System

Abstract

An object of the system according to the embodiment is to enable a new father to obtain appropriate advice on childcare and to eliminate anxiety and questions about childcare.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects information related to the situation of the father. The analysis unit analyzes the information collected by the collection unit and proposes a behavior pattern. The providing unit provides the childcare advice based on the analysis result obtained by the analyzing unit. The monitoring unit continuously monitors the care situation of the father based on the advice provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult for new fathers to obtain appropriate advice on child-rearing, and there was a lack of means to alleviate their concerns and questions about child-rearing.

[0005] The system according to the embodiment aims to enable new fathers to obtain appropriate advice regarding child-rearing and resolve their anxieties and questions about child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects information on the father's situation. The analysis unit analyzes the information collected by the collection unit and suggests behavioral patterns. The provision unit provides child-rearing advice based on the analysis results obtained by the analysis unit. The monitoring unit continuously monitors the father's child-rearing situation based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows new fathers to obtain appropriate advice regarding child-rearing and resolve their anxieties and questions about child-rearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A childcare support system according to an embodiment of the present invention provides specific childcare advice tailored to each father's situation. The childcare support system collects information about each father's situation, analyzes it using a generation AI, proposes appropriate behavioral patterns, and provides childcare advice. The childcare support system also shares the childcare experiences of other fathers and combines them with professional support to alleviate fathers' concerns and questions about childcare. For example, the childcare support system collects information about each father's situation, such as the baby's age in months, the father's childcare experience, and current childcare concerns. The childcare support system then analyzes the collected information using a generation AI and proposes appropriate behavioral patterns, such as babycare methods tailored to the baby's age and solutions to specific problems the father is facing. The childcare support system then provides specific childcare advice based on the analysis results. For example, specific advice such as "hold the baby and rock him" or "use a toy to distract him when changing his diaper" is provided to address nighttime crying. Furthermore, the childcare support system also shares the childcare experiences of other fathers. For example, other fathers share how they overcame nighttime crying and tips for changing diapers. Finally, the childcare support system continuously monitors the father's childcare situation and provides new advice as needed. For example, if the father inputs a new concern, appropriate advice is provided. This allows the childcare support system to help the father enjoy raising his child with confidence and support the harmonious development of the family. This allows the childcare support system to alleviate the father's anxieties and doubts about childcare and support the harmonious development of the family. For example, it provides the necessary information and support so that the father can enjoy raising his child with confidence.

[0029] A childcare support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects information about the father's situation. The father's situation includes, but is not limited to, the baby's age in months, the father's childcare experience, and current childcare concerns. The collection unit, for example, accepts information entered by the father. The collection unit can also collect information using sensors or application data. For example, the collection unit accepts text information entered by the father. The collection unit can also accept voice input and image input. The analysis unit uses a generation AI to analyze the information collected by the collection unit and propose an appropriate behavior pattern. The behavior pattern includes, but is not limited to, examples of baby care methods according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit allows the generation AI to propose a baby care method according to the baby's age. The analysis unit can also allow the generation AI to provide specific advice based on the father's childcare experience. The analysis unit can also allow the generation AI to propose solutions to the father's childcare concerns. The providing unit provides specific childcare advice based on the analysis results obtained by the analyzing unit. Examples of childcare advice include, but are not limited to, tips on how to deal with night crying and tips on changing diapers. For example, the providing unit provides specific advice such as "hold the baby and rock him" to deal with night crying. The providing unit can also provide advice such as "use a toy to attract the baby's attention" when changing a diaper. The providing unit can also provide the childcare advice as a text message or a voice message. The monitoring unit continuously monitors the father's childcare situation based on the advice provided by the providing unit. Examples of monitoring include, but are not limited to, periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit provides corresponding advice. The monitoring unit can also continuously monitor the father's childcare situation and provide new advice as needed. As a result, the childcare support system according to the embodiment can alleviate fathers' anxieties and doubts about childcare and support the harmonious development of families.

[0030] The collection unit can accept input information from the father. The input information includes, but is not limited to, for example, text input, voice input, and image input. For example, the collection unit accepts text information entered by the father. The collection unit can also accept voice input. For example, the collection unit converts what the father says into text using voice recognition technology. The collection unit can also accept image input. For example, the collection unit analyzes photos of the baby taken by the father and provides information about childcare. In this way, by accepting the input information from the father, the collection unit can collect information according to individual situations. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the text information entered by the father to a generation AI, which then performs analysis.

[0031] The analysis unit can suggest behavioral patterns according to the father's situation. Examples of behavioral patterns include, but are not limited to, methods of caring for the baby according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit allows the generation AI to suggest methods of caring for the baby according to the baby's age. The analysis unit can also allow the generation AI to provide specific advice based on the father's child-rearing experience. The analysis unit can also allow the generation AI to suggest solutions to the father's child-rearing worries. In this way, the analysis unit suggests appropriate behavioral patterns according to the father's situation, thereby providing specific child-rearing advice. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information about the father's situation into the generation AI, which can then suggest behavioral patterns.

[0032] The providing unit can provide childcare advice. Examples of childcare advice include, but are not limited to, measures to prevent night crying and tips for changing diapers. For example, the providing unit can provide specific advice such as "hold the baby and rock him" to prevent night crying. The providing unit can also provide advice such as "attract the baby with a toy" when changing a diaper. The providing unit can also provide the childcare advice as a text message or a voice message. In this way, the providing unit can alleviate the father's concerns and questions about childcare by providing specific childcare advice. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate childcare advice using a generation AI and provide it to the father.

[0033] The childcare support system includes a sharing unit that shares the childcare experiences of fathers. The sharing unit shares the childcare experiences of other fathers. The childcare experiences include, but are not limited to, success stories, failure stories, and specific anecdotes. For example, the sharing unit shares how other fathers overcame nighttime crying or tips for changing diapers. The sharing unit can also share the childcare experiences as text messages or voice messages. In this way, the sharing unit alleviates fathers' anxieties and questions about childcare by sharing the childcare experiences of other fathers. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the childcare experiences of other fathers into a generation AI, which then performs analysis.

[0034] The monitoring unit can continuously monitor the father's child-rearing situation and provide advice. Examples of monitoring include, but are not limited to, periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit can provide advice in response to that concern. The monitoring unit can also continuously monitor the father's child-rearing situation and provide new advice as needed. In this way, the monitoring unit continuously monitors the father's child-rearing situation and provides new advice, thereby resolving his concerns and questions about child-rearing. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's child-rearing situation into a generation AI, which then performs analysis.

[0035] The collection unit can analyze the father's past child-rearing history and select the optimal information collection method. For example, the collection unit preferentially selects an information collection method that the father has used favorably in the past. The collection unit can also select an effective information collection method from the father's past child-rearing history. The collection unit can also select the most efficient information collection method based on the father's past child-rearing history. In this way, the collection unit can select the optimal information collection method by analyzing the father's past child-rearing history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the father's past child-rearing history data into the generation AI, which can select the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the dad's current living situation and areas of interest. The collection unit filters the information based on, for example, childcare themes that the dad is currently interested in. The collection unit can also prioritize collecting highly relevant information depending on the dad's living situation (such as how busy he is at work). The collection unit can also filter and provide necessary information based on the dad's current living situation. In this way, the collection unit can provide highly relevant information by filtering information based on the dad's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the dad's living situation data into the generation AI, which then filters the information.

[0037] When collecting information, the collection unit can select the optimal collection means depending on dad's input method. For example, if dad prefers voice input, the collection unit can prioritize voice information collection. Also, if dad prefers text input, the collection unit can prioritize text information collection. Also, if dad prefers image input, the collection unit can prioritize information collection using images. In this way, the collection unit can select the optimal collection means depending on dad's input method, thereby enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input dad's input method data into a generation AI, which can select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the father's geographical location information. For example, the collection unit prioritizes collecting childcare information for the area where the father lives. The collection unit can also prioritize collecting childcare information related to places the father often visits. The collection unit can also prioritize collecting childcare information specific to the area based on the father's geographical location information. In this way, the collection unit can provide childcare information specific to the area by taking into account the father's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the father's geographical location information data to the generation AI, which can select highly relevant information.

[0039] When collecting information, the collection unit can analyze the father's social media activities and collect relevant information. For example, the collection unit collects information on childcare-related accounts that the father follows on social media. The collection unit can also analyze the content of the father's social media posts to collect relevant childcare information. The collection unit can also collect relevant childcare information by referring to the activities of the father's friends on social media. In this way, the collection unit can provide highly relevant information by analyzing the father's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the father's social media data into a generation AI, which can select relevant information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting dad's past feedback. The collection unit customizes the information collection method based on, for example, dad's past feedback. The collection unit can also preferentially select effective information collection methods from dad's past feedback. The collection unit can also improve the accuracy of information collection by reflecting dad's past feedback. In this way, the collection unit improves the accuracy of information collection by reflecting dad's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input dad's feedback data into the generation AI, which can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of childcare. For example, the analysis unit performs a detailed analysis for childcare issues with high importance. The analysis unit can also perform a concise analysis for childcare issues with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of childcare. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of childcare. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input importance data of childcare issues into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the childcare category during analysis. For example, the analysis unit can apply a specialized algorithm to an analysis related to a baby's health. The analysis unit can also apply an algorithm according to the baby's growth stage to an analysis related to the baby's growth. The analysis unit can also apply the optimal analysis algorithm depending on the childcare category. This allows the analysis unit to provide more appropriate analysis results by applying the optimal analysis algorithm depending on the childcare category. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI, for example. For example, the analysis unit can input childcare category data into the generation AI, which can select the optimal analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to dad's past analysis results. The analysis unit, for example, improves the accuracy of the analysis based on dad's past analysis results. The analysis unit can also preferentially apply effective analysis methods from dad's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to dad's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to dad's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input dad's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the childcare questions. For example, the analysis unit prioritizes analysis of childcare questions with high urgency. The analysis unit can also prioritize analysis of childcare questions whose submission date is approaching. The analysis unit can also determine the priority of analysis based on the time of submission of the childcare questions. This allows the analysis unit to determine the priority of analysis based on the time of submission of the childcare questions, thereby allowing for quick response to problems with high urgency. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of submission of the childcare questions into the generation AI, and the generation AI can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of childcare. For example, the analysis unit prioritizes analysis of highly relevant childcare problems. The analysis unit can also postpone analysis of less relevant childcare problems. The analysis unit can also adjust the order of analysis based on the relevance of childcare. In this way, the analysis unit can prioritize highly relevant problems by adjusting the order of analysis based on the relevance of childcare. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of childcare problems into the generation AI, and the generation AI can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the father's level of expertise. For example, if the father has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the father does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the father's level of expertise. In this way, the analysis unit can provide easy-to-understand analysis results by adjusting the use of technical terminology in the analysis according to the father's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input the father's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0047] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of child-rearing. For example, the providing unit provides detailed advice for child-rearing issues of high importance. The providing unit can also provide concise advice for child-rearing issues of low importance. The providing unit can also adjust the level of detail of the advice according to the importance of child-rearing. As a result, the providing unit can provide more appropriate advice by adjusting the level of detail of the advice according to the importance of child-rearing. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of child-rearing issues to a generating AI, and the generating AI can adjust the level of detail of the advice.

[0048] The providing unit can apply different advice algorithms depending on the childcare category when providing advice. For example, the providing unit can apply a specialized algorithm to advice regarding baby health. The providing unit can also apply an algorithm according to the baby's growth stage to advice regarding baby growth. The providing unit can also apply the optimal advice algorithm depending on the childcare category. In this way, the providing unit can provide more appropriate advice by applying the optimal advice algorithm depending on the childcare category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input childcare category data to a generating AI, which can select the optimal advice algorithm.

[0049] When providing advice, the providing unit can improve the accuracy of the advice by referring to dad's past advice results. The providing unit improves the accuracy of the advice, for example, based on dad's past advice results. The providing unit can also preferentially apply effective advice methods from dad's past advice results. The providing unit can also improve the accuracy of the advice by referring to dad's past advice results. In this way, the providing unit improves the accuracy of the advice by referring to dad's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input dad's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0050] When providing advice, the providing unit can determine the priority of the advice based on the time of submission of the childcare request. For example, the providing unit can prioritize providing advice for a childcare problem with a high urgency. The providing unit can also prioritize providing advice for a childcare problem whose submission date is approaching. The providing unit can also determine the priority of the advice based on the time of submission of the childcare request. This allows the providing unit to determine the priority of the advice based on the time of submission of the childcare request, thereby enabling quick response to a problem with a high urgency. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the childcare request into a generating AI, and the generating AI can determine the priority of the advice.

[0051] The providing unit can adjust the order of advice based on the relevance of childcare when providing advice. For example, the providing unit can provide advice preferentially for childcare problems with high relevance. The providing unit can also postpone providing advice for childcare problems with low relevance. The providing unit can also adjust the order of advice based on the relevance of childcare. In this way, the providing unit can prioritize addressing highly relevant problems by adjusting the order of advice based on the relevance of childcare. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of childcare problems to a generating AI, and the generating AI can adjust the order of advice.

[0052] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the dad's level of expertise. For example, if the dad has technical expertise, the providing unit can provide advice that uses a lot of technical terminology. Furthermore, if the dad does not have technical expertise, the providing unit can also provide concise and easy-to-understand advice. Furthermore, the providing unit can adjust the use of technical terminology in the advice according to the dad's level of expertise. In this way, the providing unit can provide easy-to-understand advice by adjusting the use of technical terminology in the advice according to the dad's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input dad's level of expertise data into the generating AI, which can adjust the use of technical terminology.

[0053] The sharing unit can adjust the level of detail of the sharing based on the importance of the other dads' childcare experiences when sharing. For example, the sharing unit shares detailed information for childcare experiences with high importance. The sharing unit can also share concise information for childcare experiences with low importance. The sharing unit can also adjust the level of detail of the sharing based on the importance of the other dads' childcare experiences. In this way, the sharing unit can provide more appropriate information by adjusting the level of detail of the sharing based on the importance of the other dads' childcare experiences. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the childcare experience data of the other dads into a generation AI, and the generation AI can adjust the level of detail of the sharing.

[0054] The sharing unit can apply different sharing algorithms depending on the childcare category when sharing. For example, the sharing unit can apply a specialized algorithm to childcare experiences related to baby health. The sharing unit can also apply an algorithm according to the baby's developmental stage to childcare experiences related to baby growth. The sharing unit can also apply the most appropriate sharing algorithm depending on the childcare category. This allows the sharing unit to provide more appropriate information by applying the most appropriate sharing algorithm depending on the childcare category. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input childcare category data into a generation AI, which can select the most appropriate sharing algorithm.

[0055] When sharing, the sharing unit can prioritize sharing highly relevant childcare experiences by taking into account the geographical location information of other dads. For example, the sharing unit prioritizes sharing childcare experiences in the area where the dad lives. The sharing unit can also prioritize sharing childcare experiences related to places the dad often visits. The sharing unit can also prioritize sharing childcare experiences specific to the area based on the dad's geographical location information. In this way, the sharing unit can provide childcare experiences specific to the area by taking into account the geographical location information of other dads. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input geographical location information data of other dads into the generation AI, which can select highly relevant childcare experiences.

[0056] When sharing, the sharing unit can analyze the social media activities of other dads and share related parenting experiences. For example, the sharing unit prioritizes sharing parenting experiences shared by other dads on social media. The sharing unit can also analyze the content of other dads' social media posts and share related parenting experiences. The sharing unit can also share related parenting experiences by referring to the activities of other dads' friends on social media. In this way, the sharing unit can provide highly relevant parenting experiences by analyzing the social media activities of other dads. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input social media data of other dads into a generation AI, which can select related parenting experiences.

[0057] During monitoring, the monitoring unit can analyze the father's past child-rearing behavior and select the optimal monitoring method. The monitoring unit selects the optimal monitoring method based on, for example, the father's past child-rearing behavior. The monitoring unit can also prioritize and select monitoring methods that were effective based on the father's past child-rearing behavior. The monitoring unit can also analyze the father's past child-rearing behavior and select the most efficient monitoring method. In this way, the monitoring unit can select the optimal monitoring method by analyzing the father's past child-rearing behavior. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the father's past child-rearing behavior into a generation AI, which can select the optimal monitoring method.

[0058] During monitoring, the monitoring unit can customize the monitoring means based on dad's current living situation. The monitoring unit customizes the monitoring means according to dad's current living situation (such as how busy he is at work). The monitoring unit can also provide the necessary monitoring means based on dad's current living situation. The monitoring unit can also select the optimal monitoring means taking dad's current living situation into consideration. This allows the monitoring unit to customize the monitoring means based on dad's current living situation, enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input dad's living situation data into a generation AI, which can then customize the monitoring means.

[0059] The monitoring unit can improve the monitoring method by reflecting dad's feedback during monitoring. The monitoring unit can improve the monitoring method based on dad's feedback, for example. The monitoring unit can also preferentially apply monitoring methods that were found to be effective based on dad's feedback. The monitoring unit can also improve the accuracy of monitoring by reflecting dad's feedback. In this way, the monitoring unit improves the accuracy of monitoring by reflecting dad's feedback. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or can be performed without using AI. For example, the monitoring unit can input dad's feedback data into a generation AI, which can then improve the monitoring method.

[0060] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the father's geographical location information. The monitoring unit, for example, monitors the childcare situation in the area where the father lives. The monitoring unit can also monitor the childcare situation related to places the father frequently visits. The monitoring unit can also monitor the childcare situation specific to the area based on the father's geographical location information. In this way, the monitoring unit can monitor the childcare situation specific to the area by taking into account the father's geographical location information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's geographical location information data into the generation AI, which can select the optimal monitoring method.

[0061] During monitoring, the monitoring unit can analyze the father's social media activity and suggest monitoring measures. For example, the monitoring unit monitors information on childcare-related accounts that the father follows on social media. The monitoring unit can also analyze the content of the father's social media posts to monitor related childcare situations. The monitoring unit can also monitor related childcare situations by referring to the activities of the father's friends on social media. In this way, the monitoring unit can monitor highly relevant childcare situations by analyzing the father's social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's social media data into a generation AI, which can suggest monitoring measures.

[0062] During monitoring, the monitoring unit can customize the monitoring method by reflecting dad's past feedback. The monitoring unit customizes the monitoring method based on dad's past feedback, for example. The monitoring unit can also preferentially select effective monitoring methods from dad's past feedback. The monitoring unit can also improve the accuracy of monitoring by reflecting dad's past feedback. In this way, the monitoring unit improves the accuracy of monitoring by reflecting dad's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input dad's feedback data into a generation AI, which can customize the monitoring method.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The analysis unit can estimate the father's level of knowledge about child-rearing and adjust the level of detail of the advice based on the estimated level of knowledge. For example, basic information can be provided to a father who is new to child-rearing, and more specialized advice can be provided to a father with extensive child-rearing experience. The analysis unit can also adjust the difficulty of the advice depending on the father's level of knowledge. This allows the analysis unit to provide appropriate advice according to the father's level of knowledge.

[0065] The providing unit can estimate the father's parenting style and customize the content of the advice based on the estimated parenting style. For example, advice on natural parenting is provided to a father who prefers natural parenting, and advice based on scientific evidence is provided to a father who prefers a scientific approach. The providing unit can also adjust the format of the advice depending on the father's parenting style. This allows the providing unit to provide appropriate advice according to the father's parenting style.

[0066] The collection unit can estimate the dad's lifestyle rhythm and adjust the timing of information collection based on the estimated lifestyle rhythm. For example, information collection can be performed at night for a dad who is a night owl, and in the early morning for a dad who is a morning person. The collection unit can also adjust the frequency of information collection according to the dad's lifestyle rhythm. This allows the collection unit to collect information appropriate to the dad's lifestyle rhythm.

[0067] The monitoring unit can estimate the father's motivation for child-rearing and adjust the monitoring method based on the estimated motivation. For example, detailed monitoring can be performed on a father who is highly motivated, and brief monitoring can be performed on a father who is less motivated. The monitoring unit can also adjust the frequency of monitoring according to the father's motivation. This allows the monitoring unit to perform appropriate monitoring according to the father's motivation.

[0068] The sharing unit can estimate the father's areas of interest in child-rearing and customize the content of the child-rearing experiences to be shared based on the estimated areas of interest. For example, it can share health-related child-rearing experiences with a father who is interested in the health aspects of child-rearing, and share education-related child-rearing experiences with a father who is interested in the education aspects. The sharing unit can also adjust the format of the information to be shared depending on the father's areas of interest. This allows the sharing unit to share appropriate child-rearing experiences according to the father's areas of interest.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects information about the father's situation. The father's situation includes, for example, the baby's age in months, the father's child-rearing experience, and current child-rearing concerns. The collection unit not only accepts information entered by the father, but can also collect information using sensors and application data. For example, the collection unit accepts text information, voice input, and image input entered by the father. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit and propose appropriate behavioral patterns. These behavioral patterns include care methods according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit uses the generation AI to propose care methods according to the baby's age, specific advice based on the father's child-rearing experience, and solutions to child-rearing concerns. Step 3: The provider provides specific childcare advice based on the analysis results obtained by the analyzer. The childcare advice includes tips on how to deal with nighttime crying and tips on changing diapers. For example, the provider may provide specific advice such as "hold the baby and rock him" to deal with nighttime crying, or "distract the baby with a toy" when changing a diaper. The provider can also provide the childcare advice as a text message or a voice message. Step 4: The monitoring unit continuously monitors the father's child-rearing situation based on the advice provided by the providing unit. Monitoring includes periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit provides corresponding advice. The monitoring unit can also continuously monitor the father's child-rearing situation and provide new advice as needed.

[0071] (Example 2) A childcare support system according to an embodiment of the present invention provides specific childcare advice tailored to each father's situation. The childcare support system collects information about each father's situation, analyzes it using a generation AI, proposes appropriate behavioral patterns, and provides childcare advice. The childcare support system also shares the childcare experiences of other fathers and combines them with professional support to alleviate fathers' concerns and questions about childcare. For example, the childcare support system collects information about each father's situation, such as the baby's age in months, the father's childcare experience, and current childcare concerns. The childcare support system then analyzes the collected information using a generation AI and proposes appropriate behavioral patterns, such as babycare methods tailored to the baby's age and solutions to specific problems the father is facing. The childcare support system then provides specific childcare advice based on the analysis results. For example, specific advice such as "hold the baby and rock him" or "use a toy to distract him when changing his diaper" is provided to address nighttime crying. Furthermore, the childcare support system also shares the childcare experiences of other fathers. For example, other fathers share how they overcame nighttime crying and tips for changing diapers. Finally, the childcare support system continuously monitors the father's childcare situation and provides new advice as needed. For example, if the father inputs a new concern, appropriate advice is provided. This allows the childcare support system to help the father enjoy raising his child with confidence and support the harmonious development of the family. This allows the childcare support system to alleviate the father's anxieties and doubts about childcare and support the harmonious development of the family. For example, it provides the necessary information and support so that the father can enjoy raising his child with confidence.

[0072] A childcare support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects information about the father's situation. The father's situation includes, but is not limited to, the baby's age in months, the father's childcare experience, and current childcare concerns. The collection unit, for example, accepts information entered by the father. The collection unit can also collect information using sensors or application data. For example, the collection unit accepts text information entered by the father. The collection unit can also accept voice input and image input. The analysis unit uses a generation AI to analyze the information collected by the collection unit and propose an appropriate behavior pattern. The behavior pattern includes, but is not limited to, examples of baby care methods according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit allows the generation AI to propose a baby care method according to the baby's age. The analysis unit can also allow the generation AI to provide specific advice based on the father's childcare experience. The analysis unit can also allow the generation AI to propose solutions to the father's childcare concerns. The providing unit provides specific childcare advice based on the analysis results obtained by the analyzing unit. Examples of childcare advice include, but are not limited to, tips on how to deal with night crying and tips on changing diapers. For example, the providing unit provides specific advice such as "hold the baby and rock him" to deal with night crying. The providing unit can also provide advice such as "use a toy to attract the baby's attention" when changing a diaper. The providing unit can also provide the childcare advice as a text message or a voice message. The monitoring unit continuously monitors the father's childcare situation based on the advice provided by the providing unit. Examples of monitoring include, but are not limited to, periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit provides corresponding advice. The monitoring unit can also continuously monitor the father's childcare situation and provide new advice as needed. As a result, the childcare support system according to the embodiment can alleviate fathers' anxieties and doubts about childcare and support the harmonious development of families.

[0073] The collection unit can accept input information from the father. The input information includes, but is not limited to, for example, text input, voice input, and image input. For example, the collection unit accepts text information entered by the father. The collection unit can also accept voice input. For example, the collection unit converts what the father says into text using voice recognition technology. The collection unit can also accept image input. For example, the collection unit analyzes photos of the baby taken by the father and provides information about childcare. In this way, by accepting the input information from the father, the collection unit can collect information according to individual situations. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the text information entered by the father to a generation AI, which then performs analysis.

[0074] The analysis unit can suggest behavioral patterns according to the father's situation. Examples of behavioral patterns include, but are not limited to, methods of caring for the baby according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit allows the generation AI to suggest methods of caring for the baby according to the baby's age. The analysis unit can also allow the generation AI to provide specific advice based on the father's child-rearing experience. The analysis unit can also allow the generation AI to suggest solutions to the father's child-rearing worries. In this way, the analysis unit suggests appropriate behavioral patterns according to the father's situation, thereby providing specific child-rearing advice. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information about the father's situation into the generation AI, which can then suggest behavioral patterns.

[0075] The providing unit can provide childcare advice. Examples of childcare advice include, but are not limited to, measures to prevent night crying and tips for changing diapers. For example, the providing unit can provide specific advice such as "hold the baby and rock him" to prevent night crying. The providing unit can also provide advice such as "attract the baby with a toy" when changing a diaper. The providing unit can also provide the childcare advice as a text message or a voice message. In this way, the providing unit can alleviate the father's concerns and questions about childcare by providing specific childcare advice. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can generate childcare advice using a generation AI and provide it to the father.

[0076] The childcare support system includes a sharing unit that shares the childcare experiences of fathers. The sharing unit shares the childcare experiences of other fathers. The childcare experiences include, but are not limited to, success stories, failure stories, and specific anecdotes. For example, the sharing unit shares how other fathers overcame nighttime crying or tips for changing diapers. The sharing unit can also share the childcare experiences as text messages or voice messages. In this way, the sharing unit alleviates fathers' anxieties and questions about childcare by sharing the childcare experiences of other fathers. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the childcare experiences of other fathers into a generation AI, which then performs analysis.

[0077] The monitoring unit can continuously monitor the father's child-rearing situation and provide advice. Examples of monitoring include, but are not limited to, periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit can provide advice in response to that concern. The monitoring unit can also continuously monitor the father's child-rearing situation and provide new advice as needed. In this way, the monitoring unit continuously monitors the father's child-rearing situation and provides new advice, thereby resolving his concerns and questions about child-rearing. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's child-rearing situation into a generation AI, which then performs analysis.

[0078] The collection unit can estimate dad's emotions and adjust the timing of information collection based on the estimated dad's emotions. For example, if dad is feeling stressed, the collection unit can collect information during a time when dad is able to relax. Furthermore, if dad is relaxed, the collection unit can collect information immediately and provide prompt advice. Furthermore, if dad is busy, the collection unit can collect information during his free time. This allows the collection unit to adjust the timing of information collection according to dad's emotions, enabling more appropriate information collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input dad's emotion data into a generation AI, which can then estimate the emotion.

[0079] The collection unit can analyze the father's past child-rearing history and select the optimal information collection method. For example, the collection unit preferentially selects an information collection method that the father has used favorably in the past. The collection unit can also select an effective information collection method from the father's past child-rearing history. The collection unit can also select the most efficient information collection method based on the father's past child-rearing history. In this way, the collection unit can select the optimal information collection method by analyzing the father's past child-rearing history. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the father's past child-rearing history data into the generation AI, which can select the optimal information collection method.

[0080] When collecting information, the collection unit can filter the information based on the dad's current living situation and areas of interest. The collection unit filters the information based on, for example, childcare themes that the dad is currently interested in. The collection unit can also prioritize collecting highly relevant information depending on the dad's living situation (such as how busy he is at work). The collection unit can also filter and provide necessary information based on the dad's current living situation. In this way, the collection unit can provide highly relevant information by filtering information based on the dad's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the dad's living situation data into the generation AI, which then filters the information.

[0081] When collecting information, the collection unit can select the optimal collection means depending on dad's input method. For example, if dad prefers voice input, the collection unit can prioritize voice information collection. Also, if dad prefers text input, the collection unit can prioritize text information collection. Also, if dad prefers image input, the collection unit can prioritize information collection using images. In this way, the collection unit can select the optimal collection means depending on dad's input method, thereby enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input dad's input method data into a generation AI, which can select the optimal collection means.

[0082] The collection unit can estimate the dad's emotions and determine the priority of information to be collected based on the estimated dad's emotions. For example, if the dad is feeling anxious, the collection unit can prioritize collecting information that provides a sense of security. Furthermore, if the dad is excited, the collection unit can prioritize collecting interesting information. Furthermore, if the dad is relaxed, the collection unit can prioritize collecting detailed information. This allows the collection unit to prioritize the information to be collected according to the dad's emotions, enabling more appropriate information collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input dad's emotion data into a generation AI, which can then prioritize the information.

[0083] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the father's geographical location information. For example, the collection unit prioritizes collecting childcare information for the area where the father lives. The collection unit can also prioritize collecting childcare information related to places the father often visits. The collection unit can also prioritize collecting childcare information specific to the area based on the father's geographical location information. In this way, the collection unit can provide childcare information specific to the area by taking into account the father's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the father's geographical location information data to the generation AI, which can select highly relevant information.

[0084] When collecting information, the collection unit can analyze the father's social media activities and collect relevant information. For example, the collection unit collects information on childcare-related accounts that the father follows on social media. The collection unit can also analyze the content of the father's social media posts to collect relevant childcare information. The collection unit can also collect relevant childcare information by referring to the activities of the father's friends on social media. In this way, the collection unit can provide highly relevant information by analyzing the father's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the father's social media data into a generation AI, which can select relevant information.

[0085] When collecting information, the collection unit can customize the collection method by reflecting dad's past feedback. The collection unit customizes the information collection method based on, for example, dad's past feedback. The collection unit can also preferentially select effective information collection methods from dad's past feedback. The collection unit can also improve the accuracy of information collection by reflecting dad's past feedback. In this way, the collection unit improves the accuracy of information collection by reflecting dad's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input dad's feedback data into the generation AI, which can customize the collection method.

[0086] The analysis unit can estimate the dad's emotions and adjust the way the analysis is presented based on the estimated dad's emotions. For example, if the dad is feeling anxious, the analysis unit can provide the analysis result in an expression that gives a sense of security. Furthermore, if the dad is excited, the analysis unit can provide the analysis result in an interesting expression. Furthermore, if the dad is relaxed, the analysis unit can provide a detailed analysis result. Thus, the analysis unit can adjust the way the analysis is presented according to the dad's emotions to provide more appropriate analysis results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input dad's emotion data into the generation AI, which can then adjust the way the analysis is presented.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of childcare. For example, the analysis unit performs a detailed analysis for childcare issues with high importance. The analysis unit can also perform a concise analysis for childcare issues with low importance. The analysis unit can also adjust the level of detail of the analysis according to the importance of childcare. This allows the analysis unit to provide more appropriate analysis results by adjusting the level of detail of the analysis according to the importance of childcare. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input importance data of childcare issues into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the childcare category during analysis. For example, the analysis unit can apply a specialized algorithm to an analysis related to a baby's health. The analysis unit can also apply an algorithm according to the baby's growth stage to an analysis related to the baby's growth. The analysis unit can also apply the optimal analysis algorithm depending on the childcare category. This allows the analysis unit to provide more appropriate analysis results by applying the optimal analysis algorithm depending on the childcare category. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI, for example. For example, the analysis unit can input childcare category data into the generation AI, which can select the optimal analysis algorithm.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to dad's past analysis results. The analysis unit, for example, improves the accuracy of the analysis based on dad's past analysis results. The analysis unit can also preferentially apply effective analysis methods from dad's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to dad's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to dad's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input dad's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0090] The analysis unit can estimate the father's emotions and adjust the length of the analysis based on the estimated father's emotions. For example, if the father is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the father is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the father is excited, the analysis unit can provide a visually stimulating analysis result. Thus, the analysis unit can adjust the length of the analysis according to the father's emotions to provide more appropriate analysis results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the father's emotion data into the generation AI, which can then adjust the length of the analysis.

[0091] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the childcare questions. For example, the analysis unit prioritizes analysis of childcare questions with high urgency. The analysis unit can also prioritize analysis of childcare questions whose submission date is approaching. The analysis unit can also determine the priority of analysis based on the time of submission of the childcare questions. This allows the analysis unit to determine the priority of analysis based on the time of submission of the childcare questions, thereby allowing for quick response to problems with high urgency. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of submission of the childcare questions into the generation AI, and the generation AI can determine the priority of analysis.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of childcare. For example, the analysis unit prioritizes analysis of highly relevant childcare problems. The analysis unit can also postpone analysis of less relevant childcare problems. The analysis unit can also adjust the order of analysis based on the relevance of childcare. In this way, the analysis unit can prioritize highly relevant problems by adjusting the order of analysis based on the relevance of childcare. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of childcare problems into the generation AI, and the generation AI can adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the father's level of expertise. For example, if the father has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terminology. Furthermore, if the father does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the father's level of expertise. In this way, the analysis unit can provide easy-to-understand analysis results by adjusting the use of technical terminology in the analysis according to the father's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input the father's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0094] The providing unit can estimate the dad's emotions and adjust the way the advice is expressed based on the estimated dad's emotions. For example, if the dad is feeling anxious, the providing unit can provide advice in an expression that gives a sense of security. Furthermore, if the dad is excited, the providing unit can provide advice in an expression that attracts attention. Furthermore, if the dad is relaxed, the providing unit can provide detailed advice. In this way, the providing unit can adjust the way the advice is expressed according to the dad's emotions, thereby providing more appropriate advice. The estimation of emotions is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input dad's emotion data into the generation AI, which can adjust the way the advice is expressed.

[0095] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of child-rearing. For example, the providing unit provides detailed advice for child-rearing issues of high importance. The providing unit can also provide concise advice for child-rearing issues of low importance. The providing unit can also adjust the level of detail of the advice according to the importance of child-rearing. As a result, the providing unit can provide more appropriate advice by adjusting the level of detail of the advice according to the importance of child-rearing. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input importance data of child-rearing issues to a generating AI, and the generating AI can adjust the level of detail of the advice.

[0096] The providing unit can apply different advice algorithms depending on the childcare category when providing advice. For example, the providing unit can apply a specialized algorithm to advice regarding baby health. The providing unit can also apply an algorithm according to the baby's growth stage to advice regarding baby growth. The providing unit can also apply the optimal advice algorithm depending on the childcare category. In this way, the providing unit can provide more appropriate advice by applying the optimal advice algorithm depending on the childcare category. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input childcare category data to a generating AI, which can select the optimal advice algorithm.

[0097] When providing advice, the providing unit can improve the accuracy of the advice by referring to dad's past advice results. The providing unit improves the accuracy of the advice, for example, based on dad's past advice results. The providing unit can also preferentially apply effective advice methods from dad's past advice results. The providing unit can also improve the accuracy of the advice by referring to dad's past advice results. In this way, the providing unit improves the accuracy of the advice by referring to dad's past advice results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input dad's past advice result data into the generation AI, which can improve the accuracy of the advice.

[0098] The providing unit can estimate the dad's emotions and adjust the length of the advice based on the estimated dad's emotions. For example, if the dad is in a hurry, the providing unit can provide short, to-the-point advice. Furthermore, if the dad is relaxed, the providing unit can provide detailed advice. Furthermore, if the dad is excited, the providing unit can provide visually stimulating advice. This allows the providing unit to adjust the length of the advice according to the dad's emotions, thereby providing more appropriate advice. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input dad's emotion data into the generation AI, which can then adjust the length of the advice.

[0099] When providing advice, the providing unit can determine the priority of the advice based on the time of submission of the childcare request. For example, the providing unit can prioritize providing advice for a childcare problem with a high urgency. The providing unit can also prioritize providing advice for a childcare problem whose submission date is approaching. The providing unit can also determine the priority of the advice based on the time of submission of the childcare request. This allows the providing unit to determine the priority of the advice based on the time of submission of the childcare request, thereby enabling quick response to a problem with a high urgency. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the childcare request into a generating AI, and the generating AI can determine the priority of the advice.

[0100] The providing unit can adjust the order of advice based on the relevance of childcare when providing advice. For example, the providing unit can provide advice preferentially for childcare problems with high relevance. The providing unit can also postpone providing advice for childcare problems with low relevance. The providing unit can also adjust the order of advice based on the relevance of childcare. In this way, the providing unit can prioritize addressing highly relevant problems by adjusting the order of advice based on the relevance of childcare. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of childcare problems to a generating AI, and the generating AI can adjust the order of advice.

[0101] When providing advice, the providing unit can adjust the use of technical terminology in the advice according to the dad's level of expertise. For example, if the dad has technical expertise, the providing unit can provide advice that uses a lot of technical terminology. Furthermore, if the dad does not have technical expertise, the providing unit can also provide concise and easy-to-understand advice. Furthermore, the providing unit can adjust the use of technical terminology in the advice according to the dad's level of expertise. In this way, the providing unit can provide easy-to-understand advice by adjusting the use of technical terminology in the advice according to the dad's level of expertise. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input dad's level of expertise data into the generating AI, which can adjust the use of technical terminology.

[0102] The sharing unit can estimate the father's emotions and select childcare experiences to share based on the estimated father's emotions. For example, if the father is feeling anxious, the sharing unit can share childcare experiences that give him a sense of security. Furthermore, if the father is excited, the sharing unit can share interesting childcare experiences. Furthermore, if the father is relaxed, the sharing unit can share detailed childcare experiences. In this way, the sharing unit can provide more appropriate information by selecting childcare experiences to share based on the father's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the father's emotion data into the generation AI, and the generation AI can select childcare experiences to share.

[0103] The sharing unit can adjust the level of detail of the sharing based on the importance of the other dads' childcare experiences when sharing. For example, the sharing unit shares detailed information for childcare experiences with high importance. The sharing unit can also share concise information for childcare experiences with low importance. The sharing unit can also adjust the level of detail of the sharing based on the importance of the other dads' childcare experiences. In this way, the sharing unit can provide more appropriate information by adjusting the level of detail of the sharing based on the importance of the other dads' childcare experiences. Some or all of the above-described processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input the childcare experience data of the other dads into a generation AI, and the generation AI can adjust the level of detail of the sharing.

[0104] The sharing unit can apply different sharing algorithms depending on the childcare category when sharing. For example, the sharing unit can apply a specialized algorithm to childcare experiences related to baby health. The sharing unit can also apply an algorithm according to the baby's developmental stage to childcare experiences related to baby growth. The sharing unit can also apply the most appropriate sharing algorithm depending on the childcare category. This allows the sharing unit to provide more appropriate information by applying the most appropriate sharing algorithm depending on the childcare category. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input childcare category data into a generation AI, which can select the most appropriate sharing algorithm.

[0105] The sharing unit can estimate the father's emotions and determine the priority of the childcare experiences to be shared based on the estimated father's emotions. For example, if the father is feeling anxious, the sharing unit can prioritize sharing childcare experiences that provide a sense of security. Furthermore, if the father is excited, the sharing unit can prioritize sharing interesting childcare experiences. Furthermore, if the father is relaxed, the sharing unit can prioritize sharing detailed childcare experiences. In this way, the sharing unit can provide more appropriate information by determining the priority of the childcare experiences to be shared according to the father's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit may be performed using an AI, for example, or without an AI. For example, the sharing unit can input the father's emotion data into the generation AI, and the generation AI can determine the priority of the childcare experiences to be shared.

[0106] When sharing, the sharing unit can prioritize sharing highly relevant childcare experiences by taking into account the geographical location information of other dads. For example, the sharing unit prioritizes sharing childcare experiences in the area where the dad lives. The sharing unit can also prioritize sharing childcare experiences related to places the dad often visits. The sharing unit can also prioritize sharing childcare experiences specific to the area based on the dad's geographical location information. In this way, the sharing unit can provide childcare experiences specific to the area by taking into account the geographical location information of other dads. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input geographical location information data of other dads into the generation AI, which can select highly relevant childcare experiences.

[0107] When sharing, the sharing unit can analyze the social media activities of other dads and share related parenting experiences. For example, the sharing unit prioritizes sharing parenting experiences shared by other dads on social media. The sharing unit can also analyze the content of other dads' social media posts and share related parenting experiences. The sharing unit can also share related parenting experiences by referring to the activities of other dads' friends on social media. In this way, the sharing unit can provide highly relevant parenting experiences by analyzing the social media activities of other dads. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input social media data of other dads into a generation AI, which can select related parenting experiences.

[0108] The monitoring unit can estimate the dad's emotions and adjust the monitoring method based on the estimated dad's emotions. For example, if the dad is feeling anxious, the monitoring unit can provide a monitoring method that provides a sense of security. Furthermore, if the dad is excited, the monitoring unit can provide an interesting monitoring method. Furthermore, if the dad is relaxed, the monitoring unit can provide a detailed monitoring method. This allows the monitoring unit to adjust the monitoring method according to the dad's emotions, enabling more appropriate monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the monitoring unit can input dad's emotion data into the generation AI, which can then adjust the monitoring method.

[0109] During monitoring, the monitoring unit can analyze the father's past child-rearing behavior and select the optimal monitoring method. The monitoring unit selects the optimal monitoring method based on, for example, the father's past child-rearing behavior. The monitoring unit can also prioritize and select monitoring methods that were effective based on the father's past child-rearing behavior. The monitoring unit can also analyze the father's past child-rearing behavior and select the most efficient monitoring method. In this way, the monitoring unit can select the optimal monitoring method by analyzing the father's past child-rearing behavior. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the father's past child-rearing behavior into a generation AI, which can select the optimal monitoring method.

[0110] During monitoring, the monitoring unit can customize the monitoring means based on dad's current living situation. The monitoring unit customizes the monitoring means according to dad's current living situation (such as how busy he is at work). The monitoring unit can also provide the necessary monitoring means based on dad's current living situation. The monitoring unit can also select the optimal monitoring means taking dad's current living situation into consideration. This allows the monitoring unit to customize the monitoring means based on dad's current living situation, enabling more appropriate monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input dad's living situation data into a generation AI, which can then customize the monitoring means.

[0111] The monitoring unit can improve the monitoring method by reflecting dad's feedback during monitoring. The monitoring unit can improve the monitoring method based on dad's feedback, for example. The monitoring unit can also preferentially apply monitoring methods that were found to be effective based on dad's feedback. The monitoring unit can also improve the accuracy of monitoring by reflecting dad's feedback. In this way, the monitoring unit improves the accuracy of monitoring by reflecting dad's feedback. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or can be performed without using AI. For example, the monitoring unit can input dad's feedback data into a generation AI, which can then improve the monitoring method.

[0112] The monitoring unit can estimate the dad's emotions and determine monitoring priorities based on the estimated dad's emotions. For example, if the dad is feeling anxious, the monitoring unit can prioritize monitoring that provides a sense of security. Also, if the dad is excited, the monitoring unit can prioritize monitoring that attracts interest. Also, if the dad is relaxed, the monitoring unit can prioritize detailed monitoring. This allows the monitoring unit to determine monitoring priorities according to the dad's emotions, enabling more appropriate monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the monitoring unit can input dad's emotion data into the generation AI, which can then determine the monitoring priorities.

[0113] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the father's geographical location information. The monitoring unit, for example, monitors the childcare situation in the area where the father lives. The monitoring unit can also monitor the childcare situation related to places the father frequently visits. The monitoring unit can also monitor the childcare situation specific to the area based on the father's geographical location information. In this way, the monitoring unit can monitor the childcare situation specific to the area by taking into account the father's geographical location information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's geographical location information data into the generation AI, which can select the optimal monitoring method.

[0114] During monitoring, the monitoring unit can analyze the father's social media activity and suggest monitoring measures. For example, the monitoring unit monitors information on childcare-related accounts that the father follows on social media. The monitoring unit can also analyze the content of the father's social media posts to monitor related childcare situations. The monitoring unit can also monitor related childcare situations by referring to the activities of the father's friends on social media. In this way, the monitoring unit can monitor highly relevant childcare situations by analyzing the father's social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the father's social media data into a generation AI, which can suggest monitoring measures.

[0115] During monitoring, the monitoring unit can customize the monitoring method by reflecting dad's past feedback. The monitoring unit customizes the monitoring method based on dad's past feedback, for example. The monitoring unit can also preferentially select effective monitoring methods from dad's past feedback. The monitoring unit can also improve the accuracy of monitoring by reflecting dad's past feedback. In this way, the monitoring unit improves the accuracy of monitoring by reflecting dad's past feedback. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input dad's feedback data into a generation AI, which can customize the monitoring method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and sharing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information using sensors and application data of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides specific child-rearing advice based on the analysis results. The monitoring unit is realized by the control unit 46A of the smart device 14 and continuously monitors the father's child-rearing situation. The sharing unit is realized by the control unit 46A of the smart device 14 and shares the child-rearing experiences of other fathers. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, monitoring unit, and sharing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information using sensors and application data of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides specific child-rearing advice based on the analysis results. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and continuously monitors the father's child-rearing situation. The sharing unit is realized by the control unit 46A of the smart glasses 214 and shares the child-rearing experiences of other fathers. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, monitoring unit, and sharing unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect information using sensors and application data of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides specific child-rearing advice based on the analysis results. The monitoring unit is realized by the control unit 46A of the headset-type terminal 314 and continuously monitors the father's child-rearing situation. The sharing unit is realized by the control unit 46A of the headset-type terminal 314 and shares the child-rearing experiences of other fathers. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, monitoring unit, and sharing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information using sensors and application data of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit is realized by the control unit 46A of the robot 414 and provides specific child-rearing advice based on the analysis results. The monitoring unit is realized by the control unit 46A of the robot 414 and continuously monitors the father's child-rearing situation. The sharing unit is realized by the control unit 46A of the robot 414 and shares the child-rearing experiences of other fathers.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the father's level of knowledge about child-rearing and adjust the level of detail of the advice based on the estimated level of knowledge. For example, basic information can be provided to a father who is new to child-rearing, and more specialized advice can be provided to a father with extensive child-rearing experience. The analysis unit can also adjust the difficulty of the advice depending on the father's level of knowledge. This allows the analysis unit to provide appropriate advice according to the father's level of knowledge.

[0118] The providing unit can estimate the father's parenting style and customize the content of the advice based on the estimated parenting style. For example, advice on natural parenting is provided to a father who prefers natural parenting, and advice based on scientific evidence is provided to a father who prefers a scientific approach. The providing unit can also adjust the format of the advice depending on the father's parenting style. This allows the providing unit to provide appropriate advice according to the father's parenting style.

[0119] The collection unit can estimate the dad's lifestyle rhythm and adjust the timing of information collection based on the estimated lifestyle rhythm. For example, information collection can be performed at night for a dad who is a night owl, and in the early morning for a dad who is a morning person. The collection unit can also adjust the frequency of information collection according to the dad's lifestyle rhythm. This allows the collection unit to collect information appropriate to the dad's lifestyle rhythm.

[0120] The monitoring unit can estimate the father's motivation for child-rearing and adjust the monitoring method based on the estimated motivation. For example, detailed monitoring can be performed on a father who is highly motivated, and brief monitoring can be performed on a father who is less motivated. The monitoring unit can also adjust the frequency of monitoring according to the father's motivation. This allows the monitoring unit to perform appropriate monitoring according to the father's motivation.

[0121] The sharing unit can estimate the father's areas of interest in child-rearing and customize the content of the child-rearing experiences to be shared based on the estimated areas of interest. For example, it can share health-related child-rearing experiences with a father who is interested in the health aspects of child-rearing, and share education-related child-rearing experiences with a father who is interested in the education aspects. The sharing unit can also adjust the format of the information to be shared depending on the father's areas of interest. This allows the sharing unit to share appropriate child-rearing experiences according to the father's areas of interest.

[0122] The analysis unit can estimate the father's emotions and determine the priority of the analysis based on the estimated father's emotions. For example, if the father is feeling stressed, analysis related to stress reduction can be prioritized. Also, if the father is relaxed, analysis related to a long-term child-rearing plan can be prioritized. This allows the analysis unit to perform an appropriate analysis according to the father's emotions.

[0123] The providing unit can estimate the dad's emotions and adjust the timing of advice based on the estimated dad's emotions. For example, if the dad is tired, the advice can be provided at a time when the dad can relax. Also, if the dad is in good spirits, the advice can be provided immediately. This allows the providing unit to provide advice at an appropriate timing according to the dad's emotions.

[0124] The collection unit can estimate the father's emotions and adjust the type of information to be collected based on the estimated father's emotions. For example, if the father is feeling stressed, brief information can be collected. On the other hand, if the father is relaxed, detailed information can be collected. This allows the collection unit to collect appropriate information according to the father's emotions.

[0125] The monitoring unit can estimate the dad's emotions and adjust the frequency of monitoring based on the estimated dad's emotions. For example, if the dad is feeling anxious, the monitoring unit can monitor more frequently. Also, if the dad is relaxed, the monitoring unit can reduce the frequency of monitoring. This allows the monitoring unit to perform appropriate monitoring according to the dad's emotions.

[0126] The sharing unit can estimate the father's emotions and adjust the way in which the childcare experience is expressed to be shared based on the estimated father's emotions. For example, if the father is feeling anxious, the sharing unit can share the childcare experience in an expression that gives a sense of security. Also, if the father is excited, the sharing unit can share the childcare experience in an expression that attracts the father's interest. In this way, the sharing unit can share the childcare experience in an appropriate expression according to the father's emotions.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The collection unit collects information about the father's situation. The father's situation includes, for example, the baby's age in months, the father's child-rearing experience, and current child-rearing concerns. The collection unit not only accepts information entered by the father, but can also collect information using sensors and application data. For example, the collection unit accepts text information, voice input, and image input entered by the father. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit and propose appropriate behavioral patterns. These behavioral patterns include care methods according to the baby's age and solutions to specific problems the father is facing. For example, the analysis unit uses the generation AI to propose care methods according to the baby's age, specific advice based on the father's child-rearing experience, and solutions to child-rearing concerns. Step 3: The provider provides specific childcare advice based on the analysis results obtained by the analyzer. The childcare advice includes tips on how to deal with nighttime crying and tips on changing diapers. For example, the provider may provide specific advice such as "hold the baby and rock him" to deal with nighttime crying, or "distract the baby with a toy" when changing a diaper. The provider can also provide the childcare advice as a text message or a voice message. Step 4: The monitoring unit continuously monitors the father's child-rearing situation based on the advice provided by the providing unit. Monitoring includes periodic check-ins and real-time monitoring. For example, if the father inputs a new concern, the monitoring unit provides corresponding advice. The monitoring unit can also continuously monitor the father's child-rearing situation and provide new advice as needed.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] 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.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information about Dad's situation; an analysis unit that analyzes the information collected by the collection unit and proposes a behavior pattern; a providing unit that provides childcare advice based on the analysis results obtained by the analyzing unit; a monitoring unit that continuously monitors the father's child-rearing situation based on the advice provided by the providing unit. A system characterized by:

2. The collecting unit Accept dad's input information 2. The system of claim 1.

3. The analysis unit Suggesting behavior patterns that suit the dad's situation 2. The system of claim 1.

4. The providing unit Providing parenting advice 2. The system of claim 1.

5. A shared area for fathers to share their child-rearing experiences 2. The system of claim 1.

6. The monitoring unit Continuously monitor fathers' parenting status and provide advice 2. The system of claim 1.

7. The collecting unit Estimate the father's emotions and adjust the timing of information gathering based on the estimated father's emotions.

2. The system of claim 1.

8. The collecting unit Analyze the father's past child-rearing history and select the most appropriate method of information gathering 2. The system of claim 1.

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