system

Abstract

The system according to the embodiment aims to enable dual-income parents to efficiently manage child-rearing schedules and provide advice. [Solution] A system according to an embodiment includes a management unit, a providing unit, and a support providing unit. The management unit manages a schedule. The providing unit provides advice based on the schedule managed by the management unit. The support providing unit provides support based on the advice provided by the providing unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for dual-income parents to efficiently manage child-rearing schedules and provide advice.

[0005] The system according to the embodiment aims to enable dual-income parents to efficiently manage child-rearing schedules and provide advice. [Means for solving the problem]

[0006] A system according to an embodiment includes a management unit, a providing unit, and a support providing unit. The management unit manages a schedule. The providing unit provides advice based on the schedule managed by the management unit. The support providing unit provides support based on the advice provided by the providing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows dual-income parents to efficiently manage child-rearing schedules and provide advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A child-rearing support system according to an embodiment of the present invention is an innovative web service designed to support dual-income parents. This system utilizes AI to manage child-rearing tasks and schedules, provide advice, set reminders, and more. By providing customized support tailored to each family, it helps parents reduce stress and enjoy raising their children more effectively. For example, the system uses AI to automatically organize schedules and send reminders based on information entered by parents. This allows parents to efficiently manage their schedules without forgetting important appointments. Next, the system uses AI to provide child-rearing advice. For example, it provides advice on appropriate educational methods and health management based on a child's developmental stage. Based on information entered by parents, the AI ​​provides advice tailored to the child's age and developmental stage. This allows parents to respond appropriately to their children's development. Furthermore, the system uses AI to provide customized support tailored to each family. For example, it proposes an optimal child-rearing plan based on the family's situation and needs. Based on information entered by parents, the AI ​​analyzes the family's situation and needs and proposes an optimal child-rearing plan. This allows parents to effectively raise their children in a way that suits their circumstances. In this way, the child-rearing support system supports dual-income parents and helps them enjoy raising their children more effectively. With AI support, parents can reduce the stress of raising their children and spend more quality time. In this way, the child-rearing support system provides comprehensive support for parents' child-rearing, reducing stress and allowing them to enjoy raising their children more effectively.

[0029] A child-rearing support system according to an embodiment includes a management unit, a providing unit, and a support providing unit. The management unit manages schedules. The management unit organizes schedules based on, for example, information input by a parent. The management unit organizes a schedule of events related to child-rearing input by a parent and sends reminders. The management unit can send reminders based on, for example, the information input by a parent to prevent the parent from forgetting important appointments. The providing unit provides advice. The providing unit provides, for example, advice on appropriate educational methods and health management according to a child's developmental stage. The providing unit can provide advice based on a child's age and developmental stage based on, for example, the information input by a parent. The support providing unit provides customized support. The support providing unit proposes an optimal child-rearing plan based on, for example, the situation and needs of the family. The support providing unit can analyze, for example, the situation and needs of the family based on the information input by the parent and propose an optimal child-rearing plan. As a result, the child-rearing support system according to an embodiment comprehensively supports parents in raising their children, reduces stress, and enables parents to enjoy raising their children more effectively.

[0030] The child-rearing support system includes a reminder unit that sends reminders. The reminder unit sends the reminders. The reminder unit sends the reminders based on, for example, a schedule input by a parent. The reminder unit can send the reminders by, for example, email, SMS, app notification, or other methods. The reminder unit can send reminders at appropriate times so that, for example, a parent does not forget important appointments. By sending reminders, the parent can thereby manage important appointments without forgetting them. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input schedule data input by a parent into a generation AI and have the generation AI determine the timing of sending the reminder.

[0031] The child-rearing support system includes an analysis unit that analyzes the family situation. The analysis unit analyzes the family situation. The analysis unit analyzes the family situation based on, for example, information input by a parent. The analysis unit can analyze information such as family structure, economic situation, and lifestyle habits. The analysis unit can perform a detailed analysis of the family situation based on, for example, the information input by a parent, and generate data for providing optimal support. This allows for more appropriate support to be provided by analyzing the family situation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the family situation data input by a parent into a generation AI and have the generation AI analyze the family situation.

[0032] The management unit can organize the schedule based on information input by the parent. The management unit, for example, organizes the schedule based on information input by the parent. The management unit can, for example, organize a schedule of events related to child-rearing input by the parent and send reminders. The management unit can, for example, send reminders based on information input by the parent to prevent important appointments from being forgotten. This enables efficient schedule management by organizing the schedule based on information input by the parent. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input schedule data input by the parent into a generation AI and have the generation AI organize the schedule.

[0033] The providing unit can provide advice according to the child's developmental stage. The providing unit, for example, provides advice on appropriate educational methods or health management according to the child's developmental stage. The providing unit can provide advice according to the child's age and developmental stage, for example, based on information input by a parent. For example, by providing advice according to the child's developmental stage, the providing unit allows parents to take appropriate measures according to the child's development. As a result, appropriate measures are possible by providing advice according to the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's developmental stage data input by the parent into a generating AI and cause the generating AI to provide advice.

[0034] The support providing unit can propose a parenting plan that suits the family's situation. The support providing unit can propose an optimal parenting plan, for example, based on the family's situation and needs. The support providing unit can analyze the family's situation and needs based on information input by the parents, and propose an optimal parenting plan. For example, by proposing a parenting plan that suits the family's situation, the support providing unit can enable parents to raise their children effectively according to their own situation. This enables effective parenting by proposing a parenting plan that suits the family's situation and needs. Some or all of the above-mentioned processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input family situation data input by the parents into a generation AI and have the generation AI execute a parenting plan proposal.

[0035] The management unit can analyze the parent's past schedule history and select an appropriate schedule management method. The management unit, for example, analyzes the parent's past schedule history and selects the optimal schedule management method. The management unit, for example, can propose a similar schedule based on a schedule pattern that was successful for the parent in the past. The management unit, for example, can propose a different approach to avoid a schedule pattern that was unsuccessful for the parent in the past. The management unit, for example, can propose the most efficient task for a specific time period based on the parent's past schedule history. This enables optimal schedule management by analyzing the past schedule history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the parent's past schedule data into a generation AI and have the generation AI select a schedule management method.

[0036] The management unit can filter the schedule based on the parent's current living situation when managing the schedule. For example, the management unit can filter the schedule based on the parent's current living situation and work load when managing the schedule. For example, if the parent is currently busy, the management unit can incorporate only important tasks into the schedule. For example, if the parent currently has free time, the management unit can incorporate additional tasks or time for relaxation into the schedule. For example, if the parent's work load is high, the management unit can suggest a schedule that takes into account the balance between work and child-rearing. This enables efficient schedule management by filtering the schedule based on the parent's living situation and work load. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input data on the parent's living situation into the generation AI and have the generation AI perform schedule filtering.

[0037] The management unit can prioritize highly relevant schedules based on the parent's geographical location information when managing a schedule. For example, the management unit prioritizes highly relevant schedules based on the parent's geographical location information when managing a schedule. For example, when the parent is in a specific location, the management unit can prioritize tasks related to that location in the schedule. For example, when the parent is traveling, the management unit can prioritize tasks that can be performed while traveling in the schedule. For example, when the parent is at home, the management unit can prioritize tasks that can be performed at home in the schedule. This enables highly relevant schedule management by taking geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the parent's geographical location information data into a generation AI and cause the generation AI to manage the schedule priorities.

[0038] The management unit can manage related schedules based on the parent's social media activity when managing a schedule. For example, when managing a schedule, the management unit manages related schedules based on the parent's social media activity. For example, when the parent shares a plan on social media, the management unit can automatically incorporate the plan into the schedule. For example, when the parent participates in an event on social media, the management unit can incorporate the event into the schedule. For example, the management unit can incorporate related tasks into the schedule based on the parent's social media activity. This enables related schedule management by analyzing social media activity. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the parent's social media activity data into a generation AI and have the generation AI manage the schedule.

[0039] The providing unit can adjust the level of detail of the advice based on the child's developmental stage when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the child's developmental stage when providing advice. For example, if the child is an infant, the providing unit can provide basic advice. For example, if the child is an elementary school student, the providing unit can provide advice on specific educational methods and health management. For example, if the child is a junior high school student or older, the providing unit can provide advice on further education and careers. In this way, appropriate advice is provided by adjusting the level of detail of the advice based on the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the child's developmental stage into the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0040] The providing unit can apply different advice algorithms depending on the child's category when providing advice. For example, the providing unit can apply different advice algorithms depending on the child's category when providing advice. For example, if a child needs special support, the providing unit can provide specialized advice. For example, if a child is interested in sports, the providing unit can provide sports-related advice. For example, if a child is interested in art, the providing unit can provide art-related advice. By applying different advice algorithms depending on the child's category, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input child category data into the generating AI and cause the generating AI to apply the advice algorithm.

[0041] The providing unit can determine the priority of advice based on the child's developmental stage when providing advice. For example, the providing unit can determine the priority of advice based on the child's developmental stage when providing advice. For example, if the child is an infant, the providing unit can prioritize advice on basic health management. For example, if the child is an elementary school student, the providing unit can prioritize advice on educational methods. For example, if the child is a junior high school student or older, the providing unit can prioritize advice on further education and careers. In this way, appropriate advice is provided by determining the priority of advice based on the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input child developmental stage data into the generating AI and cause the generating AI to determine the priority of advice.

[0042] The providing unit can adjust the order of advice based on the child's related information when providing advice. For example, the providing unit can adjust the order of advice based on the child's related information when providing advice. For example, the providing unit can first provide health management advice based on the child's health condition. For example, the providing unit can first provide educational method advice based on the child's learning situation. For example, the providing unit can first provide related advice based on the child's interests and concerns. In this way, appropriate advice is provided by adjusting the order of advice based on the child's related information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's related information data to the generating AI and cause the generating AI to adjust the order of advice.

[0043] The support providing unit can analyze the parent's past parenting behavior and select an appropriate customization method during customization. For example, the support providing unit analyzes the parent's past parenting behavior and selects the optimal customization method during customization. For example, the support providing unit can suggest similar customization based on parenting methods that the parent has used successfully in the past. For example, the support providing unit can suggest a different approach to avoid parenting methods that the parent has used unsuccessfully in the past. For example, the support providing unit can select the most effective customization method from the parent's past parenting behavior. In this way, the optimal customization method is provided by analyzing the parenting behavior. Some or all of the above-described processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input data on the parent's past parenting behavior into the generation AI and cause the generation AI to select a customization method.

[0044] The support providing unit can adjust the customization means based on the parent's current living situation during customization. The support providing unit, for example, adjusts the customization means based on the parent's current living situation during customization. For example, if the parent is currently busy, the support providing unit can suggest a customization method that can be easily and quickly implemented. For example, if the parent currently has time, the support providing unit can provide detailed customization options. For example, the support providing unit can suggest an optimal customization method according to the parent's living situation. As a result, optimal customization for the parent is provided by adjusting the customization means based on the current living situation. Some or all of the above-mentioned processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input the parent's living situation data into the generation AI and cause the generation AI to adjust the customization means.

[0045] The support providing unit can select an appropriate customization method based on the geographical location information of the parent during customization. For example, the support providing unit selects an appropriate customization method based on the geographical location information of the parent during customization. For example, if the parent is in a specific location, the support providing unit can suggest a customization method related to the location. For example, if the parent is traveling, the support providing unit can suggest a customization method that can be performed while traveling. For example, if the parent is at home, the support providing unit can suggest a customization method that can be performed at home. In this way, the optimal customization method is provided by taking the geographical location information into consideration. Some or all of the above-described processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input the geographical location information data of the parent into the generation AI and cause the generation AI to select a customization method.

[0046] The support providing unit can suggest customization methods based on the parent's social media activity during customization. For example, the support providing unit can suggest customization methods based on the parent's social media activity during customization. For example, the support providing unit can suggest an optimal customization method based on information shared by the parent on social media. For example, the support providing unit can suggest a customization method related to an event the parent will be attending on social media. For example, the support providing unit can suggest a related customization method based on the parent's social media activity. In this way, the optimal customization method is provided by analyzing the social media activity. Some or all of the above-described processing in the support providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the support providing unit can input the parent's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0047] The reminder unit can select the optimal sending method by analyzing the parent's past reminder history when sending a reminder. For example, when sending a reminder, the reminder unit can select the optimal sending method by analyzing the parent's past reminder history. For example, the reminder unit can suggest a similar method based on a reminder sending method that the parent has used successfully in the past. For example, the reminder unit can suggest a different approach to avoid a reminder sending method that the parent has used unsuccessfully in the past. For example, the reminder unit can select the most effective sending method from the parent's past reminder history. In this way, the optimal sending method is provided by analyzing the past reminder history. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the parent's past reminder history data into a generation AI and have the generation AI select a sending method.

[0048] The reminder unit can prioritize sending highly relevant reminders by taking into account the geographical location information of the parent when sending a reminder. For example, the reminder unit prioritizes sending highly relevant reminders by taking into account the geographical location information of the parent when sending a reminder. For example, if the parent is in a specific location, the reminder unit can send a reminder related to the location. For example, if the parent is traveling, the reminder unit can send a reminder related to the parent's travel. For example, if the parent is at home, the reminder unit can send a reminder related to tasks to be performed at home. In this way, highly relevant reminders are provided by taking into account the geographical location information. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the parent's geographical location information data into the generation AI and cause the generation AI to send a reminder.

[0049] The analysis unit can apply an appropriate analysis algorithm by referring to the parent's past data during analysis. For example, the analysis unit can apply an appropriate analysis algorithm by referring to the parent's past data during analysis. For example, the analysis unit can apply a similar analysis algorithm based on data on which the parent was successful in the past. For example, the analysis unit can apply a different analysis algorithm to avoid data on which the parent was unsuccessful in the past. For example, the analysis unit can select the most effective analysis algorithm from the parent's past data. In this way, the optimal analysis algorithm is provided by referring to the past data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the parent's past data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0050] The analysis unit can select an appropriate analysis method based on the geographical location information of the parent during analysis. For example, the analysis unit can select an appropriate analysis method based on the geographical location information of the parent during analysis. For example, if the parent is in a specific location, the analysis unit can prioritize analyzing data related to that location. For example, if the parent is traveling, the analysis unit can prioritize analyzing data related to the travel. For example, if the parent is at home, the analysis unit can prioritize analyzing data related to tasks to be performed at home. This provides an optimal analysis method by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical location information data of the parent to the generation AI and cause the generation AI to select an analysis method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The child-rearing support system may further include a health management unit that monitors the health status of the parent. The health management unit monitors the health status of the parent, for example, based on health data entered by the parent or data acquired from a wearable device. The health management unit can, for example, analyze the parent's stress level and sleep patterns and provide appropriate health management advice. For example, if the parent is overworked, the health management unit can send a reminder to encourage the parent to take a rest. In this way, more comprehensive support can be provided by monitoring the parent's health status.

[0053] The child-rearing support system can further include a refreshment suggestion unit that suggests a refreshment method based on the parent's hobbies and interests. For example, if the parent likes music, the refreshment suggestion unit can suggest relaxing music. For example, if the parent likes exercise, the refreshment suggestion unit can suggest an appropriate exercise plan. For example, if the parent likes reading, the refreshment suggestion unit can suggest relaxing reading time. In this way, refreshment methods are suggested based on the parent's hobbies and interests.

[0054] The child-rearing support system may further include an experience analysis unit that analyzes the parent's past child-rearing experience and proposes a new child-rearing plan based on successful methods. For example, the experience analysis unit may propose a similar method based on the parent's past successful child-rearing methods. For example, the experience analysis unit may propose a different approach to avoid the parent's past unsuccessful child-rearing methods. For example, the experience analysis unit may select the most effective child-rearing plan based on the parent's past child-rearing experience. In this way, an optimal child-rearing plan is provided by analyzing the parent's past child-rearing experience.

[0055] The child-rearing support system can further include a local resource providing unit that provides local child-rearing resources based on the geographical location information of the parent. The local resource providing unit can, for example, provide information on child-rearing support facilities and events in the area where the parent lives. For example, if the parent is traveling, the local resource providing unit can provide local child-rearing resources in the area where the parent is traveling. For example, if the parent is at home, the local resource providing unit can provide local child-rearing resources around the parent's home. In this way, local child-rearing resources are provided based on the geographical location information.

[0056] The child-rearing support system may further include a social media analysis unit that analyzes the social media activities of parents and provides relevant child-rearing information. The social media analysis unit may, for example, provide relevant child-rearing information based on information shared by parents on social media. The social media analysis unit may, for example, provide information related to events in which parents will participate on social media. The social media analysis unit may, for example, provide relevant child-rearing resources based on the parents' social media activities. In this way, relevant child-rearing information is provided by analyzing social media activities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The management department manages the schedule. For example, it organizes the schedule based on the information entered by the parent and sends reminders, so that important appointments will not be forgotten. Step 2: The provider provides advice based on the schedule managed by the manager. For example, it provides advice on appropriate educational methods and health care according to the child's developmental stage. This allows the provider to provide advice according to the child's age and developmental stage based on the information entered by the parent. Step 3: The support providing unit provides support based on the advice provided by the providing unit. For example, it proposes an optimal child-rearing plan based on the family's situation and needs. This makes it possible to analyze the family's situation and needs based on the information entered by the parents and propose an optimal child-rearing plan.

[0059] (Example 2) A child-rearing support system according to an embodiment of the present invention is an innovative web service designed to support dual-income parents. This system utilizes AI to manage child-rearing tasks and schedules, provide advice, set reminders, and more. By providing customized support tailored to each family, it helps parents reduce stress and enjoy raising their children more effectively. For example, the system uses AI to automatically organize schedules and send reminders based on information entered by parents. This allows parents to efficiently manage their schedules without forgetting important appointments. Next, the system uses AI to provide child-rearing advice. For example, it provides advice on appropriate educational methods and health management based on a child's developmental stage. Based on information entered by parents, the AI ​​provides advice tailored to the child's age and developmental stage. This allows parents to respond appropriately to their children's development. Furthermore, the system uses AI to provide customized support tailored to each family. For example, it proposes an optimal child-rearing plan based on the family's situation and needs. Based on information entered by parents, the AI ​​analyzes the family's situation and needs and proposes an optimal child-rearing plan. This allows parents to effectively raise their children in a way that suits their circumstances. In this way, the child-rearing support system supports dual-income parents and helps them enjoy raising their children more effectively. With AI support, parents can reduce the stress of raising their children and spend more quality time. In this way, the child-rearing support system provides comprehensive support for parents' child-rearing, reducing stress and allowing them to enjoy raising their children more effectively.

[0060] A child-rearing support system according to an embodiment includes a management unit, a providing unit, and a support providing unit. The management unit manages schedules. The management unit organizes schedules based on, for example, information input by a parent. The management unit organizes a schedule of events related to child-rearing input by a parent and sends reminders. The management unit can send reminders based on, for example, the information input by a parent to prevent the parent from forgetting important appointments. The providing unit provides advice. The providing unit provides, for example, advice on appropriate educational methods and health management according to a child's developmental stage. The providing unit can provide advice based on a child's age and developmental stage based on, for example, the information input by a parent. The support providing unit provides customized support. The support providing unit proposes an optimal child-rearing plan based on, for example, the situation and needs of the family. The support providing unit can analyze, for example, the situation and needs of the family based on the information input by the parent and propose an optimal child-rearing plan. As a result, the child-rearing support system according to an embodiment comprehensively supports parents in raising their children, reduces stress, and enables parents to enjoy raising their children more effectively.

[0061] The child-rearing support system includes a reminder unit that sends reminders. The reminder unit sends the reminders. The reminder unit sends the reminders based on, for example, a schedule input by a parent. The reminder unit can send the reminders by, for example, email, SMS, app notification, or other methods. The reminder unit can send reminders at appropriate times so that, for example, a parent does not forget important appointments. By sending reminders, the parent can thereby manage important appointments without forgetting them. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input schedule data input by a parent into a generation AI and have the generation AI determine the timing of sending the reminder.

[0062] The child-rearing support system includes an analysis unit that analyzes the family situation. The analysis unit analyzes the family situation. The analysis unit analyzes the family situation based on, for example, information input by a parent. The analysis unit can analyze information such as family structure, economic situation, and lifestyle habits. The analysis unit can perform a detailed analysis of the family situation based on, for example, the information input by a parent, and generate data for providing optimal support. This allows for more appropriate support to be provided by analyzing the family situation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the family situation data input by a parent into a generation AI and have the generation AI analyze the family situation.

[0063] The management unit can organize the schedule based on information input by the parent. The management unit, for example, organizes the schedule based on information input by the parent. The management unit can, for example, organize a schedule of events related to child-rearing input by the parent and send reminders. The management unit can, for example, send reminders based on information input by the parent to prevent important appointments from being forgotten. This enables efficient schedule management by organizing the schedule based on information input by the parent. Some or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input schedule data input by the parent into a generation AI and have the generation AI organize the schedule.

[0064] The providing unit can provide advice according to the child's developmental stage. The providing unit, for example, provides advice on appropriate educational methods or health management according to the child's developmental stage. The providing unit can provide advice according to the child's age and developmental stage, for example, based on information input by a parent. For example, by providing advice according to the child's developmental stage, the providing unit allows parents to take appropriate measures according to the child's development. As a result, appropriate measures are possible by providing advice according to the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's developmental stage data input by the parent into a generating AI and cause the generating AI to provide advice.

[0065] The support providing unit can propose a parenting plan that suits the family's situation. The support providing unit can propose an optimal parenting plan, for example, based on the family's situation and needs. The support providing unit can analyze the family's situation and needs based on information input by the parents, and propose an optimal parenting plan. For example, by proposing a parenting plan that suits the family's situation, the support providing unit can enable parents to raise their children effectively according to their own situation. This enables effective parenting by proposing a parenting plan that suits the family's situation and needs. Some or all of the above-mentioned processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input family situation data input by the parents into a generation AI and have the generation AI execute a parenting plan proposal.

[0066] The management unit can estimate the parent's emotions and adjust schedule priorities based on the estimated parent's emotions. For example, the management unit can estimate the parent's emotions and adjust schedule priorities based on the estimated parent's emotions. For example, if the parent is feeling stressed, the management unit can prioritize important tasks and suggest a less burdensome schedule. For example, if the parent is relaxed, the management unit can suggest a relaxed schedule to ensure time for relaxation. For example, if the parent is in a hurry, the management unit can suggest the most efficient schedule to make the most of their time. This reduces the parent's burden by adjusting schedule priorities based on the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 management unit may be performed using an AI, or may be performed without an AI. For example, the management unit can input the parent's emotion data into the generation AI and cause the generation AI to adjust schedule priorities.

[0067] The management unit can analyze the parent's past schedule history and select an appropriate schedule management method. The management unit, for example, analyzes the parent's past schedule history and selects the optimal schedule management method. The management unit, for example, can propose a similar schedule based on a schedule pattern that was successful for the parent in the past. The management unit, for example, can propose a different approach to avoid a schedule pattern that was unsuccessful for the parent in the past. The management unit, for example, can propose the most efficient task for a specific time period based on the parent's past schedule history. This enables optimal schedule management by analyzing the past schedule history. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the parent's past schedule data into a generation AI and have the generation AI select a schedule management method.

[0068] The management unit can filter the schedule based on the parent's current living situation when managing the schedule. For example, the management unit can filter the schedule based on the parent's current living situation and work load when managing the schedule. For example, if the parent is currently busy, the management unit can incorporate only important tasks into the schedule. For example, if the parent currently has free time, the management unit can incorporate additional tasks or time for relaxation into the schedule. For example, if the parent's work load is high, the management unit can suggest a schedule that takes into account the balance between work and child-rearing. This enables efficient schedule management by filtering the schedule based on the parent's living situation and work load. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input data on the parent's living situation into the generation AI and have the generation AI perform schedule filtering.

[0069] The management unit can estimate the parent's emotions and adjust the schedule notification method based on the estimated parent's emotions. For example, the management unit can estimate the parent's emotions and adjust the schedule notification method based on the estimated parent's emotions. For example, if the parent is stressed, the management unit can notify the parent of the schedule with a gentle notification sound. For example, if the parent is relaxed, the management unit can notify the parent of the schedule with a bright notification sound. For example, if the parent is in a hurry, the management unit can use a quick and concise notification method. This allows the optimal notification for the parent to be provided by adjusting the notification method based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 management unit may be performed using an AI, for example, or without an AI. For example, the management unit can input the parent's emotion data into the generation AI and cause the generation AI to adjust the notification method.

[0070] The management unit can prioritize highly relevant schedules based on the parent's geographical location information when managing a schedule. For example, the management unit prioritizes highly relevant schedules based on the parent's geographical location information when managing a schedule. For example, when the parent is in a specific location, the management unit can prioritize tasks related to that location in the schedule. For example, when the parent is traveling, the management unit can prioritize tasks that can be performed while traveling in the schedule. For example, when the parent is at home, the management unit can prioritize tasks that can be performed at home in the schedule. This enables highly relevant schedule management by taking geographical location information into consideration. Some or all of the above-described processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can input the parent's geographical location information data into a generation AI and cause the generation AI to manage the schedule priorities.

[0071] The management unit can manage related schedules based on the parent's social media activity when managing a schedule. For example, the management unit can manage related schedules based on the parent's social media activity when managing a schedule. For example, if the parent shares a plan on social media, the management unit can automatically incorporate the plan into the schedule. For example, if the parent is attending an event on social media, the management unit can incorporate the event into the schedule. For example, the management unit can incorporate related tasks into the schedule based on the parent's social media activity. This makes it possible to manage related schedules by analyzing social media activity. Some or all of the above-mentioned processing in the management unit can be performed using, for example, AI, or can be performed without using AI. For example, the management unit can input the parent's social media activity data into a generation AI and have the generation AI manage the schedule.

[0072] The providing unit can estimate the parent's emotions and adjust the way the advice is expressed based on the estimated parent's emotions. For example, the providing unit can estimate the parent's emotions and adjust the way the advice is expressed based on the estimated parent's emotions. For example, if the parent is stressed, the providing unit can provide advice in gentle words. For example, if the parent is relaxed, the providing unit can provide detailed advice. For example, if the parent is in a hurry, the providing unit can provide concise and to-the-point advice. By adjusting the way the advice is expressed based on the parent's emotions, optimal advice for the parent is provided. The emotion estimation is realized using an emotion estimation function, for example, using 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-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 the parent's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0073] The providing unit can adjust the level of detail of the advice based on the child's developmental stage when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the child's developmental stage when providing advice. For example, if the child is an infant, the providing unit can provide basic advice. For example, if the child is an elementary school student, the providing unit can provide advice on specific educational methods and health management. For example, if the child is a junior high school student or older, the providing unit can provide advice on further education and careers. In this way, appropriate advice is provided by adjusting the level of detail of the advice based on the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the child's developmental stage into the generating AI and cause the generating AI to adjust the level of detail of the advice.

[0074] The providing unit can apply different advice algorithms depending on the child's category when providing advice. For example, the providing unit can apply different advice algorithms depending on the child's category when providing advice. For example, if a child needs special support, the providing unit can provide specialized advice. For example, if a child is interested in sports, the providing unit can provide sports-related advice. For example, if a child is interested in art, the providing unit can provide art-related advice. By applying different advice algorithms depending on the child's category, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input child category data into the generating AI and cause the generating AI to apply the advice algorithm.

[0075] The providing unit can estimate the parent's emotions and adjust the length of the advice based on the estimated parent's emotions. For example, the providing unit can estimate the parent's emotions and adjust the length of the advice based on the estimated parent's emotions. For example, if the parent is stressed, the providing unit can provide short and to-the-point advice. For example, if the parent is relaxed, the providing unit can provide detailed advice. For example, if the parent is in a hurry, the providing unit can provide concise and quick advice. By adjusting the length of the advice based on the parent's emotions, optimal advice for the parent is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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 the parent's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0076] The providing unit can determine the priority of advice based on the child's developmental stage when providing advice. For example, the providing unit can determine the priority of advice based on the child's developmental stage when providing advice. For example, if the child is an infant, the providing unit can prioritize advice on basic health management. For example, if the child is an elementary school student, the providing unit can prioritize advice on educational methods. For example, if the child is a junior high school student or older, the providing unit can prioritize advice on further education and careers. In this way, appropriate advice is provided by determining the priority of advice based on the child's developmental stage. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input child developmental stage data into the generating AI and cause the generating AI to determine the priority of advice.

[0077] The providing unit can adjust the order of advice based on the child's related information when providing advice. For example, the providing unit can adjust the order of advice based on the child's related information when providing advice. For example, the providing unit can first provide health management advice based on the child's health condition. For example, the providing unit can first provide educational method advice based on the child's learning situation. For example, the providing unit can first provide related advice based on the child's interests and concerns. In this way, appropriate advice is provided by adjusting the order of advice based on the child's related information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's related information data to the generating AI and cause the generating AI to adjust the order of advice.

[0078] The support providing unit can estimate the parent's emotions and adjust the customization method based on the estimated parent's emotions. For example, the support providing unit can estimate the parent's emotions and adjust the customization method based on the estimated parent's emotions. For example, if the parent is stressed, the support providing unit can suggest a simple and less burdensome customization method. For example, if the parent is relaxed, the support providing unit can provide detailed customization options. For example, if the parent is in a hurry, the support providing unit can suggest a quick customization method. This adjusts the customization method based on the parent's emotions, thereby providing optimal customization for the parent. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support providing unit may be performed using an AI, for example, or without an AI. For example, the support providing unit can input the parent's emotion data into the generation AI and cause the generation AI to adjust the customization method.

[0079] The support providing unit can analyze the parent's past parenting behavior and select an appropriate customization method during customization. For example, the support providing unit analyzes the parent's past parenting behavior and selects the optimal customization method during customization. For example, the support providing unit can suggest similar customization based on parenting methods that the parent has used successfully in the past. For example, the support providing unit can suggest a different approach to avoid parenting methods that the parent has used unsuccessfully in the past. For example, the support providing unit can select the most effective customization method from the parent's past parenting behavior. In this way, the optimal customization method is provided by analyzing the parenting behavior. Some or all of the above-described processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input data on the parent's past parenting behavior into the generation AI and cause the generation AI to select a customization method.

[0080] The support providing unit can adjust the customization means based on the parent's current living situation during customization. The support providing unit, for example, adjusts the customization means based on the parent's current living situation during customization. For example, if the parent is currently busy, the support providing unit can suggest a customization method that can be easily and quickly implemented. For example, if the parent currently has time, the support providing unit can provide detailed customization options. For example, the support providing unit can suggest an optimal customization method according to the parent's living situation. As a result, optimal customization for the parent is provided by adjusting the customization means based on the current living situation. Some or all of the above-mentioned processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input the parent's living situation data into the generation AI and cause the generation AI to adjust the customization means.

[0081] The support providing unit can estimate the parent's emotions and determine the priority of customization based on the estimated parent's emotions. For example, the support providing unit can estimate the parent's emotions and determine the priority of customization based on the estimated parent's emotions. For example, if the parent is stressed, the support providing unit can prioritize important customization. For example, if the parent is relaxed, the support providing unit can prioritize detailed customization. For example, if the parent is in a hurry, the support providing unit can prioritize customization that can be performed quickly. In this way, by determining the priority of customization based on the parent's emotions, optimal customization for the parent is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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 support providing unit may be performed using an AI, for example, or without an AI. For example, the support providing unit can input the parent's emotion data into the generation AI and cause the generation AI to determine the priority of customization.

[0082] The support providing unit can select an appropriate customization method based on the geographical location information of the parent during customization. For example, the support providing unit selects an appropriate customization method based on the geographical location information of the parent during customization. For example, if the parent is in a specific location, the support providing unit can suggest a customization method related to the location. For example, if the parent is traveling, the support providing unit can suggest a customization method that can be performed while traveling. For example, if the parent is at home, the support providing unit can suggest a customization method that can be performed at home. In this way, the optimal customization method is provided by taking the geographical location information into consideration. Some or all of the above-described processing in the support providing unit may be performed using AI, for example, or may be performed without using AI. For example, the support providing unit can input the geographical location information data of the parent into the generation AI and cause the generation AI to select a customization method.

[0083] The support providing unit can suggest customization methods based on the parent's social media activity during customization. For example, the support providing unit can suggest customization methods based on the parent's social media activity during customization. For example, the support providing unit can suggest an optimal customization method based on information shared by the parent on social media. For example, the support providing unit can suggest a customization method related to an event the parent will be attending on social media. For example, the support providing unit can suggest a related customization method based on the parent's social media activity. In this way, the optimal customization method is provided by analyzing the social media activity. Some or all of the above-described processing in the support providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the support providing unit can input the parent's social media activity data into the generation AI and cause the generation AI to suggest customization methods.

[0084] The reminder unit can estimate the parent's emotions and adjust the timing of sending a reminder based on the estimated parent's emotions. The reminder unit, for example, estimates the parent's emotions and adjusts the timing of sending a reminder based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reminder unit can send a reminder at a calm timing. For example, if the parent is relaxed, the reminder unit can send a reminder at an appropriate timing. For example, if the parent is in a hurry, the reminder unit can send a reminder quickly. In this way, by adjusting the timing of sending a reminder based on the parent's emotions, the reminder is sent at the optimal timing for the parent. 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-mentioned processing in the reminder unit may be performed using, for example, AI, or without AI. For example, the reminder unit can input the parent's emotional data into the generation AI and have the generation AI adjust the timing of sending reminders.

[0085] The reminder unit can select the optimal sending method by analyzing the parent's past reminder history when sending a reminder. For example, when sending a reminder, the reminder unit can select the optimal sending method by analyzing the parent's past reminder history. For example, the reminder unit can suggest a similar method based on a reminder sending method that the parent has used successfully in the past. For example, the reminder unit can suggest a different approach to avoid a reminder sending method that the parent has used unsuccessfully in the past. For example, the reminder unit can select the most effective sending method from the parent's past reminder history. In this way, the optimal sending method is provided by analyzing the past reminder history. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the parent's past reminder history data into a generation AI and have the generation AI select a sending method.

[0086] The reminder unit can estimate the parent's emotions and adjust the content of the reminder based on the estimated parent's emotions. For example, the reminder unit can estimate the parent's emotions and adjust the content of the reminder based on the estimated parent's emotions. For example, if the parent is stressed, the reminder unit can send a calming reminder. For example, if the parent is relaxed, the reminder unit can send a detailed reminder. For example, if the parent is in a hurry, the reminder unit can send a concise and to-the-point reminder. By adjusting the content of the reminder based on the parent's emotions, an optimal reminder for the parent is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or without AI. For example, the reminder unit can input the parent's emotional data into the generation AI and have the generation AI adjust the content of the reminder.

[0087] The reminder unit can prioritize sending highly relevant reminders by taking into account the geographical location information of the parent when sending a reminder. For example, the reminder unit prioritizes sending highly relevant reminders by taking into account the geographical location information of the parent when sending a reminder. For example, if the parent is in a specific location, the reminder unit can send a reminder related to the location. For example, if the parent is traveling, the reminder unit can send a reminder related to the parent's travel. For example, if the parent is at home, the reminder unit can send a reminder related to tasks to be performed at home. In this way, highly relevant reminders are provided by taking into account the geographical location information. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the parent's geographical location information data into the generation AI and cause the generation AI to send a reminder.

[0088] The analysis unit can estimate the parent's emotions and adjust the analysis method based on the estimated parent's emotions. For example, the analysis unit can estimate the parent's emotions and adjust the analysis method based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit can provide a concise and to-the-point analysis result. For example, if the parent is relaxed, the analysis unit can provide a detailed analysis result. For example, if the parent is in a hurry, the analysis unit can provide a quick analysis result. By adjusting the analysis method based on the parent's emotions, optimal analysis results can be provided for the parent. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the parent's emotion data into the generation AI and cause the generation AI to adjust the analysis method.

[0089] The analysis unit can apply an appropriate analysis algorithm by referring to the parent's past data during analysis. For example, the analysis unit can apply an appropriate analysis algorithm by referring to the parent's past data during analysis. For example, the analysis unit can apply a similar analysis algorithm based on data on which the parent was successful in the past. For example, the analysis unit can apply a different analysis algorithm to avoid data on which the parent was unsuccessful in the past. For example, the analysis unit can select the most effective analysis algorithm from the parent's past data. In this way, the optimal analysis algorithm is provided by referring to the past data. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the parent's past data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0090] The analysis unit can estimate the parent's emotions and determine the analysis priorities based on the estimated parent's emotions. For example, the analysis unit can estimate the parent's emotions and determine the analysis priorities based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit can prioritize important analyses. For example, if the parent is relaxed, the analysis unit can prioritize detailed analyses. For example, if the parent is in a hurry, the analysis unit can prioritize analyses that can be performed quickly. This allows the parent to receive optimal analysis results by prioritizing the analyses based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 AI, for example, or without AI. For example, the analysis unit can input the parent's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0091] The analysis unit can select an appropriate analysis method based on the geographical location information of the parent during analysis. For example, the analysis unit can select an appropriate analysis method based on the geographical location information of the parent during analysis. For example, if the parent is in a specific location, the analysis unit can prioritize analyzing data related to that location. For example, if the parent is traveling, the analysis unit can prioritize analyzing data related to the travel. For example, if the parent is at home, the analysis unit can prioritize analyzing data related to tasks to be performed at home. This provides an optimal analysis method by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical location information data of the parent to the generation AI and cause the generation AI to select an analysis method. === Hard Collateral 1-1 === Each of the multiple elements, including the management unit, provision unit, support provision unit, reminder unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the smart device 14 and organizes a schedule based on information input by the parent and sends reminders. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate educational methods and health management advice according to the child's developmental stage. The support provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the family situation and needs and proposes an optimal parenting plan. The reminder unit is realized, for example, by the control unit 46A of the smart device 14 and sends reminders based on the schedule input by the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the family situation in detail and generates data for providing optimal support. === Hard Collateral 1-2 === Each of the multiple elements, including the management unit, provision unit, support provision unit, reminder unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the smart glasses 214, and organizes a schedule based on information input by the parent and sends reminders. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides appropriate educational methods and health management advice according to the child's developmental stage. The support provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the family situation and needs and proposes an optimal parenting plan. The reminder unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends reminders based on the schedule input by the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the family situation in detail and generates data for providing optimal support. === Hard Collateral 1-3 === Each of the multiple elements, including the management unit, provision unit, support provision unit, reminder unit, and analysis unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the management unit is implemented by the control unit 46A of the headset-type terminal 314, and organizes a schedule based on information entered by the parent and sends reminders. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides appropriate educational methods and health management advice according to the child's developmental stage. The support provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the family's situation and needs and proposes an optimal parenting plan. The reminder unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and sends reminders based on the schedule entered by the parent. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and performs a detailed analysis of the family's situation and generates data for providing optimal support. === Hard Collateral 1-4 === Each of the multiple elements, including the management unit, provision unit, support provision unit, reminder unit, and analysis unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the management unit is realized by the control unit 46A of the robot 414 and organizes a schedule based on information input by the parent and sends reminders. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate educational methods and health management advice according to the child's developmental stage. The support provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the family situation and needs and proposes an optimal parenting plan. The reminder unit is realized, for example, by the control unit 46A of the robot 414 and sends reminders based on the schedule input by the parent. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the family situation in detail and generates data for providing optimal support.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The child-rearing support system may further include a health management unit that monitors the health status of the parent. The health management unit monitors the health status of the parent, for example, based on health data entered by the parent or data acquired from a wearable device. The health management unit can, for example, analyze the parent's stress level and sleep patterns and provide appropriate health management advice. For example, if the parent is overworked, the health management unit can send a reminder to encourage the parent to take a rest. In this way, more comprehensive support can be provided by monitoring the parent's health status.

[0094] The child-rearing support system may further include a communication support unit that estimates the parent's emotions and suggests a communication method with the child based on the estimated emotions. For example, the communication support unit may suggest a gentle communication method when the parent is stressed. For example, the communication support unit may suggest a proactive communication method when the parent is relaxed. For example, the communication support unit may suggest an efficient communication method when the parent is in a hurry. In this way, an appropriate communication method is suggested based on the parent's emotions.

[0095] The child-rearing support system can further include a refreshment suggestion unit that suggests a refreshment method based on the parent's hobbies and interests. For example, if the parent likes music, the refreshment suggestion unit can suggest relaxing music. For example, if the parent likes exercise, the refreshment suggestion unit can suggest an appropriate exercise plan. For example, if the parent likes reading, the refreshment suggestion unit can suggest relaxing reading time. In this way, refreshment methods are suggested based on the parent's hobbies and interests.

[0096] The child-rearing support system may further include an education adjustment unit that estimates the parent's emotions and adjusts the child's education method based on the estimated emotions. For example, if the parent is feeling stressed, the education adjustment unit may suggest a simple and less burdensome education method. For example, if the parent is relaxed, the education adjustment unit may suggest a detailed education plan. For example, if the parent is in a hurry, the education adjustment unit may suggest an efficient education method. In this way, an appropriate education method is suggested based on the parent's emotions.

[0097] The child-rearing support system may further include an experience analysis unit that analyzes the parent's past child-rearing experience and proposes a new child-rearing plan based on successful methods. For example, the experience analysis unit may propose a similar method based on the parent's past successful child-rearing methods. For example, the experience analysis unit may propose a different approach to avoid the parent's past unsuccessful child-rearing methods. For example, the experience analysis unit may select the most effective child-rearing plan based on the parent's past child-rearing experience. In this way, an optimal child-rearing plan is provided by analyzing the parent's past child-rearing experience.

[0098] The child-rearing support system may further include a play suggestion unit that estimates the parent's emotions and suggests ways for the child to play based on the estimated emotions. For example, if the parent is feeling stressed, the play suggestion unit may suggest a calm way to play. For example, if the parent is relaxed, the play suggestion unit may suggest an active way to play. For example, if the parent is in a hurry, the play suggestion unit may suggest an efficient way to play. In this way, appropriate ways to play are suggested based on the parent's emotions.

[0099] The child-rearing support system can further include a local resource providing unit that provides local child-rearing resources based on the geographical location information of the parent. The local resource providing unit can, for example, provide information on child-rearing support facilities and events in the area where the parent lives. For example, if the parent is traveling, the local resource providing unit can provide local child-rearing resources in the area where the parent is traveling. For example, if the parent is at home, the local resource providing unit can provide local child-rearing resources around the parent's home. In this way, local child-rearing resources are provided based on the geographical location information.

[0100] The child-rearing support system may further include a meal suggestion unit that estimates a parent's emotions and suggests a meal plan for the child based on the estimated emotions. For example, if the parent is stressed, the meal suggestion unit may suggest a simple, nutritionally balanced meal plan. For example, if the parent is relaxed, the meal suggestion unit may suggest detailed recipes. For example, if the parent is in a hurry, the meal suggestion unit may suggest a meal plan that can be quickly prepared. In this way, an appropriate meal plan is suggested based on the parent's emotions.

[0101] The child-rearing support system may further include a social media analysis unit that analyzes the social media activities of parents and provides relevant child-rearing information. The social media analysis unit may, for example, provide relevant child-rearing information based on information shared by parents on social media. The social media analysis unit may, for example, provide information related to events in which parents will participate on social media. The social media analysis unit may, for example, provide relevant child-rearing resources based on the parents' social media activities. In this way, relevant child-rearing information is provided by analyzing social media activities.

[0102] The child-rearing support system may further include a sleep management unit that estimates the parent's emotions and suggests a child's sleep management method based on the estimated emotions. For example, if the parent is stressed, the sleep management unit may suggest a simple and effective sleep management method. For example, if the parent is relaxed, the sleep management unit may suggest a detailed sleep management plan. For example, if the parent is in a hurry, the sleep management unit may suggest a sleep management method that can be quickly implemented. In this way, an appropriate sleep management method is suggested based on the parent's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The management department manages the schedule. For example, it organizes the schedule based on the information entered by the parent and sends reminders, so that important appointments will not be forgotten. Step 2: The provider provides advice based on the schedule managed by the manager. For example, it provides advice on appropriate educational methods and health care according to the child's developmental stage. This allows the provider to provide advice according to the child's age and developmental stage based on the information entered by the parent. Step 3: The support providing unit provides support based on the advice provided by the providing unit. For example, it proposes an optimal child-rearing plan based on the family's situation and needs. This makes it possible to analyze the family's situation and needs based on the information entered by the parents and propose an optimal child-rearing plan.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. The management department manages the schedule, a providing unit that provides advice based on a schedule managed by the management unit; a support providing unit that provides support based on the advice provided by the providing unit; Equipped with A system characterized by:

2. Equipped with a reminder section that sends reminders The system of claim 1 .

3. Equipping an analysis department to analyze the situation at home The system of claim 1 .

4. The management unit Organize schedules based on parent-entered information The system of claim 1 .

5. The providing unit Providing advice according to the child's developmental stage The system of claim 1 .

6. The support providing unit Proposing child-rearing plans tailored to the family situation The system of claim 1 .

7. The management unit Estimate parental emotions and adjust schedule priorities based on the estimated parental emotions The system of claim 1 .

8. The management unit Analyze parents' past schedule history and select the appropriate schedule management method The system of claim 1 .

9. The management unit Filter schedules based on parents' current living situations The system of claim 1 .

10. The management unit Estimate the parent's emotions and adjust the schedule notification method based on the estimated parent's emotions The system of claim 1 .

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