system

Abstract

The system according to this embodiment aims to alleviate anxieties and worries related to child-rearing and to provide personalized child-rearing books. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a childcare book creation unit, and a provision unit. The reception unit receives questions and consultations from users. The analysis unit analyzes the information received by the reception unit and generates answers and advice. The storage unit stores the answers and advice generated by the analysis unit. The childcare book creation unit creates individual childcare books based on the data stored by the storage unit. The provision unit provides the childcare books created by the childcare book creation unit to the users.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, appropriate support for relieving anxieties and worries related to child-rearing is not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to relieve anxieties and worries related to child-rearing and provide an individual parenting book.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a childcare book creation unit, and a provision unit. The reception unit receives questions and consultations from users. The analysis unit analyzes the information received by the reception unit and generates answers and advice. The storage unit stores the answers and advice generated by the analysis unit. The childcare book creation unit creates individual childcare books based on the data stored by the storage unit. The provision unit provides the childcare books created by the childcare book creation unit to users. [Effects of the Invention]

[0007] The system according to this embodiment can alleviate anxieties and worries related to child-rearing and provide personalized child-rearing books. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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). <​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The childcare support system according to an embodiment of the present invention is a new childcare support AI that supports the anxieties and worries of parents. This childcare support system provides the following functions by linking a smart speaker and an app. First, the user inputs questions and consultations through the smart speaker or app. For example, specific questions such as "Is my child's growth rate normal?" or "What should I do when my child cries a lot at night?" are entered. This information is input to the AI. Next, the AI ​​analyzes the input information and provides appropriate answers and advice. The AI ​​generates the optimal answer to the user's question based on childcare books, information on the internet, past data, etc. For example, it provides specific advice such as "Your child's growth rate is within the normal range. There is no need to worry," or "If your child cries a lot at night, try adjusting the room temperature." Furthermore, the AI ​​accumulates user data and creates an individualized childcare book. For example, based on questions and consultations entered by the user in the past, and answers and advice provided by the AI, a specialized childcare book just for the child can be created. This allows the user to refer to their own personalized childcare book at any time. In addition, the AI ​​functions as a conversational partner and consultant for the user 24 hours a day, 7 days a week. For example, even if a user feels anxious or worried in the middle of the night, the AI ​​will respond at any time and provide appropriate advice and support. This allows users to raise their children with peace of mind without feeling lonely. Furthermore, the smart speaker provides voice-activated support for when users are in trouble. For example, if a user says, "Tell me how to change a diaper," the smart speaker will explain the specific steps by voice. This allows users to obtain information without using their hands and concentrate on the actual task. In this way, the present invention provides a new childcare support AI to alleviate the anxieties and worries of parents, making their parenting lives more comfortable. As a result, the childcare support system can support parents by providing appropriate answers and advice to users' questions and concerns, and by creating personalized parenting guides.

[0029] The childcare support system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a childcare book creation unit, and a provision unit. The reception unit receives questions and consultations from users. These questions and consultations include, but are not limited to, questions about childcare and consultations about health. The reception unit receives questions and consultations from users, for example, through a smart speaker or an app. The reception unit can also transmit information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit and generates answers and advice. The analysis unit generates the best answers to the user's questions based, for example, on childcare books, information on the internet, and past data. For example, the analysis unit uses natural language processing technology to analyze the user's questions and generate appropriate answers. The analysis unit can also use machine learning algorithms to analyze past data and provide the best advice. The storage unit stores the answers and advice generated by the analysis unit. The storage unit stores, for example, questions and consultations entered by users in the past, and answers and advice provided by the AI ​​in a database. The storage unit can manage data according to the type of database and retention period. The childcare book creation unit creates individual childcare books based on the data stored by the storage unit. For example, the childcare book creation unit creates a specialized childcare book just for the user's child based on questions and consultations previously entered by the user, and answers and advice provided by AI. The childcare book creation unit can create childcare books in various formats, such as e-books and print. The provision unit provides the childcare books created by the childcare book creation unit to the user. For example, the provision unit can provide voice support via a smart speaker to help users when they are in trouble. The provision unit can also function as a 24 / 7 conversation partner and advisor for the user. As a result, the childcare support system according to this embodiment can support the anxieties and worries of parents by providing appropriate answers and advice to users' questions and consultations and by creating individual childcare books.

[0030] The reception desk receives questions and consultations from users. These questions and consultations include, but are not limited to, questions about childcare or health. The reception desk receives questions and consultations from users through, for example, smart speakers or apps. Specifically, smart speakers use speech recognition technology to convert user speech into text data, and apps directly receive text data entered by users. This data is sent to the reception desk in real time, initiating the processing of the entire system. The reception desk can also send user-entered information to an AI. The AI ​​analyzes the user's questions and consultations and uses this as initial data to generate appropriate answers and advice. Furthermore, the reception desk can encrypt and anonymize data to protect user privacy. This allows users to ask questions and seek advice with peace of mind. To enhance user convenience, the reception desk has built a system that is available 24 hours a day, 365 days a year, allowing users to ask questions and seek advice anytime, anywhere. The reception desk can also refer to a user's past question and consultation history to provide faster and more accurate responses. For example, users who have asked similar questions in the past can be given advice based on their previous answers. This allows the reception department to respond flexibly to user needs and improve the overall efficiency of the system.

[0031] The analysis unit analyzes information received by the reception unit and generates answers and advice. For example, the analysis unit generates the optimal answer to a user's question based on information from parenting books, the internet, and past data. Specifically, the analysis unit uses natural language processing technology to analyze the user's question and generate an appropriate answer. Natural language processing technology uses algorithms to grammatically analyze the user's question and understand its meaning. For example, if a user asks, "What causes my baby to cry at night?", the analysis unit extracts keywords such as "baby," "crying at night," and "cause," and searches for relevant information. The analysis unit can also use machine learning algorithms to analyze past data and provide optimal advice. For example, it provides the most effective advice based on data from users who have asked similar questions in the past. Furthermore, the analysis unit can generate customized answers tailored to the user's individual situation and needs. For example, it provides specific advice based on the user's child's age and health condition. This allows the analysis unit to provide quick and accurate answers to user questions, improving user satisfaction. Additionally, the analysis unit can continuously update the AI's learning data to improve the accuracy and quality of its answers. This allows the analysis unit to consistently provide high-quality answers based on the latest information, thereby improving the overall reliability of the system.

[0032] The storage unit stores the answers and advice generated by the analysis unit. For example, the storage unit saves questions and consultations previously entered by users, as well as answers and advice provided by the AI, into a database. Specifically, the storage unit creates a separate database for each user and stores each user's questions, consultations, and provided answers and advice in chronological order. This allows for easy reference of the user's past history, enabling continuous support. The storage unit can manage data according to the type of database and retention period. For example, important data is stored for a long period, and unnecessary data is automatically deleted after a certain period. The storage unit can also perform regular data backups and take measures to prevent data loss or corruption. Furthermore, the storage unit uses access control and encryption technologies to ensure data security. This protects user privacy and prevents unauthorized access to data. The storage unit can quickly provide the data required by the analysis unit and the childcare book creation unit, improving the overall efficiency of the system. In addition, the storage unit can use data analysis and statistical processing to improve the system and develop new services. For example, by analyzing trends in user questions and inquiries, the automated response function for frequently asked questions can be enhanced. This allows the data storage unit to improve the overall system performance and increase user satisfaction.

[0033] The parenting guide creation department creates individual parenting guides based on data accumulated by the data storage department. For example, the parenting guide creation department creates a specialized parenting guide just for each child, based on questions and consultations previously entered by the user, as well as answers and advice provided by AI. Specifically, the parenting guide creation department organizes the user's questions and consultations by category and compiles answers and advice for each category. For example, it divides the information into categories such as diet, sleep, health, and education, providing information that meets the user's needs. The parenting guide creation department can create parenting guides in various formats, such as e-books and print. The e-book format allows users to easily view the guide on their smartphones and tablets, while the print format allows users to keep it on hand for reference at any time. In addition, the parenting guide creation department can continuously update the content of the parenting guides based on user feedback, providing the latest information. For example, if a user asks a new question or seeks advice, the content is reflected in the parenting guide, ensuring that the information is always up-to-date. Furthermore, the parenting guide creation department can create customized parenting guides tailored to the user's individual situation and needs. For example, it can provide specific advice tailored to the user's child's age and health condition. This allows the childcare book creation department to respond flexibly to user needs and improve the overall efficiency of the system.

[0034] The service provider department provides users with parenting books created by the parenting book creation department. The service provider department also offers voice support, such as assistance via smart speakers. Specifically, smart speakers allow users to listen to the contents of parenting books in audio format, providing necessary information quickly. Furthermore, the service provider department can function as a 24 / 7 conversational partner and advisor for users. For example, if a user's child becomes ill in the middle of the night, they can receive quick advice through their smart speaker. In addition, the service provider department can regularly update the contents of parenting books to provide the latest information. For example, if new parenting or health information is added, the service provider department will notify users and update the book. The service provider department can also improve the content of parenting books based on user feedback, making them more user-friendly. For example, they can enhance the table of contents and index of parenting books to make it easier for users to find specific information. This allows the service provider department to provide users with quick and accurate information, reducing anxiety and worries about parenting. Furthermore, the service provider department can provide customized information tailored to the user's needs. For example, they can provide specific advice based on the user's child's age and health condition. This allows the service provider to respond flexibly to user needs and improve the overall efficiency of the system.

[0035] The analysis unit can generate the optimal answer to a user's question based on information from parenting books, the internet, and past data. For example, the analysis unit collects information from parenting books and the internet to generate the optimal answer to a user's question. Furthermore, the analysis unit can also generate the optimal answer to a user's question based on past data. For instance, if a similar question has been asked in the past, the analysis unit will use those answers as a reference to generate the optimal answer. This allows for the provision of more accurate advice by generating the optimal answer to a user's question based on information from parenting books, the internet, and past data.

[0036] The data storage unit can store questions and consultations previously entered by users, as well as answers and advice provided by the AI. For example, the data storage unit can save questions and consultations previously entered by users in a database. It can also save answers and advice provided by the AI ​​in a database. For example, the data storage unit can save questions and consultations previously entered by users in text format. It can also save answers and advice provided by the AI ​​in audio format. By accumulating questions and consultations previously entered by users, as well as answers and advice provided by the AI, it is possible to accumulate data for creating personalized parenting guides.

[0037] The parenting guide creation department can create personalized parenting guides based on accumulated data. For example, it can create guides that include optimal advice for users' questions and concerns, based on the accumulated data. Furthermore, the parenting guide creation department can create individual guides based on questions and concerns previously entered by users. For instance, it can analyze previously entered questions and concerns and create a guide based on that analysis. This allows for the creation of personalized parenting guides based on accumulated data, tailored specifically to each user's child.

[0038] The service provider can offer voice-activated support to help users in need. For example, if a user says, "Tell me how to change a diaper," the smart speaker will explain the specific steps verbally. Similarly, if a user asks, "What should I do if my baby cries a lot at night?", the smart speaker can provide appropriate advice verbally. For instance, the service provider might say, "If your baby cries a lot at night, try adjusting the room temperature." This allows users to obtain information without using their hands by providing voice-activated support through their smart speaker.

[0039] The support unit can function as a conversational partner and advisor for users 24 hours a day, 7 days a week. For example, if a user feels anxious or worried in the middle of the night, the AI ​​will respond at any time and provide appropriate advice and support. The support unit can also respond to users' questions and consultations 24 hours a day, 7 days a week, even if the user wants to ask questions or seek advice during the day. For example, if a user asks, "What should I do if my child won't stop crying in the middle of the night?", the support unit will provide appropriate advice. In this way, by functioning as a conversational partner and advisor for users 24 hours a day, 7 days a week, users can consult or ask questions at any time.

[0040] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, it can automatically display as suggestions the type of question the user has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions that the user may ask at specific times of day based on their past question history. For example, if the reception desk has frequently asked about "how to deal with a crying baby at night" in the past, it will automatically display similar questions as suggestions at night. This allows for efficient response by selecting the most suitable reception method through analysis of the user's past question history.

[0041] The reception desk can filter questions and consultations based on the user's current living situation and areas of interest. For example, if a user is raising a newborn, the reception desk will prioritize relevant questions. Similarly, if a user is interested in a particular parenting method, the reception desk can prioritize questions related to that area. Furthermore, the reception desk can filter and receive appropriate questions based on the user's living situation (e.g., dual-income household). For example, if a user asks about "parenting methods in dual-income households," the reception desk will prioritize relevant questions. This allows for more appropriate questions and consultations to be received by filtering based on the user's current living situation and areas of interest.

[0042] The reception desk can prioritize receiving questions and consultations by considering the user's geographical location. For example, if a user lives in a specific region, the reception desk will prioritize questions related to that region. Similarly, if a user is traveling, the reception desk can prioritize questions related to their travel destination. Furthermore, if a user is planning to move, the reception desk can prioritize questions related to their new area. For instance, if a user asks about "childcare support in a new area," the reception desk will prioritize questions related to that area. This allows the reception desk to prioritize region-related questions by considering the user's geographical location.

[0043] The reception desk can analyze a user's social media activity when receiving questions or inquiries and receive relevant questions. For example, if a user posts about childcare on social media, the reception desk will prioritize questions related to that content. Furthermore, if a user participates in a specific childcare group, the reception desk can also receive questions based on the group's activities. Additionally, the reception desk can receive relevant questions based on articles or links shared by the user on social media. For example, the reception desk might receive a question from a user titled "Questions based on activities in a childcare group." This allows the reception desk to prioritize relevant questions by analyzing the user's social media activity.

[0044] The analysis unit can adjust the level of detail in its analysis based on the importance of the question. For example, for urgent questions, the analysis unit will perform a detailed analysis and provide specific advice. For general questions, the analysis unit can perform a standard analysis and provide appropriate advice. Furthermore, for less important questions, the analysis unit can perform a concise analysis and provide basic advice. For instance, if a user asks about an "urgent problem," the analysis unit will perform a detailed analysis and provide specific advice. This allows for detailed analysis of urgent questions by adjusting the level of detail based on the importance of the question.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for health-related questions, the analysis unit can apply an algorithm that references medical databases. Similarly, for education-related questions, it can apply an algorithm that references specialized education databases. Furthermore, for general childcare-related questions, it can apply an algorithm that references childcare books or information on the internet. For instance, if a user asks a "health-related question," the analysis unit will apply an algorithm that references medical databases. This allows for the application of different analysis algorithms depending on the question category, thereby providing more appropriate analysis results.

[0046] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit will prioritize analyzing urgent questions immediately after they are submitted. The analysis unit can also analyze general questions with normal priority. Furthermore, the analysis unit can analyze less important questions after the analysis of other questions has been completed. For example, if a user submits an "urgent question," the analysis unit will analyze that question with the highest priority. This allows for a quick response to urgent questions by determining the priority of analysis based on when the questions were submitted.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis process. For example, if there are multiple questions in the same category, the analysis unit will analyze them together. Furthermore, if the content of the questions is related, the analysis unit can prioritize the analysis of related questions. Additionally, if the content of the questions differs, the analysis unit can analyze each question individually. For example, if a user submits multiple questions in the same category, the analysis unit will analyze them together. This allows related questions to be analyzed together by adjusting the order of analysis based on the relevance of the questions.

[0048] The data storage unit can optimize its storage algorithm by referring to past data during the storage process. For example, it can analyze past data and select the most effective storage method. It can also adjust the storage algorithm based on trends in past data. Furthermore, it can evaluate the quality of past data and prioritize the storage of high-quality data. For example, it can analyze past data and select the most effective storage method. By optimizing the storage algorithm by referring to past data, more effective data storage becomes possible.

[0049] The data storage unit can weight data based on when questions and consultations were submitted. For example, the storage unit will store data related to urgent questions with a higher weight. It can also store data related to general questions with a normal weight. Furthermore, it can store data related to less important questions with a lower weight. For example, if a user submits an "urgent question," the storage unit will store that data with a higher weight. This allows for the priority storage of important data by weighting data based on when questions and consultations were submitted.

[0050] The parenting guide creation department can create the most suitable parenting guide by analyzing users' past questions and consultations. For example, the department can include relevant information in the guide based on questions users have asked in the past. It can also include specific advice in the guide based on consultations users have had in the past. Furthermore, the department can analyze users' past questions and consultations to include the most appropriate information in the guide. For example, the department can include relevant information in the guide based on questions users have asked in the past. This allows the department to create the most suitable parenting guide by analyzing users' past questions and consultations.

[0051] The parenting guide creation department can customize the content of parenting guides based on the user's current living situation. For example, if the user is in a dual-income household, the department will include time-efficient parenting methods in the guide. If the user is a single parent, the department can also include information about support systems. Furthermore, the department can include optimal parenting methods tailored to the user's specific circumstances. For instance, if the user asks about "parenting methods for dual-income households," the department will include time-efficient parenting methods. This allows the department to provide more appropriate parenting guides by customizing the content based on the user's current living situation.

[0052] The parenting guide creation department can create optimal parenting guides by considering the user's geographical location. For example, if a user lives in a specific region, the department will include information relevant to that region in the guide. It can also include information relevant to the travel destination if the user is traveling. Furthermore, if a user is planning to move, the department can include information relevant to the new region. For instance, if a user asks about "parenting support in a new region," the department will include information relevant to that region. This allows the department to create optimal parenting guides that include region-specific information by considering the user's geographical location.

[0053] The parenting book creation department can analyze users' social media activity and suggest content for parenting books when creating them. For example, if a user posts about parenting on social media, the department can include information related to that content in the parenting book. Furthermore, if a user participates in a specific parenting group, the department can suggest content based on the group's activities. In addition, the department can include relevant information in parenting books based on articles and links shared by users on social media. For example, the department could include "information based on the user's activities in parenting groups" in the parenting book. This allows for the creation of parenting books that include relevant information by analyzing users' social media activity.

[0054] The service provider can provide optimal information by referring to the user's past usage history at the time of delivery. For example, the service provider can provide relevant information based on information the user has used in the past. Furthermore, the service provider can provide the most appropriate information from the user's past usage history. In addition, the service provider can analyze the user's past usage history and provide information at the optimal time. For example, the service provider can provide relevant information based on "information the user has used in the past." This allows the service provider to provide optimal information by referring to the user's past usage history.

[0055] The information provider can provide optimal information by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the provider will provide information tailored to the screen size. Furthermore, if the user is using a tablet, the provider can provide information optimized for a larger screen. Additionally, if the user is using a smartwatch, the provider can provide concise and highly visible information. For example, if the user says they are using a smartphone, the provider will provide information tailored to the screen size. This allows the provider to provide device-optimized information by considering the user's device information.

[0056] The service provider can provide multilingual information at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the information based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide information in a specific language if the user selects one. For example, if the user says, "Provide information based on the device's language settings," the service provider will provide the information in that language. This enables more appropriate information provision by providing multilingual information according to the user's language settings.

[0057] The information provider can deliver the most relevant information to the user based on their current situation at the time of delivery. For example, if the user is raising children, the provider will prioritize providing information related to childcare. Similarly, if the user is working, the provider can prioritize providing work-related information. Furthermore, if the user is on vacation, the provider can prioritize providing information that promotes relaxation. For instance, if the user states they are "raising children," the provider will prioritize providing information related to childcare. This allows for more appropriate information delivery by providing the most relevant information based on the user's current situation.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze a user's past question history and select the most appropriate reception method. For example, it can automatically display questions the user has frequently asked in the past as suggestions. It can also prioritize suggesting reception methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest questions that the user may ask at specific times of day based on their past question history. For example, if a user has frequently asked "how to deal with a crying baby at night" in the past, similar questions will be automatically displayed as suggestions at night. By analyzing the user's past question history, the system can select the most appropriate reception method and provide efficient support.

[0060] The reception desk can filter questions and consultations based on the user's current living situation and areas of interest. For example, if a user is raising a newborn, relevant questions will be prioritized. Similarly, if a user is interested in a particular parenting method, questions related to that area will be prioritized. Furthermore, appropriate questions can be filtered based on the user's living situation (e.g., dual-income household). For example, if a user asks about "parenting methods in dual-income households," relevant questions will be prioritized. This allows for more appropriate questions and consultations to be received by filtering based on the user's current living situation and areas of interest.

[0061] The reception desk can prioritize questions and consultations based on the user's geographical location, taking into account the user's location. For example, if a user lives in a specific region, questions related to that region will be prioritized. Similarly, if a user is traveling, questions related to their travel destination will be prioritized. Furthermore, if a user is planning to move, questions related to their new area will be prioritized. For instance, if a user asks about "childcare support in a new area," questions related to that area will be prioritized. This allows the system to prioritize region-specific questions by considering the user's geographical location.

[0062] The analysis unit can adjust the level of detail in the analysis based on the importance of the question. For example, for urgent questions, a detailed analysis is performed to provide specific advice. For general questions, a standard analysis is performed to provide appropriate advice. Furthermore, for less important questions, a concise analysis is performed to provide basic advice. For example, if a user asks about an "urgent problem," a detailed analysis is performed to provide specific advice. In this way, by adjusting the level of detail in the analysis based on the importance of the question, a detailed analysis can be performed for urgent questions.

[0063] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for health-related questions, an algorithm that references medical databases can be applied. Similarly, for education-related questions, an algorithm that references specialized education databases can be applied. Furthermore, for general childcare questions, an algorithm that references childcare books or information on the internet can be applied. For example, if a user asks a "health-related question," an algorithm that references medical databases will be applied. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0064] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, urgent questions will be analyzed immediately after submission. General questions can be analyzed with normal priority. Furthermore, less important questions can be analyzed after the analysis of other questions is complete. For example, if a user submits an "urgent question," that question will be analyzed with the highest priority. This allows for a quick response to urgent questions by determining the priority of analysis based on when the questions were submitted.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives questions and inquiries from users. These questions and inquiries may include those related to childcare or health. The reception desk can receive questions and inquiries from users via smart speakers or apps, and can also send information entered by users to the AI. Step 2: The analysis unit analyzes the information received by the reception unit and generates answers and advice. The analysis unit generates the optimal answer to the user's question based on parenting books, information from the internet, and past data. It uses natural language processing technology and machine learning algorithms to analyze the user's question and provide appropriate answers and advice. Step 3: The storage unit stores the answers and advice generated by the analysis unit. The storage unit saves questions and consultations previously entered by users, as well as answers and advice provided by the AI, into a database and manages the data according to the type of database and retention period. Step 4: The parenting guide creation department creates individual parenting guides based on the data accumulated by the data storage department. The parenting guide creation department creates a specialized parenting guide just for the child, based on questions and consultations previously entered by the user, as well as answers and advice provided by the AI. The parenting guides are created in various formats, such as ebooks and print. Step 5: The provision department provides users with parenting books created by the parenting book creation department. The provision department also provides voice support via smart speakers to help users when they encounter difficulties, acting as a 24 / 7 conversation partner and advisor for users.

[0067] (Example of form 2) The childcare support system according to an embodiment of the present invention is a new childcare support AI that supports the anxieties and worries of parents. This childcare support system provides the following functions by linking a smart speaker and an app. First, the user inputs questions and consultations through the smart speaker or app. For example, specific questions such as "Is my child's growth rate normal?" or "What should I do when my child cries a lot at night?" are entered. This information is input to the AI. Next, the AI ​​analyzes the input information and provides appropriate answers and advice. The AI ​​generates the optimal answer to the user's question based on childcare books, information on the internet, past data, etc. For example, it provides specific advice such as "Your child's growth rate is within the normal range. There is no need to worry," or "If your child cries a lot at night, try adjusting the room temperature." Furthermore, the AI ​​accumulates user data and creates an individualized childcare book. For example, based on questions and consultations entered by the user in the past, and answers and advice provided by the AI, a specialized childcare book just for the child can be created. This allows the user to refer to their own personalized childcare book at any time. In addition, the AI ​​functions as a conversational partner and consultant for the user 24 hours a day, 7 days a week. For example, even if a user feels anxious or worried in the middle of the night, the AI ​​will respond at any time and provide appropriate advice and support. This allows users to raise their children with peace of mind without feeling lonely. Furthermore, the smart speaker provides voice-activated support for when users are in trouble. For example, if a user says, "Tell me how to change a diaper," the smart speaker will explain the specific steps by voice. This allows users to obtain information without using their hands and concentrate on the actual task. In this way, the present invention provides a new childcare support AI to alleviate the anxieties and worries of parents, making their parenting lives more comfortable. As a result, the childcare support system can support parents by providing appropriate answers and advice to users' questions and concerns, and by creating personalized parenting guides.

[0068] The childcare support system according to this embodiment comprises a reception unit, an analysis unit, a storage unit, a childcare book creation unit, and a provision unit. The reception unit receives questions and consultations from users. These questions and consultations include, but are not limited to, questions about childcare and consultations about health. The reception unit receives questions and consultations from users, for example, through a smart speaker or an app. The reception unit can also transmit information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit and generates answers and advice. The analysis unit generates the best answers to the user's questions based, for example, on childcare books, information on the internet, and past data. For example, the analysis unit uses natural language processing technology to analyze the user's questions and generate appropriate answers. The analysis unit can also use machine learning algorithms to analyze past data and provide the best advice. The storage unit stores the answers and advice generated by the analysis unit. The storage unit stores, for example, questions and consultations entered by users in the past, and answers and advice provided by the AI ​​in a database. The storage unit can manage data according to the type of database and retention period. The childcare book creation unit creates individual childcare books based on the data stored by the storage unit. For example, the childcare book creation unit creates a specialized childcare book just for the user's child based on questions and consultations previously entered by the user, and answers and advice provided by AI. The childcare book creation unit can create childcare books in various formats, such as e-books and print. The provision unit provides the childcare books created by the childcare book creation unit to the user. For example, the provision unit can provide voice support via a smart speaker to help users when they are in trouble. The provision unit can also function as a 24 / 7 conversation partner and advisor for the user. As a result, the childcare support system according to this embodiment can support the anxieties and worries of parents by providing appropriate answers and advice to users' questions and consultations and by creating individual childcare books.

[0069] The reception desk receives questions and consultations from users. These questions and consultations include, but are not limited to, questions about childcare or health. The reception desk receives questions and consultations from users through, for example, smart speakers or apps. Specifically, smart speakers use speech recognition technology to convert user speech into text data, and apps directly receive text data entered by users. This data is sent to the reception desk in real time, initiating the processing of the entire system. The reception desk can also send user-entered information to an AI. The AI ​​analyzes the user's questions and consultations and uses this as initial data to generate appropriate answers and advice. Furthermore, the reception desk can encrypt and anonymize data to protect user privacy. This allows users to ask questions and seek advice with peace of mind. To enhance user convenience, the reception desk has built a system that is available 24 hours a day, 365 days a year, allowing users to ask questions and seek advice anytime, anywhere. The reception desk can also refer to a user's past question and consultation history to provide faster and more accurate responses. For example, users who have asked similar questions in the past can be given advice based on their previous answers. This allows the reception department to respond flexibly to user needs and improve the overall efficiency of the system.

[0070] The analysis unit analyzes information received by the reception unit and generates answers and advice. For example, the analysis unit generates the optimal answer to a user's question based on information from parenting books, the internet, and past data. Specifically, the analysis unit uses natural language processing technology to analyze the user's question and generate an appropriate answer. Natural language processing technology uses algorithms to grammatically analyze the user's question and understand its meaning. For example, if a user asks, "What causes my baby to cry at night?", the analysis unit extracts keywords such as "baby," "crying at night," and "cause," and searches for relevant information. The analysis unit can also use machine learning algorithms to analyze past data and provide optimal advice. For example, it provides the most effective advice based on data from users who have asked similar questions in the past. Furthermore, the analysis unit can generate customized answers tailored to the user's individual situation and needs. For example, it provides specific advice based on the user's child's age and health condition. This allows the analysis unit to provide quick and accurate answers to user questions, improving user satisfaction. Additionally, the analysis unit can continuously update the AI's learning data to improve the accuracy and quality of its answers. This allows the analysis unit to consistently provide high-quality answers based on the latest information, thereby improving the overall reliability of the system.

[0071] The storage unit stores the answers and advice generated by the analysis unit. For example, the storage unit saves questions and consultations previously entered by users, as well as answers and advice provided by the AI, into a database. Specifically, the storage unit creates a separate database for each user and stores each user's questions, consultations, and provided answers and advice in chronological order. This allows for easy reference of the user's past history, enabling continuous support. The storage unit can manage data according to the type of database and retention period. For example, important data is stored for a long period, and unnecessary data is automatically deleted after a certain period. The storage unit can also perform regular data backups and take measures to prevent data loss or corruption. Furthermore, the storage unit uses access control and encryption technologies to ensure data security. This protects user privacy and prevents unauthorized access to data. The storage unit can quickly provide the data required by the analysis unit and the childcare book creation unit, improving the overall efficiency of the system. In addition, the storage unit can use data analysis and statistical processing to improve the system and develop new services. For example, by analyzing trends in user questions and inquiries, the automated response function for frequently asked questions can be enhanced. This allows the data storage unit to improve the overall system performance and increase user satisfaction.

[0072] The parenting guide creation department creates individual parenting guides based on data accumulated by the data storage department. For example, the parenting guide creation department creates a specialized parenting guide just for each child, based on questions and consultations previously entered by the user, as well as answers and advice provided by AI. Specifically, the parenting guide creation department organizes the user's questions and consultations by category and compiles answers and advice for each category. For example, it divides the information into categories such as diet, sleep, health, and education, providing information that meets the user's needs. The parenting guide creation department can create parenting guides in various formats, such as e-books and print. The e-book format allows users to easily view the guide on their smartphones and tablets, while the print format allows users to keep it on hand for reference at any time. In addition, the parenting guide creation department can continuously update the content of the parenting guides based on user feedback, providing the latest information. For example, if a user asks a new question or seeks advice, the content is reflected in the parenting guide, ensuring that the information is always up-to-date. Furthermore, the parenting guide creation department can create customized parenting guides tailored to the user's individual situation and needs. For example, it can provide specific advice tailored to the user's child's age and health condition. This allows the childcare book creation department to respond flexibly to user needs and improve the overall efficiency of the system.

[0073] The service provider department provides users with parenting books created by the parenting book creation department. The service provider department also offers voice support, such as assistance via smart speakers. Specifically, smart speakers allow users to listen to the contents of parenting books in audio format, providing necessary information quickly. Furthermore, the service provider department can function as a 24 / 7 conversational partner and advisor for users. For example, if a user's child becomes ill in the middle of the night, they can receive quick advice through their smart speaker. In addition, the service provider department can regularly update the contents of parenting books to provide the latest information. For example, if new parenting or health information is added, the service provider department will notify users and update the book. The service provider department can also improve the content of parenting books based on user feedback, making them more user-friendly. For example, they can enhance the table of contents and index of parenting books to make it easier for users to find specific information. This allows the service provider department to provide users with quick and accurate information, reducing anxiety and worries about parenting. Furthermore, the service provider department can provide customized information tailored to the user's needs. For example, they can provide specific advice based on the user's child's age and health condition. This allows the service provider to respond flexibly to user needs and improve the overall efficiency of the system.

[0074] The analysis unit can generate the optimal answer to a user's question based on information from parenting books, the internet, and past data. For example, the analysis unit collects information from parenting books and the internet to generate the optimal answer to a user's question. Furthermore, the analysis unit can also generate the optimal answer to a user's question based on past data. For instance, if a similar question has been asked in the past, the analysis unit will use those answers as a reference to generate the optimal answer. This allows for the provision of more accurate advice by generating the optimal answer to a user's question based on information from parenting books, the internet, and past data.

[0075] The data storage unit can store questions and consultations previously entered by users, as well as answers and advice provided by the AI. For example, the data storage unit can save questions and consultations previously entered by users in a database. It can also save answers and advice provided by the AI ​​in a database. For example, the data storage unit can save questions and consultations previously entered by users in text format. It can also save answers and advice provided by the AI ​​in audio format. By accumulating questions and consultations previously entered by users, as well as answers and advice provided by the AI, it is possible to accumulate data for creating personalized parenting guides.

[0076] The parenting guide creation department can create personalized parenting guides based on accumulated data. For example, it can create guides that include optimal advice for users' questions and concerns, based on the accumulated data. Furthermore, the parenting guide creation department can create individual guides based on questions and concerns previously entered by users. For instance, it can analyze previously entered questions and concerns and create a guide based on that analysis. This allows for the creation of personalized parenting guides based on accumulated data, tailored specifically to each user's child.

[0077] The service provider can offer voice-activated support to help users in need. For example, if a user says, "Tell me how to change a diaper," the smart speaker will explain the specific steps verbally. Similarly, if a user asks, "What should I do if my baby cries a lot at night?", the smart speaker can provide appropriate advice verbally. For instance, the service provider might say, "If your baby cries a lot at night, try adjusting the room temperature." This allows users to obtain information without using their hands by providing voice-activated support through their smart speaker.

[0078] The support unit can function as a conversational partner and advisor for users 24 hours a day, 7 days a week. For example, if a user feels anxious or worried in the middle of the night, the AI ​​will respond at any time and provide appropriate advice and support. The support unit can also respond to users' questions and consultations 24 hours a day, 7 days a week, even if the user wants to ask questions or seek advice during the day. For example, if a user asks, "What should I do if my child won't stop crying in the middle of the night?", the support unit will provide appropriate advice. In this way, by functioning as a conversational partner and advisor for users 24 hours a day, 7 days a week, users can consult or ask questions at any time.

[0079] The reception desk can estimate the user's emotions and adjust how questions and consultations are handled based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to quickly handle questions and consultations. For example, if the user says, "I'm in a hurry and just want a quick question," the reception desk will prioritize voice input. This allows for more appropriate responses by adjusting how questions and consultations are handled according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, it can automatically display as suggestions the type of question the user has frequently asked in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions that the user may ask at specific times of day based on their past question history. For example, if the reception desk has frequently asked about "how to deal with a crying baby at night" in the past, it will automatically display similar questions as suggestions at night. This allows for efficient response by selecting the most suitable reception method through analysis of the user's past question history.

[0081] The reception desk can filter questions and consultations based on the user's current living situation and areas of interest. For example, if a user is raising a newborn, the reception desk will prioritize relevant questions. Similarly, if a user is interested in a particular parenting method, the reception desk can prioritize questions related to that area. Furthermore, the reception desk can filter and receive appropriate questions based on the user's living situation (e.g., dual-income household). For example, if a user asks about "parenting methods in dual-income households," the reception desk will prioritize relevant questions. This allows for more appropriate questions and consultations to be received by filtering based on the user's current living situation and areas of interest.

[0082] The reception desk can estimate the user's emotions and determine the priority of questions and consultations based on those emotions. For example, if a user has an urgent problem, the reception desk will prioritize that question. If the user is relaxed, the reception desk can also prioritize questions with normal priority. Furthermore, if the user is feeling anxious, the reception desk can also prioritize those questions. For example, if the user says, "I'm in a hurry and need a quick answer," the reception desk will prioritize that question. This allows for a rapid response to urgent issues by prioritizing questions and consultations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reception desk can prioritize receiving questions and consultations by considering the user's geographical location. For example, if a user lives in a specific region, the reception desk will prioritize questions related to that region. Similarly, if a user is traveling, the reception desk can prioritize questions related to their travel destination. Furthermore, if a user is planning to move, the reception desk can prioritize questions related to their new area. For instance, if a user asks about "childcare support in a new area," the reception desk will prioritize questions related to that area. This allows the reception desk to prioritize region-related questions by considering the user's geographical location.

[0084] The reception desk can analyze a user's social media activity when receiving questions or inquiries and receive relevant questions. For example, if a user posts about childcare on social media, the reception desk will prioritize questions related to that content. Furthermore, if a user participates in a specific childcare group, the reception desk can also receive questions based on the group's activities. Additionally, the reception desk can receive relevant questions based on articles or links shared by the user on social media. For example, the reception desk might receive a question from a user titled "Questions based on activities in a childcare group." This allows the reception desk to prioritize relevant questions by analyzing the user's social media activity.

[0085] The analysis unit can estimate the user's emotions and adjust the way responses and advice are expressed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will provide responses and advice in gentle language. If the user is relaxed, the analysis unit can also provide responses and advice that include detailed explanations. Furthermore, if the user is in a hurry, the analysis unit can provide concise and to-the-point responses and advice. For example, if the user says, "I'm in a hurry, so please give me a concise answer," the analysis unit will provide concise and to-the-point responses and advice. This allows for more appropriate responses by adjusting the way responses and advice are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can adjust the level of detail in its analysis based on the importance of the question. For example, for urgent questions, the analysis unit will perform a detailed analysis and provide specific advice. For general questions, the analysis unit can perform a standard analysis and provide appropriate advice. Furthermore, for less important questions, the analysis unit can perform a concise analysis and provide basic advice. For instance, if a user asks about an "urgent problem," the analysis unit will perform a detailed analysis and provide specific advice. This allows for detailed analysis of urgent questions by adjusting the level of detail based on the importance of the question.

[0087] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for health-related questions, the analysis unit can apply an algorithm that references medical databases. Similarly, for education-related questions, it can apply an algorithm that references specialized education databases. Furthermore, for general childcare-related questions, it can apply an algorithm that references childcare books or information on the internet. For instance, if a user asks a "health-related question," the analysis unit will apply an algorithm that references medical databases. This allows for the application of different analysis algorithms depending on the question category, thereby providing more appropriate analysis results.

[0088] The analysis unit can estimate the user's emotions and adjust the length of responses and advice based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide short, concise responses and advice. If the user is relaxed, the analysis unit can also provide longer responses and advice that include detailed explanations. Furthermore, if the user is feeling anxious, the analysis unit can provide responses and advice that include careful explanations to reassure them. For example, if the user says, "I'm in a hurry, so please give me a short answer," the analysis unit will provide short, concise responses and advice. This allows for more appropriate responses by adjusting the length of responses and advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, the analysis unit will prioritize analyzing urgent questions immediately after they are submitted. The analysis unit can also analyze general questions with normal priority. Furthermore, the analysis unit can analyze less important questions after the analysis of other questions has been completed. For example, if a user submits an "urgent question," the analysis unit will analyze that question with the highest priority. This allows for a quick response to urgent questions by determining the priority of analysis based on when the questions were submitted.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the questions during the analysis process. For example, if there are multiple questions in the same category, the analysis unit will analyze them together. Furthermore, if the content of the questions is related, the analysis unit can prioritize the analysis of related questions. Additionally, if the content of the questions differs, the analysis unit can analyze each question individually. For example, if a user submits multiple questions in the same category, the analysis unit will analyze them together. This allows related questions to be analyzed together by adjusting the order of analysis based on the relevance of the questions.

[0091] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is feeling anxious, the storage unit will prioritize storing data related to that anxiety. It can also store general data if the user is relaxed. Furthermore, if the user is in a hurry, the storage unit can store only important data. For example, if the user says they are feeling anxious, the storage unit will prioritize storing data related to that anxiety. This allows for the storage of more appropriate data by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The data storage unit can optimize its storage algorithm by referring to past data during the storage process. For example, it can analyze past data and select the most effective storage method. It can also adjust the storage algorithm based on trends in past data. Furthermore, it can evaluate the quality of past data and prioritize the storage of high-quality data. For example, it can analyze past data and select the most effective storage method. By optimizing the storage algorithm by referring to past data, more effective data storage becomes possible.

[0093] The data storage unit can estimate the user's emotions and adjust the storage frequency based on the estimated emotions. For example, if the user is feeling anxious, the storage unit will store data more frequently. Conversely, if the user is relaxed, the storage unit can store data at a normal frequency. Furthermore, if the user is in a hurry, the storage unit can store only important data. For example, if the storage unit says "I'm feeling anxious," it will store data more frequently. This allows for more appropriate data storage by adjusting the storage frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The data storage unit can weight data based on when questions and consultations were submitted. For example, the storage unit will store data related to urgent questions with a higher weight. It can also store data related to general questions with a normal weight. Furthermore, it can store data related to less important questions with a lower weight. For example, if a user submits an "urgent question," the storage unit will store that data with a higher weight. This allows for the priority storage of important data by weighting data based on when questions and consultations were submitted.

[0095] The parenting guide creation system can estimate the user's emotions and adjust the content of the guide based on those emotions. For example, if the user is feeling anxious, the system will prioritize content that provides reassurance. If the user is relaxed, the system can also include detailed information. Furthermore, if the user is in a hurry, the system can include concise and to-the-point content. For instance, if the system says the user is feeling anxious, it will prioritize content that provides reassurance. By adjusting the content of the guide according to the user's emotions, a more appropriate guide can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The parenting guide creation department can create the most suitable parenting guide by analyzing users' past questions and consultations. For example, the department can include relevant information in the guide based on questions users have asked in the past. It can also include specific advice in the guide based on consultations users have had in the past. Furthermore, the department can analyze users' past questions and consultations to include the most appropriate information in the guide. For example, the department can include relevant information in the guide based on questions users have asked in the past. This allows the department to create the most suitable parenting guide by analyzing users' past questions and consultations.

[0097] The parenting guide creation department can customize the content of parenting guides based on the user's current living situation. For example, if the user is in a dual-income household, the department will include time-efficient parenting methods in the guide. If the user is a single parent, the department can also include information about support systems. Furthermore, the department can include optimal parenting methods tailored to the user's specific circumstances. For instance, if the user asks about "parenting methods for dual-income households," the department will include time-efficient parenting methods. This allows the department to provide more appropriate parenting guides by customizing the content based on the user's current living situation.

[0098] The parenting guide creation system can estimate the user's emotions and determine the priority of the parenting guide based on those emotions. For example, if the user is feeling anxious, the system will prioritize including content related to that anxiety in the parenting guide. Conversely, if the user is relaxed, the system can create the parenting guide with normal priorities. Furthermore, if the user is in a hurry, the system can prioritize including important content in the parenting guide. For example, if the user says they are feeling anxious, the system will prioritize including content related to that anxiety in the parenting guide. This allows for the provision of more appropriate parenting guides by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The parenting guide creation department can create optimal parenting guides by considering the user's geographical location. For example, if a user lives in a specific region, the department will include information relevant to that region in the guide. It can also include information relevant to the travel destination if the user is traveling. Furthermore, if a user is planning to move, the department can include information relevant to the new region. For instance, if a user asks about "parenting support in a new region," the department will include information relevant to that region. This allows the department to create optimal parenting guides that include region-specific information by considering the user's geographical location.

[0100] The parenting book creation department can analyze users' social media activity and suggest content for parenting books when creating them. For example, if a user posts about parenting on social media, the department can include information related to that content in the parenting book. Furthermore, if a user participates in a specific parenting group, the department can suggest content based on the group's activities. In addition, the department can include relevant information in parenting books based on articles and links shared by users on social media. For example, the department could include "information based on the user's activities in parenting groups" in the parenting book. This allows for the creation of parenting books that include relevant information by analyzing users' social media activity.

[0101] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is feeling anxious, the provider will provide information in gentle language. If the user is relaxed, the provider can also provide information with detailed explanations. Furthermore, if the user is in a hurry, the provider can provide concise and to-the-point information. For example, if the user says they are feeling anxious, the provider will provide information in gentle language. This allows for more appropriate information to be provided by adjusting the way information is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The service provider can provide optimal information by referring to the user's past usage history at the time of delivery. For example, the service provider can provide relevant information based on information the user has used in the past. Furthermore, the service provider can provide the most appropriate information from the user's past usage history. In addition, the service provider can analyze the user's past usage history and provide information at the optimal time. For example, the service provider can provide relevant information based on "information the user has used in the past." This allows the service provider to provide optimal information by referring to the user's past usage history.

[0103] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is feeling anxious, the provider will prioritize providing information related to that anxiety. If the user is relaxed, the provider can also provide information with normal priority. Furthermore, if the user is in a hurry, the provider can prioritize providing important information. For example, if the provider says "I'm feeling anxious," it will prioritize providing information related to that anxiety. This allows for more appropriate information delivery by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The information provider can provide optimal information by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the provider will provide information tailored to the screen size. Furthermore, if the user is using a tablet, the provider can provide information optimized for a larger screen. Additionally, if the user is using a smartwatch, the provider can provide concise and highly visible information. For example, if the user says they are using a smartphone, the provider will provide information tailored to the screen size. This allows the provider to provide device-optimized information by considering the user's device information.

[0105] The service provider can provide multilingual information at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the information based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide information in a specific language if the user selects one. For example, if the user says, "Provide information based on the device's language settings," the service provider will provide the information in that language. This enables more appropriate information provision by providing multilingual information according to the user's language settings.

[0106] The information provider can deliver the most relevant information to the user based on their current situation at the time of delivery. For example, if the user is raising children, the provider will prioritize providing information related to childcare. Similarly, if the user is working, the provider can prioritize providing work-related information. Furthermore, if the user is on vacation, the provider can prioritize providing information that promotes relaxation. For instance, if the user states they are "raising children," the provider will prioritize providing information related to childcare. This allows for more appropriate information delivery by providing the most relevant information based on the user's current situation.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception desk can estimate the user's emotions and adjust the way questions and consultations are handled based on those emotions. For example, if the user is stressed, a simple interface and minimal input steps can be provided. If the user is relaxed, detailed input options and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quicker handling of questions and consultations. For example, if the user says, "I'm in a hurry and just want a quick question," voice input will be prioritized. This allows for more appropriate responses by adjusting the way questions and consultations are handled according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The reception desk can analyze a user's past question history and select the most appropriate reception method. For example, it can automatically display questions the user has frequently asked in the past as suggestions. It can also prioritize suggesting reception methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest questions that the user may ask at specific times of day based on their past question history. For example, if a user has frequently asked "how to deal with a crying baby at night" in the past, similar questions will be automatically displayed as suggestions at night. By analyzing the user's past question history, the system can select the most appropriate reception method and provide efficient support.

[0110] The reception desk can filter questions and consultations based on the user's current living situation and areas of interest. For example, if a user is raising a newborn, relevant questions will be prioritized. Similarly, if a user is interested in a particular parenting method, questions related to that area will be prioritized. Furthermore, appropriate questions can be filtered based on the user's living situation (e.g., dual-income household). For example, if a user asks about "parenting methods in dual-income households," relevant questions will be prioritized. This allows for more appropriate questions and consultations to be received by filtering based on the user's current living situation and areas of interest.

[0111] The reception desk can estimate the user's emotions and determine the priority of questions and consultations based on those emotions. For example, if a user has an urgent problem, that question will be given top priority. If the user is relaxed, questions can be given the normal priority. Furthermore, if the user is feeling anxious, that question can also be given priority. For example, if a user says, "I'm in a hurry and need a quick answer," that question will be given top priority. This allows for a rapid response to urgent issues by prioritizing questions and consultations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The reception desk can prioritize questions and consultations based on the user's geographical location, taking into account the user's location. For example, if a user lives in a specific region, questions related to that region will be prioritized. Similarly, if a user is traveling, questions related to their travel destination will be prioritized. Furthermore, if a user is planning to move, questions related to their new area will be prioritized. For instance, if a user asks about "childcare support in a new area," questions related to that area will be prioritized. This allows the system to prioritize region-specific questions by considering the user's geographical location.

[0113] The analysis unit can estimate the user's emotions and adjust the way responses and advice are expressed based on the estimated emotions. For example, if the user is feeling anxious, it can provide responses and advice in gentle language. If the user is relaxed, it can provide responses and advice that include detailed explanations. Furthermore, if the user is in a hurry, it can provide concise and to-the-point responses and advice. For example, if the user says, "I'm in a hurry, so please give me a concise answer," it will provide concise and to-the-point responses and advice. In this way, by adjusting the way responses and advice are expressed according to the user's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The analysis unit can adjust the level of detail in the analysis based on the importance of the question. For example, for urgent questions, a detailed analysis is performed to provide specific advice. For general questions, a standard analysis is performed to provide appropriate advice. Furthermore, for less important questions, a concise analysis is performed to provide basic advice. For example, if a user asks about an "urgent problem," a detailed analysis is performed to provide specific advice. In this way, by adjusting the level of detail in the analysis based on the importance of the question, a detailed analysis can be performed for urgent questions.

[0115] The analysis unit can apply different analysis algorithms depending on the category of the question during analysis. For example, for health-related questions, an algorithm that references medical databases can be applied. Similarly, for education-related questions, an algorithm that references specialized education databases can be applied. Furthermore, for general childcare questions, an algorithm that references childcare books or information on the internet can be applied. For example, if a user asks a "health-related question," an algorithm that references medical databases will be applied. By applying different analysis algorithms depending on the category of the question, more appropriate analysis results can be provided.

[0116] The analysis unit can estimate the user's emotions and adjust the length of responses and advice based on the estimated emotions. For example, if the user is in a hurry, it can provide short, to-the-point responses and advice. If the user is relaxed, it can provide longer responses and advice that include detailed explanations. Furthermore, if the user is feeling anxious, it can provide responses and advice that include careful explanations to reassure them. For example, if the user says, "I'm in a hurry, so please give me a short answer," it will provide short, to-the-point responses and advice. By adjusting the length of responses and advice according to the user's emotions, more appropriate responses become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The analysis unit can determine the priority of analysis based on when the questions were submitted. For example, urgent questions will be analyzed immediately after submission. General questions can be analyzed with normal priority. Furthermore, less important questions can be analyzed after the analysis of other questions is complete. For example, if a user submits an "urgent question," that question will be analyzed with the highest priority. This allows for a quick response to urgent questions by determining the priority of analysis based on when the questions were submitted.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk receives questions and inquiries from users. These questions and inquiries may include those related to childcare or health. The reception desk can receive questions and inquiries from users via smart speakers or apps, and can also send information entered by users to the AI. Step 2: The analysis unit analyzes the information received by the reception unit and generates answers and advice. The analysis unit generates the optimal answer to the user's question based on parenting books, information from the internet, and past data. It uses natural language processing technology and machine learning algorithms to analyze the user's question and provide appropriate answers and advice. Step 3: The storage unit stores the answers and advice generated by the analysis unit. The storage unit saves questions and consultations previously entered by users, as well as answers and advice provided by the AI, into a database and manages the data according to the type of database and retention period. Step 4: The parenting guide creation department creates individual parenting guides based on the data accumulated by the data storage department. The parenting guide creation department creates a specialized parenting guide just for the child, based on questions and consultations previously entered by the user, as well as answers and advice provided by the AI. The parenting guides are created in various formats, such as ebooks and print. Step 5: The provision department provides users with parenting books created by the parenting book creation department. The provision department also provides voice support via smart speakers to help users when they encounter difficulties, acting as a 24 / 7 conversation partner and advisor for users.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0123] Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, childcare book creation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives questions and consultations from users through the reception device 38 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on childcare books and information on the internet. The storage unit stores the answers and advice in the database 24 of the data processing unit 12. The childcare book creation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that creates individual childcare books based on the stored data. The provision unit provides childcare books and advice to the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, childcare book creation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives questions and consultations from the user through the microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on childcare books and information on the internet. The storage unit stores the answers and advice in the database 24 of the data processing unit 12. The childcare book creation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that creates individual childcare books based on the stored data. The provision unit provides childcare books and advice to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, childcare book creation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives questions and consultations from the user through the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on childcare books and information on the internet. The storage unit stores the answers and advice in the database 24 of the data processing unit 12. The childcare book creation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that creates individual childcare books based on the stored data. The provision unit provides childcare books and advice to the user through the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, storage unit, childcare book creation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives questions and consultations from the user through the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates answers based on childcare books and information on the internet. The storage unit stores the answers and advice in the database 24 of the data processing unit 12. The childcare book creation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that creates individual childcare books based on the stored data. The provision unit provides childcare books and advice to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) A reception desk that handles questions and inquiries from users, An analysis unit analyzes the information received by the reception unit and generates answers and advice, A storage unit that stores the answers and advice generated by the analysis unit, A childcare book creation unit that creates individual childcare books based on the data accumulated by the aforementioned storage unit, The system includes a provisioning unit that provides the childcare book created by the aforementioned childcare book creation unit to the user. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Based on parenting books, online information, and historical data, it generates the best possible answers to user questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The storage unit is The system stores questions and consultations previously entered by users, as well as answers and advice provided by the AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned childcare book creation department, Based on accumulated data, we create a specialized parenting guide tailored specifically for your child. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Smart speakers provide voice-activated support to help you when you need assistance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It functions as a 24 / 7 conversational partner and advisor for users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the way questions and consultations are handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions or inquiries, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions and consultations it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions or inquiries, the system prioritizes receiving questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions or inquiries, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way responses and advice are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of responses and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The storage unit is The system estimates the user's emotions and selects the data to be stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The storage unit is During data storage, the storage algorithm is optimized by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The storage unit is It estimates the user's emotions and adjusts the frequency of accumulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The storage unit is During data accumulation, the data is weighted based on when the questions or consultations were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned childcare book creation department, The system estimates the user's emotions and adjusts the content of the parenting book based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned childcare book creation department, When creating parenting books, we analyze users' past questions and consultations to create the most suitable parenting books. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned childcare book creation department, When creating a parenting guide, customize the content based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned childcare book creation department, It estimates the user's emotions and prioritizes parenting books based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned childcare book creation department, When creating a parenting guide, we take the user's geographical location into consideration to create the most suitable guide. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned childcare book creation department, When creating parenting books, we analyze users' social media activity to propose content for the books. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we refer to the user's past usage history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing information, we will consider the user's device information to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, we will provide information in multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing information, we will deliver the most relevant information based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles questions and inquiries from users, An analysis unit analyzes the information received by the reception unit and generates answers and advice, A storage unit that stores the answers and advice generated by the analysis unit, A childcare book creation unit that creates individual childcare books based on the data accumulated by the aforementioned storage unit, The system includes a provisioning unit that provides the childcare book created by the aforementioned childcare book creation unit to the user. A system characterized by the following features.

2. The aforementioned analysis unit, Based on parenting books, online information, and historical data, it generates the best possible answers to user questions. The system according to feature 1.

3. The storage unit is The system stores questions and consultations previously entered by users, as well as answers and advice provided by the AI. The system according to feature 1.

4. The aforementioned childcare book creation department, Based on accumulated data, we create a specialized parenting guide tailored specifically for your child. The system according to feature 1.

5. The aforementioned supply unit is, Smart speakers provide voice-activated support to help you when you need assistance. The system according to feature 1.

6. The aforementioned supply unit is, It functions as a 24 / 7 conversational partner and advisor for users. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the way questions and consultations are handled based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

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