System

Abstract

An object of a system according to an embodiment is to integrally provide information provision based on a pregnancy cycle, child care coaching, and child growth monitoring.SOLUTION: A system according to an embodiment includes an information providing unit, a coaching unit, and a monitoring unit. The information providing unit provides information based on the pregnancy cycle. The coaching unit performs child care coaching based on the information provided by the information providing unit. The monitoring unit monitors the growth of the child based on the coaching provided by the coaching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technologies do not integrate information provision based on the pregnancy cycle, parenting coaching, and child growth monitoring, leaving room for improvement.

[0005] The system according to the embodiment aims to provide information based on the pregnancy cycle, parenting coaching, and child growth monitoring in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, a coaching unit, and a monitoring unit. The information providing unit provides information based on the pregnancy cycle. The coaching unit provides childcare coaching based on the information provided by the information providing unit. The monitoring unit monitors the child's growth based on the coaching provided by the coaching unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information based on the pregnancy cycle, parenting coaching, and child growth monitoring in an integrated manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A childcare support system according to an embodiment of the present invention utilizes generative AI to support parents struggling with childcare and address the trend toward later marriage in Japan. The system provides information based on pregnancy cycles, provides childcare coaching, and monitors child development. For example, the system visualizes tasks for parents based on the pregnancy cycle, establishes a follow-up system during pregnancy, and provides postnatal support. Specifically, the system visualizes status explanations and precautions based on the pregnancy cycle, and visualizes precautions to take after birth as the child grows. The system then provides childcare coaching to parents. For example, it provides advice on children's learning methods, discipline, and building parent-child relationships. Furthermore, the system monitors children's development. For example, it supports language and physical development using data such as sleep and diet, and visualizes childcare records so they can be shared with grandparents. This system is expected to support parents struggling with childcare and address the trend toward later marriage in Japan. The system supports parents struggling with childcare and address the trend toward later marriage in Japan. For example, by establishing a support system during pregnancy and providing support for childcare after birth, the burden of childcare can be reduced and the whole family can watch over the child's growth.

[0029] A childcare support system according to an embodiment includes an information providing unit, a coaching unit, and a monitoring unit. The information providing unit provides information based on the pregnancy cycle. For example, it provides health information and nutritional information for each week of pregnancy. The information providing unit can also visualize explanations of a pregnant woman's condition and important points based on the pregnancy cycle. For example, it emphasizes the importance of nutritional management and stress management during the early stages of pregnancy and supports breastfeeding and sleep management after birth. The coaching unit provides childcare coaching. For example, it provides advice on child learning methods, discipline, and building parent-child relationships. The coaching unit specifically suggests learning methods and discipline points appropriate for the child's age and provides advice to facilitate parent-child communication. The monitoring unit monitors the child's growth. For example, it supports language and physical development based on data such as sleep and diet. The monitoring unit can also visualize childcare records so that they can be shared with grandparents. For example, it stores childcare records on the cloud so that all family members can access them. This enables the childcare support system according to an embodiment to provide information based on the pregnancy cycle, childcare coaching, and childcare growth monitoring.

[0030] The information providing unit can visualize an explanation of the pregnant woman's condition and points to note based on the pregnancy cycle. The information providing unit, for example, visualizes physical changes and points to note for each week of pregnancy. For example, it emphasizes the importance of nutritional management and stress management in the early stages of pregnancy, and indicates the importance of weight management and exercise in the middle stages of pregnancy. The information providing unit can also provide information on preparation for childbirth and postpartum care in the late stages of pregnancy. This allows the pregnant woman to deepen her understanding by visualizing the explanation of her condition and points to note. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input information for each week of pregnancy into the generation AI and have the generation AI execute the visualized information.

[0031] The coaching unit can provide advice on children's learning methods, discipline, and building parent-child relationships. For example, the coaching unit provides learning methods appropriate to the child's age. For example, it suggests playful learning methods for toddlers and advice on reading and homework for elementary school children. The coaching unit can also provide advice on discipline. For example, it can suggest key points on setting rules and how to praise and scold. The coaching unit can also provide advice on building parent-child relationships. For example, it can suggest specific dialogue methods and joint activities to facilitate parent-child communication. This makes it possible to provide specific advice on children's learning methods, discipline, and building parent-child relationships. Some or all of the above-described processing in the coaching unit may be performed using, or without, AI. For example, the coaching unit can input advice appropriate to the child's age and developmental stage into the generation AI and have the generation AI execute the specific advice.

[0032] The monitoring unit can support language and physical growth based on sleep and dietary data. The monitoring unit, for example, records the child's sleep time and dietary content and monitors growth indicators. For example, based on the record of sleep time, the monitoring unit can suggest an appropriate sleep rhythm. The monitoring unit can also suggest a nutritionally balanced diet based on the record of dietary content. The monitoring unit can also monitor the stage of language development and provide appropriate support. For example, if a delay in language development is observed, measures can be taken early. This makes it possible to support the child's growth based on data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input sleep and dietary data into the generation AI and have the generation AI execute growth indicators.

[0033] The monitoring unit can visualize the childcare records so that they can be shared with grandparents. For example, the monitoring unit stores the child's growth record on the cloud so that all family members can access it. For example, the growth record can be displayed in graphs or charts, providing it in a visually easy-to-understand format. The monitoring unit also has a notification function for sharing the growth record. For example, when the growth record is updated, a notification can be sent to all family members. The monitoring unit can also provide a function for leaving comments and feedback on the growth record. This allows the childcare record to be shared with all family members, reducing the burden of childcare. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the growth record into a generation AI and have the generation AI execute the visualized record.

[0034] The information providing unit can provide nutritional management and stress management during the early stages of pregnancy, and support breastfeeding and sleep management after birth. For example, the information providing unit emphasizes the importance of nutritional management and stress management during the early stages of pregnancy. For example, it can suggest a balanced diet and relaxation methods. The information providing unit can also support breastfeeding and sleep management after birth. For example, it can suggest the timing of breastfeeding and an appropriate sleep rhythm. This can support important management points from the early stages of pregnancy to postpartum. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input information on nutritional management and stress management into the generation AI and have the generation AI provide specific advice.

[0035] The information providing unit can provide information that specifically indicates the division of roles between the father and the mother based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit indicates specific tasks that the father should support. For example, in the middle stages of pregnancy, the information providing unit provides health management points that the mother should pay attention to. The information providing unit can also indicate specific division of roles related to preparation for childbirth in the late stages of pregnancy. This clarifies the division of roles between the father and the mother, thereby improving the efficiency of childcare. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input information on the division of roles based on the pregnancy cycle into the generation AI and have the generation AI execute the specific division of roles.

[0036] The information providing unit can monitor the health condition of the pregnant woman in real time based on the pregnancy cycle and issue an alert if an abnormality is detected. The information providing unit can, for example, monitor the pregnant woman's blood pressure and body temperature in real time and issue an alert if an abnormality is detected. For example, the information providing unit can monitor the pregnant woman's heart rate and issue an alert if an abnormality is detected. The information providing unit can also monitor the pregnant woman's weight gain and issue an alert if an abnormality is detected. This allows the pregnant woman's health condition to be monitored in real time and a prompt response to be made if an abnormality is detected. Some or all of the above-mentioned processing in the information providing unit can be performed using, for example, AI or without AI. For example, the information providing unit can input the pregnant woman's vital signs data to the generation AI and cause the generation AI to detect abnormalities and issue an alert.

[0037] The information providing unit can provide information on psychological support for pregnant women based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit provides information on stress management and relaxation methods. For example, in the middle stages of pregnancy, it provides information to encourage positive thinking. The information providing unit can also provide information on mental preparation for childbirth in the late stages of pregnancy. In this way, by providing psychological support to pregnant women, stress during pregnancy can be reduced. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, or may be performed without using AI, for example. For example, the information providing unit can input information on psychological support into the generation AI and have the generation AI execute specific advice.

[0038] The information providing unit can provide information tailored to the lifestyle rhythms of mom and dad based on the pregnancy cycle. For example, if dad works the night shift, the information providing unit provides information at night. For example, if mom takes a rest during the day, the information providing unit provides information during the day. Furthermore, if mom and dad both work, the information providing unit can also provide information on weekends. This allows for more effective support by providing information tailored to the lifestyle rhythms of mom and dad. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input lifestyle rhythm data into the generation AI and have the generation AI execute the timing of information provision.

[0039] The information providing unit can provide customized advice regarding diet and exercise to pregnant women based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit can provide advice regarding a nutritionally balanced diet. For example, in the middle stages of pregnancy, the information providing unit can provide advice regarding moderate exercise. In addition, in the late stages of pregnancy, the information providing unit can also provide advice regarding physical fitness for childbirth. This makes it possible to support health management by providing customized advice regarding diet and exercise to pregnant women. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input data regarding diet and exercise into the generation AI and have the generation AI execute customized advice.

[0040] The information providing unit can monitor the stress level of the pregnant woman based on the pregnancy cycle and suggest relaxation methods. The information providing unit, for example, monitors the stress level of the pregnant woman and suggests relaxation methods. For example, if the stress level of the pregnant woman is high, it preferentially suggests relaxation methods. The information providing unit can also suggest complementary relaxation methods if the stress level of the pregnant woman is low. This makes it possible to reduce stress by monitoring the stress level of the pregnant woman and suggesting appropriate relaxation methods. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input stress level data into the generation AI and have the generation AI suggest relaxation methods.

[0041] The coaching unit can provide specific child-rearing methods according to the child's age and developmental stage. For example, during the newborn period, the coaching unit provides methods for breastfeeding and changing diapers. For example, during the infant period, the coaching unit provides methods for introducing solid food and managing sleep. The coaching unit can also provide advice on discipline and play methods during the toddler period. This allows the quality of child-rearing to be improved by providing specific child-rearing methods according to the child's age and developmental stage. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input child-rearing methods according to the child's age and developmental stage into the generation AI and have the generation AI execute the specific child-rearing methods.

[0042] The coaching unit can provide a specific dialogue method for facilitating communication between a parent and a child. The coaching unit provides, for example, a specific dialogue method for facilitating communication between a parent and a child. For example, if communication between a parent and a child is not smooth, the coaching unit can provide a dialogue method preferentially. Furthermore, if communication between a parent and a child is smooth, the coaching unit can also provide a dialogue method supplementarily. This makes it possible to strengthen the parent-child relationship by providing a specific dialogue method for facilitating communication between a parent and a child. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input parent-child communication data into a generation AI and cause the generation AI to execute the specific dialogue method.

[0043] The coaching unit can propose a customized learning method according to the child's learning style. For example, if the child has a visual learning style, the coaching unit proposes visual learning materials. For example, if the child has an auditory learning style, the coaching unit proposes auditory learning materials. Furthermore, if the child has an experiential learning style, the coaching unit can also propose experiential learning materials. This makes it possible to improve learning effectiveness by proposing a customized learning method according to the child's learning style. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input the child's learning style data into the generation AI and cause the generation AI to execute the customized learning method.

[0044] The coaching unit can suggest optimal child-rearing methods based on a child's personality and interests. For example, if a child has an introverted personality, the coaching unit can suggest child-rearing methods that suit that introverted personality. For example, if a child has an extroverted personality, the coaching unit can suggest child-rearing methods that suit that extroverted personality. Furthermore, if a child has specific interests, the coaching unit can also suggest child-rearing methods based on those interests. This makes it possible to improve the quality of child-rearing by suggesting child-rearing methods based on a child's personality and interests. Some or all of the above-described processing in the coaching unit may be performed using, or without, AI, for example. For example, the coaching unit can input data on a child's personality and interests into a generation AI and have the generation AI execute the optimal child-rearing method.

[0045] The coaching unit can suggest joint activities to strengthen the parent-child relationship. For example, the coaching unit suggests joint activities to strengthen the parent-child relationship. For example, if the parent-child relationship is not strengthened, the coaching unit preferentially suggests joint activities. Furthermore, if the parent-child relationship is strengthened, the coaching unit can also suggest joint activities as a complement. In this way, by suggesting joint activities to strengthen the parent-child relationship, the parent-child relationship can be deepened. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input parent-child relationship data into the generation AI and have the generation AI execute the joint activity suggestions.

[0046] The coaching unit can suggest specific activities for developing a child's social skills. For example, the coaching unit suggests specific activities for developing a child's social skills. For example, if a child's social skills are not being developed, the coaching unit can preferentially suggest activities. Furthermore, if a child's social skills are being developed, the coaching unit can also suggest complementary activities. In this way, by suggesting specific activities for developing a child's social skills, it is possible to promote the development of social skills. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input the child's social skills data into a generation AI and cause the generation AI to execute specific activities.

[0047] The monitoring unit can analyze the child's growth data and issue an alert if an abnormality is detected. The monitoring unit, for example, analyzes the child's growth data and issues an alert if an abnormality is detected. For example, if the child's growth data is abnormal, an alert is issued preferentially. The monitoring unit can also issue a supplementary alert if the child's growth data is normal. This makes it possible to analyze the child's growth data and respond quickly if an abnormality is detected. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data to a generation AI and cause the generation AI to detect abnormalities and issue an alert.

[0048] The monitoring unit can predict future growth based on the child's growth data. The monitoring unit, for example, predicts future growth based on the child's growth data. For example, if the child's growth data is abnormal, growth prediction is performed as a priority. Furthermore, if the child's growth data is normal, the monitoring unit can also perform growth prediction as a supplement. This makes it easier to make childcare plans by predicting future growth based on the child's growth data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data into a generation AI and cause the generation AI to perform a future growth prediction.

[0049] The monitoring unit can suggest an optimal child-rearing method based on the child's growth data. The monitoring unit suggests an optimal child-rearing method, for example, based on the child's growth data. For example, if the child's growth data is abnormal, the monitoring unit preferentially suggests a child-rearing method. Furthermore, if the child's growth data is normal, the monitoring unit can also suggest a complementary child-rearing method. This makes it possible to improve the quality of child-rearing by suggesting an optimal child-rearing method based on the child's growth data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data into a generation AI and cause the generation AI to execute the optimal child-rearing method.

[0050] The monitoring unit can store the child's growth data on the cloud so that all family members can access it. For example, the monitoring unit can store the child's growth data on the cloud so that all family members can access it. For example, when the growth data is updated, a notification is sent to all family members. The monitoring unit can also share the growth data on the cloud so that all family members can leave comments and feedback. This can reduce the burden of childcare by storing the child's growth data on the cloud so that all family members can access it. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input the growth data into a generation AI and cause the generation AI to execute a process of storing the data on the cloud.

[0051] The monitoring unit can automatically generate a childcare record based on the child's growth data. The monitoring unit, for example, automatically generates a daily childcare record based on the child's growth data. For example, when the growth data is updated, the childcare record is also automatically updated. The monitoring unit can also automatically generate weekly or monthly childcare reports based on the child's growth data. This makes it possible to streamline childcare records by automatically generating a childcare record based on the child's growth data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input growth data into a generation AI and cause the generation AI to automatically generate a childcare record.

[0052] The monitoring unit can visualize the progress of childcare based on the child's growth data. The monitoring unit, for example, visualizes the progress of childcare in the form of graphs or charts based on the child's growth data. For example, when the growth data is updated, the progress is automatically updated. The monitoring unit can also enable the entire family to share the progress of childcare. This makes it easier to understand the situation of childcare by visualizing the progress of childcare based on the child's growth data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the growth data to a generation AI and cause the generation AI to visualize the progress.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The childcare support system can further include a lifestyle adjustment unit that adjusts the timing of information provision based on the user's lifestyle. For example, if the father works the night shift, information can be provided at night. If the mother takes a daytime rest, information can also be provided during the day. Furthermore, if both the father and mother work, information can be provided on weekends. This allows for more effective support by providing information that is tailored to the lifestyles of the father and mother.

[0055] The childcare support system may further include a growth prediction unit that predicts future growth based on the child's growth data. For example, if the child's growth data is abnormal, growth prediction may be performed as a priority. Also, if the child's growth data is normal, growth prediction may be performed as a supplement. Furthermore, the growth prediction unit may provide advice based on the child's growth data to make it easier to plan childcare. In this way, future growth prediction based on the child's growth data makes it easier to plan childcare.

[0056] The childcare support system may further include a childcare suggestion unit that suggests optimal childcare methods based on the child's growth data. For example, if the child's growth data is abnormal, the system may suggest childcare methods preferentially. Also, if the child's growth data is normal, the system may suggest complementary childcare methods. Furthermore, the childcare suggestion unit may provide specific advice to improve the quality of childcare based on the child's growth data. In this way, the quality of childcare can be improved by suggesting optimal childcare methods based on the child's growth data.

[0057] The childcare support system can further include a childcare record generation unit that automatically generates childcare records based on the child's growth data. For example, a daily childcare record can be automatically generated based on the child's growth data. When the growth data is updated, the childcare record can also be automatically updated. Furthermore, the childcare record generation unit can automatically generate weekly or monthly childcare reports based on the child's growth data. This makes it possible to streamline childcare records by automatically generating childcare records based on the child's growth data.

[0058] The childcare support system may further include a progress visualization unit that visualizes the progress of childcare based on the child's growth data. For example, the progress of childcare may be visualized in graphs or charts based on the child's growth data. When the growth data is updated, the progress may also be automatically updated. Furthermore, the progress visualization unit may enable the entire family to share the progress of childcare. This makes it easier to understand the status of childcare by visualizing the progress of childcare based on the child's growth data.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The information section provides information based on the pregnancy cycle. For example, it provides health and nutritional information for each week of pregnancy, and can also visualize the pregnant woman's condition and precautions. It emphasizes the importance of nutritional management and stress management during the early stages of pregnancy, and supports breastfeeding and sleep management after birth. Step 2: The coaching department provides parenting coaching. For example, they offer advice on children's learning methods, discipline, and building parent-child relationships. They provide specific learning methods and discipline tips appropriate for the child's age, and offer advice to facilitate smooth parent-child communication. Step 3: The monitoring unit monitors the child's growth. For example, it supports language and physical development using data on sleep, diet, etc. It also visualizes childcare records so they can be shared with grandparents, and stores the child's growth records on the cloud for access by the whole family.

[0061] (Example 2) A childcare support system according to an embodiment of the present invention utilizes generative AI to support parents struggling with childcare and address the trend toward later marriage in Japan. The system provides information based on pregnancy cycles, provides childcare coaching, and monitors child development. For example, the system visualizes tasks for parents based on the pregnancy cycle, establishes a follow-up system during pregnancy, and provides postnatal support. Specifically, the system visualizes status explanations and precautions based on the pregnancy cycle, and visualizes precautions to take after birth as the child grows. The system then provides childcare coaching to parents. For example, it provides advice on children's learning methods, discipline, and building parent-child relationships. Furthermore, the system monitors children's development. For example, it supports language and physical development using data such as sleep and diet, and visualizes childcare records so they can be shared with grandparents. This system is expected to support parents struggling with childcare and address the trend toward later marriage in Japan. The system supports parents struggling with childcare and address the trend toward later marriage in Japan. For example, by establishing a support system during pregnancy and providing support for childcare after birth, the burden of childcare can be reduced and the whole family can watch over the child's growth.

[0062] A childcare support system according to an embodiment includes an information providing unit, a coaching unit, and a monitoring unit. The information providing unit provides information based on the pregnancy cycle. For example, it provides health information and nutritional information for each week of pregnancy. The information providing unit can also visualize explanations of a pregnant woman's condition and important points based on the pregnancy cycle. For example, it emphasizes the importance of nutritional management and stress management during the early stages of pregnancy and supports breastfeeding and sleep management after birth. The coaching unit provides childcare coaching. For example, it provides advice on child learning methods, discipline, and building parent-child relationships. The coaching unit specifically suggests learning methods and discipline points appropriate for the child's age and provides advice to facilitate parent-child communication. The monitoring unit monitors the child's growth. For example, it supports language and physical development based on data such as sleep and diet. The monitoring unit can also visualize childcare records so that they can be shared with grandparents. For example, it stores childcare records on the cloud so that all family members can access them. This enables the childcare support system according to an embodiment to provide information based on the pregnancy cycle, childcare coaching, and childcare growth monitoring.

[0063] The information providing unit can visualize an explanation of the pregnant woman's condition and points to note based on the pregnancy cycle. The information providing unit, for example, visualizes physical changes and points to note for each week of pregnancy. For example, it emphasizes the importance of nutritional management and stress management in the early stages of pregnancy, and indicates the importance of weight management and exercise in the middle stages of pregnancy. The information providing unit can also provide information on preparation for childbirth and postpartum care in the late stages of pregnancy. This allows the pregnant woman to deepen her understanding by visualizing the explanation of her condition and points to note. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input information for each week of pregnancy into the generation AI and have the generation AI execute the visualized information.

[0064] The coaching unit can provide advice on children's learning methods, discipline, and building parent-child relationships. For example, the coaching unit provides learning methods appropriate to the child's age. For example, it suggests playful learning methods for toddlers and advice on reading and homework for elementary school children. The coaching unit can also provide advice on discipline. For example, it can suggest key points on setting rules and how to praise and scold. The coaching unit can also provide advice on building parent-child relationships. For example, it can suggest specific dialogue methods and joint activities to facilitate parent-child communication. This makes it possible to provide specific advice on children's learning methods, discipline, and building parent-child relationships. Some or all of the above-described processing in the coaching unit may be performed using, or without, AI. For example, the coaching unit can input advice appropriate to the child's age and developmental stage into the generation AI and have the generation AI execute the specific advice.

[0065] The monitoring unit can support language and physical growth based on sleep and dietary data. The monitoring unit, for example, records the child's sleep time and dietary content and monitors growth indicators. For example, based on the record of sleep time, the monitoring unit can suggest an appropriate sleep rhythm. The monitoring unit can also suggest a nutritionally balanced diet based on the record of dietary content. The monitoring unit can also monitor the stage of language development and provide appropriate support. For example, if a delay in language development is observed, measures can be taken early. This makes it possible to support the child's growth based on data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input sleep and dietary data into the generation AI and have the generation AI execute growth indicators.

[0066] The monitoring unit can visualize the childcare records so that they can be shared with grandparents. For example, the monitoring unit stores the child's growth record on the cloud so that all family members can access it. For example, the growth record can be displayed in graphs or charts, providing it in a visually easy-to-understand format. The monitoring unit also has a notification function for sharing the growth record. For example, when the growth record is updated, a notification can be sent to all family members. The monitoring unit can also provide a function for leaving comments and feedback on the growth record. This allows the childcare record to be shared with all family members, reducing the burden of childcare. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the growth record into a generation AI and have the generation AI execute the visualized record.

[0067] The information providing unit can provide nutritional management and stress management during the early stages of pregnancy, and support breastfeeding and sleep management after birth. For example, the information providing unit emphasizes the importance of nutritional management and stress management during the early stages of pregnancy. For example, it can suggest a balanced diet and relaxation methods. The information providing unit can also support breastfeeding and sleep management after birth. For example, it can suggest the timing of breastfeeding and an appropriate sleep rhythm. This can support important management points from the early stages of pregnancy to postpartum. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input information on nutritional management and stress management into the generation AI and have the generation AI provide specific advice.

[0068] The childcare support system further includes an information providing unit that estimates the user's emotions and adjusts the timing of providing information about the pregnancy cycle based on the estimated user emotions. For example, if the user is feeling stressed, the information providing unit provides information about the pregnancy cycle at a time when the user is relaxed. For example, if the user is relaxed, detailed information is provided to promote deeper understanding. Furthermore, if the user is busy, the information providing unit can provide concise, to-the-point information. This allows for more appropriate support by adjusting the timing of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit may input the user's emotion data into the generation AI and have the generation AI determine the timing of information provision.

[0069] The information providing unit can provide information that specifically indicates the division of roles between the father and the mother based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit indicates specific tasks that the father should support. For example, in the middle stages of pregnancy, the information providing unit provides health management points that the mother should pay attention to. The information providing unit can also indicate specific division of roles related to preparation for childbirth in the late stages of pregnancy. This clarifies the division of roles between the father and the mother, thereby improving the efficiency of childcare. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the information providing unit can input information on the division of roles based on the pregnancy cycle into the generation AI and have the generation AI execute the specific division of roles.

[0070] The information providing unit can monitor the health condition of the pregnant woman in real time based on the pregnancy cycle and issue an alert if an abnormality is detected. The information providing unit can, for example, monitor the pregnant woman's blood pressure and body temperature in real time and issue an alert if an abnormality is detected. For example, the information providing unit can monitor the pregnant woman's heart rate and issue an alert if an abnormality is detected. The information providing unit can also monitor the pregnant woman's weight gain and issue an alert if an abnormality is detected. This allows the pregnant woman's health condition to be monitored in real time and a prompt response to be made if an abnormality is detected. Some or all of the above-mentioned processing in the information providing unit can be performed using, for example, AI or without AI. For example, the information providing unit can input the pregnant woman's vital signs data to the generation AI and cause the generation AI to detect abnormalities and issue an alert.

[0071] The information providing unit can provide information on psychological support for pregnant women based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit provides information on stress management and relaxation methods. For example, in the middle stages of pregnancy, it provides information to encourage positive thinking. The information providing unit can also provide information on mental preparation for childbirth in the late stages of pregnancy. In this way, by providing psychological support to pregnant women, stress during pregnancy can be reduced. Some or all of the above-mentioned processing in the information providing unit may be performed using AI, or may be performed without using AI, for example. For example, the information providing unit can input information on psychological support into the generation AI and have the generation AI execute specific advice.

[0072] The information providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the information providing unit can prioritize providing information that gives a sense of security. For example, if the user is excited, the information providing unit can prioritize providing information to help the user stay calm. Furthermore, if the user is tired, the information providing unit can prioritize providing information that helps the user relax. This allows more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information providing unit can be performed using an AI, for example, or without an AI. For example, the information providing unit can input the user's emotion data into the generation AI and have the generation AI execute the information priority order.

[0073] The information providing unit can provide information tailored to the lifestyle rhythms of mom and dad based on the pregnancy cycle. For example, if dad works the night shift, the information providing unit provides information at night. For example, if mom takes a rest during the day, the information providing unit provides information during the day. Furthermore, if mom and dad both work, the information providing unit can also provide information on weekends. This allows for more effective support by providing information tailored to the lifestyle rhythms of mom and dad. Some or all of the above-described processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input lifestyle rhythm data into the generation AI and have the generation AI execute the timing of information provision.

[0074] The information providing unit can provide customized advice regarding diet and exercise to pregnant women based on the pregnancy cycle. For example, in the early stages of pregnancy, the information providing unit can provide advice regarding a nutritionally balanced diet. For example, in the middle stages of pregnancy, the information providing unit can provide advice regarding moderate exercise. In addition, in the late stages of pregnancy, the information providing unit can also provide advice regarding physical fitness for childbirth. This makes it possible to support health management by providing customized advice regarding diet and exercise to pregnant women. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input data regarding diet and exercise into the generation AI and have the generation AI execute customized advice.

[0075] The information providing unit can monitor the stress level of the pregnant woman based on the pregnancy cycle and suggest relaxation methods. The information providing unit, for example, monitors the stress level of the pregnant woman and suggests relaxation methods. For example, if the stress level of the pregnant woman is high, it preferentially suggests relaxation methods. The information providing unit can also suggest complementary relaxation methods if the stress level of the pregnant woman is low. This makes it possible to reduce stress by monitoring the stress level of the pregnant woman and suggesting appropriate relaxation methods. Some or all of the above-mentioned processing in the information providing unit may be performed using, or without, AI, for example. For example, the information providing unit can input stress level data into the generation AI and have the generation AI suggest relaxation methods.

[0076] The coaching unit can estimate the user's emotions and adjust the content of the parenting coaching based on the estimated user's emotions. For example, if the user is feeling stressed, the coaching unit can provide relaxing parenting coaching. For example, if the user is relaxed, the coaching unit can provide detailed parenting coaching. Furthermore, if the user is busy, the coaching unit can provide concise and to-the-point parenting coaching. This allows the content of the parenting coaching to be adjusted according to the user's emotions, thereby providing more appropriate coaching. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the coaching unit may be performed using an AI, for example, or without an AI. For example, the coaching unit can input the user's emotion data into the generation AI and have the generation AI adjust the coaching content.

[0077] The coaching unit can provide specific child-rearing methods according to the child's age and developmental stage. For example, during the newborn period, the coaching unit provides methods for breastfeeding and changing diapers. For example, during the infant period, the coaching unit provides methods for introducing solid food and managing sleep. The coaching unit can also provide advice on discipline and play methods during the toddler period. This allows the quality of child-rearing to be improved by providing specific child-rearing methods according to the child's age and developmental stage. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input child-rearing methods according to the child's age and developmental stage into the generation AI and have the generation AI execute the specific child-rearing methods.

[0078] The coaching unit can provide a specific dialogue method for facilitating communication between a parent and a child. The coaching unit provides, for example, a specific dialogue method for facilitating communication between a parent and a child. For example, if communication between a parent and a child is not smooth, the coaching unit can provide a dialogue method preferentially. Furthermore, if communication between a parent and a child is smooth, the coaching unit can also provide a dialogue method supplementarily. This makes it possible to strengthen the parent-child relationship by providing a specific dialogue method for facilitating communication between a parent and a child. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input parent-child communication data into a generation AI and cause the generation AI to execute the specific dialogue method.

[0079] The coaching unit can propose a customized learning method according to the child's learning style. For example, if the child has a visual learning style, the coaching unit proposes visual learning materials. For example, if the child has an auditory learning style, the coaching unit proposes auditory learning materials. Furthermore, if the child has an experiential learning style, the coaching unit can also propose experiential learning materials. This makes it possible to improve learning effectiveness by proposing a customized learning method according to the child's learning style. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input the child's learning style data into the generation AI and cause the generation AI to execute the customized learning method.

[0080] The coaching unit can estimate the user's emotions and determine the priority of coaching based on the estimated user's emotions. For example, if the user is feeling anxious, the coaching unit can prioritize providing coaching that provides a sense of security. For example, if the user is excited, the coaching unit can prioritize providing coaching to help the user stay calm. Furthermore, if the user is tired, the coaching unit can prioritize providing coaching that helps the user relax. This allows for more appropriate coaching to be provided by determining the priority of coaching according to the user'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 coaching unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the coaching unit can input the user's emotion data into the generation AI and have the generation AI execute the coaching priority.

[0081] The coaching unit can suggest optimal child-rearing methods based on a child's personality and interests. For example, if a child has an introverted personality, the coaching unit can suggest child-rearing methods that suit that introverted personality. For example, if a child has an extroverted personality, the coaching unit can suggest child-rearing methods that suit that extroverted personality. Furthermore, if a child has specific interests, the coaching unit can also suggest child-rearing methods based on those interests. This makes it possible to improve the quality of child-rearing by suggesting child-rearing methods based on a child's personality and interests. Some or all of the above-described processing in the coaching unit may be performed using, or without, AI, for example. For example, the coaching unit can input data on a child's personality and interests into a generation AI and have the generation AI execute the optimal child-rearing method.

[0082] The coaching unit can suggest joint activities to strengthen the parent-child relationship. For example, the coaching unit suggests joint activities to strengthen the parent-child relationship. For example, if the parent-child relationship is not strengthened, the coaching unit preferentially suggests joint activities. Furthermore, if the parent-child relationship is strengthened, the coaching unit can also suggest joint activities as a complement. In this way, by suggesting joint activities to strengthen the parent-child relationship, the parent-child relationship can be deepened. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input parent-child relationship data into the generation AI and have the generation AI execute the joint activity suggestions.

[0083] The coaching unit can suggest specific activities for developing a child's social skills. For example, the coaching unit suggests specific activities for developing a child's social skills. For example, if a child's social skills are not being developed, the coaching unit can preferentially suggest activities. Furthermore, if a child's social skills are being developed, the coaching unit can also suggest complementary activities. In this way, by suggesting specific activities for developing a child's social skills, it is possible to promote the development of social skills. Some or all of the above-described processing in the coaching unit may be performed using, for example, AI, or may be performed without using AI. For example, the coaching unit can input the child's social skills data into a generation AI and cause the generation AI to execute specific activities.

[0084] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, the monitoring unit increases the monitoring frequency when the user is feeling anxious. For example, the monitoring unit decreases the monitoring frequency when the user is relaxed. The monitoring unit can also adjust the monitoring frequency when the user is busy. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved 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 monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI execute the monitoring frequency.

[0085] The monitoring unit can analyze the child's growth data and issue an alert if an abnormality is detected. The monitoring unit, for example, analyzes the child's growth data and issues an alert if an abnormality is detected. For example, if the child's growth data is abnormal, an alert is issued preferentially. The monitoring unit can also issue a supplementary alert if the child's growth data is normal. This makes it possible to analyze the child's growth data and respond quickly if an abnormality is detected. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data to a generation AI and cause the generation AI to detect abnormalities and issue an alert.

[0086] The monitoring unit can predict future growth based on the child's growth data. The monitoring unit, for example, predicts future growth based on the child's growth data. For example, if the child's growth data is abnormal, growth prediction is performed as a priority. Furthermore, if the child's growth data is normal, the monitoring unit can also perform growth prediction as a supplement. This makes it easier to make childcare plans by predicting future growth based on the child's growth data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data into a generation AI and cause the generation AI to perform a future growth prediction.

[0087] The monitoring unit can suggest an optimal child-rearing method based on the child's growth data. The monitoring unit suggests an optimal child-rearing method, for example, based on the child's growth data. For example, if the child's growth data is abnormal, the monitoring unit preferentially suggests a child-rearing method. Furthermore, if the child's growth data is normal, the monitoring unit can also suggest a complementary child-rearing method. This makes it possible to improve the quality of child-rearing by suggesting an optimal child-rearing method based on the child's growth data. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the child's growth data into a generation AI and cause the generation AI to execute the optimal child-rearing method.

[0088] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit provides a simple, highly visible display method. For example, if the user is relaxed, the monitoring unit provides a display method including detailed information. Furthermore, if the user is busy, the monitoring unit can also provide a display method that focuses on the main points. This allows for adjusting the display method of the monitoring results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0089] The monitoring unit can store the child's growth data on the cloud so that all family members can access it. For example, the monitoring unit can store the child's growth data on the cloud so that all family members can access it. For example, when the growth data is updated, a notification is sent to all family members. The monitoring unit can also share the growth data on the cloud so that all family members can leave comments and feedback. This can reduce the burden of childcare by storing the child's growth data on the cloud so that all family members can access it. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input the growth data into a generation AI and cause the generation AI to execute a process of storing the data on the cloud.

[0090] The monitoring unit can automatically generate a childcare record based on the child's growth data. The monitoring unit, for example, automatically generates a daily childcare record based on the child's growth data. For example, when the growth data is updated, the childcare record is also automatically updated. The monitoring unit can also automatically generate weekly or monthly childcare reports based on the child's growth data. This makes it possible to streamline childcare records by automatically generating a childcare record based on the child's growth data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input growth data into a generation AI and cause the generation AI to automatically generate a childcare record.

[0091] The monitoring unit can visualize the progress of childcare based on the child's growth data. The monitoring unit, for example, visualizes the progress of childcare in the form of graphs or charts based on the child's growth data. For example, when the growth data is updated, the progress is automatically updated. The monitoring unit can also enable the entire family to share the progress of childcare. This makes it easier to understand the situation of childcare by visualizing the progress of childcare based on the child's growth data. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the growth data to a generation AI and cause the generation AI to visualize the progress. === Hard Collateral 1-1 === Each of the multiple elements, including the information providing unit, coaching unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information providing unit visualizes information based on the pregnancy cycle using the display 40A of the smart device 14 and provides health and nutrition information for each week of pregnancy using the specific processing unit 290 of the data processing device 12. The coaching unit provides parenting coaching using the control unit 46A of the smart device 14 and provides advice on child learning methods and discipline using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the child's growth using the camera 42 and microphone 38B of the smart device 14 and stores parenting records on the cloud using the specific processing unit 290 of the data processing device 12 so that all family members can access them. Furthermore, the information providing unit has a function of estimating the user's emotions and adjusting the timing of information provision based on the estimated user emotions, and is realized, for example, using an emotion engine or generative AI. === Hard Collateral 1-2 === Each of the multiple elements, including the information providing unit, coaching unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information providing unit visualizes information based on the pregnancy cycle using the display of the smart glasses 214 and provides health and nutritional information for each week of pregnancy using the specific processing unit 290 of the data processing device 12. The coaching unit provides parenting coaching using the control unit 46A of the smart glasses 214 and provides advice on child learning methods and discipline using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the child's growth using the camera 42 and microphone 238 of the smart glasses 214 and stores parenting records on the cloud using the specific processing unit 290 of the data processing device 12 so that all family members can access them. Furthermore, the information providing unit has a function of estimating the user's emotions and adjusting the timing of information provision based on the estimated user emotions, and is realized, for example, using an emotion engine or generative AI. === Hard Collateral 1-3 === Each of the multiple elements, including the information providing unit, coaching unit, and monitoring unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information providing unit visualizes information based on the pregnancy cycle using the display 343 of the headset terminal 314 and provides health and nutritional information for each week of pregnancy using the specific processing unit 290 of the data processing device 12. The coaching unit provides parenting coaching using the control unit 46A of the headset terminal 314 and provides advice on child learning methods and discipline using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the child's growth using the camera 42 and microphone 238 of the headset terminal 314 and stores parenting records on the cloud using the specific processing unit 290 of the data processing device 12 so that all family members can access them. Furthermore, the information providing unit has a function of estimating the user's emotions and adjusting the timing of information provision based on the estimated user emotions, and is realized, for example, using an emotion engine or generative AI. === Hard Collateral 1-4 === Each of the multiple elements, including the information providing unit, coaching unit, and monitoring unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information providing unit visualizes information based on the pregnancy cycle using the display of the robot 414 and provides health and nutritional information for each week of pregnancy using the specific processing unit 290 of the data processing device 12. The coaching unit provides parenting coaching using the control unit 46A of the robot 414 and provides advice on child learning methods and discipline using the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the child's growth using the camera 42 and microphone 238 of the robot 414 and stores parenting records on the cloud using the specific processing unit 290 of the data processing device 12 so that all family members can access them. Furthermore, the information providing unit has a function of estimating the user's emotions and adjusting the timing of information provision based on the estimated user emotions, and is realized, for example, using an emotion engine or generative AI.

[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 childcare support system may further include an emotion customization unit that estimates the user's emotions and customizes childcare advice based on the estimated emotions. For example, if the user is feeling stressed, the system may suggest a childcare method that will help the user relax. If the user is feeling happy, the system may provide advice that emphasizes positive feedback. Furthermore, if the user is tired, the system may provide concise advice that focuses on the main points. This allows for more effective support by providing childcare advice that is customized according to the user's emotions.

[0094] The childcare support system can further include a lifestyle adjustment unit that adjusts the timing of information provision based on the user's lifestyle. For example, if the father works the night shift, information can be provided at night. If the mother takes a daytime rest, information can also be provided during the day. Furthermore, if both the father and mother work, information can be provided on weekends. This allows for more effective support by providing information that is tailored to the lifestyles of the father and mother.

[0095] The childcare support system may further include an emotion visualization unit that estimates the user's emotions and visualizes the progress of childcare based on the estimated emotions. For example, if the user is feeling anxious, a simple, highly visible display method may be provided. Alternatively, if the user is relaxed, a display method including detailed information may be provided. Furthermore, if the user is busy, a display method that focuses on the main points may be provided. In this way, more appropriate information can be provided by visualizing the progress of childcare according to the user's emotions.

[0096] The childcare support system may further include a growth prediction unit that predicts future growth based on the child's growth data. For example, if the child's growth data is abnormal, growth prediction may be performed as a priority. Also, if the child's growth data is normal, growth prediction may be performed as a supplement. Furthermore, the growth prediction unit may provide advice based on the child's growth data to make it easier to plan childcare. In this way, future growth prediction based on the child's growth data makes it easier to plan childcare.

[0097] The childcare support system can further include an emotion sharing unit that estimates the user's emotions and adjusts the method of sharing the childcare records based on the estimated emotions. For example, if the user is feeling anxious, a simple and highly visible sharing method can be provided. Alternatively, if the user is relaxed, a sharing method that includes detailed information can be provided. Furthermore, if the user is busy, a sharing method that focuses on the main points can be provided. In this way, by adjusting the method of sharing the childcare records according to the user's emotions, more appropriate information can be provided.

[0098] The childcare support system may further include a childcare suggestion unit that suggests optimal childcare methods based on the child's growth data. For example, if the child's growth data is abnormal, the system may suggest childcare methods preferentially. Also, if the child's growth data is normal, the system may suggest complementary childcare methods. Furthermore, the childcare suggestion unit may provide specific advice to improve the quality of childcare based on the child's growth data. In this way, the quality of childcare can be improved by suggesting optimal childcare methods based on the child's growth data.

[0099] The childcare support system may further include an emotion prioritization unit that estimates the user's emotions and determines the priority of childcare based on the estimated emotions. For example, if the user is feeling anxious, childcare methods that provide a sense of security may be provided preferentially. Also, if the user is excited, childcare methods that help the user stay calm may be provided preferentially. Furthermore, if the user is tired, childcare methods that help the user relax may be provided preferentially. In this way, by determining the priority of childcare based on the user's emotions, more appropriate childcare methods can be provided.

[0100] The childcare support system can further include a childcare record generation unit that automatically generates childcare records based on the child's growth data. For example, a daily childcare record can be automatically generated based on the child's growth data. When the growth data is updated, the childcare record can also be automatically updated. Furthermore, the childcare record generation unit can automatically generate weekly or monthly childcare reports based on the child's growth data. This makes it possible to streamline childcare records by automatically generating childcare records based on the child's growth data.

[0101] The childcare support system may further include an emotion visualization unit that estimates the user's emotions and visualizes the progress of childcare based on the estimated emotions. For example, if the user is feeling anxious, a simple, highly visible display method may be provided. Alternatively, if the user is relaxed, a display method including detailed information may be provided. Furthermore, if the user is busy, a display method that focuses on the main points may be provided. In this way, more appropriate information can be provided by visualizing the progress of childcare according to the user's emotions.

[0102] The childcare support system may further include a progress visualization unit that visualizes the progress of childcare based on the child's growth data. For example, the progress of childcare may be visualized in graphs or charts based on the child's growth data. When the growth data is updated, the progress may also be automatically updated. Furthermore, the progress visualization unit may enable the entire family to share the progress of childcare. This makes it easier to understand the status of childcare by visualizing the progress of childcare based on the child's growth data.

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

[0104] Step 1: The information section provides information based on the pregnancy cycle. For example, it provides health and nutritional information for each week of pregnancy, and can also visualize the pregnant woman's condition and precautions. It emphasizes the importance of nutritional management and stress management during the early stages of pregnancy, and supports breastfeeding and sleep management after birth. Step 2: The coaching department provides parenting coaching. For example, they offer advice on children's learning methods, discipline, and building parent-child relationships. They provide specific learning methods and discipline tips appropriate for the child's age, and offer advice to facilitate smooth parent-child communication. Step 3: The monitoring unit monitors the child's growth. For example, it supports language and physical development using data on sleep, diet, etc. It also visualizes childcare records so they can be shared with grandparents, and stores the child's growth records on the cloud for access by the whole family.

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

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

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

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

[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 above example, 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, in order to avoid confusion and to 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. an information providing unit that provides information based on the pregnancy cycle; a coaching unit that provides childcare coaching based on the information provided by the information providing unit; a monitoring unit that monitors the child's growth based on the coaching provided by the coaching unit. A system characterized by:

2. The information providing unit Visualizing the status and precautions of pregnant women based on the pregnancy cycle 2. The system of claim 1.

3. The coaching department Providing advice on child learning, discipline, and parent-child relationship building 2. The system of claim 1.

4. The monitoring unit Supporting language and physical development through sleep and diet data 2. The system of claim 1.

5. The monitoring unit Visualize childcare records so they can be shared with grandparents 2. The system of claim 1.

6. The information providing unit Nutritional and stress management during early pregnancy, and support for breastfeeding and sleep management after birth 2. The system of claim 1.

7. The information providing unit The method estimates the user's emotions and adjusts the timing of providing information about the pregnancy cycle based on the estimated user emotions.

2. The system of claim 1.

8. The information providing unit Provide specific information on the division of roles between mom and dad based on the pregnancy cycle 2. The system of claim 1.

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