access unit can access the childcare support content using, for example, a smartphone or a tablet. For example, the access unit can access the childcare support content through a web browser or a mobile app. This allows the childcare support system according to the embodiment to allow parents to receive optimal childcare support tailored to their child's developmental stage. For example, parents can access the childcare support content anytime, anywhere using a smartphone or a tablet.

[0030] The generation unit can use the developmental database to generate parenting advice videos, electronic picture books, and expert-supervised content. The developmental database includes, for example, information on a child's developmental stage, expert parenting advice, and past parenting support content, but is not limited to these examples. The generation unit can use the developmental database to generate parenting advice videos. For example, the generation unit can use a text generation AI to generate scripts for the parenting advice videos. The generation unit can also use the developmental database to generate electronic picture books. For example, the generation unit can use a multimodal generation AI to generate content for the electronic picture book. The generation unit can also use the developmental database to generate expert-supervised content. For example, the generation unit can use a text generation AI to generate scripts for the expert-supervised content. In this way, by using the developmental database, optimal parenting support content tailored to a child's developmental stage can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input information obtained from the developmental database into the generation AI and cause the generation AI to generate parenting support content.

[0031] The analysis unit can analyze the viewing history to identify the child's interests. The viewing history includes, but is not limited to, the title of the content viewed, the viewing time, and the viewing frequency. The analysis unit can analyze the viewing history to identify the child's interests. For example, the analysis unit can determine whether the child is interested in a particular character based on the viewing frequency and viewing time. The analysis unit can also identify changes in the child's interests based on the viewing history. For example, the analysis unit can analyze changes in the viewing history to determine whether the child has developed a new interest. The analysis unit can also identify trends in the child's interests based on the viewing history. For example, the analysis unit can analyze trends in the viewing history to determine whether the child is interested in a particular genre. By analyzing the viewing history, the child's interests can be identified and more appropriate content can be provided. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input viewing history data into AI and have the AI ​​identify the child's interests.

[0032] The recommendation unit can provide personalized content based on the child's interests. Recommended content includes, but is not limited to, individually customized videos, articles, and advice. The recommendation unit can provide personalized content based on the child's interests. For example, if a child is interested in a particular character, the recommendation unit can recommend content featuring that character. The recommendation unit can also provide personalized content based on changes in the child's interests. For example, if a child develops a new interest, the recommendation unit can recommend content related to that interest. The recommendation unit can also provide personalized content based on trends in the child's interests. For example, if a child is interested in a particular genre, the recommendation unit can recommend content related to that genre. This can support the child's development by providing personalized content based on the child's interests. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the child's interest data identified by the analysis unit into the AI ​​and have the AI ​​perform personalized content recommendations.

[0033] The access unit can access the childcare support content using a smartphone or tablet. The access unit can access the childcare support content using, for example, a smartphone or tablet. For example, the access unit can access the childcare support content through a web browser or a mobile app. The access unit can also access the childcare support content using a device other than a smartphone or tablet. For example, the access unit can access the childcare support content using a smart TV or a PC. This allows parents to easily use the content at any time by accessing the childcare support content using a smartphone or tablet. Some or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without AI. For example, the access unit can input the parent's device information into AI and have the AI ​​select the optimal access method.

[0034] The input unit can analyze past profile input history and select the optimal input method. For example, if the parent has preferred text input in the past, the input unit can preferentially suggest text input. Also, if the parent has used voice input in the past, the input unit can preferentially suggest voice input. For example, if the parent has used image input in the past, the input unit can preferentially suggest image input. In this way, by analyzing the past profile input history, the optimal input method for the parent can be provided. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past profile input history data into AI and have the AI ​​select the optimal input method.

[0035] When entering a profile, the input unit can filter based on the parent's current living situation and areas of interest. For example, if the parent frequently searches for information about childcare, the input unit can prioritize displaying input items related to childcare. Also, if the parent frequently searches for information about work, the input unit can prioritize displaying input items related to balancing work and childcare. For example, if the parent frequently searches for information about health, the input unit can prioritize displaying input items related to health management. This allows for more appropriate profile entry by filtering based on the parent's living situation and areas of interest. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the parent's search history data into AI and have the AI ​​perform the filtering.

[0036] When entering a profile, the input unit can select the optimal input means depending on the parent's input method. For example, if the parent selects voice input, the input unit converts the input content into text using voice recognition technology. Furthermore, if the parent selects text input, the input unit can also provide an interface that supports keyboard input. For example, if the parent selects image input, the input unit analyzes the input content using image recognition technology. This allows for smooth profile entry by selecting the optimal input means depending on the parent's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the parent's input method data into AI and have the AI ​​select the optimal input means.

[0037] When entering a profile, the input unit can prioritize inputting highly relevant information by taking into account the parent's geographical location information. For example, if the parent lives in an urban area, the input unit can prioritize inputting childcare information related to the urban area. Furthermore, if the parent lives in a suburban area, the input unit can also prioritize inputting childcare information related to the suburban area. For example, if the parent lives overseas, the input unit can prioritize inputting local childcare information. This allows more relevant information to be entered by taking the parent's geographical location information into consideration. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the parent's geographical location information data into AI and cause the AI ​​to execute the prioritized input of highly relevant information.

[0038] The input unit can analyze the parent's social media activity and input relevant information when entering a profile. For example, the input unit automatically inputs childcare information shared by the parent on social media. The input unit can also input information about childcare experts that the parent follows on social media. For example, the input unit inputs information about childcare groups that the parent participates in on social media. This allows more relevant information to be input by analyzing the parent's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the parent's social media activity data into AI and cause the AI ​​to input relevant information.

[0039] When entering a profile, the input unit can customize the input method by reflecting the parent's past feedback. For example, if the parent has provided feedback on the input method in the past, the input unit can improve the input method by reflecting that feedback. Furthermore, if the parent has been dissatisfied with an input method in the past, the input unit can also provide an input method to resolve that dissatisfaction. For example, if the parent was satisfied with an input method in the past, the input unit can continue to provide that input method. In this way, by reflecting the parent's past feedback, an input method that is easier to use can be provided. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the parent's feedback data into AI and have the AI ​​customize the input method.

[0040] When generating content, the generation unit can adjust the level of detail of the content based on the child's developmental stage. For example, when the child is in an early developmental stage, the generation unit generates content that mainly includes basic information. Furthermore, when the child is in an intermediate developmental stage, the generation unit can also generate content that includes detailed information. For example, when the child is in a later developmental stage, the generation unit generates content that includes specialized information. In this way, by adjusting the level of detail of the content based on the child's developmental stage, it is possible to provide optimal content for the child. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input child developmental stage data into the generation AI and cause the generation AI to adjust the level of detail of the content generation.

[0041] When generating content, the generation unit can apply different generation algorithms depending on the child's preferences. For example, if a child is interested in animals, the generation unit can apply an algorithm that generates animal-related content. Also, if a child is interested in vehicles, the generation unit can apply an algorithm that generates vehicle-related content. For example, if a child is interested in music, the generation unit can apply an algorithm that generates music-related content. In this way, by applying different generation algorithms depending on the child's preferences, content that is interesting to children can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input child preference data into the generation AI and cause the generation AI to apply different generation algorithms.

[0042] When generating content, the generation unit can improve the accuracy of generation by referring to the parent's past content usage history. For example, the generation unit analyzes the trends of content viewed by the parent in the past and generates similar content. The generation unit can also generate similar content by referring to content that the parent has previously given a high rating. For example, the generation unit analyzes the viewing time of content viewed by the parent in the past and generates content with the optimal viewing time. By referring to the parent's past content usage history, more accurate content can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the parent's past content usage history data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0043] When generating content, the generation unit can determine generation priorities based on the child's developmental stage. For example, if the child is in an early developmental stage, the generation unit can prioritize generating basic information. Furthermore, if the child is in a middle developmental stage, the generation unit can prioritize generating detailed information. For example, if the child is in a later developmental stage, the generation unit can prioritize generating specialized information. By determining generation priorities based on the child's developmental stage, optimal content for the child can be provided. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input child developmental stage data into the generation AI and have the generation AI determine the generation priorities.

[0044] When generating content, the generation unit can adjust the use of technical terminology in the generation according to the parent's level of expertise. For example, if the parent has technical expertise, the generation unit generates content that uses a lot of technical terminology. Furthermore, if the parent does not have technical expertise, the generation unit can generate content that explains things in simple terms. For example, if the parent has medium level of expertise, the generation unit generates content that uses a moderate amount of technical terminology. This allows content that is easy for the parent to understand to be provided by adjusting the use of technical terminology according to the parent's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the parent's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0045] The analysis unit can improve the accuracy of the analysis by taking into account changes in a child's interests when analyzing the viewing history. For example, if a child begins to become interested in a particular character, the analysis unit can prioritize analyzing viewing history related to that character. Furthermore, if a child begins to develop a new interest, the analysis unit can also prioritize analyzing viewing history related to that interest. For example, if a child's interest in a content that they previously had wanes, the analysis unit can analyze the viewing history taking into account such changes. This allows for more accurate analysis results by taking into account changes in a child's interests. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on changes in a child's interests into AI and have the AI ​​improve the accuracy of the analysis.

[0046] When analyzing the viewing history, the analysis unit can apply an analysis method based on the child's developmental stage. For example, if the child is in an early developmental stage, the analysis unit analyzes a basic viewing history. Furthermore, if the child is in a middle developmental stage, the analysis unit can also analyze a detailed viewing history. For example, if the child is in a late developmental stage, the analysis unit analyzes a specialized viewing history. By applying an analysis method based on the child's developmental stage, more appropriate analysis results can be obtained. 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 child's developmental stage data into AI and have the AI ​​apply the analysis method.

[0047] The analysis unit can improve the analysis method by reflecting parental feedback when analyzing the viewing history. For example, if a parent provides feedback on the analysis results of the viewing history, the analysis unit can improve the analysis method by reflecting that feedback. Furthermore, if a parent is dissatisfied with the analysis results of the viewing history, the analysis unit can provide an analysis method to resolve that dissatisfaction. For example, if the parent is satisfied with the analysis results of the viewing history, the analysis unit can continue to use that analysis method. In this way, by reflecting the parent's feedback, a more appropriate analysis method can be provided. 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 parental feedback data into AI and have the AI ​​improve the analysis method.

[0048] The analysis unit can take into account the geographical distribution of children when analyzing viewing histories. For example, if a child lives in an urban area, the analysis unit can prioritize analyzing viewing histories related to urban areas. Also, if a child lives in a suburban area, the analysis unit can prioritize analyzing viewing histories related to suburban areas. For example, if a child lives overseas, the analysis unit can prioritize analyzing viewing histories from that area. This allows for more appropriate analysis results to be obtained by taking the geographical distribution of children into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the geographical distribution of children into AI and have the AI ​​perform the analysis.

[0049] When analyzing the viewing history, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, performs the analysis by referring to the latest research literature related to the viewing history. The analysis unit can also perform the analysis by referring to past research literature related to the viewing history. For example, the analysis unit performs the analysis by referring to the opinions of experts related to the viewing history. By referring to related literature, 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 related literature data into AI and have the AI ​​improve the accuracy of the analysis.

[0050] The analysis unit can take into account the market value of the child when analyzing the viewing history. For example, if the child is likely to have future market value, the analysis unit analyzes the viewing history taking that market value into account. Furthermore, if the child is likely to have market value in a particular field, the analysis unit can prioritize analysis of viewing history related to that field. For example, if the child is unlikely to have market value, the analysis unit analyzes the viewing history without taking that market value into account. This allows for more appropriate analysis results to be obtained by taking the child's market value into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's market value data into AI and have the AI ​​perform the analysis.

[0051] The recommendation unit can improve the accuracy of recommendations by taking into account changes in a child's interests when making recommendations. For example, if a child begins to take an interest in a particular character, the recommendation unit can recommend content related to that character. Furthermore, if a child begins to develop a new interest, the recommendation unit can also recommend content related to that interest. For example, if a child's interest in a subject that he or she previously had wanes, the recommendation unit can recommend content taking into account such changes. This allows for more accurate recommendations to be provided by taking into account changes in a child's interests. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on changes in a child's interests into AI and have the AI ​​improve the accuracy of recommendations.

[0052] The recommendation unit can apply a recommendation method based on the child's developmental stage when making a recommendation. For example, if the child is in an early developmental stage, the recommendation unit can recommend basic content. The recommendation unit can also recommend detailed content if the child is in an intermediate developmental stage. For example, the recommendation unit can recommend specialized content if the child is in a later developmental stage. This makes it possible to provide more appropriate content by applying a recommendation method based on the child's developmental stage. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI, for example. For example, the recommendation unit can input child developmental stage data into AI and have the AI ​​apply the recommendation method.

[0053] The recommendation unit can improve the recommendation method by reflecting parental feedback when making a recommendation. For example, if the parent provides feedback on the recommended content, the recommendation unit improves the recommendation method by reflecting that feedback. Furthermore, if the parent is dissatisfied with the recommended content, the recommendation unit can also provide a recommendation method to resolve that dissatisfaction. For example, if the parent is satisfied with the recommended content, the recommendation unit continues to use that recommendation method. In this way, by reflecting the parent's feedback, a more appropriate recommendation method can be provided. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input parental feedback data into AI and cause the AI ​​to improve the recommendation method.

[0054] The recommendation unit can make recommendations taking into account the geographical distribution of children. For example, if the child lives in an urban area, the recommendation unit can recommend content related to urban areas. Furthermore, if the child lives in a suburban area, the recommendation unit can also recommend content related to suburban areas. For example, if the child lives overseas, the recommendation unit can recommend local content. This allows more appropriate content to be provided by taking the geographical distribution of children into consideration. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI, for example. For example, the recommendation unit can input data on the geographical distribution of children into AI and have the AI ​​execute the recommendations.

[0055] The recommendation unit can improve the accuracy of recommendations by referring to related literature when making recommendations. The recommendation unit, for example, makes recommendations by referring to the latest research literature related to the content to be recommended. The recommendation unit can also make recommendations by referring to past research literature related to the content to be recommended. For example, the recommendation unit makes recommendations by referring to the opinions of experts related to the content to be recommended. By referring to related literature, more accurate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input related literature data into AI and have the AI ​​improve the accuracy of recommendations.

[0056] The recommendation unit can make recommendations taking into account the market value of the child. For example, if the child is likely to have future market value, the recommendation unit recommends content taking that market value into account. Furthermore, if the child is likely to have market value in a particular field, the recommendation unit can also recommend content related to that field. For example, if the child is unlikely to have market value, the recommendation unit recommends content without taking that market value into account. This makes it possible to provide more appropriate content by taking the child's market value into account. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the child's market value data into AI and have the AI ​​execute the recommendations.

[0057] When accessing, the access unit can select the optimal access method by referring to the parent's past access history. For example, the access unit can prioritize providing access methods that the parent has used in the past. The access unit can also suggest the optimal access method based on content that the parent has accessed in the past. For example, the access unit can provide the optimal access method by taking into account the time period during which the parent accessed the content in the past. In this way, by referring to the parent's past access history, a more appropriate access method can be provided. Some or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit can input the parent's past access history data into AI and have the AI ​​select the optimal access method.

[0058] The access unit can improve the access method by reflecting parental feedback at the time of access. For example, if the parent provides feedback on the access method, the access unit can improve the access method by reflecting that feedback. Furthermore, if the parent is dissatisfied with the access method, the access unit can provide an access method to resolve that dissatisfaction. For example, if the parent is satisfied with the access method, the access unit can continue to provide that access method. In this way, a more appropriate access method can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input parental feedback data into AI and have the AI ​​improve the access method.

[0059] At the time of access, the access unit can select the optimal access method by taking into account the parent's geographical location information. For example, if the parent lives in an urban area, the access unit can provide an access method related to the urban area. Furthermore, if the parent lives in a suburban area, the access unit can also provide an access method related to the suburban area. For example, if the parent lives overseas, the access unit can provide a local access method. This allows for a more appropriate access method to be provided by taking into account the parent's geographical location information. Some or all of the above-described processing in the access unit may be performed using, or without, AI. For example, the access unit can input the parent's geographical location information data into AI and have the AI ​​select the optimal access method.

[0060] At the time of access, the access unit can analyze the parent's social media activity and suggest an access method. For example, the access unit can suggest an access method based on content shared by the parent on social media. The access unit can also suggest an access method based on information about experts the parent follows on social media. For example, the access unit can suggest an access method based on information about groups the parent joins on social media. This makes it possible to provide more appropriate access methods by analyzing the parent's social media activity. Some or all of the above-described processing in the access unit may be performed using, or without, AI, for example. For example, the access unit can input the parent's social media activity data into AI and have the AI ​​execute the access method suggestion.

[0061] The access unit can customize the access method by reflecting the parent's past feedback at the time of access. For example, if the parent has provided feedback on the access method in the past, the access unit can improve the access method by reflecting that feedback. Furthermore, if the parent has been dissatisfied with the access method in the past, the access unit can provide an access method that resolves that dissatisfaction. For example, if the parent was satisfied with the access method in the past, the access unit can continue to provide that access method. In this way, by reflecting the parent's past feedback, an easier-to-use access method can be provided. Some or all of the above-described processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input parental feedback data into AI and have the AI ​​customize the access method.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The childcare support system may further include a notification unit. The notification unit enables parents to receive important childcare information in a timely manner according to their child's developmental stage. For example, the notification unit may send reminders for vaccination schedules and health checkups based on the child's developmental stage. The notification unit may also send relevant childcare advice or congratulatory messages based on specific events set by the parent (e.g., the child's birthday or growth milestones). Furthermore, the notification unit may regularly provide updated childcare information so that parents can receive new research and expert advice on childcare. This allows parents to receive important information according to their child's developmental stage without missing anything, thereby improving the quality of childcare.

[0064] The generator can further customize the content based on the child's learning style. For example, if the child is a visual learner, the generator can generate visually rich content (e.g., illustrations and videos). If the child is an auditory learner, the generator can generate content that makes extensive use of audio and music. Furthermore, if the child is a tactile learner, the generator can generate content that includes interactive elements. This allows the provision of optimal content according to the child's learning style, thereby improving the child's learning effectiveness.

[0065] The analysis unit can further analyze the parenting style of the parent and evaluate the degree of match between the child's interests and the parent's parenting style. For example, if the parent has a strict parenting style, the analysis unit can evaluate the degree to which the child's interests match that parenting style. Alternatively, if the parent has a liberal parenting style, the analysis unit can evaluate the degree to which the child's interests match that parenting style. Furthermore, the analysis unit can suggest adjustments to the parenting style based on the degree of match between the parent's parenting style and the child's interests. This allows parents to adopt an optimal parenting style that suits their child's interests, thereby more effectively supporting their child's development.

[0066] The recommendation unit can further provide personalized content based on the parent's child-rearing goals. For example, if a parent places importance on their child's language development, the recommendation unit can recommend content related to language development (e.g., picture books and language games to increase vocabulary). If a parent places importance on their child's social development, the recommendation unit can also recommend content related to social development (e.g., stories to teach cooperation and empathy). If a parent places importance on their child's motor skill development, the recommendation unit can also recommend content related to motor skill development (e.g., physical games and exercise programs). This makes it possible to provide optimal content according to the parent's child-rearing goals, thereby effectively supporting the child's growth.

[0067] The access unit can further suggest the optimal access method based on the parent's device usage habits. For example, if the parent mainly uses a smartphone, the access unit can provide an interface optimized for the smartphone. If the parent mainly uses a tablet, the access unit can also provide an interface optimized for the tablet. If the parent mainly uses a PC, the access unit can also provide an interface optimized for the PC. This makes it possible to provide the optimal access method according to the parent's device usage habits, making it easier to use childcare support content.

[0068] The input unit can analyze the parent's past profile input history and provide an input assistance function based on the parent's input patterns. For example, if the parent has frequently input a specific input item in the past, the input unit can provide a function to automatically complete that item. Also, if the parent has frequently made input errors in the past, the input unit can display guidelines to prevent input errors. Furthermore, if the parent has taken a long time to input data in the past, the input unit can provide a simple input option to shorten the input time. This makes it possible to make the parent's input work more efficient by utilizing the parent's past profile input history.

[0069] When entering a profile, the input unit can suggest the timing of entry based on the parent's current lifestyle. For example, if the parent has a nocturnal lifestyle, the input unit can send a notification prompting the parent to enter their profile at night. Alternatively, if the parent has a morning lifestyle, the input unit can send a notification prompting the parent to enter their profile in the morning. Furthermore, if the parent has an irregular lifestyle, the input unit can detect the time period when the parent is relaxed and send a notification prompting the parent to enter their profile during that time period. This makes it possible to provide the optimal entry timing according to the parent's lifestyle, making profile entry smoother.

[0070] The input unit can provide an input assistance function according to the parent's input speed when entering a profile. For example, if the parent is typing quickly, the input unit can provide a function to automatically complete the input content in real time. If the parent is typing slowly, the input unit can also display guidelines to help the parent proceed while checking the input content. Furthermore, if the parent is typing at a medium speed, the input unit can provide a function to appropriately save the input content and allow the parent to resume input even if it is interrupted midway. This makes it possible to provide an optimal input assistance function according to the parent's input speed, making profile entry smoother.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The parent inputs the child's profile in the input unit. The profile includes the child's name, age, gender, interests, etc. The input unit can input the profile using methods such as text input, voice input, and image input. Step 2: The generation unit uses generation AI to generate childcare support content from the development database based on the information input by the input unit. The generated content includes childcare advice videos, digital picture books, and expert-supervised content. The generation AI generates content using text generation AI and multimodal generation AI. Step 3: The analysis unit analyzes the viewing history of the content generated by the generation unit. The viewing history includes the title of the content viewed, the viewing time, the viewing frequency, etc. The analysis unit analyzes the viewing history and identifies the child's interests. Step 4: The recommendation unit recommends personalized content based on the analysis results obtained by the analysis unit. Recommended content may include individually customized videos, articles, advice, etc. Step 5: The access unit accesses the childcare support content online. The access unit can access the childcare support content using a smartphone or tablet. The childcare support content can be accessed through a web browser or mobile app.

[0073] (Example 2) A childcare support system according to an embodiment of the present invention utilizes a generation AI to automatically generate and provide childcare support content tailored to a child's developmental stage. The childcare support system allows parents to input their child's profile, view the generated childcare support content, and receive personalized content recommendations, enabling them to receive optimal childcare support tailored to their child's developmental stage. For example, in the childcare support system, parents select their child's profile (age, developmental stage, and preferences). This information is input into a generation AI. The generation AI then automatically generates optimal childcare advice videos, e-picture books, and expert-supervised content from a developmental database. For example, advice videos tailored to the child's developmental stage according to the child's age and e-picture books tailored to the child's preferences are generated. Furthermore, the generation AI analyzes the child's interests based on the viewing history and recommends personalized content. For example, if a child is interested in a particular character, content featuring that character is recommended. This service is provided online, allowing parents to easily access it at any time. For example, the childcare support content can be viewed using a smartphone or tablet. This allows parents to receive optimal childcare support tailored to their child's developmental stage and support their child's growth by providing content tailored to the child's interests.

[0074] A childcare support system according to an embodiment includes an input unit, a generation unit, an analysis unit, a recommendation unit, and an access unit. A parent inputs a child's profile through the input unit. The profile includes, but is not limited to, information such as the child's name, age, gender, and interests. The input unit can input the profile using, for example, text input, voice input, or image input. The generation unit uses a generation AI to generate childcare support content from a development database based on the information input by the input unit. The generated content includes, but is not limited to, information such as parenting advice videos, digital picture books, and expert-supervised content. For example, the generation AI generates a script for a parenting advice video using a text generation AI (e.g., LLM). The generation unit can also generate content for a digital picture book using a multimodal generation AI. The generation unit can also generate expert-supervised content using the generation AI. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation AI uses keyword extraction technology to select particularly important information from the childcare support content and generates content based on that information. The analysis unit analyzes the viewing history of the content generated by the generation unit. The viewing history includes, but is not limited to, the title of the content viewed, viewing time, and viewing frequency. The analysis unit, for example, analyzes the viewing history to identify the child's interests. For example, the analysis unit determines whether the child is interested in a particular character based on the viewing frequency and viewing time. The recommendation unit recommends personalized content based on the analysis results obtained by the analysis unit. The recommended content includes, but is not limited to, individually customized videos, articles, advice, etc. For example, if the child is interested in a particular character, the recommendation unit recommends content featuring that character. The access unit accesses the childcare support content online.The access unit can access the childcare support content using, for example, a smartphone or a tablet. For example, the access unit can access the childcare support content through a web browser or a mobile app. This allows the childcare support system according to the embodiment to allow parents to receive optimal childcare support tailored to their child's developmental stage. For example, parents can access the childcare support content anytime, anywhere using a smartphone or a tablet.

[0075] The generation unit can use the developmental database to generate parenting advice videos, electronic picture books, and expert-supervised content. The developmental database includes, for example, information on a child's developmental stage, expert parenting advice, and past parenting support content, but is not limited to these examples. The generation unit can use the developmental database to generate parenting advice videos. For example, the generation unit can use a text generation AI to generate scripts for the parenting advice videos. The generation unit can also use the developmental database to generate electronic picture books. For example, the generation unit can use a multimodal generation AI to generate content for the electronic picture book. The generation unit can also use the developmental database to generate expert-supervised content. For example, the generation unit can use a text generation AI to generate scripts for the expert-supervised content. In this way, by using the developmental database, optimal parenting support content tailored to a child's developmental stage can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input information obtained from the developmental database into the generation AI and cause the generation AI to generate parenting support content.

[0076] The analysis unit can analyze the viewing history to identify the child's interests. The viewing history includes, but is not limited to, the title of the content viewed, the viewing time, and the viewing frequency. The analysis unit can analyze the viewing history to identify the child's interests. For example, the analysis unit can determine whether the child is interested in a particular character based on the viewing frequency and viewing time. The analysis unit can also identify changes in the child's interests based on the viewing history. For example, the analysis unit can analyze changes in the viewing history to determine whether the child has developed a new interest. The analysis unit can also identify trends in the child's interests based on the viewing history. For example, the analysis unit can analyze trends in the viewing history to determine whether the child is interested in a particular genre. By analyzing the viewing history, the child's interests can be identified and more appropriate content can be provided. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input viewing history data into AI and have the AI ​​identify the child's interests.

[0077] The recommendation unit can provide personalized content based on the child's interests. Recommended content includes, but is not limited to, individually customized videos, articles, and advice. The recommendation unit can provide personalized content based on the child's interests. For example, if a child is interested in a particular character, the recommendation unit can recommend content featuring that character. The recommendation unit can also provide personalized content based on changes in the child's interests. For example, if a child develops a new interest, the recommendation unit can recommend content related to that interest. The recommendation unit can also provide personalized content based on trends in the child's interests. For example, if a child is interested in a particular genre, the recommendation unit can recommend content related to that genre. This can support the child's development by providing personalized content based on the child's interests. Some or all of the above-described processing in the recommendation unit can be performed using, for example, AI, or without AI. For example, the recommendation unit can input the child's interest data identified by the analysis unit into the AI ​​and have the AI ​​perform personalized content recommendations.

[0078] The access unit can access the childcare support content using a smartphone or tablet. The access unit can access the childcare support content using, for example, a smartphone or tablet. For example, the access unit can access the childcare support content through a web browser or a mobile app. The access unit can also access the childcare support content using a device other than a smartphone or tablet. For example, the access unit can access the childcare support content using a smart TV or a PC. This allows parents to easily use the content at any time by accessing the childcare support content using a smartphone or tablet. Some or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without AI. For example, the access unit can input the parent's device information into AI and have the AI ​​select the optimal access method.

[0079] The input unit can estimate the parent's emotions and adjust the timing of profile entry based on the estimated emotions. For example, if the parent is feeling stressed, the input unit can send a notification prompting the parent to enter their profile during a time when they can relax. Furthermore, if the parent is busy, the input unit can provide a simplified profile entry form that can be completed in a short amount of time. For example, if the parent is feeling relaxed, the input unit can send a notification prompting the parent to enter detailed profile information. This allows the parent to enter their profile without feeling stressed by adjusting the timing of profile entry based on the parent's emotions. 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 input unit can be performed using, for example, an AI, or without an AI. For example, the input unit can input the parent's emotion data into an AI and have the AI ​​adjust the timing of profile entry.

[0080] The input unit can analyze past profile input history and select the optimal input method. For example, if the parent has preferred text input in the past, the input unit can preferentially suggest text input. Also, if the parent has used voice input in the past, the input unit can preferentially suggest voice input. For example, if the parent has used image input in the past, the input unit can preferentially suggest image input. In this way, by analyzing the past profile input history, the optimal input method for the parent can be provided. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past profile input history data into AI and have the AI ​​select the optimal input method.

[0081] When entering a profile, the input unit can filter based on the parent's current living situation and areas of interest. For example, if the parent frequently searches for information about childcare, the input unit can prioritize displaying input items related to childcare. Also, if the parent frequently searches for information about work, the input unit can prioritize displaying input items related to balancing work and childcare. For example, if the parent frequently searches for information about health, the input unit can prioritize displaying input items related to health management. This allows for more appropriate profile entry by filtering based on the parent's living situation and areas of interest. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the parent's search history data into AI and have the AI ​​perform the filtering.

[0082] When entering a profile, the input unit can select the optimal input means depending on the parent's input method. For example, if the parent selects voice input, the input unit converts the input content into text using voice recognition technology. Furthermore, if the parent selects text input, the input unit can also provide an interface that supports keyboard input. For example, if the parent selects image input, the input unit analyzes the input content using image recognition technology. This allows for smooth profile entry by selecting the optimal input means depending on the parent's input method. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the parent's input method data into AI and have the AI ​​select the optimal input means.

[0083] The input unit can estimate the parent's emotions and determine the priority of the profile information to be input based on the estimated emotions. For example, if the parent is stressed, the input unit can prioritize input of only important information. Furthermore, if the parent is relaxed, the input unit can also prompt the parent to input detailed information. For example, if the parent is in a hurry, the input unit prompts the parent to input minimal information. This allows the parent to input important information without feeling stressed by determining the priority of the profile information based on the parent's emotions. 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 input unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the input unit can input the parent's emotion data into an AI and have the AI ​​determine the priority of the profile information.

[0084] When entering a profile, the input unit can prioritize inputting highly relevant information by taking into account the parent's geographical location information. For example, if the parent lives in an urban area, the input unit can prioritize inputting childcare information related to the urban area. Furthermore, if the parent lives in a suburban area, the input unit can also prioritize inputting childcare information related to the suburban area. For example, if the parent lives overseas, the input unit can prioritize inputting local childcare information. This allows more relevant information to be entered by taking the parent's geographical location information into consideration. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the parent's geographical location information data into AI and cause the AI ​​to execute the prioritized input of highly relevant information.

[0085] The input unit can analyze the parent's social media activity and input relevant information when entering a profile. For example, the input unit automatically inputs childcare information shared by the parent on social media. The input unit can also input information about childcare experts that the parent follows on social media. For example, the input unit inputs information about childcare groups that the parent participates in on social media. This allows more relevant information to be input by analyzing the parent's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the parent's social media activity data into AI and cause the AI ​​to input relevant information.

[0086] When entering a profile, the input unit can customize the input method by reflecting the parent's past feedback. For example, if the parent has provided feedback on the input method in the past, the input unit can improve the input method by reflecting that feedback. Furthermore, if the parent has been dissatisfied with an input method in the past, the input unit can also provide an input method to resolve that dissatisfaction. For example, if the parent was satisfied with an input method in the past, the input unit can continue to provide that input method. In this way, by reflecting the parent's past feedback, an input method that is easier to use can be provided. Some or all of the above-described processing in the input unit may be performed, for example, using AI, or may be performed without using AI. For example, the input unit can input the parent's feedback data into AI and have the AI ​​customize the input method.

[0087] The generation unit can estimate the parent's emotions and adjust the expression method of the generated content based on the estimated emotions. For example, if the parent is relaxed, the generation unit generates content in a calm tone. Furthermore, if the parent is stressed, the generation unit can generate concise and easy-to-understand content. For example, if the parent is excited, the generation unit generates visually stimulating content. This allows the optimal content for the parent to be provided by adjusting the expression method of the generated content based on the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the parent's emotion data into an AI and cause the AI ​​to adjust the expression method of the generated content.

[0088] When generating content, the generation unit can adjust the level of detail of the content based on the child's developmental stage. For example, when the child is in an early developmental stage, the generation unit generates content that mainly includes basic information. Furthermore, when the child is in an intermediate developmental stage, the generation unit can also generate content that includes detailed information. For example, when the child is in a later developmental stage, the generation unit generates content that includes specialized information. In this way, by adjusting the level of detail of the content based on the child's developmental stage, it is possible to provide optimal content for the child. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input child developmental stage data into the generation AI and cause the generation AI to adjust the level of detail of the content generation.

[0089] When generating content, the generation unit can apply different generation algorithms depending on the child's preferences. For example, if a child is interested in animals, the generation unit can apply an algorithm that generates animal-related content. Also, if a child is interested in vehicles, the generation unit can apply an algorithm that generates vehicle-related content. For example, if a child is interested in music, the generation unit can apply an algorithm that generates music-related content. In this way, by applying different generation algorithms depending on the child's preferences, content that is interesting to children can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input child preference data into the generation AI and cause the generation AI to apply different generation algorithms.

[0090] When generating content, the generation unit can improve the accuracy of generation by referring to the parent's past content usage history. For example, the generation unit analyzes the trends of content viewed by the parent in the past and generates similar content. The generation unit can also generate similar content by referring to content that the parent has previously given a high rating. For example, the generation unit analyzes the viewing time of content viewed by the parent in the past and generates content with the optimal viewing time. By referring to the parent's past content usage history, more accurate content can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the parent's past content usage history data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0091] The generation unit can estimate the parent's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the parent is in a hurry, the generation unit generates short content. The generation unit can also generate longer content if the parent is relaxed. For example, if the parent is excited, the generation unit generates short, visually stimulating content. This allows the content optimal for the parent to be provided by adjusting the length of the content based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the parent's emotion data into an AI and have the AI ​​adjust the length of the content.

[0092] When generating content, the generation unit can determine generation priorities based on the child's developmental stage. For example, if the child is in an early developmental stage, the generation unit can prioritize generating basic information. Furthermore, if the child is in a middle developmental stage, the generation unit can prioritize generating detailed information. For example, if the child is in a later developmental stage, the generation unit can prioritize generating specialized information. By determining generation priorities based on the child's developmental stage, optimal content for the child can be provided. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input child developmental stage data into the generation AI and have the generation AI determine the generation priorities.

[0093] When generating content, the generation unit can adjust the use of technical terminology in the generation according to the parent's level of expertise. For example, if the parent has technical expertise, the generation unit generates content that uses a lot of technical terminology. Furthermore, if the parent does not have technical expertise, the generation unit can generate content that explains things in simple terms. For example, if the parent has medium level of expertise, the generation unit generates content that uses a moderate amount of technical terminology. This allows content that is easy for the parent to understand to be provided by adjusting the use of technical terminology according to the parent's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the parent's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0094] The analysis unit can estimate the parent's emotions and adjust the analysis criteria for the viewing history based on the estimated emotions. For example, if the parent is relaxed, the analysis unit analyzes the detailed viewing history. Furthermore, if the parent is stressed, the analysis unit can analyze the brief viewing history. For example, if the parent is excited, the analysis unit analyzes the visually stimulating viewing history. By adjusting the analysis criteria for the viewing history based on the parent's emotions, more appropriate analysis results can be obtained. 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the parent's emotion data into an AI and have the AI ​​adjust the analysis criteria for the viewing history.

[0095] The analysis unit can improve the accuracy of the analysis by taking into account changes in a child's interests when analyzing the viewing history. For example, if a child begins to become interested in a particular character, the analysis unit can prioritize analyzing viewing history related to that character. Furthermore, if a child begins to develop a new interest, the analysis unit can also prioritize analyzing viewing history related to that interest. For example, if a child's interest in a content that they previously had wanes, the analysis unit can analyze the viewing history taking into account such changes. This allows for more accurate analysis results by taking into account changes in a child's interests. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on changes in a child's interests into AI and have the AI ​​improve the accuracy of the analysis.

[0096] When analyzing the viewing history, the analysis unit can apply an analysis method based on the child's developmental stage. For example, if the child is in an early developmental stage, the analysis unit analyzes a basic viewing history. Furthermore, if the child is in a middle developmental stage, the analysis unit can also analyze a detailed viewing history. For example, if the child is in a late developmental stage, the analysis unit analyzes a specialized viewing history. By applying an analysis method based on the child's developmental stage, more appropriate analysis results can be obtained. 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 child's developmental stage data into AI and have the AI ​​apply the analysis method.

[0097] The analysis unit can improve the analysis method by reflecting parental feedback when analyzing the viewing history. For example, if a parent provides feedback on the analysis results of the viewing history, the analysis unit can improve the analysis method by reflecting that feedback. Furthermore, if a parent is dissatisfied with the analysis results of the viewing history, the analysis unit can provide an analysis method to resolve that dissatisfaction. For example, if the parent is satisfied with the analysis results of the viewing history, the analysis unit can continue to use that analysis method. In this way, by reflecting the parent's feedback, a more appropriate analysis method can be provided. 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 parental feedback data into AI and have the AI ​​improve the analysis method.

[0098] The analysis unit can estimate the parent's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the parent is relaxed, the analysis unit displays detailed analysis results. Furthermore, if the parent is stressed, the analysis unit can display concise analysis results. For example, if the parent is excited, the analysis unit displays visually stimulating analysis results. By adjusting the display method of the analysis results based on the parent's emotions, analysis results that are easy for the parent to understand 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 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 parent's emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0099] The analysis unit can take into account the geographical distribution of children when analyzing viewing histories. For example, if a child lives in an urban area, the analysis unit can prioritize analyzing viewing histories related to urban areas. Also, if a child lives in a suburban area, the analysis unit can prioritize analyzing viewing histories related to suburban areas. For example, if a child lives overseas, the analysis unit can prioritize analyzing viewing histories from that area. This allows for more appropriate analysis results to be obtained by taking the geographical distribution of children into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the geographical distribution of children into AI and have the AI ​​perform the analysis.

[0100] When analyzing the viewing history, the analysis unit can improve the accuracy of the analysis by referring to related literature. The analysis unit, for example, performs the analysis by referring to the latest research literature related to the viewing history. The analysis unit can also perform the analysis by referring to past research literature related to the viewing history. For example, the analysis unit performs the analysis by referring to the opinions of experts related to the viewing history. By referring to related literature, 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 related literature data into AI and have the AI ​​improve the accuracy of the analysis.

[0101] The analysis unit can take into account the market value of the child when analyzing the viewing history. For example, if the child is likely to have future market value, the analysis unit analyzes the viewing history taking that market value into account. Furthermore, if the child is likely to have market value in a particular field, the analysis unit can prioritize analysis of viewing history related to that field. For example, if the child is unlikely to have market value, the analysis unit analyzes the viewing history without taking that market value into account. This allows for more appropriate analysis results to be obtained by taking the child's market value into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's market value data into AI and have the AI ​​perform the analysis.

[0102] The recommendation unit can estimate the parent's emotions and adjust the recommendation method based on the estimated emotions. For example, if the parent is relaxed, the recommendation unit can provide detailed recommendations. Furthermore, if the parent is stressed, the recommendation unit can provide concise recommendations. For example, if the parent is excited, the recommendation unit can provide visually stimulating recommendations. This allows the recommendation method to be adjusted based on the parent's emotions, thereby providing optimal content for the parent. 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 recommendation unit can be performed using an AI, for example, or without an AI. For example, the recommendation unit can input the parent's emotion data into an AI and have the AI ​​adjust the recommendation method.

[0103] The recommendation unit can improve the accuracy of recommendations by taking into account changes in a child's interests when making recommendations. For example, if a child begins to take an interest in a particular character, the recommendation unit can recommend content related to that character. Furthermore, if a child begins to develop a new interest, the recommendation unit can also recommend content related to that interest. For example, if a child's interest in a subject that he or she previously had wanes, the recommendation unit can recommend content taking into account such changes. This allows for more accurate recommendations to be provided by taking into account changes in a child's interests. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on changes in a child's interests into AI and have the AI ​​improve the accuracy of recommendations.

[0104] The recommendation unit can apply a recommendation method based on the child's developmental stage when making a recommendation. For example, if the child is in an early developmental stage, the recommendation unit can recommend basic content. The recommendation unit can also recommend detailed content if the child is in an intermediate developmental stage. For example, the recommendation unit can recommend specialized content if the child is in a later developmental stage. This makes it possible to provide more appropriate content by applying a recommendation method based on the child's developmental stage. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI, for example. For example, the recommendation unit can input child developmental stage data into AI and have the AI ​​apply the recommendation method.

[0105] The recommendation unit can improve the recommendation method by reflecting parental feedback when making a recommendation. For example, if the parent provides feedback on the recommended content, the recommendation unit improves the recommendation method by reflecting that feedback. Furthermore, if the parent is dissatisfied with the recommended content, the recommendation unit can also provide a recommendation method to resolve that dissatisfaction. For example, if the parent is satisfied with the recommended content, the recommendation unit continues to use that recommendation method. In this way, by reflecting the parent's feedback, a more appropriate recommendation method can be provided. Some or all of the above-described processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input parental feedback data into AI and cause the AI ​​to improve the recommendation method.

[0106] The recommendation unit can estimate the parent's emotions and determine the priority of recommendations based on the estimated emotions. For example, if the parent is relaxed, the recommendation unit can prioritize detailed content. Furthermore, if the parent is stressed, the recommendation unit can prioritize concise content. For example, if the parent is excited, the recommendation unit can prioritize visually stimulating content. By determining the priority of recommendations based on the parent's emotions, the system can provide optimal content for the parent. 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 recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the parent's emotion data into an AI and have the AI ​​determine the priority of recommendations.

[0107] The recommendation unit can make recommendations taking into account the geographical distribution of children. For example, if the child lives in an urban area, the recommendation unit can recommend content related to urban areas. Furthermore, if the child lives in a suburban area, the recommendation unit can also recommend content related to suburban areas. For example, if the child lives overseas, the recommendation unit can recommend local content. This allows more appropriate content to be provided by taking the geographical distribution of children into consideration. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI, for example. For example, the recommendation unit can input data on the geographical distribution of children into AI and have the AI ​​execute the recommendations.

[0108] The recommendation unit can improve the accuracy of recommendations by referring to related literature when making recommendations. The recommendation unit, for example, makes recommendations by referring to the latest research literature related to the content to be recommended. The recommendation unit can also make recommendations by referring to past research literature related to the content to be recommended. For example, the recommendation unit makes recommendations by referring to the opinions of experts related to the content to be recommended. By referring to related literature, more accurate recommendations can be provided. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input related literature data into AI and have the AI ​​improve the accuracy of recommendations.

[0109] The recommendation unit can make recommendations taking into account the market value of the child. For example, if the child is likely to have future market value, the recommendation unit recommends content taking that market value into account. Furthermore, if the child is likely to have market value in a particular field, the recommendation unit can also recommend content related to that field. For example, if the child is unlikely to have market value, the recommendation unit recommends content without taking that market value into account. This makes it possible to provide more appropriate content by taking the child's market value into account. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the child's market value data into AI and have the AI ​​execute the recommendations.

[0110] The access unit can estimate the parent's emotions and adjust the access method based on the estimated emotions. For example, if the parent is relaxed, the access unit provides a detailed access method. Furthermore, if the parent is stressed, the access unit can provide a concise access method. For example, if the parent is excited, the access unit provides a visually stimulating access method. By adjusting the access method based on the parent's emotions, the optimal access method for the parent 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 may 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 access unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the access unit can input the parent's emotion data into an AI and have the AI ​​adjust the access method.

[0111] When accessing, the access unit can select the optimal access method by referring to the parent's past access history. For example, the access unit can prioritize providing access methods that the parent has used in the past. The access unit can also suggest the optimal access method based on content that the parent has accessed in the past. For example, the access unit can provide the optimal access method by taking into account the time period during which the parent accessed the content in the past. In this way, by referring to the parent's past access history, a more appropriate access method can be provided. Some or all of the above-mentioned processing in the access unit may be performed using, for example, AI, or may be performed without using AI. For example, the access unit can input the parent's past access history data into AI and have the AI ​​select the optimal access method.

[0112] The access unit can improve the access method by reflecting parental feedback at the time of access. For example, if the parent provides feedback on the access method, the access unit can improve the access method by reflecting that feedback. Furthermore, if the parent is dissatisfied with the access method, the access unit can provide an access method to resolve that dissatisfaction. For example, if the parent is satisfied with the access method, the access unit can continue to provide that access method. In this way, a more appropriate access method can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input parental feedback data into AI and have the AI ​​improve the access method.

[0113] The access unit can estimate the parent's emotions and determine access priorities based on the estimated emotions. For example, if the parent is relaxed, the access unit can prioritize access to detailed content. Furthermore, if the parent is stressed, the access unit can prioritize access to concise content. For example, if the parent is excited, the access unit can prioritize access to visually stimulating content. By determining access priorities based on the parent's emotions, the parent can be provided with access to content that is optimal for the parent. 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 access unit can be performed using, for example, an AI, or without an AI. For example, the access unit can input the parent's emotion data into an AI and have the AI ​​determine the access priorities.

[0114] At the time of access, the access unit can select the optimal access method by taking into account the parent's geographical location information. For example, if the parent lives in an urban area, the access unit can provide an access method related to the urban area. Furthermore, if the parent lives in a suburban area, the access unit can also provide an access method related to the suburban area. For example, if the parent lives overseas, the access unit can provide a local access method. This allows for a more appropriate access method to be provided by taking into account the parent's geographical location information. Some or all of the above-described processing in the access unit may be performed using, or without, AI. For example, the access unit can input the parent's geographical location information data into AI and have the AI ​​select the optimal access method.

[0115] At the time of access, the access unit can analyze the parent's social media activity and suggest an access method. For example, the access unit can suggest an access method based on content shared by the parent on social media. The access unit can also suggest an access method based on information about experts the parent follows on social media. For example, the access unit can suggest an access method based on information about groups the parent joins on social media. This makes it possible to provide more appropriate access methods by analyzing the parent's social media activity. Some or all of the above-described processing in the access unit may be performed using, or without, AI, for example. For example, the access unit can input the parent's social media activity data into AI and have the AI ​​execute the access method suggestion.

[0116] The access unit can customize the access method by reflecting the parent's past feedback at the time of access. For example, if the parent has provided feedback on the access method in the past, the access unit can improve the access method by reflecting that feedback. Furthermore, if the parent has been dissatisfied with the access method in the past, the access unit can provide an access method that resolves that dissatisfaction. For example, if the parent was satisfied with the access method in the past, the access unit can continue to provide that access method. In this way, by reflecting the parent's past feedback, an easier-to-use access method can be provided. Some or all of the above-described processing in the access unit may be performed, for example, using AI, or may be performed without using AI. For example, the access unit can input parental feedback data into AI and have the AI ​​customize the access method. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, generation unit, analysis unit, recommendation unit, and access unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit allows a parent to input their child's profile using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates child-rearing support content using a generation AI. The analysis unit analyzes the viewing history using the specific processing unit 290 of the data processing device 12 and identifies the child's interests. The recommendation unit recommends content personalized by the specific processing unit 290 of the data processing device 12. The access unit can access the child-rearing support content using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, generation unit, analysis unit, recommendation unit, and access unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit allows a parent to input a child's profile using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates child-rearing support content using a generation AI. The analysis unit analyzes the viewing history using the specific processing unit 290 of the data processing device 12 and identifies the child's interests. The recommendation unit recommends content personalized by the specific processing unit 290 of the data processing device 12. The access unit allows access to the child-rearing support content using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, generation unit, analysis unit, recommendation unit, and access unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit allows a parent to input a child's profile using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates child-rearing support content using a generation AI. The analysis unit analyzes the viewing history using the specific processing unit 290 of the data processing device 12 to identify the child's interests. The recommendation unit recommends content personalized by the specific processing unit 290 of the data processing device 12. The access unit allows access to the child-rearing support content using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, generation unit, analysis unit, recommendation unit, and access unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit allows a parent to input a child's profile using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates child-rearing support content using a generation AI. The analysis unit analyzes the viewing history using the specific processing unit 290 of the data processing device 12 and identifies the child's interests. The recommendation unit recommends content personalized by the specific processing unit 290 of the data processing device 12. The access unit can access the child-rearing support content using the speaker 240 of the robot 414.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] The childcare support system may further include a notification unit. The notification unit enables parents to receive important childcare information in a timely manner according to their child's developmental stage. For example, the notification unit may send reminders for vaccination schedules and health checkups based on the child's developmental stage. The notification unit may also send relevant childcare advice or congratulatory messages based on specific events set by the parent (e.g., the child's birthday or growth milestones). Furthermore, the notification unit may regularly provide updated childcare information so that parents can receive new research and expert advice on childcare. This allows parents to receive important information according to their child's developmental stage without missing anything, thereby improving the quality of childcare.

[0119] The generator can further customize the content based on the child's learning style. For example, if the child is a visual learner, the generator can generate visually rich content (e.g., illustrations and videos). If the child is an auditory learner, the generator can generate content that makes extensive use of audio and music. Furthermore, if the child is a tactile learner, the generator can generate content that includes interactive elements. This allows the provision of optimal content according to the child's learning style, thereby improving the child's learning effectiveness.

[0120] The analysis unit can further analyze the parenting style of the parent and evaluate the degree of match between the child's interests and the parent's parenting style. For example, if the parent has a strict parenting style, the analysis unit can evaluate the degree to which the child's interests match that parenting style. Alternatively, if the parent has a liberal parenting style, the analysis unit can evaluate the degree to which the child's interests match that parenting style. Furthermore, the analysis unit can suggest adjustments to the parenting style based on the degree of match between the parent's parenting style and the child's interests. This allows parents to adopt an optimal parenting style that suits their child's interests, thereby more effectively supporting their child's development.

[0121] The recommendation unit can further provide personalized content based on the parent's child-rearing goals. For example, if a parent places importance on their child's language development, the recommendation unit can recommend content related to language development (e.g., picture books and language games to increase vocabulary). If a parent places importance on their child's social development, the recommendation unit can also recommend content related to social development (e.g., stories to teach cooperation and empathy). If a parent places importance on their child's motor skill development, the recommendation unit can also recommend content related to motor skill development (e.g., physical games and exercise programs). This makes it possible to provide optimal content according to the parent's child-rearing goals, thereby effectively supporting the child's growth.

[0122] The access unit can further suggest the optimal access method based on the parent's device usage habits. For example, if the parent mainly uses a smartphone, the access unit can provide an interface optimized for the smartphone. If the parent mainly uses a tablet, the access unit can also provide an interface optimized for the tablet. If the parent mainly uses a PC, the access unit can also provide an interface optimized for the PC. This makes it possible to provide the optimal access method according to the parent's device usage habits, making it easier to use childcare support content.

[0123] The input unit can estimate the parent's emotions and customize the profile entry interface based on the estimated emotions. For example, if the parent is feeling stressed, the input unit can provide a simple and intuitive interface. If the parent is feeling relaxed, the input unit can provide an interface that allows detailed information to be entered. Furthermore, if the parent is excited, the input unit can provide a visually appealing interface. This makes it possible to provide an optimal interface according to the parent's emotions, making profile entry smoother.

[0124] The input unit can analyze the parent's past profile input history and provide an input assistance function based on the parent's input patterns. For example, if the parent has frequently input a specific input item in the past, the input unit can provide a function to automatically complete that item. Also, if the parent has frequently made input errors in the past, the input unit can display guidelines to prevent input errors. Furthermore, if the parent has taken a long time to input data in the past, the input unit can provide a simple input option to shorten the input time. This makes it possible to make the parent's input work more efficient by utilizing the parent's past profile input history.

[0125] When entering a profile, the input unit can suggest the timing of entry based on the parent's current lifestyle. For example, if the parent has a nocturnal lifestyle, the input unit can send a notification prompting the parent to enter their profile at night. Alternatively, if the parent has a morning lifestyle, the input unit can send a notification prompting the parent to enter their profile in the morning. Furthermore, if the parent has an irregular lifestyle, the input unit can detect the time period when the parent is relaxed and send a notification prompting the parent to enter their profile during that time period. This makes it possible to provide the optimal entry timing according to the parent's lifestyle, making profile entry smoother.

[0126] The input unit can provide an input assistance function according to the parent's input speed when entering a profile. For example, if the parent is typing quickly, the input unit can provide a function to automatically complete the input content in real time. If the parent is typing slowly, the input unit can also display guidelines to help the parent proceed while checking the input content. Furthermore, if the parent is typing at a medium speed, the input unit can provide a function to appropriately save the input content and allow the parent to resume input even if it is interrupted midway. This makes it possible to provide an optimal input assistance function according to the parent's input speed, making profile entry smoother.

[0127] The input unit can estimate the parent's emotions and adjust the format of the profile information to be entered based on the estimated emotions. For example, if the parent is stressed, a simple checkbox-style input is provided. If the parent is relaxed, a detailed text input format can be provided. Furthermore, if the parent is excited, a visually appealing drag-and-drop input format can be provided. This makes it possible to provide the optimal input format according to the parent's emotions, making profile entry smoother.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The parent inputs the child's profile in the input unit. The profile includes the child's name, age, gender, interests, etc. The input unit can input the profile using methods such as text input, voice input, and image input. Step 2: The generation unit uses generation AI to generate childcare support content from the development database based on the information input by the input unit. The generated content includes childcare advice videos, digital picture books, and expert-supervised content. The generation AI generates content using text generation AI and multimodal generation AI. Step 3: The analysis unit analyzes the viewing history of the content generated by the generation unit. The viewing history includes the title of the content viewed, the viewing time, the viewing frequency, etc. The analysis unit analyzes the viewing history and identifies the child's interests. Step 4: The recommendation unit recommends personalized content based on the analysis results obtained by the analysis unit. Recommended content may include individually customized videos, articles, advice, etc. Step 5: The access unit accesses the childcare support content online. The access unit can access the childcare support content using a smartphone or tablet. The childcare support content can be accessed through a web browser or mobile app.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 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. an input section where parents input their child's profile; a generation unit that generates child-rearing support content from a development database based on the information input by the input unit; an analysis unit that analyzes the viewing history of the content generated by the generation unit; a recommendation unit that recommends personalized content based on the analysis result obtained by the analysis unit; and an access unit for accessing online. A system characterized by:

2. The generation unit Use developmental databases to generate parenting advice videos, e-books, and expert-supervised content 2. The system of claim 1.

3. The analysis unit Analyze viewing history to identify children's interests 2. The system of claim 1.

4. The recommendation unit Providing personalized content based on a child's interests 2. The system of claim 1.

5. The access unit is Access parenting support content using your smartphone or tablet 2. The system of claim 1.

6. The input unit Estimate parental emotions and adjust profile entry timing based on the estimated emotions 2. The system of claim 1.

7. The input unit Analyze your past profile entry history and select the best entry method 2. The system of claim 1.

8. The input unit Filter profiles based on parents' current living situation and interests 2. The system of claim 1.

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