system

Abstract

The system according to this embodiment aims to provide individually optimized information related to pregnancy and childcare. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides optimal childcare methods and matching with other parents based on the analysis results obtained by the analysis unit. The advice unit provides advice and childcare tips for pregnant women.

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, it is difficult to individually optimize and provide information and advice regarding pregnancy and child-rearing, and there is room for improvement.

[0005] The system according to the embodiment aims to individually optimize and provide information regarding pregnancy and child-rearing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides optimal childcare methods and matching with other parents based on the analysis results obtained by the analysis unit. The advice unit provides advice and childcare tips for pregnant women. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually optimized information related to pregnancy and childcare. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable 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).

[0019] 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 matching and community service according to an embodiment of the present invention is a system for sharing and resolving the "fun," "uncertainties," and "anxieties" related to pregnancy, childbirth, and childcare. This system uses AI to analyze the experiences, information, and problems of individual families and provides each parent with the most suitable childcare method and matching with other parents. For example, users input their own experiences, information, and problems. For example, they input specific worries about childcare, past experiences, and current situations. This information is input into the AI. Next, the AI ​​analyzes the input information and provides each parent with the most suitable childcare method and matching with other parents. The AI ​​analyzes the user's information and experiences with its own algorithm and presents the best solutions for each parent's experiences and problems. For example, it matches parents with similar worries or provides advice from parents who have solved the same problems in the past. Furthermore, it provides advice and childcare tips for pregnant women and predicts future childcare problems and difficulties. This allows for pinpoint preparation for anxieties during and after childbirth. Through this mechanism, parents do not have to face problems alone, but can learn from other parents and share their own experiences, and receive more specific and individualized advice on childcare stress and problems. Furthermore, it is expected to contribute to solving the declining birthrate problem, alleviate the vague anxieties of future and current parents, and reduce the hurdles to raising children. This means that matching and community services can enable parents to share and resolve childcare-related issues.

[0029] The matching and community service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects user information. For example, the collection unit collects information such as specific childcare concerns entered by the user, past experiences, and current situations. The collection unit can collect information by methods such as text input, voice input, and image input. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and presents optimal solutions to the experiences and problems of individual parents. For example, the analysis unit uses techniques such as data mining, statistical analysis, and machine learning to analyze the information. The provision unit provides optimal childcare methods and matching with other parents based on the analysis results obtained by the analysis unit. For example, the provision unit matches parents with similar concerns. The provision unit can also provide advice from parents who have solved the same problems in the past. The advice unit provides advice and childcare tips for pregnant women. For example, the advice unit predicts future childcare problems and difficulties for pregnant women and provides advice and childcare tips. This enables the matching and community service according to the embodiment to efficiently collect, analyze, provide, and advise on user information.

[0030] The data collection unit collects user information. For example, it collects information such as specific childcare concerns entered by the user, past experiences, and current situations. The data collection unit can collect information through methods such as text input, voice input, and image input. Specifically, text information entered by users through applications and websites includes detailed concerns and questions about childcare, as well as past experiences. In the case of voice input, users use their smartphones and microphones to record their concerns and experiences by voice and send it to the system. The voice data is converted into text data using speech recognition technology, making processing by the analysis unit easier. With image input, users can upload photos and illustrations related to childcare. For example, images of babies eating, playing, and using childcare products are collected. These images are analyzed using image recognition technology and used as supplementary information to gain a more concrete understanding of the user's concerns and situation. By combining these diverse input methods, the data collection unit comprehensively collects user information and builds foundational data to provide optimal services to individual users. Furthermore, the data collection unit ensures security by encrypting and anonymizing data to protect user privacy. This allows users to confidently provide their information, improving the overall reliability of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and present optimal solutions to the experiences and problems of individual parents. Specifically, the AI ​​uses natural language processing technology to analyze text data and understand the content of the user's concerns and questions. For example, if a user inputs the concern "my baby cries at night," the AI ​​analyzes the text and extracts information related to night crying. Furthermore, audio data is converted to text using speech recognition technology and then analyzed in the same way. For image data, image recognition technology is used to detect specific objects and scenes related to childcare and understand the user's situation. The analysis unit combines these technologies to comprehensively analyze the user's concerns and situation and derive the optimal solution. For example, data mining technology is used to analyze data from users who have had similar concerns in the past and identify successful solutions. Statistical analysis is also used to reveal general trends and patterns for specific childcare problems. Machine learning algorithms continuously learn based on user feedback and improve analysis accuracy. This allows the analysis unit to quickly and accurately present optimal solutions tailored to the user's individual situation.

[0032] The service provider will provide optimal parenting methods and matching users with other parents based on the analysis results obtained by the analysis department. Specifically, it will match parents with similar concerns. For example, it will match parents struggling with nighttime crying so they can share their experiences and advice. The service provider can also provide advice from parents who have solved the same problems in the past. For example, it will provide testimonials and specific advice from parents who have implemented effective measures against nighttime crying. The service provider will provide this information to users at the appropriate time to help them resolve their parenting concerns. Furthermore, the service provider can provide customized parenting information and resources according to the user's preferences and needs. For example, it may recommend specific parenting books, online resources, or expert advice. The service provider will also collect user feedback and continuously improve the quality of the information and services it provides. In this way, the service provider can provide users with optimal parenting methods and resources and support them in resolving their parenting concerns.

[0033] The Advice Department provides advice and parenting tips for expectant mothers. Specifically, it anticipates future parenting problems and difficulties for expectant mothers and provides advice and parenting tips. For example, it provides advice on managing health and nutrition during pregnancy, and preparing for childbirth. It also provides basic knowledge and skills regarding postpartum parenting, as well as guidance on preparing for first-time parenting. The Advice Department provides this information to users in stages, providing consistent support from pregnancy to postpartum. Furthermore, the Advice Department can provide customized advice according to the user's situation and needs. For example, it can provide nutritional advice tailored to specific health conditions and lifestyles, or information on specific parenting styles. The Advice Department also collects user feedback and continuously improves the quality of the advice it provides. In this way, the Advice Department can provide expectant users with appropriate advice and parenting tips, supporting them to confidently engage in parenting.

[0034] The data collection unit can analyze the user's past information submission history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize text input. It can also recommend voice input if the user has frequently used voice input in the past. Furthermore, if the user has submitted many images in the past, the data collection unit can prioritize image collection. This allows the optimal data collection method to be selected by analyzing the user's past information submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information submission history into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the data collection unit will prioritize collecting information related to parenting. The data collection unit can also collect information related to a specific parenting method if the user is interested in that method. Furthermore, if the user is struggling to balance work and childcare, the data collection unit can collect information related to work-life balance. This allows for the collection of more relevant information by filtering it based on the user's living situation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0036] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information about childcare support services in the area where the user lives. It can also collect childcare information related to places the user plans to visit. Furthermore, the data collection unit can prioritize the collection of information about childcare facilities and events near the user. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0037] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, the data collection unit can analyze posts about childcare that the user has shared on social media and collect relevant information. The data collection unit can also collect information about childcare-related accounts that the user follows. Furthermore, the data collection unit can collect information about childcare-related groups that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a concise analysis on general information. Furthermore, the analysis unit can perform a detailed analysis on information that the user is particularly interested in. By adjusting the level of detail of the analysis based on the importance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for information related to childcare, the analysis unit can apply a specialized analysis algorithm for childcare. Similarly, for information related to health, the analysis unit can apply a specialized analysis algorithm for health. Furthermore, for information related to education, the analysis unit can apply a specialized analysis algorithm for education. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted information. It can also prioritize the analysis of information submitted by users during specific time periods. Furthermore, the analysis unit can analyze current information by referring to information previously submitted by users. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also prioritize the analysis of information that the user has shown interest in. Furthermore, the analysis unit can prioritize the analysis of highly relevant information by referring to the user's past behavior history. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide detailed information for important information. It can also provide concise information for general information. Furthermore, it can provide detailed information for information that the user has shown particular interest in. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0043] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, for information related to childcare, the delivery unit can apply a specialized delivery algorithm for childcare. Furthermore, for information related to health, the delivery unit can apply a specialized delivery algorithm for health. In addition, for information related to education, the delivery unit can apply a specialized delivery algorithm for education. This allows for the provision of more appropriate information by applying different delivery algorithms depending on the category of information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0044] The information delivery unit can determine the priority of information delivery based on when the information was submitted. For example, the delivery unit may prioritize the delivery of recently submitted information. It can also prioritize the delivery of information submitted by users during a specific time period. Furthermore, the delivery unit may provide current information by referring to information previously submitted by users. This allows for the provision of more appropriate information by determining the priority of delivery based on when the information was submitted. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input information submission time data into a generating AI and have the generating AI perform the determination of the delivery priority.

[0045] The information delivery unit can adjust the order of information delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information. It can also prioritize the delivery of information that the user has shown interest in. Furthermore, the delivery unit can prioritize the delivery of highly relevant information by referring to the user's past behavior history. By adjusting the order of information delivery based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0046] The advice unit can optimize current advice by referring to past advice data when providing advice. For example, the advice unit can provide current advice by referring to advice given in the past to solve the same problem. The advice unit can also analyze past advice data to provide the most effective advice. Furthermore, the advice unit can provide advice that is best suited to the current situation based on past advice data. In this way, current advice can be optimized by referring to past advice data. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past advice data into a generating AI and have the generating AI perform the optimization of the current advice.

[0047] The advice unit can customize the means of advice based on the user's current living situation. For example, if the user is working, the advice unit can provide advice that can be implemented in a short time. If the user is on parental leave, the advice unit can also provide advice that can be implemented over a longer period of time. Furthermore, if the user is busy, the advice unit can provide advice that can be implemented easily. In this way, by customizing the means of advice based on the user's living situation, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of advice.

[0048] The advice unit can provide optimal advice by taking into account the user's geographical location. For example, the advice unit can provide advice on childcare support services in the area where the user lives. It can also provide childcare advice on places the user plans to visit. Furthermore, the advice unit can provide advice on childcare facilities and events near the user. This allows the advice unit to provide optimal advice by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal advice.

[0049] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can analyze posts related to childcare that the user has shared on social media and provide relevant advice. The advice unit can also provide advice based on information about childcare-related accounts that the user follows. Furthermore, the advice unit can provide advice based on information about childcare-related groups that the user participates in. This allows the advice unit to propose more appropriate methods for providing advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity data into a generating AI and have the generating AI propose methods for providing advice.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit collects user health data, and the analysis unit can provide childcare advice based on the collected health data. For example, it can collect the user's sleep patterns and dietary information, and the analysis unit can provide health management advice for childcare based on this data. The data collection unit can also collect the user's exercise habits, and the analysis unit can provide advice on the impact of exercise on childcare. Furthermore, the data collection unit can collect the user's stress levels, and the analysis unit can provide advice on stress management. This allows for more comprehensive support by providing childcare advice based on the user's health data.

[0052] The analysis unit can analyze a user's past childcare data and predict future childcare problems. For example, it can analyze past sleep patterns and dietary content to predict future health problems. It can also analyze past stress levels to predict the need for future stress management. Furthermore, it can analyze past childcare activities to predict future childcare challenges. This allows users to take preventative measures by predicting future problems based on their past data.

[0053] The data collection unit collects the user's family structure data, and the analysis unit can provide childcare advice based on the collected data. For example, if the user has multiple children, it can provide individual advice for each child. It can also provide support tailored to single parents if the user is a single parent. Furthermore, if the user lives with grandparents, it can provide advice that takes the grandparents' role into consideration. This allows for more comprehensive support by providing customized advice tailored to the user's family structure.

[0054] The service provider can offer region-specific childcare advice, taking into account the user's geographical location. For example, it can provide information about childcare support services in the user's area. It can also provide childcare advice for places the user plans to visit. Furthermore, it can provide information about childcare facilities and events near the user. In this way, by considering the user's geographical location, it can provide region-specific childcare advice.

[0055] The analysis unit can analyze the user's parenting style data and provide optimal parenting advice. For example, if the user prefers natural parenting, it can provide advice on natural parenting. If the user has an education-focused parenting style, it can provide advice on education. Furthermore, if the user is a dual-income family, it can provide parenting advice tailored to dual-income families. This allows for more effective support by providing customized advice according to the user's parenting style.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects user information. The data collection unit collects information such as specific concerns about childcare entered by the user, past experiences, and current situation. The data collection unit can collect information using methods such as text input, voice input, and image input. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, AI, and presents optimal solutions to the experiences and problems of individual parents. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning. Step 3: The service provider provides optimal parenting methods and matching with other parents based on the analysis results obtained by the analysis unit. For example, the service provider matches parents with similar concerns. The service provider can also provide advice from parents who have solved the same problems in the past. Step 4: The advice section provides advice and parenting tips for expectant mothers. For example, the advice section anticipates future parenting problems and difficulties for expectant mothers and provides advice and parenting tips.

[0058] (Example of form 2) The matching and community service according to an embodiment of the present invention is a system for sharing and resolving the "fun," "uncertainties," and "anxieties" related to pregnancy, childbirth, and childcare. This system uses AI to analyze the experiences, information, and problems of individual families and provides each parent with the most suitable childcare method and matching with other parents. For example, users input their own experiences, information, and problems. For example, they input specific worries about childcare, past experiences, and current situations. This information is input into the AI. Next, the AI ​​analyzes the input information and provides each parent with the most suitable childcare method and matching with other parents. The AI ​​analyzes the user's information and experiences with its own algorithm and presents the best solutions for each parent's experiences and problems. For example, it matches parents with similar worries or provides advice from parents who have solved the same problems in the past. Furthermore, it provides advice and childcare tips for pregnant women and predicts future childcare problems and difficulties. This allows for pinpoint preparation for anxieties during and after childbirth. Through this mechanism, parents do not have to face problems alone, but can learn from other parents and share their own experiences, and receive more specific and individualized advice on childcare stress and problems. Furthermore, it is expected to contribute to solving the declining birthrate problem, alleviate the vague anxieties of future and current parents, and reduce the hurdles to raising children. This means that matching and community services can enable parents to share and resolve childcare-related issues.

[0059] The matching and community service according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and an advice unit. The collection unit collects user information. For example, the collection unit collects information such as specific childcare concerns entered by the user, past experiences, and current situations. The collection unit can collect information by methods such as text input, voice input, and image input. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and presents optimal solutions to the experiences and problems of individual parents. For example, the analysis unit uses techniques such as data mining, statistical analysis, and machine learning to analyze the information. The provision unit provides optimal childcare methods and matching with other parents based on the analysis results obtained by the analysis unit. For example, the provision unit matches parents with similar concerns. The provision unit can also provide advice from parents who have solved the same problems in the past. The advice unit provides advice and childcare tips for pregnant women. For example, the advice unit predicts future childcare problems and difficulties for pregnant women and provides advice and childcare tips. This enables the matching and community service according to the embodiment to efficiently collect, analyze, provide, and advise on user information.

[0060] The data collection unit collects user information. For example, it collects information such as specific childcare concerns entered by the user, past experiences, and current situations. The data collection unit can collect information through methods such as text input, voice input, and image input. Specifically, text information entered by users through applications and websites includes detailed concerns and questions about childcare, as well as past experiences. In the case of voice input, users use their smartphones and microphones to record their concerns and experiences by voice and send it to the system. The voice data is converted into text data using speech recognition technology, making processing by the analysis unit easier. With image input, users can upload photos and illustrations related to childcare. For example, images of babies eating, playing, and using childcare products are collected. These images are analyzed using image recognition technology and used as supplementary information to gain a more concrete understanding of the user's concerns and situation. By combining these diverse input methods, the data collection unit comprehensively collects user information and builds foundational data to provide optimal services to individual users. Furthermore, the data collection unit ensures security by encrypting and anonymizing data to protect user privacy. This allows users to confidently provide their information, improving the overall reliability of the system.

[0061] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses AI to analyze the collected information and present optimal solutions to the experiences and problems of individual parents. Specifically, the AI ​​uses natural language processing technology to analyze text data and understand the content of the user's concerns and questions. For example, if a user inputs the concern "my baby cries at night," the AI ​​analyzes the text and extracts information related to night crying. Furthermore, audio data is converted to text using speech recognition technology and then analyzed in the same way. For image data, image recognition technology is used to detect specific objects and scenes related to childcare and understand the user's situation. The analysis unit combines these technologies to comprehensively analyze the user's concerns and situation and derive the optimal solution. For example, data mining technology is used to analyze data from users who have had similar concerns in the past and identify successful solutions. Statistical analysis is also used to reveal general trends and patterns for specific childcare problems. Machine learning algorithms continuously learn based on user feedback and improve analysis accuracy. This allows the analysis unit to quickly and accurately present optimal solutions tailored to the user's individual situation.

[0062] The service provider will provide optimal parenting methods and matching users with other parents based on the analysis results obtained by the analysis department. Specifically, it will match parents with similar concerns. For example, it will match parents struggling with nighttime crying so they can share their experiences and advice. The service provider can also provide advice from parents who have solved the same problems in the past. For example, it will provide testimonials and specific advice from parents who have implemented effective measures against nighttime crying. The service provider will provide this information to users at the appropriate time to help them resolve their parenting concerns. Furthermore, the service provider can provide customized parenting information and resources according to the user's preferences and needs. For example, it may recommend specific parenting books, online resources, or expert advice. The service provider will also collect user feedback and continuously improve the quality of the information and services it provides. In this way, the service provider can provide users with optimal parenting methods and resources and support them in resolving their parenting concerns.

[0063] The Advice Department provides advice and parenting tips for expectant mothers. Specifically, it anticipates future parenting problems and difficulties for expectant mothers and provides advice and parenting tips. For example, it provides advice on managing health and nutrition during pregnancy, and preparing for childbirth. It also provides basic knowledge and skills regarding postpartum parenting, as well as guidance on preparing for first-time parenting. The Advice Department provides this information to users in stages, providing consistent support from pregnancy to postpartum. Furthermore, the Advice Department can provide customized advice according to the user's situation and needs. For example, it can provide nutritional advice tailored to specific health conditions and lifestyles, or information on specific parenting styles. The Advice Department also collects user feedback and continuously improves the quality of the advice it provides. In this way, the Advice Department can provide expectant users with appropriate advice and parenting tips, supporting them to confidently engage in parenting.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect information during a relaxed period. The data collection unit can also start collecting information immediately if the user is excited. Furthermore, if the user is tired, the data collection unit can collect information after rest. This allows for more appropriate information collection by adjusting the timing of information collection 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past information submission history and select the optimal data collection method. For example, if the user has preferred using text input in the past, the data collection unit will prioritize text input. It can also recommend voice input if the user has frequently used voice input in the past. Furthermore, if the user has submitted many images in the past, the data collection unit can prioritize image collection. This allows the optimal data collection method to be selected by analyzing the user's past information submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information submission history into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the data collection unit will prioritize collecting information related to parenting. The data collection unit can also collect information related to a specific parenting method if the user is interested in that method. Furthermore, if the user is struggling to balance work and childcare, the data collection unit can collect information related to work-life balance. This allows for the collection of more relevant information by filtering it based on the user's living situation and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that alleviates anxiety. Similarly, if the user is enjoying themselves, the data collection unit can prioritize collecting information that enhances their enjoyment. Furthermore, if the user has questions, the data collection unit can prioritize collecting information that answers those questions. This allows for the collection of more appropriate information 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0068] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of information about childcare support services in the area where the user lives. It can also collect childcare information related to places the user plans to visit. Furthermore, the data collection unit can prioritize the collection of information about childcare facilities and events near the user. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0069] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, the data collection unit can analyze posts about childcare that the user has shared on social media and collect relevant information. The data collection unit can also collect information about childcare-related accounts that the user follows. Furthermore, the data collection unit can collect information about childcare-related groups that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can use a reassuring presentation. It can also use a positive presentation if the user is enjoying themselves. Furthermore, if the user has questions, the analysis unit can use a clear and concise presentation. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a concise analysis on general information. Furthermore, the analysis unit can perform a detailed analysis on information that the user is particularly interested in. By adjusting the level of detail of the analysis based on the importance of the information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, for information related to childcare, the analysis unit can apply a specialized analysis algorithm for childcare. Similarly, for information related to health, the analysis unit can apply a specialized analysis algorithm for health. Furthermore, for information related to education, the analysis unit can apply a specialized analysis algorithm for education. This allows for the provision of more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0074] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted information. It can also prioritize the analysis of information submitted by users during specific time periods. Furthermore, the analysis unit can analyze current information by referring to information previously submitted by users. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also prioritize the analysis of information that the user has shown interest in. Furthermore, the analysis unit can prioritize the analysis of highly relevant information by referring to the user's past behavior history. By adjusting the order of analysis based on the relevance of the information, it is possible to provide more appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is feeling anxious, the service provider can use a reassuring way of presenting the information. If the user is having fun, the service provider can also use a cheerful way of presenting the information. Furthermore, if the user has questions, the service provider can use a clear and concise way of presenting the information. By adjusting the way the information is presented according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the information is presented.

[0077] The information delivery unit can adjust the level of detail provided based on the importance of the information at the time of delivery. For example, the delivery unit can provide detailed information for important information. It can also provide concise information for general information. Furthermore, it can provide detailed information for information that the user has shown particular interest in. By adjusting the level of detail based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0078] The information delivery unit can apply different delivery algorithms depending on the category of information at the time of delivery. For example, for information related to childcare, the delivery unit can apply a specialized delivery algorithm for childcare. Furthermore, for information related to health, the delivery unit can apply a specialized delivery algorithm for health. In addition, for information related to education, the delivery unit can apply a specialized delivery algorithm for education. This allows for the provision of more appropriate information by applying different delivery algorithms depending on the category of information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information category data into a generating AI and have the generating AI execute the application of different delivery algorithms.

[0079] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the information provider can prioritize providing information that alleviates anxiety. Similarly, if the user is enjoying themselves, the information provider can prioritize providing information that enhances their enjoyment. Furthermore, if the user has questions, the information provider can prioritize providing information that answers those questions. This allows for the provision of more appropriate information 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provider may be performed using AI, or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0080] The information delivery unit can determine the priority of information delivery based on when the information was submitted. For example, the delivery unit may prioritize the delivery of recently submitted information. It can also prioritize the delivery of information submitted by users during a specific time period. Furthermore, the delivery unit may provide current information by referring to information previously submitted by users. This allows for the provision of more appropriate information by determining the priority of delivery based on when the information was submitted. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input information submission time data into a generating AI and have the generating AI perform the determination of the delivery priority.

[0081] The information delivery unit can adjust the order of information delivery based on the relevance of the information. For example, the delivery unit can prioritize the delivery of highly relevant information. It can also prioritize the delivery of information that the user has shown interest in. Furthermore, the delivery unit can prioritize the delivery of highly relevant information by referring to the user's past behavior history. By adjusting the order of information delivery based on the relevance of the information, more appropriate information can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0082] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is feeling anxious, the advice unit can use reassuring language. It can also use cheerful language if the user is having fun. Furthermore, if the user has questions, the advice unit can use clear and concise language. This allows for more appropriate advice to be provided by adjusting the way it expresses advice 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses advice.

[0083] The advice unit can optimize current advice by referring to past advice data when providing advice. For example, the advice unit can provide current advice by referring to advice given in the past to solve the same problem. The advice unit can also analyze past advice data to provide the most effective advice. Furthermore, the advice unit can provide advice that is best suited to the current situation based on past advice data. In this way, current advice can be optimized by referring to past advice data. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past advice data into a generating AI and have the generating AI perform the optimization of the current advice.

[0084] The advice unit can customize the means of advice based on the user's current living situation. For example, if the user is working, the advice unit can provide advice that can be implemented in a short time. If the user is on parental leave, the advice unit can also provide advice that can be implemented over a longer period of time. Furthermore, if the user is busy, the advice unit can provide advice that can be implemented easily. In this way, by customizing the means of advice based on the user's living situation, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the means of advice.

[0085] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling anxious, the advice unit will prioritize providing advice that alleviates anxiety. Similarly, if the user is enjoying themselves, the advice unit can prioritize providing advice that enhances their enjoyment. Furthermore, if the user has questions, the advice unit can prioritize providing advice that answers those questions. This allows for more appropriate advice to be provided by prioritizing advice 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, or not. For example, the advice unit can input user emotion data into a generative AI and have the generative AI determine the priority of advice.

[0086] The advice unit can provide optimal advice by taking into account the user's geographical location. For example, the advice unit can provide advice on childcare support services in the area where the user lives. It can also provide childcare advice on places the user plans to visit. Furthermore, the advice unit can provide advice on childcare facilities and events near the user. This allows the advice unit to provide optimal advice by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into a generating AI and have the generating AI perform the task of providing optimal advice.

[0087] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can analyze posts related to childcare that the user has shared on social media and provide relevant advice. The advice unit can also provide advice based on information about childcare-related accounts that the user follows. Furthermore, the advice unit can provide advice based on information about childcare-related groups that the user participates in. This allows the advice unit to propose more appropriate methods for providing advice by analyzing the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity data into a generating AI and have the generating AI propose methods for providing advice.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit collects user health data, and the analysis unit can provide childcare advice based on the collected health data. For example, it can collect the user's sleep patterns and dietary information, and the analysis unit can provide health management advice for childcare based on this data. The data collection unit can also collect the user's exercise habits, and the analysis unit can provide advice on the impact of exercise on childcare. Furthermore, the data collection unit can collect the user's stress levels, and the analysis unit can provide advice on stress management. This allows for more comprehensive support by providing childcare advice based on the user's health data.

[0090] The data collection unit can estimate the user's emotions and customize the content of parenting advice based on those estimated emotions. For example, if the user is feeling anxious, it can provide specific advice to alleviate that anxiety. If the user is having fun, it can suggest activities to enhance that enjoyment. Furthermore, if the user has questions, it can provide clear answers to those questions. This allows for more effective support by providing customized advice tailored to the user's emotions.

[0091] The analysis unit can analyze a user's past childcare data and predict future childcare problems. For example, it can analyze past sleep patterns and dietary content to predict future health problems. It can also analyze past stress levels to predict the need for future stress management. Furthermore, it can analyze past childcare activities to predict future childcare challenges. This allows users to take preventative measures by predicting future problems based on their past data.

[0092] The service provider can estimate the user's emotions and adjust the timing of childcare advice based on those estimates. For example, it can provide advice when the user is relaxed. It can also immediately provide advice to reduce stress if the user is stressed. Furthermore, if the user is agitated, it can provide advice to calm them down. This allows for more effective support by providing advice at the appropriate time according to the user's emotions.

[0093] The data collection unit collects the user's family structure data, and the analysis unit can provide childcare advice based on the collected data. For example, if the user has multiple children, it can provide individual advice for each child. It can also provide support tailored to single parents if the user is a single parent. Furthermore, if the user lives with grandparents, it can provide advice that takes the grandparents' role into consideration. This allows for more comprehensive support by providing customized advice tailored to the user's family structure.

[0094] The analysis unit can estimate the user's emotions and adjust the content of parenting advice based on those emotions. For example, if the user is feeling anxious, it can provide specific advice to alleviate that anxiety. If the user is enjoying themselves, it can suggest activities to enhance their enjoyment. Furthermore, if the user has questions, it can provide clear answers to those questions. This allows for more effective support by providing customized advice tailored to the user's emotions.

[0095] The service provider can offer region-specific childcare advice, taking into account the user's geographical location. For example, it can provide information about childcare support services in the user's area. It can also provide childcare advice for places the user plans to visit. Furthermore, it can provide information about childcare facilities and events near the user. In this way, by considering the user's geographical location, it can provide region-specific childcare advice.

[0096] The data collection unit can estimate the user's emotions and prioritize parenting advice based on those emotions. For example, if the user is feeling anxious, it can prioritize advice that reduces anxiety. If the user is having fun, it can prioritize advice that enhances that fun. Furthermore, if the user has questions, it can prioritize advice that answers those questions. By prioritizing advice according to the user's emotions, it is possible to provide more appropriate advice.

[0097] The analysis unit can analyze the user's parenting style data and provide optimal parenting advice. For example, if the user prefers natural parenting, it can provide advice on natural parenting. If the user has an education-focused parenting style, it can provide advice on education. Furthermore, if the user is a dual-income family, it can provide parenting advice tailored to dual-income families. This allows for more effective support by providing customized advice according to the user's parenting style.

[0098] The service provider can estimate the user's emotions and adjust the way parenting advice is expressed based on those emotions. For example, if the user is feeling anxious, it can use reassuring language. If the user is having fun, it can use cheerful language. Furthermore, if the user has questions, it can use clear and concise language. By adjusting the way advice is expressed according to the user's emotions, the service provider can offer more appropriate advice.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects user information. The data collection unit collects information such as specific concerns about childcare entered by the user, past experiences, and current situation. The data collection unit can collect information using methods such as text input, voice input, and image input. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the collected information using, for example, AI, and presents optimal solutions to the experiences and problems of individual parents. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning. Step 3: The service provider provides optimal parenting methods and matching with other parents based on the analysis results obtained by the analysis unit. For example, the service provider matches parents with similar concerns. The service provider can also provide advice from parents who have solved the same problems in the past. Step 4: The advice section provides advice and parenting tips for expectant mothers. For example, the advice section anticipates future parenting problems and difficulties for expectant mothers and provides advice and parenting tips.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the receiving device 38 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit provides the analysis results to the user using the output device 40 of the smart device 14. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice and childcare tips for pregnant women. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit provides the analysis results to the user using the speaker 240 of the smart glasses 214. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice and childcare tips for pregnant women. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit provides the analysis results to the user using the speaker 240 of the headset terminal 314. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice and childcare tips for pregnant women. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and advice unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user information using the microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using AI. The provision unit provides the analysis results to the user using the speaker 240 of the robot 414. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice and childcare tips for pregnant women. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the optimal childcare method and matching with other parents based on the analysis results obtained by the aforementioned analysis unit, It includes an advice section that provides advice and parenting tips for pregnant women. A system characterized by the following features. (Note 2) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past information submission history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting information, we analyze users' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, we optimize the current advice by referring to past advice data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, customize the advice based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, we analyze the user's social media activity and suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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 collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, A providing unit that provides the optimal childcare method and matching with other parents based on the analysis results obtained by the aforementioned analysis unit, It includes an advice section that provides advice and parenting tips for pregnant women. A system characterized by the following features.

2. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past information submission history to select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting information, we analyze users' social media activity and gather relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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