System

Abstract

An object of a system according to an embodiment is to eliminate anxiety and worries about childcare and provide an individual child care document.SOLUTION: A system includes a reception unit, a generation unit, a provision unit, an accumulation unit, and a creation unit. The reception unit receives a question from a user. The generation unit generates an answer based on the question received by the reception unit. The providing unit provides the answer generated by the generating unit. The storage unit stores data relating to growth and development of the child of the user. The creation unit creates an individual child care document based on the data accumulated by the accumulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not provided sufficient support to alleviate concerns and worries about child-rearing, and it has been difficult to provide individual child-rearing books.

[0005] The system according to the embodiment aims to alleviate concerns and worries about child-rearing and to provide individualized child-rearing books. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a storage unit, and a creation unit. The reception unit receives questions from a user. The generation unit generates answers based on the questions received by the reception unit. The provision unit provides the answers generated by the generation unit. The storage unit stores data related to the growth and development of the user's child. The creation unit creates an individual parenting manual based on the data stored by the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate concerns and worries about child-rearing and provide individualized child-rearing books. [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 child-rearing support system according to an embodiment of the present invention accepts questions from users and generates and provides answers using a generation AI. The child-rearing support system accumulates data on the growth and development of the user's child and creates a personalized child-rearing manual based on that data. For example, when a user inputs a question through a smart speaker or app, the generation AI answers the question like a knowledgeable expert. The child-rearing support system then accumulates data on the growth and development of the user's child and creates a personalized child-rearing manual based on that data. This allows parents to obtain information specific to their child. The child-rearing support system also serves as a 24-hour mom / dad friend that users can easily talk to. For example, when parents are in trouble, such as when their child won't stop crying in the middle of the night, the generation AI provides voice support. This reduces the anxiety and worries of parents and provides an environment where parents can raise their children with greater peace of mind. This allows the child-rearing support system to provide appropriate answers to users' questions, accumulate data on children's growth and development, and create a personalized child-rearing manual, thereby supporting the anxiety and worries of parents. For example, when a user inputs a question through a smart speaker or app, the generation AI answers the question like a knowledgeable expert. Next, the child-rearing support system accumulates data on the growth and development of the user's child and creates an individualized child-rearing manual based on that data. This allows parents to obtain information specific to their child. The child-rearing support system also acts as a 24-hour mom / dad friend that users can easily talk to. For example, when a parent is in trouble, such as when their child won't stop crying in the middle of the night, the generating AI will provide voice support. In this way, the child-rearing support system reduces the anxiety and worries of the parenting generation and provides an environment in which parents can raise their children with greater peace of mind.

[0029] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a provision unit, a storage unit, and a creation unit. The reception unit receives questions from a user. Questions from a user may be in, for example, text format, audio format, or image format, but are not limited to these examples. The reception unit receives questions from a user, for example, through a smart speaker or an app. The reception unit can also estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of receiving questions can be delayed. The generation unit generates answers based on the questions received by the reception unit using a generation AI. The generation AI analyzes the questions using, for example, natural language processing technology and generates appropriate answers. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can apply an optimal generation algorithm depending on the content of the question. For example, a generation algorithm specialized for child-rearing is applied to questions about child-rearing. The provision unit provides the answers generated by the generation unit to the user. For example, the provision unit provides the generated answers to the user in audio or text format. The providing unit can also estimate the user's emotions and adjust the method of providing answers based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide answers in a calm voice tone. The storage unit stores data related to the growth and development of the user's child. For example, the storage unit stores data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also estimate the user's emotions and adjust the method of storing data based on the estimated user's emotions. For example, if the user is feeling stressed, the storage of data can be temporarily stopped. The creating unit creates an individual parenting book based on the data stored by the storage unit. For example, the creating unit analyzes growth and development patterns based on the stored data and creates an individual parenting book. The creating unit can also estimate the user's emotions and adjust the method of creating the parenting book based on the estimated user's emotions. For example, if the user is feeling stressed, the creating unit creates a concise and easy-to-understand parenting book.As a result, the child-rearing support system of the embodiment can provide appropriate answers to users' questions, accumulate data on children's growth and development, and create individual child-rearing books, thereby supporting the anxieties and worries of the parenting generation.

[0030] The reception unit can accept questions from a user through a smart speaker or an app. Examples of smart speakers include, but are not limited to, Amazon Echo (registered trademark) and Google Home (registered trademark). Examples of apps include, but are not limited to, iOS apps and Android apps. The reception unit can accept questions from a user in voice format through a smart speaker. The reception unit can also accept questions from a user in text format through an app. For example, if a user speaks to a smart speaker, "Tell me about my child's development," the reception unit accepts the question. Alternatively, if a user inputs into an app, "Tell me about my child's growth record," the reception unit accepts the question. In this way, by accepting questions from a user through a smart speaker or an app, the user can easily input questions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice data acquired from a smart speaker into a generation AI and have the generation AI convert the voice data into text data.

[0031] The generation unit can analyze the question using natural language processing and generate an appropriate answer. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the generation unit can analyze the words of the question using morphological analysis and generate an appropriate answer. The generation unit can also analyze the structure of the question sentence using grammatical analysis and generate an appropriate answer. Furthermore, the generation unit can analyze the meaning of the question using semantic analysis and generate an appropriate answer. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of the words. Grammatical analysis is used to analyze the structure of the question sentence and understand the meaning of the sentence. Semantic analysis is used to analyze the meaning of the entire question sentence and generate an appropriate answer. In this way, natural language processing improves the accuracy of question analysis and enables the generation of an appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input text data of a question into the generation AI and cause the generation AI to generate an appropriate answer.

[0032] The providing unit can provide the generated answer to the user in the form of voice or text. Voice includes, but is not limited to, voice files and real-time voice. Text includes, but is not limited to, chat formats and email formats. The providing unit can provide, for example, the generated answer to the user as an voice file. The providing unit can also provide the generated answer to the user as real-time voice. The providing unit can also provide the generated answer to the user in chat format. For example, the providing unit can save the generated answer as an audio file so that the user can play it. The real-time voice is used to provide the generated answer as voice in real time. The chat format is used to display the generated answer as text in a chat app. This makes it easier for the user to receive the answer by providing the answer in voice or text. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input text data of the generated answer to the generation AI and have the generation AI convert it into voice data.

[0033] The storage unit can store data related to the growth and development of the user's child in the cloud. Examples of clouds include, but are not limited to, AWS (registered trademark), Google (registered trademark), Microsoft Azure (registered trademark), etc. The storage unit stores data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also store the user's child's growth record in the cloud. The storage unit can also store the user's child's health data in the cloud. For example, the storage unit can store the user's child's height data in the cloud for later reference. The weight data is used to record the user's child's weight change. The developmental stage data is used to record the user's child's developmental progress. Storing data in the cloud improves data security and accessibility. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's child's growth data into a generation AI and have the generation AI analyze and store the data.

[0034] The creation unit can analyze growth and development patterns based on the accumulated data and create an individualized parenting book. Growth and development patterns include, but are not limited to, temporal changes and developmental stages. For example, the creation unit can analyze the growth patterns of the user's child based on the accumulated data and create an individualized parenting book. The creation unit can also analyze the developmental patterns of the user's child based on the accumulated data and create an individualized parenting book. Furthermore, the creation unit can analyze the health patterns of the user's child based on the accumulated data and create an individualized parenting book. For example, the creation unit can analyze the growth data of the user's child and create a parenting book showing the progress of growth. The developmental patterns are used to indicate the developmental stages of the user's child. The health patterns are used to indicate the health status of the user's child. In this way, an individualized parenting book can be created by analyzing the growth and development patterns. Some or all of the above-described processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the accumulated data into a generation AI and cause the generation AI to create an individualized parenting book.

[0035] The providing unit can provide the created individual parenting book to the user. The individual parenting book can be in, for example, a text format, an image format, or a video format, but is not limited to these examples. For example, the providing unit can provide the created individual parenting book to the user in a text format. The providing unit can also provide the created individual parenting book to the user in an image format. Furthermore, the providing unit can also provide the created individual parenting book to the user in a video format. For example, the providing unit can save the created individual parenting book as a text file and make it available for download by the user. The image format is used to visually present the contents of the parenting book. The video format is used to present the contents of the parenting book in a video format. By providing the individual parenting book, the user can obtain information specific to their child. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data of the created parenting book to a generation AI and have the generation AI convert the data into text, image, or video format.

[0036] The providing unit can provide a 24-hour best friend with whom the user can easily talk. 24-hour service includes, but is not limited to, a shift system, an AI chatbot, and the like. The providing unit can provide a 24-hour best friend with whom the user can easily talk, for example, using an AI chatbot. The providing unit can also provide a 24-hour best friend with whom the user can easily talk, using a shift system. For example, the providing unit can use an AI chatbot to enable the user to ask questions or seek advice at any time. A shift system is used to achieve 24-hour service by having multiple operators take turns responding. This provides the user with a 24-hour best friend with whom the user can easily talk, thereby alleviating the user's anxiety and worries. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's questions or inquiries into a generation AI and cause the generation AI to generate appropriate answers and advice.

[0037] The providing unit can provide support via voice when the user is in trouble. Voice support includes, but is not limited to, real-time voice, recorded voice, and the like. The providing unit, for example, provides support to the user via real-time voice. The providing unit can also provide support to the user via recorded voice. For example, the providing unit provides instant support via real-time voice when the user is in trouble. The recorded voice is used to play a pre-recorded support message. This allows the user to quickly solve the problem by providing voice support when the user is in trouble. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's troublesome situation into a generation AI and cause the generation AI to generate an appropriate support message.

[0038] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the user in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a question format to use during a specific time period based on the user's past question history. For example, if the user has asked many questions about childcare in the past, the reception unit prioritizes suggesting related questions. The question format is used to prioritize suggesting voice format if the user has previously input questions in voice format. The time period prediction is used to suggest a question format appropriate for a specific time period if the user has previously asked a question during that time period. In this way, by analyzing the past question history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0039] The reception unit can filter questions based on the user's current lifestyle and areas of interest when receiving a question. For example, if the user has many questions about childcare, the reception unit prioritizes receiving related questions. Furthermore, if the user has many questions about work, the reception unit can postpone questions about childcare. Furthermore, the reception unit can provide appropriate answers based on the user's lifestyle (e.g., questions asked at night). For example, if the user has many questions about childcare, the reception unit prioritizes receiving related questions. If the user has questions about work, questions about childcare are used to postpone questions about childcare. If the user has questions asked at night, the lifestyle is used to provide answers appropriate for the night. Thus, by filtering questions based on the user's lifestyle and areas of interest, more appropriate answers can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI perform question filtering.

[0040] When receiving a question, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question in text, the reception unit can also analyze the question using natural language processing technology. Furthermore, when the user submits an image, the reception unit can also receive the question using image recognition technology. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. When the user inputs a question in text, the reception unit uses natural language processing technology to analyze the question. When the user submits a question in image, the reception unit uses image recognition technology to receive the question. This improves the accuracy of question reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0041] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to the home. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to the area. The travel questions are used to prioritize receiving travel-related questions when the user is traveling. The home-related questions are used to prioritize receiving home-related questions when the user is at home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of questions.

[0042] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, if the user posts about childcare on social media, the reception unit can prioritize receiving related questions. Furthermore, if the user posts about work on social media, the reception unit can prioritize receiving questions related to work. Furthermore, the reception unit can also receive related questions by taking into account the activities of the user's friends on social media. For example, if the user posts about childcare on social media, the reception unit prioritizes receiving related questions. If the user posts about work on social media, questions related to work are used to prioritize receiving questions related to work. The activities of the user's friends on social media are used to receive related questions. In this way, by analyzing social media activity, questions based on the user's interests can be received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related questions.

[0043] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can suggest an optimal reception method based on the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit preferentially suggests voice input. If the user has preferred text input in the past, the text input is used to preferentially suggest text input. The optimal reception method is suggested based on the user's past feedback. This allows the user to be provided with an optimal reception method by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0044] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the answer according to the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. A concise answer is generated for a question of low importance. The dynamic adjustment is used to dynamically adjust the level of detail of the answer according to the importance of the question. In this way, an appropriate answer can be provided by adjusting the level of detail of the answer according to the importance of the question. 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 text data of a question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0045] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a generation algorithm specialized for childcare to a question about childcare. The generation unit can also apply a generation algorithm specialized for health to a question about health. The generation unit can also apply a generation algorithm specialized for education to a question about education. For example, the generation unit applies a generation algorithm specialized for childcare to a question about childcare. A question about health is used to apply a generation algorithm specialized for health. A question about education is used to apply a generation algorithm specialized for education. This allows for applying an appropriate generation algorithm depending on the category of the question, thereby providing a more accurate answer. Some or all of the above-mentioned 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 text data of the question into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0046] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, generates a more accurate answer based on answers received by the user in the past. The generation unit can also improve the accuracy of the answer by referring to the user's past feedback. Furthermore, the generation unit can analyze the user's past question history to generate an optimal answer. For example, the generation unit generates a more accurate answer based on answers received by the user in the past. The past feedback is used to improve the accuracy of the answer by referring to the user's past feedback. The question history is analyzed to analyze the user's past question history and generate an optimal answer. As a result, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned 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 user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0047] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. The generation unit can determine the priority of answers based on, for example, the time of day when the question was submitted. The generation unit can also determine the priority of answers based on the day of the week when the question was submitted. The generation unit can also determine the priority of answers based on the season when the question was submitted. For example, the generation unit can determine the priority of answers based on the time of day when the question was submitted. The priority based on the day of the week is used to determine the priority of answers based on the day of the week when the question was submitted. The priority based on the season is used to determine the priority of answers based on the season when the question was submitted. In this way, by determining the priority of answers based on the time of submission of the question, answers can be provided at an appropriate time. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input question submission time data into the generation AI and cause the generation AI to determine the priority of answers.

[0048] The generation unit can adjust the order of answers based on the relevance of the question when generating answers. For example, the generation unit prioritizes generating the most relevant answer based on the relevance of the question. The generation unit can also dynamically adjust the order of answers based on the relevance of the question. Furthermore, the generation unit can provide related answers collectively based on the relevance of the question. For example, the generation unit prioritizes generating the most relevant answer based on the relevance of the question. The dynamic adjustment is used to dynamically adjust the order of answers based on the relevance of the question. The provision of related answers is used to provide related answers collectively based on the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, it is possible to provide highly relevant answers preferentially. Some or all of the above-mentioned 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 question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0049] When generating an answer, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can generate a concise answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates an answer that uses a lot of technical terms. If the user does not have technical expertise, the generation unit is used to generate a concise answer that avoids technical terms. The dynamic adjustment is used to dynamically adjust the use of technical terms in the answer according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing an answer that is easy for the user to understand. 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 user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the answer.

[0050] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past feedback. For example, if the user has preferred voice answers in the past, the providing unit can provide the answer by voice. Furthermore, if the user has preferred text answers in the past, the providing unit can also provide the answer by text. Furthermore, the providing unit can select the optimal providing method based on the user's past feedback. For example, if the user has preferred voice answers in the past, the providing unit can provide the answer by voice. If the user has preferred text answers in the past, the text answer can be used to provide the answer by text. The optimal providing method is selected based on the user's past feedback. In this way, the optimal providing method for the user can be selected by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to select the providing method.

[0051] When providing an answer, the providing unit can customize the provided content according to the user's current task. For example, if the user is raising a child, the providing unit can prioritize providing answers related to childcare. Furthermore, if the user is working, the providing unit can also prioritize providing answers related to work. Furthermore, the providing unit can provide an optimal answer according to the user's current task. For example, if the user is raising a child, the providing unit can prioritize providing answers related to childcare. The answer for work is used to prioritize providing answers related to work when the user is working. The optimal answer is used to provide an optimal answer according to the user's current task. In this way, by customizing the provided content according to the user's current task, a more appropriate answer can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data to the generation AI and cause the generation AI to customize the provided content.

[0052] The providing unit can analyze the user's lifestyle rhythm when providing an answer and select the optimal timing for providing the answer. For example, if the user asks a question at night, the providing unit provides the answer the next morning. Furthermore, if the user asks a question during the day, the providing unit can also provide the answer immediately. Furthermore, the providing unit can select the optimal timing for providing the answer based on the user's lifestyle rhythm. For example, if the user asks a question at night, the providing unit provides the answer the next morning. If the user asks a question during the day, the answer is used to provide an immediate answer. The optimal timing for providing the answer is selected based on the user's lifestyle rhythm. In this way, by selecting the optimal timing for providing the answer based on the user's lifestyle rhythm, the answer can be provided at the optimal timing for the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to select the timing for providing the answer.

[0053] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. When the user is using a tablet, the providing unit can provide a display method optimized for a large screen. When the user is using a smartwatch, the providing unit can provide a display method that is concise and highly visible. This allows the optimal providing method to be selected by taking into account the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the providing method.

[0054] When providing an answer, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, the providing unit can provide the answer in a specific language when the user selects that language. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The use of multiple languages ​​is used to provide a language switching function when the user uses multiple languages. The selection of a specific language is used to provide the answer in that language when the user selects that language. This allows the provision of multilingual content in accordance with the user's language setting, thereby providing answers that are easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to perform multilingual support for the provided content.

[0055] When providing an answer, the providing unit can customize the content to be provided by referring to the user's past question history. For example, the providing unit can prioritize providing relevant answers based on the content of questions frequently asked by the user in the past. The providing unit can also prioritize providing question formats (audio, text, etc.) used by the user in the past. Furthermore, the providing unit can predict and provide question formats to be used in a specific time period from the user's past question history. For example, the providing unit prioritizes providing relevant answers based on the content of questions frequently asked by the user in the past. The question format priority is used to prioritize providing question formats (audio, text, etc.) used by the user in the past. The time period prediction is used to predict and provide question formats to be used in a specific time period from the user's past question history. In this way, by referring to the past question history, it is possible to provide content that is optimal for the user. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past question history data into a generation AI and cause the generation AI to customize the content to be provided.

[0056] When storing data, the storage unit can select the optimal storage method by referring to the user's past data. For example, the storage unit prioritizes storing related data based on data stored by the user in the past. The storage unit can also select the optimal storage method by referring to the user's past data storage history. Furthermore, the storage unit can analyze the user's past data storage patterns and propose the optimal storage method. For example, the storage unit prioritizes storing related data based on data stored by the user in the past. The data storage history is used to select the optimal storage method by referring to the user's past data storage history. The analysis of the data storage pattern is used to analyze the user's past data storage pattern and propose the optimal storage method. In this way, the optimal data storage method can be provided by referring to the past data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past data into a generation AI and have the generation AI select the storage method.

[0057] The storage unit can filter data based on the user's current living situation when storing data. For example, if the user is raising a child, the storage unit can prioritize storing data related to childcare. Also, if the user is working, the storage unit can prioritize storing data related to work. Furthermore, the storage unit can filter and store optimal data based on the user's current living situation. For example, if the user is raising a child, the storage unit prioritizes storing data related to childcare. Data from work is used to prioritize storing data related to work when the user is working. The filtering of optimal data is used to filter and store optimal data based on the user's current living situation. In this way, appropriate data can be stored by filtering data based on the user's living situation. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's living situation data to a generation AI and have the generation AI filter the data.

[0058] The storage unit can improve the storage method by reflecting user feedback when storing data. The storage unit can improve the data storage method, for example, based on feedback previously provided by the user. The storage unit can also propose an optimal data storage method by referring to the user feedback. The storage unit can also dynamically adjust the data storage method by reflecting the user feedback. For example, the storage unit improves the data storage method based on feedback previously provided by the user. The reference to the feedback is used to propose an optimal data storage method by referring to the user feedback. The dynamic adjustment is used to dynamically adjust the data storage method by reflecting the user feedback. In this way, an optimal data storage method can be provided by reflecting the user feedback. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI or without AI. For example, the storage unit can input user feedback data to a generation AI and cause the generation AI to improve the storage method.

[0059] When storing data, the storage unit can prioritize storing highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the storage unit prioritizes storing data related to that area. Furthermore, when the user is traveling, the storage unit can prioritize storing data related to the travel. Furthermore, when the user is at home, the storage unit can prioritize storing data related to the home. For example, when the user is in a specific area, the storage unit prioritizes storing data related to that area. Data from the travel period is used to prioritize storing data related to the travel when the user is traveling. Data related to the home is used to prioritize storing data related to the home when the user is at home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information data to the generation AI and have the generation AI determine the data storage priority.

[0060] The storage unit can analyze the user's social media activity and store relevant data when storing data. For example, if the user posts about childcare on social media, the storage unit can prioritize storing relevant data. Furthermore, if the user posts about work on social media, the storage unit can prioritize storing work-related data. Furthermore, the storage unit can also store relevant data by referring to the activities of the user's friends on social media. For example, if the user posts about childcare on social media, the storage unit prioritizes storing relevant data. If the user posts about work on social media, the storage unit can prioritize storing work-related data. The activities of the user's friends on social media can be used to store relevant data. In this way, by analyzing social media activity, data based on the user's interests can be stored. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's social media activity data into a generation AI and cause the generation AI to store relevant data.

[0061] The storage unit can customize the storage method by reflecting the user's past feedback when storing data. For example, if the user has previously preferred voice input, the storage unit can preferentially store voice data. Furthermore, if the user has previously preferred text input, the storage unit can preferentially store text data. Furthermore, the storage unit can also suggest an optimal data storage method based on the user's past feedback. For example, if the user has previously preferred voice input, the storage unit preferentially stores voice data. If the user has previously preferred text input, the text data is used to preferentially store text data. The optimal data storage method is suggested based on the user's past feedback. This allows the user to be provided with an optimal data storage method by reflecting past feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's past feedback data into a generation AI and have the generation AI customize the storage method.

[0062] When creating a parenting book, the creation unit can adjust the level of detail in the parenting book based on the importance of the accumulated data. For example, the creation unit creates a detailed parenting book based on data with high importance. The creation unit can also create a concise parenting book based on data with low importance. Furthermore, the creation unit can dynamically adjust the level of detail in the parenting book according to the importance of the data. For example, the creation unit creates a detailed parenting book based on data with high importance. Data with low importance is used to create a concise parenting book. The dynamic adjustment is used to dynamically adjust the level of detail in the parenting book according to the importance of the data. This makes it possible to provide appropriate information by adjusting the level of detail in the parenting book according to the importance of the data. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input accumulated data into the generation AI and cause the generation AI to adjust the level of detail in the parenting book.

[0063] When creating a parenting book, the creation unit can apply different creation algorithms depending on the category of data. For example, the creation unit can apply a childcare-specialized creation algorithm to data related to childcare. The creation unit can also apply a health-specialized creation algorithm to data related to health. The creation unit can also apply an education-specialized creation algorithm to data related to education. For example, the creation unit applies a childcare-specialized creation algorithm to data related to childcare. Health-related data is used to apply a health-specialized creation algorithm. Education-related data is used to apply an education-specialized creation algorithm. This allows for the application of an appropriate creation algorithm depending on the category of data, thereby providing a more accurate parenting book. Some or all of the above-mentioned processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate creation algorithm.

[0064] When creating a parenting book, the creation unit can improve the accuracy of the creation by referring to the user's past parenting books. For example, the creation unit creates a more accurate parenting book based on parenting books created by the user in the past. The creation unit can also improve the accuracy of the parenting book by referring to the user's past feedback. Furthermore, the creation unit can analyze the user's past parenting books to create an optimal parenting book. For example, the creation unit creates a more accurate parenting book based on parenting books created by the user in the past. The past feedback is used to improve the accuracy of the parenting book by referring to the user's past feedback. The analysis of the parenting book is used to analyze the user's past parenting books and create an optimal parenting book. In this way, by referring to the past parenting books, the accuracy of the parenting book is improved. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data on the user's past parenting books into the generation AI and cause the generation AI to improve the accuracy of the parenting book.

[0065] When creating a parenting book, the creation unit can determine the priority of the parenting book based on the time of data submission. The creation unit can determine the priority of the parenting book based on, for example, the time of day when the data was submitted. The creation unit can also determine the priority of the parenting book based on the day of the week when the data was submitted. The creation unit can also determine the priority of the parenting book based on the season when the data was submitted. For example, the creation unit determines the priority of the parenting book based on the time of day when the data was submitted. The priority based on the day of the week is used to determine the priority of the parenting book based on the day of the week when the data was submitted. The priority based on the season is used to determine the priority of the parenting book based on the season when the data was submitted. In this way, by determining the priority of the parenting book based on the time of data submission, the parenting book can be provided at an appropriate time. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input data on the time of data submission to the generation AI and cause the generation AI to determine the priority of the parenting books.

[0066] When creating the parenting book, the creation unit can adjust the order of the parenting book based on the relevance of the data. For example, the creation unit prioritizes including the most relevant information in the parenting book based on the relevance of the data. The creation unit can also dynamically adjust the order of the parenting book based on the relevance of the data. Furthermore, the creation unit can include related information together in the parenting book based on the relevance of the data. For example, the creation unit prioritizes including the most relevant information in the parenting book based on the relevance of the data. The dynamic adjustment is used to dynamically adjust the order of the parenting book based on the relevance of the data. The provision of related information is used to include related information together in the parenting book based on the relevance of the data. In this way, by adjusting the order of the parenting book based on the relevance of the data, it is possible to provide highly relevant information preferentially. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of the parenting book.

[0067] When creating a parenting book, the creation unit can adjust the use of technical terms in the parenting book according to the user's level of expertise. For example, if the user has technical expertise, the creation unit can create a parenting book that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the creation unit can create a concise parenting book that avoids technical terms. Furthermore, the creation unit can dynamically adjust the use of technical terms in the parenting book according to the user's level of expertise. For example, if the user has technical expertise, the creation unit can create a parenting book that uses a lot of technical terms. If the user does not have technical expertise, the creation unit can create a concise parenting book that avoids technical terms. The dynamic adjustment is used to dynamically adjust the use of technical terms in the parenting book according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing a parenting book that is easy for the user to understand. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the parenting book.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The reception unit can analyze the user's past question history and select the optimal reception method. For example, it can automatically suggest related questions based on the content of questions the user has frequently asked in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the question format to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the user with the optimal reception method.

[0070] The providing unit can customize the content to be provided according to the user's current task. For example, if the user is raising a child, answers related to childcare can be provided preferentially. Also, if the user is working, answers related to work can be provided preferentially. Furthermore, the providing unit can provide the optimal answer according to the user's current task. In this way, by customizing the content to be provided according to the user's current task, more appropriate answers can be provided.

[0071] When creating a parenting book, the creation unit can apply different creation algorithms depending on the category of data. For example, a creation algorithm specialized for parenting can be applied to data related to parenting. A creation algorithm specialized for health can also be applied to data related to health. Furthermore, a creation algorithm specialized for education can also be applied to data related to education. In this way, by applying an appropriate creation algorithm depending on the category of data, it is possible to provide a parenting book with higher accuracy.

[0072] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area can be prioritized. Also, if the user is traveling, questions related to the trip can be prioritized. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the home. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0073] When generating an answer, the generator can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer can be generated for a question of high importance. A concise answer can also be generated for a question of low importance. Furthermore, the generator can dynamically adjust the level of detail of the answer depending on the importance of the question. This allows the generator to provide an appropriate answer by adjusting the level of detail of the answer depending on the importance of the question.

[0074] When accumulating data, the accumulation unit can analyze the user's social media activity and accumulate related data. For example, if the user posts about childcare on social media, the accumulation unit can prioritize the accumulation of related data. Also, if the user posts about work on social media, the accumulation unit can prioritize the accumulation of work-related data. Furthermore, the accumulation unit can also accumulate related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data based on the user's interests can be accumulated.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The reception unit receives questions from the user. Questions from the user may be in text, voice, or image format. The reception unit receives questions from the user through a smart speaker or app. The reception unit can also estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of receiving questions can be delayed. Step 2: The generation unit uses a generation AI to generate an answer based on the question received by the reception unit. The generation AI analyzes the question using natural language processing technology and generates an appropriate answer. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can apply the optimal generation algorithm depending on the content of the question. For example, a generation algorithm specialized for childcare is applied to a question about childcare. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit provides the generated answer to the user by voice or text. The providing unit can also estimate the user's emotions and adjust the way the answer is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide the answer by voice in a calm tone. Step 4: The storage unit stores data related to the growth and development of the user's child. The storage unit stores data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is feeling stressed, the storage of data can be temporarily stopped. Step 5: The creation unit creates an individualized parenting book based on the data accumulated by the storage unit. The creation unit analyzes growth and development patterns based on the accumulated data and creates an individualized parenting book. The creation unit can also estimate the user's emotions and adjust the way the parenting book is created based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit creates a concise and easy-to-understand parenting book.

[0077] (Example 2) A child-rearing support system according to an embodiment of the present invention accepts questions from users and generates and provides answers using a generation AI. The child-rearing support system accumulates data on the growth and development of the user's child and creates a personalized child-rearing manual based on that data. For example, when a user inputs a question through a smart speaker or app, the generation AI answers the question like a knowledgeable expert. The child-rearing support system then accumulates data on the growth and development of the user's child and creates a personalized child-rearing manual based on that data. This allows parents to obtain information specific to their child. The child-rearing support system also serves as a 24-hour mom / dad friend that users can easily talk to. For example, when parents are in trouble, such as when their child won't stop crying in the middle of the night, the generation AI provides voice support. This reduces the anxiety and worries of parents and provides an environment where parents can raise their children with greater peace of mind. This allows the child-rearing support system to provide appropriate answers to users' questions, accumulate data on children's growth and development, and create a personalized child-rearing manual, thereby supporting the anxiety and worries of parents. For example, when a user inputs a question through a smart speaker or app, the generation AI answers the question like a knowledgeable expert. Next, the child-rearing support system accumulates data on the growth and development of the user's child and creates an individualized child-rearing manual based on that data. This allows parents to obtain information specific to their child. The child-rearing support system also acts as a 24-hour mom / dad friend that users can easily talk to. For example, when a parent is in trouble, such as when their child won't stop crying in the middle of the night, the generating AI will provide voice support. In this way, the child-rearing support system reduces the anxiety and worries of the parenting generation and provides an environment in which parents can raise their children with greater peace of mind.

[0078] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a provision unit, a storage unit, and a creation unit. The reception unit receives questions from a user. Questions from a user may be in, for example, text format, audio format, or image format, but are not limited to these examples. The reception unit receives questions from a user, for example, through a smart speaker or an app. The reception unit can also estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of receiving questions can be delayed. The generation unit generates answers based on the questions received by the reception unit using a generation AI. The generation AI analyzes the questions using, for example, natural language processing technology and generates appropriate answers. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can apply an optimal generation algorithm depending on the content of the question. For example, a generation algorithm specialized for child-rearing is applied to questions about child-rearing. The provision unit provides the answers generated by the generation unit to the user. For example, the provision unit provides the generated answers to the user in audio or text format. The providing unit can also estimate the user's emotions and adjust the method of providing answers based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide answers in a calm voice tone. The storage unit stores data related to the growth and development of the user's child. For example, the storage unit stores data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also estimate the user's emotions and adjust the method of storing data based on the estimated user's emotions. For example, if the user is feeling stressed, the storage of data can be temporarily stopped. The creating unit creates an individual parenting book based on the data stored by the storage unit. For example, the creating unit analyzes growth and development patterns based on the stored data and creates an individual parenting book. The creating unit can also estimate the user's emotions and adjust the method of creating the parenting book based on the estimated user's emotions. For example, if the user is feeling stressed, the creating unit creates a concise and easy-to-understand parenting book.As a result, the child-rearing support system of the embodiment can provide appropriate answers to users' questions, accumulate data on children's growth and development, and create individual child-rearing books, thereby supporting the anxieties and worries of the parenting generation.

[0079] The reception unit can accept questions from a user through a smart speaker or an app. Examples of smart speakers include, but are not limited to, Amazon Echo and Google Home. Examples of apps include, but are not limited to, iOS apps and Android apps. The reception unit can accept questions from a user in voice format through a smart speaker. The reception unit can also accept questions from a user in text format through an app. For example, if a user speaks to a smart speaker, "Tell me about my child's development," the reception unit accepts the question. Alternatively, if a user inputs into an app, "Tell me about my child's growth record," the reception unit accepts the question. This allows users to easily input questions by accepting questions from a smart speaker or an app. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input voice data acquired from a smart speaker into a generation AI and have the generation AI convert the voice data into text data.

[0080] The generation unit can analyze the question using natural language processing and generate an appropriate answer. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the generation unit can analyze the words of the question using morphological analysis and generate an appropriate answer. The generation unit can also analyze the structure of the question sentence using grammatical analysis and generate an appropriate answer. Furthermore, the generation unit can analyze the meaning of the question using semantic analysis and generate an appropriate answer. For example, the generation unit can use morphological analysis to divide the words of the question and analyze the meaning of the words. Grammatical analysis is used to analyze the structure of the question sentence and understand the meaning of the sentence. Semantic analysis is used to analyze the meaning of the entire question sentence and generate an appropriate answer. In this way, natural language processing improves the accuracy of question analysis and enables the generation of an appropriate answer. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input text data of a question into the generation AI and cause the generation AI to generate an appropriate answer.

[0081] The providing unit can provide the generated answer to the user in the form of voice or text. Voice includes, but is not limited to, voice files and real-time voice. Text includes, but is not limited to, chat formats and email formats. The providing unit can provide, for example, the generated answer to the user as an voice file. The providing unit can also provide the generated answer to the user as real-time voice. The providing unit can also provide the generated answer to the user in chat format. For example, the providing unit can save the generated answer as an audio file so that the user can play it. The real-time voice is used to provide the generated answer as voice in real time. The chat format is used to display the generated answer as text in a chat app. This makes it easier for the user to receive the answer by providing the answer in voice or text. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input text data of the generated answer to the generation AI and have the generation AI convert it into voice data.

[0082] The storage unit can store data related to the growth and development of the user's child in the cloud. Examples of clouds include, but are not limited to, AWS, Google Cloud, and Microsoft Azure. The storage unit can store data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also store the user's child's growth record in the cloud. The storage unit can also store the user's child's health data in the cloud. For example, the storage unit can store the user's child's height data in the cloud for later reference. The weight data can be used to record changes in the user's child's weight. The developmental stage data can be used to record the user's child's developmental progress. Storing data in the cloud improves data security and accessibility. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's child's growth data into a generation AI and have the generation AI analyze and store the data.

[0083] The creation unit can analyze growth and development patterns based on the accumulated data and create an individualized parenting book. Growth and development patterns include, but are not limited to, temporal changes and developmental stages. For example, the creation unit can analyze the growth patterns of the user's child based on the accumulated data and create an individualized parenting book. The creation unit can also analyze the developmental patterns of the user's child based on the accumulated data and create an individualized parenting book. Furthermore, the creation unit can analyze the health patterns of the user's child based on the accumulated data and create an individualized parenting book. For example, the creation unit can analyze the growth data of the user's child and create a parenting book showing the progress of growth. The developmental patterns are used to indicate the developmental stages of the user's child. The health patterns are used to indicate the health status of the user's child. In this way, an individualized parenting book can be created by analyzing the growth and development patterns. Some or all of the above-described processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the accumulated data into a generation AI and cause the generation AI to create an individualized parenting book.

[0084] The providing unit can provide the created individual parenting book to the user. The individual parenting book can be in, for example, a text format, an image format, or a video format, but is not limited to these examples. For example, the providing unit can provide the created individual parenting book to the user in a text format. The providing unit can also provide the created individual parenting book to the user in an image format. Furthermore, the providing unit can also provide the created individual parenting book to the user in a video format. For example, the providing unit can save the created individual parenting book as a text file and make it available for download by the user. The image format is used to visually present the contents of the parenting book. The video format is used to present the contents of the parenting book in a video format. By providing the individual parenting book, the user can obtain information specific to their child. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input data of the created parenting book to a generation AI and have the generation AI convert the data into text, image, or video format.

[0085] The providing unit can provide a 24-hour best friend with whom the user can easily talk. 24-hour service includes, but is not limited to, a shift system, an AI chatbot, and the like. The providing unit can provide a 24-hour best friend with whom the user can easily talk, for example, using an AI chatbot. The providing unit can also provide a 24-hour best friend with whom the user can easily talk, using a shift system. For example, the providing unit can use an AI chatbot to enable the user to ask questions or seek advice at any time. A shift system is used to achieve 24-hour service by having multiple operators take turns responding. This provides the user with a 24-hour best friend with whom the user can easily talk, thereby alleviating the user's anxiety and worries. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's questions or inquiries into a generation AI and cause the generation AI to generate appropriate answers and advice.

[0086] The providing unit can provide support via voice when the user is in trouble. Voice support includes, but is not limited to, real-time voice, recorded voice, and the like. The providing unit, for example, provides support to the user via real-time voice. The providing unit can also provide support to the user via recorded voice. For example, the providing unit provides instant support via real-time voice when the user is in trouble. The recorded voice is used to play a pre-recorded support message. This allows the user to quickly solve the problem by providing voice support when the user is in trouble. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's troublesome situation into a generation AI and cause the generation AI to generate an appropriate support message.

[0087] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. The reception unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This adjusts the timing of question reception based on the user's emotions, thereby reducing the user's stress. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0088] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the user in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a question format to use during a specific time period based on the user's past question history. For example, if the user has asked many questions about childcare in the past, the reception unit prioritizes suggesting related questions. The question format is used to prioritize suggesting voice format if the user has previously input questions in voice format. The time period prediction is used to suggest a question format appropriate for a specific time period if the user has previously asked a question during that time period. In this way, by analyzing the past question history, the optimal reception method can be provided to the user. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past question history data into a generation AI and have the generation AI select the optimal reception method.

[0089] The reception unit can filter questions based on the user's current lifestyle and areas of interest when receiving a question. For example, if the user has many questions about childcare, the reception unit prioritizes receiving related questions. Furthermore, if the user has many questions about work, the reception unit can postpone questions about childcare. Furthermore, the reception unit can provide appropriate answers based on the user's lifestyle (e.g., questions asked at night). For example, if the user has many questions about childcare, the reception unit prioritizes receiving related questions. If the user has questions about work, questions about childcare are used to postpone questions about childcare. If the user has questions asked at night, the lifestyle is used to provide answers appropriate for the night. Thus, by filtering questions based on the user's lifestyle and areas of interest, more appropriate answers can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI perform question filtering.

[0090] When receiving a question, the reception unit can select the optimal reception means depending on the user's input method. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question in text, the reception unit can also analyze the question using natural language processing technology. Furthermore, when the user submits an image, the reception unit can also receive the question using image recognition technology. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. When the user inputs a question in text, the reception unit uses natural language processing technology to analyze the question. When the user submits a question in image, the reception unit uses image recognition technology to receive the question. This improves the accuracy of question reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0091] The reception unit can estimate the user's emotions and prioritize questions to be received based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows questions to be prioritized according to the user's emotions, thereby enabling quick responses to questions with high urgency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving questions related to the travel. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to the home. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to the area. The travel questions are used to prioritize receiving travel-related questions when the user is traveling. The home-related questions are used to prioritize receiving home-related questions when the user is at home. In this way, by taking the user's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of questions.

[0093] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, if the user posts about childcare on social media, the reception unit can prioritize receiving related questions. Furthermore, if the user posts about work on social media, the reception unit can prioritize receiving questions related to work. Furthermore, the reception unit can also receive related questions by taking into account the activities of the user's friends on social media. For example, if the user posts about childcare on social media, the reception unit prioritizes receiving related questions. If the user posts about work on social media, questions related to work are used to prioritize receiving questions related to work. The activities of the user's friends on social media are used to receive related questions. In this way, by analyzing social media activity, questions based on the user's interests can be received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to receive related questions.

[0094] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, the reception unit can suggest an optimal reception method based on the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit preferentially suggests voice input. If the user has preferred text input in the past, the text input is used to preferentially suggest text input. The optimal reception method is suggested based on the user's past feedback. This allows the user to be provided with an optimal reception method by reflecting past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past feedback data into a generation AI and have the generation AI customize the reception method.

[0095] The generation unit can estimate the user's emotion and adjust the way the answer is expressed based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the user to provide an answer that is easy to understand by adjusting the way the answer is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0096] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the answer according to the importance of the question. For example, the generation unit generates a detailed answer for a question of high importance. A concise answer is generated for a question of low importance. The dynamic adjustment is used to dynamically adjust the level of detail of the answer according to the importance of the question. In this way, an appropriate answer can be provided by adjusting the level of detail of the answer according to the importance of the question. 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 text data of a question to the generation AI and cause the generation AI to adjust the level of detail of the answer.

[0097] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a generation algorithm specialized for childcare to a question about childcare. The generation unit can also apply a generation algorithm specialized for health to a question about health. The generation unit can also apply a generation algorithm specialized for education to a question about education. For example, the generation unit applies a generation algorithm specialized for childcare to a question about childcare. A question about health is used to apply a generation algorithm specialized for health. A question about education is used to apply a generation algorithm specialized for education. This allows for applying an appropriate generation algorithm depending on the category of the question, thereby providing a more accurate answer. Some or all of the above-mentioned 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 text data of the question into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0098] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, generates a more accurate answer based on answers received by the user in the past. The generation unit can also improve the accuracy of the answer by referring to the user's past feedback. Furthermore, the generation unit can analyze the user's past question history to generate an optimal answer. For example, the generation unit generates a more accurate answer based on answers received by the user in the past. The past feedback is used to improve the accuracy of the answer by referring to the user's past feedback. The question history is analyzed to analyze the user's past question history and generate an optimal answer. As a result, the accuracy of the answer is improved by referring to the past answer results. Some or all of the above-mentioned 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 user's past answer result data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0099] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the length of the answer to be adjusted according to the user's emotion, thereby providing an optimal answer for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0100] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. The generation unit can determine the priority of answers based on, for example, the time of day when the question was submitted. The generation unit can also determine the priority of answers based on the day of the week when the question was submitted. The generation unit can also determine the priority of answers based on the season when the question was submitted. For example, the generation unit can determine the priority of answers based on the time of day when the question was submitted. The priority based on the day of the week is used to determine the priority of answers based on the day of the week when the question was submitted. The priority based on the season is used to determine the priority of answers based on the season when the question was submitted. In this way, by determining the priority of answers based on the time of submission of the question, answers can be provided at an appropriate time. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input question submission time data into the generation AI and cause the generation AI to determine the priority of answers.

[0101] The generation unit can adjust the order of answers based on the relevance of the question when generating answers. For example, the generation unit prioritizes generating the most relevant answer based on the relevance of the question. The generation unit can also dynamically adjust the order of answers based on the relevance of the question. Furthermore, the generation unit can provide related answers collectively based on the relevance of the question. For example, the generation unit prioritizes generating the most relevant answer based on the relevance of the question. The dynamic adjustment is used to dynamically adjust the order of answers based on the relevance of the question. The provision of related answers is used to provide related answers collectively based on the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, it is possible to provide highly relevant answers preferentially. Some or all of the above-mentioned 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 question relevance data to the generation AI and cause the generation AI to adjust the order of answers.

[0102] When generating an answer, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the generation unit can generate a concise answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates an answer that uses a lot of technical terms. If the user does not have technical expertise, the generation unit is used to generate a concise answer that avoids technical terms. The dynamic adjustment is used to dynamically adjust the use of technical terms in the answer according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing an answer that is easy for the user to understand. 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 user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the answer.

[0103] The providing unit can estimate the user's emotions and adjust the method of providing an answer based on the estimated user's emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on heart rate fluctuations. This allows the method of providing an answer to be adjusted according to the user's emotions, thereby providing an answer in a manner optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0104] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past feedback. For example, if the user has preferred voice answers in the past, the providing unit can provide the answer by voice. Furthermore, if the user has preferred text answers in the past, the providing unit can also provide the answer by text. Furthermore, the providing unit can select the optimal providing method based on the user's past feedback. For example, if the user has preferred voice answers in the past, the providing unit can provide the answer by voice. If the user has preferred text answers in the past, the text answer can be used to provide the answer by text. The optimal providing method is selected based on the user's past feedback. In this way, the optimal providing method for the user can be selected by referring to the past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to select the providing method.

[0105] When providing an answer, the providing unit can customize the provided content according to the user's current task. For example, if the user is raising a child, the providing unit can prioritize providing answers related to childcare. Furthermore, if the user is working, the providing unit can also prioritize providing answers related to work. Furthermore, the providing unit can provide an optimal answer according to the user's current task. For example, if the user is raising a child, the providing unit can prioritize providing answers related to childcare. The answer for work is used to prioritize providing answers related to work when the user is working. The optimal answer is used to provide an optimal answer according to the user's current task. In this way, by customizing the provided content according to the user's current task, a more appropriate answer can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data to the generation AI and cause the generation AI to customize the provided content.

[0106] The providing unit can analyze the user's lifestyle rhythm when providing an answer and select the optimal timing for providing the answer. For example, if the user asks a question at night, the providing unit provides the answer the next morning. Furthermore, if the user asks a question during the day, the providing unit can also provide the answer immediately. Furthermore, the providing unit can select the optimal timing for providing the answer based on the user's lifestyle rhythm. For example, if the user asks a question at night, the providing unit provides the answer the next morning. If the user asks a question during the day, the answer is used to provide an immediate answer. The optimal timing for providing the answer is selected based on the user's lifestyle rhythm. In this way, by selecting the optimal timing for providing the answer based on the user's lifestyle rhythm, the answer can be provided at the optimal timing for the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's lifestyle rhythm data into the generation AI and cause the generation AI to select the timing for providing the answer.

[0107] The providing unit can estimate the user's emotions and adjust the order in which answers are provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the order in which answers are provided to be adjusted according to the user's emotions, thereby prioritizing the provision of answers with a high degree of urgency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0108] When providing an answer, the providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. When the user is using a tablet, the providing unit can provide a display method optimized for a large screen. When the user is using a smartwatch, the providing unit can provide a display method that is concise and highly visible. This allows the optimal providing method to be selected by taking into account the user's device information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the providing method.

[0109] When providing an answer, the providing unit can make the provided content multilingual in accordance with the user's language setting. The providing unit, for example, automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, the providing unit can provide the answer in a specific language when the user selects that language. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The use of multiple languages ​​is used to provide a language switching function when the user uses multiple languages. The selection of a specific language is used to provide the answer in that language when the user selects that language. This allows the provision of multilingual content in accordance with the user's language setting, thereby providing answers that are easy for the user to understand. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to perform multilingual support for the provided content.

[0110] When providing an answer, the providing unit can customize the content to be provided by referring to the user's past question history. For example, the providing unit can prioritize providing relevant answers based on the content of questions frequently asked by the user in the past. The providing unit can also prioritize providing question formats (audio, text, etc.) used by the user in the past. Furthermore, the providing unit can predict and provide question formats to be used in a specific time period from the user's past question history. For example, the providing unit prioritizes providing relevant answers based on the content of questions frequently asked by the user in the past. The question format priority is used to prioritize providing question formats (audio, text, etc.) used by the user in the past. The time period prediction is used to predict and provide question formats to be used in a specific time period from the user's past question history. In this way, by referring to the past question history, it is possible to provide content that is optimal for the user. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past question history data into a generation AI and cause the generation AI to customize the content to be provided.

[0111] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, the storage unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on changes in facial expressions. The storage unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the storage unit analyzes the tone and speed of the voice and calculates an emotion score. The storage unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on heart rate fluctuations. This allows the data storage method to be adjusted according to the user's emotions, thereby providing an optimal data storage method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0112] When storing data, the storage unit can select the optimal storage method by referring to the user's past data. For example, the storage unit prioritizes storing related data based on data stored by the user in the past. The storage unit can also select the optimal storage method by referring to the user's past data storage history. Furthermore, the storage unit can analyze the user's past data storage patterns and propose the optimal storage method. For example, the storage unit prioritizes storing related data based on data stored by the user in the past. The data storage history is used to select the optimal storage method by referring to the user's past data storage history. The analysis of the data storage pattern is used to analyze the user's past data storage pattern and propose the optimal storage method. In this way, the optimal data storage method can be provided by referring to the past data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past data into a generation AI and have the generation AI select the storage method.

[0113] The storage unit can filter data based on the user's current living situation when storing data. For example, if the user is raising a child, the storage unit can prioritize storing data related to childcare. Also, if the user is working, the storage unit can prioritize storing data related to work. Furthermore, the storage unit can filter and store optimal data based on the user's current living situation. For example, if the user is raising a child, the storage unit prioritizes storing data related to childcare. Data from work is used to prioritize storing data related to work when the user is working. The filtering of optimal data is used to filter and store optimal data based on the user's current living situation. In this way, appropriate data can be stored by filtering data based on the user's living situation. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's living situation data to a generation AI and have the generation AI filter the data.

[0114] The storage unit can improve the storage method by reflecting user feedback when storing data. The storage unit can improve the data storage method, for example, based on feedback previously provided by the user. The storage unit can also propose an optimal data storage method by referring to the user feedback. The storage unit can also dynamically adjust the data storage method by reflecting the user feedback. For example, the storage unit improves the data storage method based on feedback previously provided by the user. The reference to the feedback is used to propose an optimal data storage method by referring to the user feedback. The dynamic adjustment is used to dynamically adjust the data storage method by reflecting the user feedback. In this way, an optimal data storage method can be provided by reflecting the user feedback. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using AI or without AI. For example, the storage unit can input user feedback data to a generation AI and cause the generation AI to improve the storage method.

[0115] The storage unit can estimate the user's emotions and determine the data storage priority based on the estimated user emotions. For example, the storage unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on changes in facial expression. The storage unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the storage unit analyzes the tone and speed of the voice and calculates the emotion score. The storage unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the storage unit calculates the emotion score based on heart rate fluctuations. This allows the data storage priority to be determined according to the user's emotions, thereby enabling important data to be stored preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0116] When storing data, the storage unit can prioritize storing highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the storage unit prioritizes storing data related to that area. Furthermore, when the user is traveling, the storage unit can prioritize storing data related to the travel. Furthermore, when the user is at home, the storage unit can prioritize storing data related to the home. For example, when the user is in a specific area, the storage unit prioritizes storing data related to that area. Data from the travel period is used to prioritize storing data related to the travel when the user is traveling. Data related to the home is used to prioritize storing data related to the home when the user is at home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information data to the generation AI and have the generation AI determine the data storage priority.

[0117] The storage unit can analyze the user's social media activity and store relevant data when storing data. For example, if the user posts about childcare on social media, the storage unit can prioritize storing relevant data. Furthermore, if the user posts about work on social media, the storage unit can prioritize storing work-related data. Furthermore, the storage unit can also store relevant data by referring to the activities of the user's friends on social media. For example, if the user posts about childcare on social media, the storage unit prioritizes storing relevant data. If the user posts about work on social media, the storage unit can prioritize storing work-related data. The activities of the user's friends on social media can be used to store relevant data. In this way, by analyzing social media activity, data based on the user's interests can be stored. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the user's social media activity data into a generation AI and cause the generation AI to store relevant data.

[0118] The storage unit can customize the storage method by reflecting the user's past feedback when storing data. For example, if the user has previously preferred voice input, the storage unit can preferentially store voice data. Furthermore, if the user has previously preferred text input, the storage unit can preferentially store text data. Furthermore, the storage unit can also suggest an optimal data storage method based on the user's past feedback. For example, if the user has previously preferred voice input, the storage unit preferentially stores voice data. If the user has previously preferred text input, the text data is used to preferentially store text data. The optimal data storage method is suggested based on the user's past feedback. This allows the user to be provided with an optimal data storage method by reflecting past feedback. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input the user's past feedback data into a generation AI and have the generation AI customize the storage method.

[0119] The creation unit can estimate the user's emotions and adjust the creation method of the parenting book based on the estimated user emotions. For example, the creation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on changes in facial expressions. The creation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the creation unit analyzes the tone and speed of the voice and calculates an emotion score. The creation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on heart rate fluctuations. This allows the creation method of the parenting book to be adjusted according to the user's emotions, thereby providing the user with an optimal parenting book. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0120] When creating a parenting book, the creation unit can adjust the level of detail in the parenting book based on the importance of the accumulated data. For example, the creation unit creates a detailed parenting book based on data with high importance. The creation unit can also create a concise parenting book based on data with low importance. Furthermore, the creation unit can dynamically adjust the level of detail in the parenting book according to the importance of the data. For example, the creation unit creates a detailed parenting book based on data with high importance. Data with low importance is used to create a concise parenting book. The dynamic adjustment is used to dynamically adjust the level of detail in the parenting book according to the importance of the data. This makes it possible to provide appropriate information by adjusting the level of detail in the parenting book according to the importance of the data. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input accumulated data into the generation AI and cause the generation AI to adjust the level of detail in the parenting book.

[0121] When creating a parenting book, the creation unit can apply different creation algorithms depending on the category of data. For example, the creation unit can apply a childcare-specialized creation algorithm to data related to childcare. The creation unit can also apply a health-specialized creation algorithm to data related to health. The creation unit can also apply an education-specialized creation algorithm to data related to education. For example, the creation unit applies a childcare-specialized creation algorithm to data related to childcare. Health-related data is used to apply a health-specialized creation algorithm. Education-related data is used to apply an education-specialized creation algorithm. This allows for the application of an appropriate creation algorithm depending on the category of data, thereby providing a more accurate parenting book. Some or all of the above-mentioned processing in the creation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the data category into the generation AI and cause the generation AI to apply an appropriate creation algorithm.

[0122] When creating a parenting book, the creation unit can improve the accuracy of the creation by referring to the user's past parenting books. For example, the creation unit creates a more accurate parenting book based on parenting books created by the user in the past. The creation unit can also improve the accuracy of the parenting book by referring to the user's past feedback. Furthermore, the creation unit can analyze the user's past parenting books to create an optimal parenting book. For example, the creation unit creates a more accurate parenting book based on parenting books created by the user in the past. The past feedback is used to improve the accuracy of the parenting book by referring to the user's past feedback. The analysis of the parenting book is used to analyze the user's past parenting books and create an optimal parenting book. In this way, by referring to the past parenting books, the accuracy of the parenting book is improved. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data on the user's past parenting books into the generation AI and cause the generation AI to improve the accuracy of the parenting book.

[0123] The creation unit can estimate the user's emotions and adjust the length of the parenting book based on the estimated user emotions. For example, the creation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on changes in facial expressions. The creation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the creation unit analyzes the tone and speed of the voice and calculates an emotion score. The creation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the creation unit calculates an emotion score based on heart rate fluctuations. This allows the length of the parenting book to be adjusted according to the user's emotions, thereby providing an optimal parenting book for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the creation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0124] When creating a parenting book, the creation unit can determine the priority of the parenting book based on the time of data submission. The creation unit can determine the priority of the parenting book based on, for example, the time of day when the data was submitted. The creation unit can also determine the priority of the parenting book based on the day of the week when the data was submitted. The creation unit can also determine the priority of the parenting book based on the season when the data was submitted. For example, the creation unit determines the priority of the parenting book based on the time of day when the data was submitted. The priority based on the day of the week is used to determine the priority of the parenting book based on the day of the week when the data was submitted. The priority based on the season is used to determine the priority of the parenting book based on the season when the data was submitted. In this way, by determining the priority of the parenting book based on the time of data submission, the parenting book can be provided at an appropriate time. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input data on the time of data submission to the generation AI and cause the generation AI to determine the priority of the parenting books.

[0125] When creating the parenting book, the creation unit can adjust the order of the parenting book based on the relevance of the data. For example, the creation unit prioritizes including the most relevant information in the parenting book based on the relevance of the data. The creation unit can also dynamically adjust the order of the parenting book based on the relevance of the data. Furthermore, the creation unit can include related information together in the parenting book based on the relevance of the data. For example, the creation unit prioritizes including the most relevant information in the parenting book based on the relevance of the data. The dynamic adjustment is used to dynamically adjust the order of the parenting book based on the relevance of the data. The provision of related information is used to include related information together in the parenting book based on the relevance of the data. In this way, by adjusting the order of the parenting book based on the relevance of the data, it is possible to provide highly relevant information preferentially. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of the parenting book.

[0126] When creating a parenting book, the creation unit can adjust the use of technical terms in the parenting book according to the user's level of expertise. For example, if the user has technical expertise, the creation unit can create a parenting book that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the creation unit can create a concise parenting book that avoids technical terms. Furthermore, the creation unit can dynamically adjust the use of technical terms in the parenting book according to the user's level of expertise. For example, if the user has technical expertise, the creation unit can create a parenting book that uses a lot of technical terms. If the user does not have technical expertise, the creation unit can create a concise parenting book that avoids technical terms. The dynamic adjustment is used to dynamically adjust the use of technical terms in the parenting book according to the user's level of expertise. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing a parenting book that is easy for the user to understand. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the parenting book. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, storage unit, and creation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive questions from a user via the reception device 38 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers to the questions using a generation AI. For example, the provision unit provides the user with answers generated by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the storage unit stores data on the growth and development of the user's child in the database 24 of the data processing device 12. For example, the creation unit creates an individual parenting manual based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, storage unit, and creation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a question from a user via the microphone 238 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer to the question using a generation AI. For example, the provision unit provides the answer generated by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 to the user. For example, the storage unit stores data on the growth and development of the user's child in the database 24 of the data processing device 12. For example, the creation unit creates an individual parenting manual based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, storage unit, and creation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive questions from a user via the microphone 238 of the headset-type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers to the questions using a generation AI. For example, the provision unit provides the user with answers generated by the speaker 240 of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the storage unit stores data on the growth and development of the user's child in the database 24 of the data processing device 12. For example, the creation unit creates an individual parenting manual based on the data stored by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, storage unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive questions from a user via the microphone 238 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers to the questions using a generation AI. For example, the provision unit provides the user with answers generated by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the storage unit accumulates data on the growth and development of the user's child in the database 24 of the data processing device 12. For example, the creation unit creates an individual parenting manual based on the data accumulated by the specific processing unit 290 of the data processing device 12.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The reception unit can analyze the user's past question history and select the optimal reception method. For example, it can automatically suggest related questions based on the content of questions the user has frequently asked in the past. The reception unit can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the question format to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the user with the optimal reception method.

[0129] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, the user's facial expression is captured with a camera and the emotion is estimated using an emotion estimation algorithm. An emotion score is calculated based on changes in facial expression. The user's voice can also be recorded and the emotion can be estimated using voice analysis technology. The tone and speed of the voice can be analyzed and the emotion score calculated. This makes it possible to adjust the way the answer is expressed based on the user's emotions, thereby providing answers that are easy for the user to understand.

[0130] The providing unit can customize the content to be provided according to the user's current task. For example, if the user is raising a child, answers related to childcare can be provided preferentially. Also, if the user is working, answers related to work can be provided preferentially. Furthermore, the providing unit can provide the optimal answer according to the user's current task. In this way, by customizing the content to be provided according to the user's current task, more appropriate answers can be provided.

[0131] The storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, the user's facial expression can be captured with a camera and emotions can be estimated using an emotion estimation algorithm. An emotion score can be calculated based on changes in facial expression. The user's voice can also be recorded and emotions can be estimated using voice analysis technology. The tone and speed of the voice can be analyzed and an emotion score calculated. This allows the data storage method to be adjusted according to the user's emotions, making it possible to provide the optimal data storage method for the user.

[0132] When creating a parenting book, the creation unit can apply different creation algorithms depending on the category of data. For example, a creation algorithm specialized for parenting can be applied to data related to parenting. A creation algorithm specialized for health can also be applied to data related to health. Furthermore, a creation algorithm specialized for education can also be applied to data related to education. In this way, by applying an appropriate creation algorithm depending on the category of data, it is possible to provide a parenting book with higher accuracy.

[0133] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, questions related to that area can be prioritized. Also, if the user is traveling, questions related to the trip can be prioritized. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the home. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0134] When generating an answer, the generator can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer can be generated for a question of high importance. A concise answer can also be generated for a question of low importance. Furthermore, the generator can dynamically adjust the level of detail of the answer depending on the importance of the question. This allows the generator to provide an appropriate answer by adjusting the level of detail of the answer depending on the importance of the question.

[0135] The providing unit can estimate the user's emotions and adjust the method of providing answers based on the estimated user emotions. For example, the user's facial expression is captured with a camera and emotions are estimated using an emotion estimation algorithm. An emotion score is calculated based on changes in facial expression. The user's voice can also be recorded and emotions can be estimated using voice analysis technology. The tone and speed of the voice can be analyzed and an emotion score calculated. This allows the method of providing answers to be adjusted according to the user's emotions, making it possible to provide answers in the most optimal way for the user.

[0136] When accumulating data, the accumulation unit can analyze the user's social media activity and accumulate related data. For example, if the user posts about childcare on social media, the accumulation unit can prioritize the accumulation of related data. Also, if the user posts about work on social media, the accumulation unit can prioritize the accumulation of work-related data. Furthermore, the accumulation unit can also accumulate related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data based on the user's interests can be accumulated.

[0137] The creation unit can estimate the user's emotions and adjust the length of the parenting book based on the estimated user emotions. For example, the user's facial expressions are captured with a camera and emotions are estimated using an emotion estimation algorithm. An emotion score is calculated based on changes in facial expressions. The user's voice can also be recorded and emotions can be estimated using voice analysis technology. The tone and speed of the voice can be analyzed and an emotion score calculated. This allows the length of the parenting book to be adjusted according to the user's emotions, making it possible to provide the user with the optimal parenting book.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The reception unit receives questions from the user. Questions from the user may be in text, voice, or image format. The reception unit receives questions from the user through a smart speaker or app. The reception unit can also estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of receiving questions can be delayed. Step 2: The generation unit uses a generation AI to generate an answer based on the question received by the reception unit. The generation AI analyzes the question using natural language processing technology and generates an appropriate answer. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and can apply the optimal generation algorithm depending on the content of the question. For example, a generation algorithm specialized for childcare is applied to a question about childcare. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit provides the generated answer to the user by voice or text. The providing unit can also estimate the user's emotions and adjust the way the answer is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide the answer by voice in a calm tone. Step 4: The storage unit stores data related to the growth and development of the user's child. The storage unit stores data such as the user's child's height, weight, and developmental stage in the cloud. The storage unit can also estimate the user's emotions and adjust the data storage method based on the estimated user emotions. For example, if the user is feeling stressed, the storage of data can be temporarily stopped. Step 5: The creation unit creates an individualized parenting book based on the data accumulated by the storage unit. The creation unit analyzes growth and development patterns based on the accumulated data and creates an individualized parenting book. The creation unit can also estimate the user's emotions and adjust the way the parenting book is created based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit creates a concise and easy-to-understand parenting book.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 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.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; a generator that generates an answer based on the question received by the receiver; a providing unit that provides the answer generated by the generating unit; a storage unit that stores data on the growth and development of the user's child; a creation unit that creates an individual child-rearing book based on the data stored by the storage unit; Equipped with A system characterized by:

2. The reception unit Accept questions from users via a smart speaker or app 2. The system of claim 1.

3. The generation unit Uses natural language processing to analyze questions and generate appropriate answers 2. The system of claim 1.

4. The providing unit Provide generated answers to users via voice or text 2. The system of claim 1.

5. The storage unit is Store data about your child's growth and development in the cloud 2. The system of claim 1.

6. The creation unit Analyzing growth and development patterns based on accumulated data and creating individual parenting manuals 2. The system of claim 1.

7. The providing unit Providing users with personalized parenting books 2. The system of claim 1.

8. The providing unit Become a 24-hour best friend that users can easily talk to 2. The system of claim 1.

Images