system

Abstract

The system according to the embodiment aims to provide advice according to a child's stage of growth, health management, and suggestions for nutritionally balanced meals in a unified manner. [Solution] A system according to an embodiment includes an advice unit, a tracking unit, a learning unit, a suggestion unit, and a creation unit. The advice unit provides advice according to a child's developmental stage. The tracking unit tracks information on the child's health management and development based on the advice provided by the advice unit. The learning unit learns the family's preferences and tastes. The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. The creation unit creates a shopping list based on the recipes suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to provide appropriate advice, health management, and nutritionally balanced meal suggestions in a unified manner according to a child's stage of development.

[0005] The system according to the embodiment aims to provide advice according to a child's stage of growth, health management, and suggestions for nutritionally balanced meals in a unified manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an advice unit, a tracking unit, a learning unit, a suggestion unit, and a creation unit. The advice unit provides advice according to the child's developmental stage. The tracking unit tracks information on the child's health management and development based on the advice provided by the advice unit. The learning unit learns the family's preferences and tastes. The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. The creation unit creates a shopping list based on the recipes suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide advice according to a child's stage of growth, health management, and suggestions for nutritionally balanced meals in a unified 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) The AI ​​service according to an embodiment of the present invention is a system that provides information useful for raising children. This system has functions to reduce the burden on parents, such as providing advice based on a child's developmental stage, managing vaccination schedules, and tracking children's health and growth. It also learns family preferences and tastes, suggests nutritionally balanced dinner recipes, and creates shopping lists. It also works with cooking appliances to automatically prepare meals and manages power consumption with compatible appliances. It also automatically manages household accounts, manages payment due dates, and suggests ways to improve household finances. For example, the AI ​​service provides advice based on a child's developmental stage. For example, it provides advice on the frequency of breastfeeding and diaper changes for infants, and on toilet training and language development for toddlers. It also manages vaccination schedules and sends reminders when vaccination dates are approaching. It also tracks information on children's health and growth, allowing parents to easily check it. It then learns family preferences and tastes and suggests nutritionally balanced dinner recipes. For example, it suggests optimal recipes based on the family's favorite ingredients and allergies. It also automatically creates shopping lists based on the suggested recipes. It also works with cooking appliances to automatically prepare meals. For example, it can connect with a smart oven or rice cooker to start cooking so that food is ready at a specified time. It also has the ability to connect with compatible home appliances to manage power consumption. For example, it can monitor the usage of home appliances such as air conditioners and washing machines and make suggestions to optimize power consumption. It also automatically manages household accounts, manages payment due dates, and makes suggestions for improving household finances. For example, it automatically records monthly income and expenses and sends reminders when payment due dates approach. It also analyzes spending trends and makes suggestions for saving money. This allows AI services to provide a variety of information related to child rearing, reducing the burden on parents. This allows AI services to provide a variety of information related to child rearing, reducing the burden on parents. Its wide range of functions include advice based on a child's developmental stage, managing vaccination schedules, tracking health and growth information, suggesting nutritionally balanced dinner recipes, creating shopping lists, connecting with cooking appliances, managing power consumption, automatically managing household accounts, managing payment due dates, and making suggestions for improving household finances.

[0029] A child-rearing support system according to an embodiment includes an advice unit, a tracking unit, a learning unit, a suggestion unit, and a creation unit. The advice unit provides advice according to a child's developmental stage. For example, advice on breastfeeding and diaper changing frequency during infancy and advice on toilet training and language development during toddlerhood can be provided. The advice unit also manages vaccination schedules and can send reminders when vaccination dates approach. The tracking unit tracks information on the child's health and growth based on the advice provided by the advice unit. For example, it collects information such as the child's height, weight, and developmental stage so that parents can easily check it. The tracking unit can also monitor the child's health and notify parents of any abnormalities. The learning unit learns the family's preferences and tastes. For example, it can collect and learn information about the family's favorite ingredients and allergies. The learning unit can also analyze the family's dietary history and learn the family's preferences and tastes. The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. For example, it can suggest optimal recipes taking into account the family's favorite ingredients and allergy information. The suggestion unit can also consider the types and amounts of nutrients in order to suggest nutritionally balanced recipes. The creation unit creates a shopping list based on the recipes suggested by the suggestion unit. For example, the shopping list can be automatically created by adding the types and amounts of ingredients needed to the list. The creation unit can also optimize the shopping list by taking into account the inventory status of ingredients for the family. As a result, the child-rearing support system according to the embodiment can reduce the burden on parents by providing advice according to the child's growth stage, health management, and suggesting nutritionally balanced dinner recipes.

[0030] Furthermore, the child-rearing support system includes a provision unit that works in conjunction with cooking appliances to automatically provide food. The provision unit works in conjunction with cooking appliances to automatically provide food. For example, it can work in conjunction with a smart oven or rice cooker to start cooking so that the food is ready at a specified time. The provision unit can also monitor the usage of the cooking appliances and suggest optimal cooking methods. For example, the provision unit can adjust the oven temperature and cooking time to optimize the quality of the food. The provision unit can also suggest efficient cooking methods, taking into account the energy efficiency of the cooking appliances. In this way, by working in conjunction with cooking appliances, food provision is automated, reducing the burden on parents.

[0031] Furthermore, the child-rearing support system includes a management unit that manages power consumption in cooperation with compatible home appliances. The management unit manages power consumption in cooperation with compatible home appliances. For example, it can monitor the usage of home appliances such as air conditioners and washing machines and make suggestions to optimize power consumption. The management unit can also suggest efficient usage methods taking into account the energy efficiency of the home appliances. For example, the management unit can adjust the temperature setting and usage time of an air conditioner to reduce power consumption. The management unit can also monitor the usage of home appliances in real time and notify if there is an abnormality. In this way, by linking with compatible home appliances, power consumption management becomes more efficient and the burden on parents is reduced.

[0032] Furthermore, the child-rearing support system includes a management unit that automatically manages the household account book. The management unit automatically manages the household account book. For example, it can automatically record monthly income and expenditures and send reminders when a payment due date approaches. The management unit can also analyze spending trends and make suggestions for saving money. For example, the management unit can analyze each category of expenditure and make suggestions for reducing wasteful spending. The management unit can also display household account book data as graphs and charts, providing it in a visually easy-to-understand format. This enables automatic management of the household account book and reduces the burden on parents.

[0033] The child-rearing support system further includes a management unit that manages payment due dates. The management unit manages payment due dates. For example, it can send reminders when a payment due date approaches to ensure that payments are not forgotten. The management unit can also manage payment history and check past payment status. For example, the management unit can display payment history in chronological order, allowing past payment status to be checked at a glance. The management unit can also set priorities when payment due dates overlap, and prioritize notifications of important payments. This automates management of payment due dates and reduces the burden on parents.

[0034] Furthermore, the child-rearing support system includes a proposal unit that makes proposals for improving household finances. The proposal unit makes proposals for improving household finances. For example, it can analyze spending trends and make proposals for saving money. The proposal unit can also make proposals for increasing income. For example, it can make proposals for side jobs or investments and provide methods for increasing income. The proposal unit can also support budget setting and management based on household account book data. For example, the proposal unit can make proposals for setting an appropriate budget, taking into account the balance between income and expenses. This automates proposals for improving household finances, reducing the burden on parents.

[0035] The advice unit can analyze the child's past growth data and provide appropriate advice. For example, the advice unit can provide advice necessary for the next growth stage based on the child's past growth data. The advice unit can also analyze the child's past health data and suggest a vaccination schedule. The advice unit can also suggest an appropriate learning method based on the child's past learning data. In this way, advice based on the child's past growth data is provided.

[0036] The advice unit can customize the advice provided based on the child's current health condition and lifestyle rhythm. For example, the advice unit can monitor the child's current health condition and provide dietary advice containing necessary nutrients. The advice unit can also analyze the child's lifestyle rhythm and suggest appropriate sleep times. The advice unit can also monitor the child's exercise volume and suggest appropriate exercise methods. In this way, advice is provided that is tailored to the child's current condition.

[0037] The advice unit can improve the accuracy of advice by reflecting the parent's past feedback when providing advice. For example, the advice unit can adjust the content of advice based on the parent's past feedback. The advice unit can also analyze the parent's past feedback and optimize the timing of advice. The advice unit can also customize the format of advice by reflecting the parent's feedback. In this way, advice based on the parent's past feedback is provided.

[0038] When providing advice, the advice unit can provide highly relevant advice by taking into account the geographical location information of the parent. For example, if the parent is in a specific area, the advice unit can provide advice about medical institutions and childcare facilities in that area. Furthermore, if the parent is traveling, the advice unit can provide advice about childcare at the parent's destination. Furthermore, if the parent is at home, the advice unit can provide advice about childcare methods that can be used at home. In this way, advice based on the geographical location information of the parent is provided.

[0039] When providing advice, the advice unit can analyze the parent's social media activity and provide relevant advice. For example, the advice unit can provide relevant child-rearing advice based on information shared by the parent on social media. The advice unit can also analyze the parent's social media activity and provide child-rearing information that may be of interest to the parent. The advice unit can also provide relevant child-rearing advice by referring to the activity of the parent's friends on social media. In this way, advice based on the parent's social media activity is provided.

[0040] The advice unit can customize the advice method by reflecting the parent's past feedback when providing advice. For example, the advice unit can adjust the format of advice based on feedback provided by the parent in the past. The advice unit can also analyze the parent's past feedback and optimize the timing of advice. The advice unit can also customize the content of advice by reflecting the parent's feedback. In this way, an advice method based on the parent's past feedback is provided.

[0041] The tracking unit can collect and analyze the child's health data in real time during tracking. For example, the tracking unit can monitor the child's body temperature and heart rate in real time and notify if any abnormalities are detected. The tracking unit can also record the child's diet in real time and analyze nutritional balance. The tracking unit can also track the child's exercise volume in real time and suggest appropriate exercise amounts. In this way, the child's health data is collected and analyzed in real time.

[0042] The tracking unit can apply different tracking algorithms depending on the child's developmental stage during tracking. For example, the tracking unit can apply an algorithm for tracking the frequency of breastfeeding and diaper changes during infancy. The tracking unit can also apply an algorithm for tracking toilet training and language development during toddlerhood. The tracking unit can also apply an algorithm for tracking learning progress and motor skills during school age. This makes it possible to track children according to their developmental stage.

[0043] The tracking unit can improve the accuracy of tracking by referring to the parent's past tracking data during tracking. For example, the tracking unit can adjust the tracking algorithm based on the tracking data previously provided by the parent. The tracking unit can also analyze the parent's past tracking data to optimize the timing of tracking. The tracking unit can also customize the tracking method by reflecting the parent's feedback. In this way, tracking based on the parent's past tracking data is provided.

[0044] During tracking, the tracking unit can prioritize tracking of highly relevant data taking into account the child's geographical location information. For example, if the child is in a specific location, the tracking unit can prioritize tracking health data related to that location. Also, if the child is at school, the tracking unit can prioritize tracking learning progress. Also, if the child is at a park, the tracking unit can prioritize tracking the amount of exercise. This provides tracking based on the child's geographical location information.

[0045] During tracking, the tracking unit can analyze the child's social media activities and track related data. For example, the tracking unit can track related health data based on the information the child has shared on social media. The tracking unit can also analyze the child's social media activities and track data that may be of interest to the child. The tracking unit can also track related data based on the activities of the child's friends on social media. This provides tracking based on the child's social media activities.

[0046] The tracking unit can customize the tracking method by reflecting the parent's past feedback during tracking. For example, the tracking unit can adjust the tracking method based on the parent's past feedback. The tracking unit can also analyze the parent's past feedback and optimize the timing of tracking. The tracking unit can also customize the tracking format by reflecting the parent's feedback. This provides a tracking method based on the parent's past feedback.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can, for example, select an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and optimize the progress of learning. The learning unit can also optimize the timing of learning by referring to past learning data. This provides optimization of the learning algorithm based on past learning data.

[0048] The learning unit can update the learning data during learning by reflecting parental feedback. For example, the learning unit can update the learning data based on feedback provided by the parent. The learning unit can also analyze the parental feedback and optimize the learning progress. The learning unit can also customize the learning format by reflecting the parental feedback. This provides updates to the learning data based on the parental feedback.

[0049] The learning unit can integrate information from different data sources during learning to enrich the learning data. For example, the learning unit can integrate information from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources to optimize the progress of learning. The learning unit can also optimize the timing of learning by referring to information from different data sources. This provides enriched learning data that integrates information from different data sources.

[0050] During learning, the learning unit can weight the learning data based on the child's developmental stage. For example, the learning unit can weight data related to breastfeeding and diaper changing more heavily during the infancy stage. The learning unit can also weight data related to toilet training and language development more heavily during the toddler stage. The learning unit can also weight data related to learning progress and motor skills more heavily during the school-age stage. This provides weighting of the learning data based on the child's developmental stage.

[0051] The learning unit can analyze the parent's social media activities during learning and incorporate relevant data into the learning. For example, the learning unit can incorporate relevant learning data based on information shared by the parent on social media. The learning unit can also analyze the parent's social media activities and incorporate learning data that may be of interest to the child. The learning unit can also incorporate relevant learning data based on the activities of the parent's friends on social media. This provides for the incorporation of learning data based on the parent's social media activities.

[0052] The learning unit can adjust the learning algorithm during learning by reflecting the parent's past feedback. For example, the learning unit can adjust the learning algorithm based on feedback provided by the parent. The learning unit can also analyze the parent's past feedback to optimize learning progress. The learning unit can also customize the learning format by reflecting the parent's feedback. This provides adjustment of the learning algorithm based on the parent's past feedback.

[0053] When making a suggestion, the suggestion unit can customize the suggestion content by taking into consideration the health condition and nutritional balance of the family members. For example, the suggestion unit can monitor the health condition of the family members and suggest recipes that contain the necessary nutrients. The suggestion unit can also analyze the nutritional balance of the family members and suggest balanced meals. The suggestion unit can also suggest recipes that avoid allergies by taking into consideration allergy information of the family members. In this way, suggestions based on the health condition and nutritional balance of the family members are provided.

[0054] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the family's past meal history. For example, the suggestion unit can suggest recipes that include favorite ingredients based on the family's past meal history. The suggestion unit can also analyze the family's past meal history and suggest nutritionally balanced meals. The suggestion unit can also suggest recipes that avoid allergies by referring to the family's past meal history. In this way, suggestions based on the family's past meal history are provided.

[0055] When making a suggestion, the suggestion unit can adjust the suggestion content by taking into account the allergy information of the family members. For example, the suggestion unit can suggest recipes that avoid allergies based on the allergy information of the family members. The suggestion unit can also analyze the allergy information of the family members and suggest nutritionally balanced meals. The suggestion unit can also suggest recipes that include favorite ingredients by referring to the allergy information of the family members. In this way, suggestions based on the allergy information of the family members are provided.

[0056] When making suggestions, the suggestion unit can suggest highly relevant recipes taking into account the geographical location information of the family members. For example, if the family members are in a specific area, the suggestion unit can suggest recipes using ingredients from that area. Also, if the family members are traveling, the suggestion unit can suggest recipes using ingredients available at the travel destination. Also, if the family members are at home, the suggestion unit can suggest recipes using ingredients available at home. In this way, recipe suggestions based on the geographical location information of the family members are provided.

[0057] When making a suggestion, the suggestion unit can analyze the social media activity of the family and suggest related recipes. For example, the suggestion unit can suggest related recipes based on information shared by the family on social media. The suggestion unit can also analyze the social media activity of the family and suggest recipes that the family may be interested in. The suggestion unit can also suggest related recipes based on the activity of the family's friends on social media. In this way, recipe suggestions based on the social media activity of the family are provided.

[0058] The suggestion unit can customize the suggestion method by reflecting the family's past feedback when making a suggestion. The suggestion unit can adjust the suggestion method based on, for example, feedback provided by the family in the past. The suggestion unit can also analyze the family's past feedback and optimize the timing of the suggestion. The suggestion unit can also customize the content of the suggestion by reflecting the family's feedback. In this way, a suggestion method based on the family's past feedback is provided.

[0059] The creation unit can optimize the shopping list by taking into account the inventory status of ingredients for the family members when creating the list. For example, the creation unit can monitor the inventory status of the family members' refrigerators and add necessary ingredients to the list. The creation unit can also analyze the inventory status of the family members' pantries and add necessary ingredients to the list. The creation unit can also add necessary ingredients to the list by taking into account the expiration dates of the family members' ingredients. This provides optimization of the shopping list based on the inventory status of ingredients for the family members.

[0060] The creation unit can improve the accuracy of the list by referring to the family's past shopping history when creating the list. For example, the creation unit can add necessary ingredients to the list based on the family's past shopping history. The creation unit can also analyze the family's past shopping history to create an efficient shopping list. The creation unit can also add ingredients to the list that are free of allergies by referring to the family's past shopping history. This provides for improved accuracy of the list based on the family's past shopping history.

[0061] The creation unit can adjust the list contents taking into account the allergy information of family members when creating the list. For example, the creation unit can add ingredients that are allergy-free to the list based on the allergy information of family members. The creation unit can also analyze the allergy information of family members and add nutritionally balanced ingredients to the list. The creation unit can also add preferred ingredients to the list by referring to the allergy information of family members. This allows the list contents to be adjusted based on the allergy information of family members.

[0062] The creation unit can create an optimal shopping list by taking into account the geographical location information of the family members when creating the list. For example, if the family members are in a specific area, the creation unit can add ingredients that are available in that area to the list. Furthermore, if the family members are traveling, the creation unit can add ingredients that are available at the travel destination to the list. Furthermore, if the family members are at home, the creation unit can add ingredients that are available at home to the list. In this way, an optimal shopping list based on the geographical location information of the family members is provided.

[0063] When creating the list, the creation unit can analyze the social media activities of the family and add related ingredients to the list. For example, the creation unit can add related ingredients to the list based on information shared by the family on social media. The creation unit can also analyze the social media activities of the family and add ingredients that may be of interest to the list. The creation unit can also add related ingredients to the list based on the activities of the family's friends on social media. This provides a list of ingredients that can be added based on the social media activities of the family.

[0064] The creation unit can customize the list contents by reflecting the family's past feedback when creating the list. For example, the creation unit can adjust the list contents based on the feedback provided by the family in the past. The creation unit can also analyze the family's past feedback to improve the accuracy of the list. The creation unit can also customize the format of the list by reflecting the family's feedback. This provides customization of the list contents based on the family's past feedback.

[0065] The providing unit can set an optimal serving time in consideration of the family's meal schedule when providing food. The providing unit can, for example, monitor the family's meal schedule and set an optimal serving time. The providing unit can also analyze the family's meal schedule and suggest an efficient serving time. The providing unit can also set an appropriate serving time by referring to the family's meal schedule. In this way, an optimal serving time based on the family's meal schedule is provided.

[0066] The serving unit can customize the serving contents by referring to the family's past meal history when serving. For example, the serving unit can serve dishes containing favorite ingredients based on the family's past meal history. The serving unit can also analyze the family's past meal history and serve nutritionally balanced dishes. The serving unit can also refer to the family's past meal history and serve dishes that avoid allergies. This allows the serving contents to be customized based on the family's past meal history.

[0067] The serving unit can adjust the serving contents taking into consideration the allergy information of the family members when serving food. For example, the serving unit can serve dishes that avoid allergies based on the allergy information of the family members. The serving unit can also analyze the allergy information of the family members and serve nutritionally balanced dishes. The serving unit can also refer to the allergy information of the family members and serve dishes that include their favorite ingredients. In this way, the serving contents can be adjusted based on the allergy information of the family members.

[0068] When providing food, the providing unit can select the optimal providing method by taking into consideration the geographical location information of the family. For example, if the family is in a specific area, the providing unit can provide food made with ingredients available in that area. Furthermore, if the family is traveling, the providing unit can also provide food made with ingredients available at the travel destination. Furthermore, if the family is at home, the providing unit can also provide food made with ingredients available at home. In this way, the optimal providing method based on the geographical location information of the family is provided.

[0069] The provision unit can analyze the social media activity of the family and provide related dishes when providing food. For example, the provision unit can provide related dishes based on information shared by the family on social media. The provision unit can also analyze the social media activity of the family and provide dishes that the family may be interested in. The provision unit can also provide related dishes by taking into account the activity of the family's friends on social media. In this way, food recommendations based on the social media activity of the family are provided.

[0070] The providing unit can customize the method of providing information by reflecting the family's past feedback when providing information. The providing unit can adjust the method of providing information based on, for example, feedback provided by the family in the past. The providing unit can also analyze the family's past feedback and optimize the timing of providing information. The providing unit can also customize the content of the information provided by reflecting the family's feedback. This allows the method of providing information to be customized based on the family's past feedback.

[0071] During management, the management unit can monitor the usage status of the home appliances in real time and propose an optimal management method. For example, the management unit can monitor the usage status of the home appliances in real time and propose an efficient usage method. The management unit can also analyze the usage status of the home appliances and propose a method to optimize power consumption. The management unit can also refer to the usage status of the home appliances and propose an appropriate usage timing. This provides an optimal management method based on the usage status of the home appliances.

[0072] During management, the management unit can improve the accuracy of management by referring to the family's past power consumption data. For example, the management unit can propose an efficient management method based on the family's past power consumption data. The management unit can also analyze the family's past power consumption data and propose a method to optimize power consumption. The management unit can also suggest appropriate usage timing by referring to the family's past power consumption data. This provides improved accuracy of management based on the family's past power consumption data.

[0073] The management unit can adjust the management method taking into account the energy efficiency of the home appliance during management. For example, the management unit can monitor the energy efficiency of the home appliance and suggest an efficient usage method. The management unit can also analyze the energy efficiency of the home appliance and suggest a method for optimizing power consumption. The management unit can also suggest an appropriate usage timing by referring to the energy efficiency of the home appliance. This provides adjustment of the management method based on the energy efficiency of the home appliance.

[0074] During management, the management unit can select the optimal management method taking into account the geographical location information of the family members. For example, if the family members are in a specific area, the management unit can propose a management method taking into account the power consumption situation in that area. Furthermore, if the family members are traveling, the management unit can propose a management method taking into account the power consumption situation at the travel destination. Furthermore, if the family members are at home, the management unit can propose a management method taking into account the power consumption situation at home. In this way, the optimal management method based on the geographical location information of the family members is provided.

[0075] During management, the management unit can analyze the social media activities of family members and suggest relevant management methods. For example, the management unit can suggest relevant management methods based on information shared by family members on social media. The management unit can also analyze the social media activities of family members and suggest management methods that may be of interest to them. The management unit can also suggest relevant management methods based on the activities of the family members' friends on social media. In this way, management method suggestions based on the social media activities of family members are provided.

[0076] The management unit can customize the management method by reflecting the family's past feedback during management. For example, the management unit can adjust the management method based on feedback provided by the family in the past. The management unit can also analyze the family's past feedback and optimize the timing of management. The management unit can also customize the content of management by reflecting the family's feedback. This provides a customized management method based on the family's past feedback.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The child-rearing support system can also monitor the parent's health and provide advice according to their health condition. For example, if the parent is tired, it can advise them to take a rest. If the parent is healthy, it can suggest active activities. Furthermore, if the parent is ill, it can refer them to an appropriate medical institution. This allows the system to provide appropriate advice according to the parent's health condition.

[0079] The parenting support system can also monitor the parent's sleep patterns and provide appropriate sleep advice. For example, if the parent is not getting enough sleep, it can advise them to go to bed earlier. If the parent is getting enough sleep, it can suggest that they increase their daytime activity. Furthermore, if the parent has irregular sleep patterns, it can suggest a regular sleep schedule. This allows the system to provide appropriate advice based on the parent's sleep patterns.

[0080] The child-rearing support system can also monitor parents' exercise habits and provide appropriate exercise advice. For example, if a parent is not getting enough exercise, it can suggest simple exercises. If a parent exercises regularly, it can suggest increasing the variety of exercises they do. Furthermore, if a parent is exercising excessively, it can advise them to take a rest. In this way, appropriate advice can be provided according to the parent's exercise habits.

[0081] The child-rearing support system can also monitor parents' eating habits and provide appropriate dietary advice. For example, if a parent has irregular eating habits, it can advise them to eat regular meals. If a parent eats a balanced diet, it can suggest more nutritious ingredients. Furthermore, if a parent is avoiding a particular ingredient, it can suggest alternative ingredients. In this way, appropriate advice can be provided according to the parent's eating habits.

[0082] The child-rearing support system can also learn about parents' hobbies and interests and suggest related information and activities based on that information. For example, if a parent enjoys reading, it can suggest books that might interest them. If a parent enjoys outdoor activities, it can suggest nearby hiking trails and campsites. Furthermore, if a parent enjoys cooking, it can suggest new recipes or cooking classes. This allows the system to provide appropriate information and activities based on parents' hobbies and interests.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The advice section provides advice according to the child's developmental stage. For example, in infancy, it provides advice on breastfeeding and diaper changing frequency, and in toddlerhood, it provides advice on toilet training and language development. It also manages vaccination schedules and sends reminders when vaccination dates are approaching. Step 2: The tracking unit tracks the child's health and growth information based on the advice provided by the advice unit. For example, it collects information such as the child's height, weight, and developmental stage, making it easy for parents to check. It also monitors the child's health and notifies them if there are any abnormalities. Step 3: The learning unit learns the family's preferences and tastes. For example, it collects and learns about the family's favorite ingredients and allergy information. It also analyzes the family's dietary history to learn their preferences and tastes. Step 4: The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. For example, it suggests optimal recipes taking into account the family's favorite ingredients and allergy information. It also suggests nutritionally balanced recipes taking into account the types and amounts of nutrients. Step 5: The creation unit creates a shopping list based on the recipes suggested by the suggestion unit. For example, the creation unit automatically creates the shopping list by adding the types and amounts of ingredients needed to the list. The shopping list is also optimized taking into account the availability of ingredients for the family.

[0085] (Example 2) The AI ​​service according to an embodiment of the present invention is a system that provides information useful for raising children. This system has functions to reduce the burden on parents, such as providing advice based on a child's developmental stage, managing vaccination schedules, and tracking children's health and growth. It also learns family preferences and tastes, suggests nutritionally balanced dinner recipes, and creates shopping lists. It also works with cooking appliances to automatically prepare meals and manages power consumption with compatible appliances. It also automatically manages household accounts, manages payment due dates, and suggests ways to improve household finances. For example, the AI ​​service provides advice based on a child's developmental stage. For example, it provides advice on the frequency of breastfeeding and diaper changes for infants, and on toilet training and language development for toddlers. It also manages vaccination schedules and sends reminders when vaccination dates are approaching. It also tracks information on children's health and growth, allowing parents to easily check it. It then learns family preferences and tastes and suggests nutritionally balanced dinner recipes. For example, it suggests optimal recipes based on the family's favorite ingredients and allergies. It also automatically creates shopping lists based on the suggested recipes. It also works with cooking appliances to automatically prepare meals. For example, it can connect with a smart oven or rice cooker to start cooking so that food is ready at a specified time. It also has the ability to connect with compatible home appliances to manage power consumption. For example, it can monitor the usage of home appliances such as air conditioners and washing machines and make suggestions to optimize power consumption. It also automatically manages household accounts, manages payment due dates, and makes suggestions for improving household finances. For example, it automatically records monthly income and expenses and sends reminders when payment due dates approach. It also analyzes spending trends and makes suggestions for saving money. This allows AI services to provide a variety of information related to child rearing, reducing the burden on parents. This allows AI services to provide a variety of information related to child rearing, reducing the burden on parents. Its wide range of functions include advice based on a child's developmental stage, managing vaccination schedules, tracking health and growth information, suggesting nutritionally balanced dinner recipes, creating shopping lists, connecting with cooking appliances, managing power consumption, automatically managing household accounts, managing payment due dates, and making suggestions for improving household finances.

[0086] A child-rearing support system according to an embodiment includes an advice unit, a tracking unit, a learning unit, a suggestion unit, and a creation unit. The advice unit provides advice according to a child's developmental stage. For example, advice on breastfeeding and diaper changing frequency during infancy and advice on toilet training and language development during toddlerhood can be provided. The advice unit also manages vaccination schedules and can send reminders when vaccination dates approach. The tracking unit tracks information on the child's health and growth based on the advice provided by the advice unit. For example, it collects information such as the child's height, weight, and developmental stage so that parents can easily check it. The tracking unit can also monitor the child's health and notify parents of any abnormalities. The learning unit learns the family's preferences and tastes. For example, it can collect and learn information about the family's favorite ingredients and allergies. The learning unit can also analyze the family's dietary history and learn the family's preferences and tastes. The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. For example, it can suggest optimal recipes taking into account the family's favorite ingredients and allergy information. The suggestion unit can also consider the types and amounts of nutrients in order to suggest nutritionally balanced recipes. The creation unit creates a shopping list based on the recipes suggested by the suggestion unit. For example, the shopping list can be automatically created by adding the types and amounts of ingredients needed to the list. The creation unit can also optimize the shopping list by taking into account the inventory status of ingredients for the family. As a result, the child-rearing support system according to the embodiment can reduce the burden on parents by providing advice according to the child's growth stage, health management, and suggesting nutritionally balanced dinner recipes.

[0087] Furthermore, the child-rearing support system includes a provision unit that works in conjunction with cooking appliances to automatically provide food. The provision unit works in conjunction with cooking appliances to automatically provide food. For example, it can work in conjunction with a smart oven or rice cooker to start cooking so that the food is ready at a specified time. The provision unit can also monitor the usage of the cooking appliances and suggest optimal cooking methods. For example, the provision unit can adjust the oven temperature and cooking time to optimize the quality of the food. The provision unit can also suggest efficient cooking methods, taking into account the energy efficiency of the cooking appliances. In this way, by working in conjunction with cooking appliances, food provision is automated, reducing the burden on parents.

[0088] Furthermore, the child-rearing support system includes a management unit that manages power consumption in cooperation with compatible home appliances. The management unit manages power consumption in cooperation with compatible home appliances. For example, it can monitor the usage of home appliances such as air conditioners and washing machines and make suggestions to optimize power consumption. The management unit can also suggest efficient usage methods taking into account the energy efficiency of the home appliances. For example, the management unit can adjust the temperature setting and usage time of an air conditioner to reduce power consumption. The management unit can also monitor the usage of home appliances in real time and notify if there is an abnormality. In this way, by linking with compatible home appliances, power consumption management becomes more efficient and the burden on parents is reduced.

[0089] Furthermore, the child-rearing support system includes a management unit that automatically manages the household account book. The management unit automatically manages the household account book. For example, it can automatically record monthly income and expenditures and send reminders when a payment due date approaches. The management unit can also analyze spending trends and make suggestions for saving money. For example, the management unit can analyze each category of expenditure and make suggestions for reducing wasteful spending. The management unit can also display household account book data as graphs and charts, providing it in a visually easy-to-understand format. This enables automatic management of the household account book and reduces the burden on parents.

[0090] The child-rearing support system further includes a management unit that manages payment due dates. The management unit manages payment due dates. For example, it can send reminders when a payment due date approaches to ensure that payments are not forgotten. The management unit can also manage payment history and check past payment status. For example, the management unit can display payment history in chronological order, allowing past payment status to be checked at a glance. The management unit can also set priorities when payment due dates overlap, and prioritize notifications of important payments. This automates management of payment due dates and reduces the burden on parents.

[0091] Furthermore, the child-rearing support system includes a proposal unit that makes proposals for improving household finances. The proposal unit makes proposals for improving household finances. For example, it can analyze spending trends and make proposals for saving money. The proposal unit can also make proposals for increasing income. For example, it can make proposals for side jobs or investments and provide methods for increasing income. The proposal unit can also support budget setting and management based on household account book data. For example, the proposal unit can make proposals for setting an appropriate budget, taking into account the balance between income and expenses. This automates proposals for improving household finances, reducing the burden on parents.

[0092] The advice unit can estimate the parent's emotions and adjust the content and timing of the advice based on the estimated parent's emotions. For example, if the parent is feeling stressed, the advice unit can provide relaxation advice and set the timing to nighttime. If the parent is relaxed, the advice unit can provide detailed advice and set the timing to morning. If the parent is busy, the advice unit can provide concise advice and set the timing to lunch break. This makes it possible to provide advice according to the parent's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The advice unit can analyze the child's past growth data and provide appropriate advice. For example, the advice unit can provide advice necessary for the next growth stage based on the child's past growth data. The advice unit can also analyze the child's past health data and suggest a vaccination schedule. The advice unit can also suggest an appropriate learning method based on the child's past learning data. In this way, advice based on the child's past growth data is provided.

[0094] The advice unit can customize the advice provided based on the child's current health condition and lifestyle rhythm. For example, the advice unit can monitor the child's current health condition and provide dietary advice containing necessary nutrients. The advice unit can also analyze the child's lifestyle rhythm and suggest appropriate sleep times. The advice unit can also monitor the child's exercise volume and suggest appropriate exercise methods. In this way, advice is provided that is tailored to the child's current condition.

[0095] The advice unit can improve the accuracy of advice by reflecting the parent's past feedback when providing advice. For example, the advice unit can adjust the content of advice based on the parent's past feedback. The advice unit can also analyze the parent's past feedback and optimize the timing of advice. The advice unit can also customize the format of advice by reflecting the parent's feedback. In this way, advice based on the parent's past feedback is provided.

[0096] The advice unit can estimate the parent's emotions and determine the priority of advice based on the estimated parent's emotions. For example, if the parent is feeling stressed, the advice unit can prioritize advice that helps the parent relax. Furthermore, if the parent is relaxed, the advice unit can also prioritize detailed advice. Furthermore, if the parent is busy, the advice unit can also prioritize concise advice. In this way, the priority of advice is determined according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] When providing advice, the advice unit can provide highly relevant advice by taking into account the geographical location information of the parent. For example, if the parent is in a specific area, the advice unit can provide advice about medical institutions and childcare facilities in that area. Furthermore, if the parent is traveling, the advice unit can provide advice about childcare at the parent's destination. Furthermore, if the parent is at home, the advice unit can provide advice about childcare methods that can be used at home. In this way, advice based on the geographical location information of the parent is provided.

[0098] When providing advice, the advice unit can analyze the parent's social media activity and provide relevant advice. For example, the advice unit can provide relevant child-rearing advice based on information shared by the parent on social media. The advice unit can also analyze the parent's social media activity and provide child-rearing information that may be of interest to the parent. The advice unit can also provide relevant child-rearing advice by referring to the activity of the parent's friends on social media. In this way, advice based on the parent's social media activity is provided.

[0099] The advice unit can customize the advice method by reflecting the parent's past feedback when providing advice. For example, the advice unit can adjust the format of advice based on feedback provided by the parent in the past. The advice unit can also analyze the parent's past feedback and optimize the timing of advice. The advice unit can also customize the content of advice by reflecting the parent's feedback. In this way, an advice method based on the parent's past feedback is provided.

[0100] The tracking unit can estimate the parent's emotion and adjust the display method of the tracking data based on the estimated parent's emotion. For example, if the parent is stressed, the tracking unit can provide a simple, highly visible display method. If the parent is relaxed, the tracking unit can provide a display method including detailed data. If the parent is busy, the tracking unit can provide a display method that focuses on the main points. This provides a display method of the tracking data according to the parent's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The tracking unit can collect and analyze the child's health data in real time during tracking. For example, the tracking unit can monitor the child's body temperature and heart rate in real time and notify if any abnormalities are detected. The tracking unit can also record the child's diet in real time and analyze nutritional balance. The tracking unit can also track the child's exercise volume in real time and suggest appropriate exercise amounts. In this way, the child's health data is collected and analyzed in real time.

[0102] The tracking unit can apply different tracking algorithms depending on the child's developmental stage during tracking. For example, the tracking unit can apply an algorithm for tracking the frequency of breastfeeding and diaper changes during infancy. The tracking unit can also apply an algorithm for tracking toilet training and language development during toddlerhood. The tracking unit can also apply an algorithm for tracking learning progress and motor skills during school age. This makes it possible to track children according to their developmental stage.

[0103] The tracking unit can improve the accuracy of tracking by referring to the parent's past tracking data during tracking. For example, the tracking unit can adjust the tracking algorithm based on the tracking data previously provided by the parent. The tracking unit can also analyze the parent's past tracking data to optimize the timing of tracking. The tracking unit can also customize the tracking method by reflecting the parent's feedback. In this way, tracking based on the parent's past tracking data is provided.

[0104] The tracking unit can estimate the parent's emotions and determine the priority of the tracking data based on the estimated parent's emotions. For example, if the parent is feeling stressed, the tracking unit can prioritize displaying important data. Furthermore, if the parent is relaxed, the tracking unit can prioritize displaying detailed data. Furthermore, if the parent is busy, the tracking unit can prioritize displaying data that highlights the main points. In this way, the priority of the tracking data is determined according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] During tracking, the tracking unit can prioritize tracking of highly relevant data taking into account the child's geographical location information. For example, if the child is in a specific location, the tracking unit can prioritize tracking health data related to that location. Also, if the child is at school, the tracking unit can prioritize tracking learning progress. Also, if the child is at a park, the tracking unit can prioritize tracking the amount of exercise. This provides tracking based on the child's geographical location information.

[0106] During tracking, the tracking unit can analyze the child's social media activities and track related data. For example, the tracking unit can track related health data based on the information the child has shared on social media. The tracking unit can also analyze the child's social media activities and track data that may be of interest to the child. The tracking unit can also track related data based on the activities of the child's friends on social media. This provides tracking based on the child's social media activities.

[0107] The tracking unit can customize the tracking method by reflecting the parent's past feedback during tracking. For example, the tracking unit can adjust the tracking method based on the parent's past feedback. The tracking unit can also analyze the parent's past feedback and optimize the timing of tracking. The tracking unit can also customize the tracking format by reflecting the parent's feedback. This provides a tracking method based on the parent's past feedback.

[0108] The learning unit can estimate the parent's emotions and select learning data based on the estimated parent's emotions. For example, if the parent is feeling stressed, the learning unit can prioritize selecting learning data that will relax the parent. Furthermore, if the parent is relaxed, the learning unit can prioritize selecting detailed learning data. Furthermore, if the parent is busy, the learning unit can prioritize selecting concise learning data. This provides a selection of learning data according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit can, for example, select an optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and optimize the progress of learning. The learning unit can also optimize the timing of learning by referring to past learning data. This provides optimization of the learning algorithm based on past learning data.

[0110] The learning unit can update the learning data during learning by reflecting parental feedback. For example, the learning unit can update the learning data based on feedback provided by the parent. The learning unit can also analyze the parental feedback and optimize the learning progress. The learning unit can also customize the learning format by reflecting the parental feedback. This provides updates to the learning data based on the parental feedback.

[0111] The learning unit can integrate information from different data sources during learning to enrich the learning data. For example, the learning unit can integrate information from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources to optimize the progress of learning. The learning unit can also optimize the timing of learning by referring to information from different data sources. This provides enriched learning data that integrates information from different data sources.

[0112] The learning unit can estimate the parent's emotions and adjust the frequency of learning based on the estimated parent's emotions. For example, if the parent is feeling stressed, the learning unit can reduce the frequency of learning and increase the time the parent can relax. Furthermore, if the parent is relaxed, the learning unit can increase the frequency of learning and provide detailed information. Furthermore, if the parent is busy, the learning unit can adjust the frequency of learning and provide information efficiently. In this way, the frequency of learning is provided according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] During learning, the learning unit can weight the learning data based on the child's developmental stage. For example, the learning unit can weight data related to breastfeeding and diaper changing more heavily during the infancy stage. The learning unit can also weight data related to toilet training and language development more heavily during the toddler stage. The learning unit can also weight data related to learning progress and motor skills more heavily during the school-age stage. This provides weighting of the learning data based on the child's developmental stage.

[0114] The learning unit can analyze the parent's social media activities during learning and incorporate relevant data into the learning. For example, the learning unit can incorporate relevant learning data based on information shared by the parent on social media. The learning unit can also analyze the parent's social media activities and incorporate learning data that may be of interest to the child. The learning unit can also incorporate relevant learning data based on the activities of the parent's friends on social media. This provides for the incorporation of learning data based on the parent's social media activities.

[0115] The learning unit can adjust the learning algorithm during learning by reflecting the parent's past feedback. For example, the learning unit can adjust the learning algorithm based on feedback provided by the parent. The learning unit can also analyze the parent's past feedback to optimize learning progress. The learning unit can also customize the learning format by reflecting the parent's feedback. This provides adjustment of the learning algorithm based on the parent's past feedback.

[0116] The suggestion unit can estimate the parent's emotions and adjust the way the suggestions are expressed based on the estimated parent's emotions. For example, if the parent is feeling stressed, the suggestion unit can provide a simple and highly visible suggestion method. If the parent is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. If the parent is busy, the suggestion unit can also provide a suggestion method that focuses on the main points. This provides a way of expressing the suggestions according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] When making a suggestion, the suggestion unit can customize the suggestion content by taking into consideration the health condition and nutritional balance of the family members. For example, the suggestion unit can monitor the health condition of the family members and suggest recipes that contain the necessary nutrients. The suggestion unit can also analyze the nutritional balance of the family members and suggest balanced meals. The suggestion unit can also suggest recipes that avoid allergies by taking into consideration allergy information of the family members. In this way, suggestions based on the health condition and nutritional balance of the family members are provided.

[0118] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the family's past meal history. For example, the suggestion unit can suggest recipes that include favorite ingredients based on the family's past meal history. The suggestion unit can also analyze the family's past meal history and suggest nutritionally balanced meals. The suggestion unit can also suggest recipes that avoid allergies by referring to the family's past meal history. In this way, suggestions based on the family's past meal history are provided.

[0119] When making a suggestion, the suggestion unit can adjust the suggestion content by taking into account the allergy information of the family members. For example, the suggestion unit can suggest recipes that avoid allergies based on the allergy information of the family members. The suggestion unit can also analyze the allergy information of the family members and suggest nutritionally balanced meals. The suggestion unit can also suggest recipes that include favorite ingredients by referring to the allergy information of the family members. In this way, suggestions based on the allergy information of the family members are provided.

[0120] The suggestion unit can estimate the parent's emotions and determine the priority of suggestions based on the estimated parent's emotions. For example, if the parent is feeling stressed, the suggestion unit can prioritize relaxing suggestions. If the parent is relaxed, the suggestion unit can also prioritize detailed suggestions. If the parent is busy, the suggestion unit can also prioritize concise suggestions. This provides a priority of suggestions according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] When making suggestions, the suggestion unit can suggest highly relevant recipes taking into account the geographical location information of the family members. For example, if the family members are in a specific area, the suggestion unit can suggest recipes using ingredients from that area. Also, if the family members are traveling, the suggestion unit can suggest recipes using ingredients available at the travel destination. Also, if the family members are at home, the suggestion unit can suggest recipes using ingredients available at home. In this way, recipe suggestions based on the geographical location information of the family members are provided.

[0122] When making a suggestion, the suggestion unit can analyze the social media activity of the family and suggest related recipes. For example, the suggestion unit can suggest related recipes based on information shared by the family on social media. The suggestion unit can also analyze the social media activity of the family and suggest recipes that the family may be interested in. The suggestion unit can also suggest related recipes based on the activity of the family's friends on social media. In this way, recipe suggestions based on the social media activity of the family are provided.

[0123] The suggestion unit can customize the suggestion method by reflecting the family's past feedback when making a suggestion. The suggestion unit can adjust the suggestion method based on, for example, feedback provided by the family in the past. The suggestion unit can also analyze the family's past feedback and optimize the timing of the suggestion. The suggestion unit can also customize the content of the suggestion by reflecting the family's feedback. In this way, a suggestion method based on the family's past feedback is provided.

[0124] The creation unit can estimate the parent's emotions and adjust the contents of the shopping list based on the estimated parent's emotions. For example, if the parent is stressed, the creation unit can provide a simple, bare-bones shopping list. If the parent is relaxed, the creation unit can also provide a detailed shopping list. If the parent is busy, the creation unit can also adjust the list to allow for efficient shopping. In this way, the shopping list contents are provided according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0125] The creation unit can optimize the shopping list by taking into account the inventory status of ingredients for the family members when creating the list. For example, the creation unit can monitor the inventory status of the family members' refrigerators and add necessary ingredients to the list. The creation unit can also analyze the inventory status of the family members' pantries and add necessary ingredients to the list. The creation unit can also add necessary ingredients to the list by taking into account the expiration dates of the family members' ingredients. This provides optimization of the shopping list based on the inventory status of ingredients for the family members.

[0126] The creation unit can improve the accuracy of the list by referring to the family's past shopping history when creating the list. For example, the creation unit can add necessary ingredients to the list based on the family's past shopping history. The creation unit can also analyze the family's past shopping history to create an efficient shopping list. The creation unit can also add ingredients to the list that are free of allergies by referring to the family's past shopping history. This provides for improved accuracy of the list based on the family's past shopping history.

[0127] The creation unit can adjust the list contents taking into account the allergy information of family members when creating the list. For example, the creation unit can add ingredients that are allergy-free to the list based on the allergy information of family members. The creation unit can also analyze the allergy information of family members and add nutritionally balanced ingredients to the list. The creation unit can also add preferred ingredients to the list by referring to the allergy information of family members. This allows the list contents to be adjusted based on the allergy information of family members.

[0128] The creation unit can estimate the parent's emotions and determine the priority of the shopping list based on the estimated parent's emotions. For example, if the parent is feeling stressed, the creation unit can prioritize adding the bare minimum of ingredients to the list. Furthermore, if the parent is relaxed, the creation unit can prioritize adding detailed ingredients to the list. Furthermore, if the parent is busy, the creation unit can adjust the priority of the list to enable efficient shopping. This provides a shopping list priority according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0129] The creation unit can create an optimal shopping list by taking into account the geographical location information of the family members when creating the list. For example, if the family members are in a specific area, the creation unit can add ingredients that are available in that area to the list. Furthermore, if the family members are traveling, the creation unit can add ingredients that are available at the travel destination to the list. Furthermore, if the family members are at home, the creation unit can add ingredients that are available at home to the list. In this way, an optimal shopping list based on the geographical location information of the family members is provided.

[0130] When creating the list, the creation unit can analyze the social media activities of the family and add related ingredients to the list. For example, the creation unit can add related ingredients to the list based on information shared by the family on social media. The creation unit can also analyze the social media activities of the family and add ingredients that may be of interest to the list. The creation unit can also add related ingredients to the list based on the activities of the family's friends on social media. This provides a list of ingredients that can be added based on the social media activities of the family.

[0131] The creation unit can customize the list contents by reflecting the family's past feedback when creating the list. For example, the creation unit can adjust the list contents based on the feedback provided by the family in the past. The creation unit can also analyze the family's past feedback to improve the accuracy of the list. The creation unit can also customize the format of the list by reflecting the family's feedback. This provides customization of the list contents based on the family's past feedback.

[0132] The serving unit can estimate the parent's emotions and adjust the timing of serving food based on the estimated parent's emotions. For example, if the parent is feeling stressed, the serving unit can serve food at a time when the parent is able to relax. Furthermore, if the parent is relaxed, the serving unit can also suggest detailed timing for serving food. Furthermore, if the parent is busy, the serving unit can efficiently adjust the timing for serving food. In this way, the timing for serving food is provided according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0133] The providing unit can set an optimal serving time in consideration of the family's meal schedule when providing food. The providing unit can, for example, monitor the family's meal schedule and set an optimal serving time. The providing unit can also analyze the family's meal schedule and suggest an efficient serving time. The providing unit can also set an appropriate serving time by referring to the family's meal schedule. In this way, an optimal serving time based on the family's meal schedule is provided.

[0134] The serving unit can customize the serving contents by referring to the family's past meal history when serving. For example, the serving unit can serve dishes containing favorite ingredients based on the family's past meal history. The serving unit can also analyze the family's past meal history and serve nutritionally balanced dishes. The serving unit can also refer to the family's past meal history and serve dishes that avoid allergies. This allows the serving contents to be customized based on the family's past meal history.

[0135] The serving unit can adjust the serving contents taking into consideration the allergy information of the family members when serving food. For example, the serving unit can serve dishes that avoid allergies based on the allergy information of the family members. The serving unit can also analyze the allergy information of the family members and serve nutritionally balanced dishes. The serving unit can also refer to the allergy information of the family members and serve dishes that include their favorite ingredients. In this way, the serving contents can be adjusted based on the allergy information of the family members.

[0136] The providing unit can estimate the parent's emotions and determine the priority of food provision based on the estimated parent's emotions. For example, if the parent is feeling stressed, the providing unit can prioritize providing relaxing dishes. Furthermore, if the parent is relaxed, the providing unit can also prioritize providing detailed dishes. Furthermore, if the parent is busy, the providing unit can adjust the priority of food provision efficiently. In this way, the priority of food provision is provided according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0137] When providing food, the providing unit can select the optimal providing method by taking into consideration the geographical location information of the family. For example, if the family is in a specific area, the providing unit can provide food made with ingredients available in that area. Furthermore, if the family is traveling, the providing unit can also provide food made with ingredients available at the travel destination. Furthermore, if the family is at home, the providing unit can also provide food made with ingredients available at home. In this way, the optimal providing method based on the geographical location information of the family is provided.

[0138] The provision unit can analyze the social media activity of the family and provide related dishes when providing food. For example, the provision unit can provide related dishes based on information shared by the family on social media. The provision unit can also analyze the social media activity of the family and provide dishes that the family may be interested in. The provision unit can also provide related dishes by taking into account the activity of the family's friends on social media. In this way, food recommendations based on the social media activity of the family are provided.

[0139] The providing unit can customize the method of providing information by reflecting the family's past feedback when providing information. The providing unit can adjust the method of providing information based on, for example, feedback provided by the family in the past. The providing unit can also analyze the family's past feedback and optimize the timing of providing information. The providing unit can also customize the content of the information provided by reflecting the family's feedback. This allows the method of providing information to be customized based on the family's past feedback.

[0140] The management unit can estimate the parent's emotions and adjust the power consumption management method based on the estimated parent's emotions. For example, if the parent is feeling stressed, the management unit can provide a simple and highly visible management method. If the parent is relaxed, the management unit can also provide a management method that includes detailed data. If the parent is busy, the management unit can also provide a management method that focuses on the main points. This provides a power consumption management method that corresponds to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0141] During management, the management unit can monitor the usage status of the home appliances in real time and propose an optimal management method. For example, the management unit can monitor the usage status of the home appliances in real time and propose an efficient usage method. The management unit can also analyze the usage status of the home appliances and propose a method to optimize power consumption. The management unit can also refer to the usage status of the home appliances and propose an appropriate usage timing. This provides an optimal management method based on the usage status of the home appliances.

[0142] During management, the management unit can improve the accuracy of management by referring to the family's past power consumption data. For example, the management unit can propose an efficient management method based on the family's past power consumption data. The management unit can also analyze the family's past power consumption data and propose a method to optimize power consumption. The management unit can also suggest appropriate usage timing by referring to the family's past power consumption data. This provides improved accuracy of management based on the family's past power consumption data.

[0143] The management unit can adjust the management method taking into account the energy efficiency of the home appliance during management. For example, the management unit can monitor the energy efficiency of the home appliance and suggest an efficient usage method. The management unit can also analyze the energy efficiency of the home appliance and suggest a method for optimizing power consumption. The management unit can also suggest an appropriate usage timing by referring to the energy efficiency of the home appliance. This provides adjustment of the management method based on the energy efficiency of the home appliance.

[0144] The management unit can estimate the parent's emotions and determine the priority of power consumption based on the estimated parent's emotions. For example, if the parent is feeling stressed, the management unit can prioritize displaying important data. Furthermore, if the parent is relaxed, the management unit can prioritize displaying detailed data. Furthermore, if the parent is busy, the management unit can prioritize displaying data that focuses on the main points. This provides a priority of power consumption according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0145] During management, the management unit can select the optimal management method taking into account the geographical location information of the family members. For example, if the family members are in a specific area, the management unit can propose a management method taking into account the power consumption situation in that area. Furthermore, if the family members are traveling, the management unit can propose a management method taking into account the power consumption situation at the travel destination. Furthermore, if the family members are at home, the management unit can propose a management method taking into account the power consumption situation at home. In this way, the optimal management method based on the geographical location information of the family members is provided.

[0146] During management, the management unit can analyze the social media activities of family members and suggest relevant management methods. For example, the management unit can suggest relevant management methods based on information shared by family members on social media. The management unit can also analyze the social media activities of family members and suggest management methods that may be of interest to them. The management unit can also suggest relevant management methods based on the activities of the family members' friends on social media. In this way, management method suggestions based on the social media activities of family members are provided.

[0147] The management unit can customize the management method by reflecting the family's past feedback during management. For example, the management unit can adjust the management method based on feedback provided by the family in the past. The management unit can also analyze the family's past feedback and optimize the timing of management. The management unit can also customize the content of management by reflecting the family's feedback. This provides a customized management method based on the family's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned advice unit, tracking unit, learning unit, suggestion unit, creation unit, provision unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the advice unit is realized by the control unit 46A of the smart device 14 and provides advice according to the child's developmental stage. The tracking unit is realized by the specific processing unit 290 of the data processing device 12 and tracks information on the child's health management and development. The learning unit is realized by the control unit 46A of the smart device 14 and learns the family's preferences and tastes. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests nutritionally balanced dinner recipes. The creation unit is realized by the control unit 46A of the smart device 14 and creates a shopping list. The provision unit is realized by the control unit 46A of the smart device 14 and cooperates with cooking appliances to automatically provide meals. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages power consumption. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs automatic management of the household account book. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs payment due date management. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals for improving household finances. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned advice unit, tracking unit, learning unit, suggestion unit, creation unit, provision unit, and management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the advice unit is realized by the control unit 46A of the smart glasses 214 and provides advice according to the child's developmental stage. The tracking unit is realized by the specific processing unit 290 of the data processing device 12 and tracks information on the child's health management and development. The learning unit is realized by the control unit 46A of the smart glasses 214 and learns the family's preferences and tastes. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests nutritionally balanced dinner recipes. The creation unit is realized by the control unit 46A of the smart glasses 214 and creates a shopping list. The provision unit is realized by the control unit 46A of the smart glasses 214 and cooperates with cooking appliances to automatically provide meals. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages power consumption. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs automatic management of the household account book. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs payment due date management. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals for improving household finances. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described advice unit, tracking unit, learning unit, suggestion unit, creation unit, provision unit, and management unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the advice unit is realized by the control unit 46A of the headset-type terminal 314 and provides advice according to the child's developmental stage. The tracking unit is realized by the specific processing unit 290 of the data processing device 12 and tracks information on the child's health management and development. The learning unit is realized by the control unit 46A of the headset-type terminal 314 and learns the family's preferences and tastes. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests nutritionally balanced dinner recipes. The creation unit is realized by the control unit 46A of the headset-type terminal 314 and creates a shopping list. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and cooperates with cooking appliances to automatically provide meals. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages power consumption. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs automatic management of the household account book. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs payment due date management. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals for improving household finances. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned advice unit, tracking unit, learning unit, suggestion unit, creation unit, provision unit, and management unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the advice unit is realized by the control unit 46A of the robot 414 and provides advice according to the child's developmental stage. The tracking unit is realized by the specific processing unit 290 of the data processing device 12 and tracks information on the child's health management and development. The learning unit is realized by the control unit 46A of the robot 414 and learns the family's preferences and tastes. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests nutritionally balanced dinner recipes. The creation unit is realized by the control unit 46A of the robot 414 and creates a shopping list. The provision unit is realized by the control unit 46A of the robot 414 and cooperates with cooking appliances to automatically provide food. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages power consumption. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs automatic management of the household account book. The management unit is realized by the specific processing unit 290 of the data processing device 12 and performs payment due date management. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and makes proposals for improving household finances.

[0148] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0149] The parenting support system can also monitor the parent's stress level and suggest relaxing activities if stress levels are high. For example, if a parent is feeling stressed, it can suggest relaxation methods such as deep breathing or a short walk. If the parent is relaxed, it can also suggest activities that the parent can enjoy together with their child. Furthermore, if the parent is busy, it can also suggest short, effective ways to relax. This makes it possible to suggest appropriate activities according to the parent's stress level.

[0150] The child-rearing support system can also monitor the parent's health and provide advice according to their health condition. For example, if the parent is tired, it can advise them to take a rest. If the parent is healthy, it can suggest active activities. Furthermore, if the parent is ill, it can refer them to an appropriate medical institution. This allows the system to provide appropriate advice according to the parent's health condition.

[0151] The child-rearing support system can also estimate the parent's emotions and suggest communication methods with their children based on the estimated emotions. For example, if a parent is feeling stressed, it can provide advice on how to communicate smoothly with their child. If the parent is relaxed, it can suggest fun activities to do with their child. Furthermore, if the parent is busy, it can suggest effective communication methods that can be done quickly. In this way, appropriate communication methods can be provided according to the parent's emotions.

[0152] The parenting support system can also monitor the parent's sleep patterns and provide appropriate sleep advice. For example, if the parent is not getting enough sleep, it can advise them to go to bed earlier. If the parent is getting enough sleep, it can suggest that they increase their daytime activity. Furthermore, if the parent has irregular sleep patterns, it can suggest a regular sleep schedule. This allows the system to provide appropriate advice based on the parent's sleep patterns.

[0153] The child-rearing support system can also estimate the parent's emotions and determine the priority of housework based on the estimated emotions. For example, if the parent is feeling stressed, it can suggest that important housework be done first. If the parent is relaxed, it can provide a detailed list of housework. Furthermore, if the parent is busy, it can suggest priorities for efficiently performing housework. In this way, the system provides housework priorities according to the parent's emotions.

[0154] The child-rearing support system can also monitor parents' exercise habits and provide appropriate exercise advice. For example, if a parent is not getting enough exercise, it can suggest simple exercises. If a parent exercises regularly, it can suggest increasing the variety of exercises they do. Furthermore, if a parent is exercising excessively, it can advise them to take a rest. In this way, appropriate advice can be provided according to the parent's exercise habits.

[0155] The child-rearing support system can also estimate the parent's emotions and suggest educational methods for the child based on the estimated emotions. For example, if the parent is feeling stressed, it can suggest a simple but effective educational method. If the parent is relaxed, it can provide a detailed educational plan. Furthermore, if the parent is busy, it can suggest a short but effective educational method. In this way, an appropriate educational method can be provided according to the parent's emotions.

[0156] The child-rearing support system can also monitor parents' eating habits and provide appropriate dietary advice. For example, if a parent has irregular eating habits, it can advise them to eat regular meals. If a parent eats a balanced diet, it can suggest more nutritious ingredients. Furthermore, if a parent is avoiding a particular ingredient, it can suggest alternative ingredients. In this way, appropriate advice can be provided according to the parent's eating habits.

[0157] The child-rearing support system can also estimate the parent's emotions and suggest recreational activities for the family based on the estimated emotions. For example, if the parent is feeling stressed, it can suggest a relaxing recreational activity. If the parent is feeling relaxed, it can suggest an activity that the whole family can enjoy. Furthermore, if the parent is busy, it can suggest a recreational activity that can be enjoyed in a short amount of time. In this way, appropriate recreational activities are provided according to the parent's emotions.

[0158] The child-rearing support system can also learn about parents' hobbies and interests and suggest related information and activities based on that information. For example, if a parent enjoys reading, it can suggest books that might interest them. If a parent enjoys outdoor activities, it can suggest nearby hiking trails and campsites. Furthermore, if a parent enjoys cooking, it can suggest new recipes or cooking classes. This allows the system to provide appropriate information and activities based on parents' hobbies and interests.

[0159] The processing flow of the second embodiment will be briefly explained below.

[0160] Step 1: The advice section provides advice according to the child's developmental stage. For example, in infancy, it provides advice on breastfeeding and diaper changing frequency, and in toddlerhood, it provides advice on toilet training and language development. It also manages vaccination schedules and sends reminders when vaccination dates are approaching. Step 2: The tracking unit tracks the child's health and growth information based on the advice provided by the advice unit. For example, it collects information such as the child's height, weight, and developmental stage, making it easy for parents to check. It also monitors the child's health and notifies them if there are any abnormalities. Step 3: The learning unit learns the family's preferences and tastes. For example, it collects and learns about the family's favorite ingredients and allergy information. It also analyzes the family's dietary history to learn their preferences and tastes. Step 4: The suggestion unit suggests nutritionally balanced dinner recipes based on the information learned by the learning unit. For example, it suggests optimal recipes taking into account the family's favorite ingredients and allergy information. It also suggests nutritionally balanced recipes taking into account the types and amounts of nutrients. Step 5: The creation unit creates a shopping list based on the recipes suggested by the suggestion unit. For example, the creation unit automatically creates the shopping list by adding the types and amounts of ingredients needed to the list. The shopping list is also optimized taking into account the availability of ingredients for the family.

[0161] 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.

[0162] 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.

[0163] 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.

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

[0165] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0166] 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.

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

[0168] The 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.

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

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] 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.

[0196] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0197] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0212] 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.

[0213] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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."

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0231] 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.

[0232] [Explanation of symbols]

[0233] 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 advice department that provides advice according to the child's developmental stage, a tracking unit that tracks information on the child's health care and growth based on the advice provided by the advising unit; A learning department that learns about family preferences and tastes; a suggestion unit that suggests nutritionally balanced dinner recipes based on the information learned by the learning unit; a creation unit that creates a shopping list based on the recipes suggested by the suggestion unit. A system characterized by:

2. Equipped with a serving unit that automatically serves food in conjunction with kitchen appliances 2. The system of claim 1.

3. Equipped with a management unit that manages power consumption in cooperation with compatible home appliances 2. The system of claim 1.

4. Equipped with a management department that automatically manages household accounts 2. The system of claim 1.

5. Equipped with a management department that manages payment due dates 2. The system of claim 1.

6. Equipped with a proposal department that makes proposals for improving household finances 2. The system of claim 1.

7. The advice unit Estimate the parent's emotions and adjust the content and timing of advice based on the estimated parent's emotions 2. The system of claim 1.

8. The advice unit Analyze your child's past growth data and provide appropriate advice 2. The system of claim 1.

9. The advice unit Advice is tailored based on the child's current health and lifestyle.

2. The system of claim 1.

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