System

Abstract

An object of a system according to an embodiment is to propose an optimal action to be taken next by a parent in childcare.SOLUTION: A system according to an embodiment includes a situation input unit, a data analysis unit, and an action suggestion unit. The situation input unit inputs a current situation. The data analysis unit analyzes the current situation input by the situation input unit. The action suggestion unit suggests an optimal action to be taken next on the basis of the result analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for parents to determine the best next steps for raising children, placing a heavy burden on them.

[0005] The system according to the embodiment aims to suggest the next optimal action that parents should take in raising their children. [Means for solving the problem]

[0006] The system according to the embodiment includes a situation input unit, a data analysis unit, and an action suggestion unit. The situation input unit inputs a current situation. The data analysis unit analyzes the current situation input by the situation input unit. The action suggestion unit suggests the next optimal action to be taken based on the results of the analysis by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the next optimal action that a parent should take in raising their child. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 child-rearing support system according to an embodiment of the present invention is a system that uses generative AI to support child-rearing, reduce the burden on parents, and save parents and children from rare tragedies. As a result, the child-rearing support system can reduce the burden on parents and ensure the safety of parents and children.

[0029] A child-rearing support system according to an embodiment includes a situation input unit, a data analysis unit, and an action suggestion unit. The situation input unit inputs the current situation. For example, a parent inputs specific situations, such as the baby crying, refusing to eat, or constant crying at night. The data analysis unit analyzes the current situation input by the situation input unit. For example, the generation AI analyzes a huge amount of past child-rearing performance data to find the most appropriate response to the current situation. The action suggestion unit suggests the next optimal action based on the results of the analysis by the data analysis unit. For example, it provides specific advice such as, "If the baby is crying, first check the diaper, then try giving him milk." In this way, the child-rearing support system uses the generation AI to support child-rearing, reduce the burden on parents, and save parents and children from rare tragedies.

[0030] The situation input unit monitors the child's behavior and condition using cameras and sensors, and the generation AI can automatically recognize and input the situation. For example, the situation input unit monitors the child's behavior using a camera, and the generation AI automatically recognizes the situation. For example, if the child is crying, it detects the crying and notifies the parent. The sensors monitor the child's condition, and the generation AI automatically recognizes the situation. For example, if the body temperature sensor detects a high fever, it notifies the parent. This automatically recognizes the child's behavior and condition, reducing the burden on parents.

[0031] The situation input unit can use voice recognition technology to enable input in natural language to reduce the effort required for parents to input information. For example, when a parent describes their child's situation by voice, the situation input unit automatically converts the description into text using voice recognition technology. For example, the situation can be input by simply saying, "The baby is crying." The voice recognition technology analyzes the parent's voice and enables input in natural language. For example, a voice recognition engine converts the parent's voice into text. This allows the parent to input information in natural language, reducing the effort required for input.

[0032] The status input unit shares information entered by parents with family members and childcare workers, enabling the creation of a system for collaborative support of child rearing. The status input unit, for example, creates a system for sharing a child's status entered by a parent with family members and childcare workers in real time. For example, all family members share information using the same app. The information sharing platform shares information entered by parents with family members and childcare workers. For example, the information is shared in real time using a data sharing protocol. This allows the information entered by parents to be shared, enabling collaborative support of child rearing.

[0033] The status input unit automatically collects the child's health condition and growth records, and can manage the health in cooperation with medical institutions. The status input unit, for example, automatically collects data such as body temperature and heart rate using a sensor that monitors the child's health condition. For example, if the body temperature is high, the medical institution is notified. The growth record automatically collects the child's growth data, and can manage the health in cooperation with medical institutions. For example, the growth curve can be shared with medical institutions and health management can be performed. This allows the child's health condition and growth records to be automatically collected, and health management can be performed in cooperation with medical institutions.

[0034] The data analysis unit learns a child's individual characteristics and patterns based on past performance data, and is able to provide more personalized advice. The data analysis unit, for example, analyzes past performance data and builds a system that learns a child's individual characteristics and patterns. For example, if a child tends to cry at a certain time of day, it will suggest measures to take at that time of day. The generation AI learns a child's characteristics and patterns based on past data and provides personalized advice. For example, it generates individual advice based on specific behavioral patterns. This makes it possible to learn a child's individual characteristics and patterns and provide more personalized advice.

[0035] When analyzing the data, the data analysis unit takes into account child-rearing methods from different cultures and regions and is able to propose a variety of countermeasures. For example, the data analysis unit incorporates child-rearing methods from different cultures and regions into the data analysis and builds a system that proposes a variety of countermeasures. For example, it provides advice that takes into account the eating habits of each region. The generation AI analyzes data from different cultures and regions and proposes a variety of countermeasures. For example, it proposes child-rearing methods that take cultural differences into account. This makes it possible to propose a variety of countermeasures that take into account child-rearing methods from different cultures and regions.

[0036] The data analysis unit can link past performance data with other childcare support apps and services to share and integrate data. The data analysis unit, for example, builds a system that shares and integrates data with other childcare support apps and services. For example, it collects data from multiple apps and manages it centrally. The data sharing platform links past performance data with other apps and services to share and integrate data. For example, it shares data using API integration. This allows past performance data to be linked with other childcare support apps and services to share and integrate data.

[0037] The data analysis unit can display the data analysis results in graphs and charts that are visually easy to understand, allowing parents to intuitively understand. The data analysis unit, for example, builds a system that generates graphs and charts for visually displaying the data analysis results. For example, a child's growth curve is displayed in a graph. The visual display system displays the data analysis results in graphs and charts, allowing parents to intuitively understand. For example, the data is visually displayed using bar graphs and line graphs. This allows the data analysis results to be displayed in graphs and charts that are visually easy to understand, allowing parents to intuitively understand.

[0038] The action suggestion unit can monitor the effects of the proposed action in real time and update the advice as necessary. The action suggestion unit, for example, builds a system that monitors the effects of the proposed action in real time. For example, it monitors the child's reactions with a camera and provides new advice if the action is ineffective. The monitoring system monitors the effects of the proposed action in real time and updates the advice as necessary. For example, it uses a sensor to collect the child's reactions as data, which is then analyzed by the generation AI. This makes it possible to monitor the effects of the proposed action in real time and update the advice as necessary.

[0039] The action suggestion unit collects parental feedback on the proposed actions, allowing the generation AI to continuously learn and improve the accuracy of the suggestions. The action suggestion unit, for example, collects parental feedback on the proposed actions, building a system in which the generation AI continuously learns. For example, a questionnaire is provided in which parents evaluate the effectiveness of actions. The feedback collection system collects parental feedback, and the generation AI learns based on that data. For example, a questionnaire is provided in which parents evaluate the effectiveness of actions, and the generation AI analyzes the results. This allows parental feedback on the proposed actions to be collected, allowing the generation AI to continuously learn and improve the accuracy of the suggestions.

[0040] The action suggestion unit can share the suggested actions with other parents and experts, thereby promoting community-based support. The action suggestion unit, for example, builds a system for sharing the suggested actions with other parents and experts. For example, the suggestions are shared in an online forum and opinions are exchanged. The sharing platform shares the suggested actions with other parents and experts, thereby promoting community-based support. For example, information is shared using a data sharing protocol. This allows the suggested actions to be shared with other parents and experts, thereby promoting community-based support.

[0041] The action suggestion unit can present the suggested action in the form of a visual guide or video, making it easier for parents to carry out the action. The action suggestion unit, for example, builds a system that presents the suggested action in the form of a visual guide or video. For example, specific steps are explained using illustrations or video. The visual guide or video visually shows the suggested action, making it easier for parents to carry out. For example, the format of the video or the content of the guide can be devised so that parents can intuitively understand. In this way, the suggested action can be presented in the form of a visual guide or video, making it easier for parents to carry out the action.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The child-rearing support system can also be equipped with a health management unit that monitors the parent's health condition. The health management unit uses sensors to monitor the parent's body temperature, heart rate, sleep patterns, etc., and evaluates their health condition. For example, if the parent is not getting enough sleep, the unit will advise them to get some rest. It can also suggest appropriate nutritional intake and exercise based on the parent's health condition. This helps parents maintain their health and reduces the burden of child-rearing.

[0044] The parenting support system can also be equipped with a learning support unit that monitors a child's learning status and provides appropriate learning advice. The learning support unit monitors a child's learning status using cameras and sensors, and the generation AI automatically analyzes the learning status. For example, it can evaluate a child's level of concentration and understanding when working on a specific task and suggest appropriate learning methods. It can also customize learning content based on the child's interests. This can improve children's learning effectiveness and reduce the burden on parents.

[0045] The child-rearing support system can also be equipped with a play suggestion unit that monitors children's play and suggests appropriate play activities. The play suggestion unit monitors children's play activities using cameras and sensors, and the generation AI automatically analyzes the play situation. For example, if a child is bored with a particular game, it can suggest a new game. It can also customize appropriate play content based on the child's age and interests. This improves the quality of children's play and reduces the burden on parents.

[0046] The child-rearing support system can also be equipped with a meal support unit that monitors children's eating habits and provides appropriate dietary advice. The meal support unit monitors children's eating habits using cameras and sensors, and the generation AI automatically analyzes the eating habits. For example, if a child dislikes a particular ingredient, it can suggest alternative ingredients. It can also suggest meal menus that take children's nutritional balance into consideration. This can improve the quality of children's meals and reduce the burden on parents.

[0047] The parenting support system can also be equipped with a sleep support unit that monitors a child's sleep status and provides appropriate sleep advice. The sleep support unit monitors a child's sleep status using cameras and sensors, and the AI ​​generation system automatically analyzes the sleep status. For example, if a child is crying at night, it will identify the cause and suggest appropriate measures. It can also suggest an appropriate sleeping environment based on the child's age and daily routine. This can improve the quality of a child's sleep and reduce the burden on parents.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The situation input unit inputs the current situation. For example, a parent inputs specific situations such as the baby crying, refusing to eat, or constant crying at night. Step 2: The data analysis unit analyzes the current situation input by the situation input unit. For example, the generation AI analyzes a huge amount of past child-rearing performance data and finds the most appropriate response to the current situation. Step 3: The action suggestion unit suggests the next best action based on the results of the analysis by the data analysis unit. For example, it provides specific advice such as, "If the baby is crying, first check the diaper, then try giving him milk."

[0050] (Example 2) The child-rearing support system according to an embodiment of the present invention is a system that uses generative AI to support child-rearing, reduce the burden on parents, and save parents and children from rare tragedies. As a result, the child-rearing support system can reduce the burden on parents and ensure the safety of parents and children.

[0051] A child-rearing support system according to an embodiment includes a situation input unit, a data analysis unit, and an action suggestion unit. The situation input unit inputs the current situation. For example, a parent inputs specific situations, such as the baby crying, refusing to eat, or constant crying at night. The data analysis unit analyzes the current situation input by the situation input unit. For example, the generation AI analyzes a huge amount of past child-rearing performance data to find the most appropriate response to the current situation. The action suggestion unit suggests the next optimal action based on the results of the analysis by the data analysis unit. For example, it provides specific advice such as, "If the baby is crying, first check the diaper, then try giving him milk." In this way, the child-rearing support system uses the generation AI to support child-rearing, reduce the burden on parents, and save parents and children from rare tragedies.

[0052] The situation input unit analyzes the parent's voice and facial expression in real time, and uses the emotion estimation function to evaluate the parent's stress level and provide appropriate advice. For example, when a parent explains their child's situation, the situation input unit uses a camera and microphone to analyze the parent's facial expression and tone of voice in real time. For example, if the parent is tired, the generative AI will suggest ways to relax. The emotion estimation function analyzes the parent's voice and facial expression to evaluate the parent's stress level. For example, it calculates a stress score based on changes in voice tone and facial expression. This makes it possible to evaluate the parent's stress level and provide appropriate advice, thereby reducing the burden on the parent.

[0053] The situation input unit monitors the child's behavior and condition using cameras and sensors, and the generation AI can automatically recognize and input the situation. For example, the situation input unit monitors the child's behavior using a camera, and the generation AI automatically recognizes the situation. For example, if the child is crying, it detects the crying and notifies the parent. The sensors monitor the child's condition, and the generation AI automatically recognizes the situation. For example, if the body temperature sensor detects a high fever, it notifies the parent. This automatically recognizes the child's behavior and condition, reducing the burden on parents.

[0054] The situation input unit can use voice recognition technology to enable input in natural language to reduce the effort required for parents to input information. For example, when a parent describes their child's situation by voice, the situation input unit automatically converts the description into text using voice recognition technology. For example, the situation can be input by simply saying, "The baby is crying." The voice recognition technology analyzes the parent's voice and enables input in natural language. For example, a voice recognition engine converts the parent's voice into text. This allows the parent to input information in natural language, reducing the effort required for input.

[0055] The status input unit shares information entered by parents with family members and childcare workers, enabling the creation of a system for collaborative support of child rearing. The status input unit, for example, creates a system for sharing a child's status entered by a parent with family members and childcare workers in real time. For example, all family members share information using the same app. The information sharing platform shares information entered by parents with family members and childcare workers. For example, the information is shared in real time using a data sharing protocol. This allows the information entered by parents to be shared, enabling collaborative support of child rearing.

[0056] The status input unit automatically collects the child's health condition and growth records, and can manage the health in cooperation with medical institutions. The status input unit, for example, automatically collects data such as body temperature and heart rate using a sensor that monitors the child's health condition. For example, if the body temperature is high, the medical institution is notified. The growth record automatically collects the child's growth data, and can manage the health in cooperation with medical institutions. For example, the growth curve can be shared with medical institutions and health management can be performed. This allows the child's health condition and growth records to be automatically collected, and health management can be performed in cooperation with medical institutions.

[0057] The situation input unit uses an emotion estimation function to analyze the emotion of the parent when entering input in real time and provide positive feedback, thereby increasing the parent's motivation. The situation input unit, for example, builds a system that analyzes the emotion of the parent when entering input in real time and provides positive feedback. For example, if the parent is tired, an encouraging message is sent. The emotion estimation function analyzes the parent's emotion and provides positive feedback. For example, an emotion recognition algorithm is used to analyze the parent's emotion and generate a positive message. In this way, the parent's emotion can be analyzed in real time and positive feedback can be provided, thereby increasing the parent's motivation.

[0058] The data analysis unit learns a child's individual characteristics and patterns based on past performance data, and is able to provide more personalized advice. The data analysis unit, for example, analyzes past performance data and builds a system that learns a child's individual characteristics and patterns. For example, if a child tends to cry at a certain time of day, it will suggest measures to take at that time of day. The generation AI learns a child's characteristics and patterns based on past data and provides personalized advice. For example, it generates individual advice based on specific behavioral patterns. This makes it possible to learn a child's individual characteristics and patterns and provide more personalized advice.

[0059] When analyzing the data, the data analysis unit takes into account child-rearing methods from different cultures and regions and is able to propose a variety of countermeasures. For example, the data analysis unit incorporates child-rearing methods from different cultures and regions into the data analysis and builds a system that proposes a variety of countermeasures. For example, it provides advice that takes into account the eating habits of each region. The generation AI analyzes data from different cultures and regions and proposes a variety of countermeasures. For example, it proposes child-rearing methods that take cultural differences into account. This makes it possible to propose a variety of countermeasures that take into account child-rearing methods from different cultures and regions.

[0060] The data analysis unit can use the emotion estimation function to analyze changes in parents' emotions from past data and propose emotionally stable countermeasures. The data analysis unit, for example, builds a system that analyzes changes in parents' emotions from past data and proposes emotionally stable countermeasures. For example, it can suggest relaxation methods during times of high stress. The emotion estimation function analyzes changes in parents' emotions and proposes stable countermeasures. For example, it can provide advice to stabilize parents' emotions based on emotion evaluation scores. This makes it possible to analyze changes in parents' emotions and propose emotionally stable countermeasures.

[0061] The data analysis unit can link past performance data with other childcare support apps and services to share and integrate data. The data analysis unit, for example, builds a system that shares and integrates data with other childcare support apps and services. For example, it collects data from multiple apps and manages it centrally. The data sharing platform links past performance data with other apps and services to share and integrate data. For example, it shares data using API integration. This allows past performance data to be linked with other childcare support apps and services to share and integrate data.

[0062] The data analysis unit can display the data analysis results in graphs and charts that are visually easy to understand, allowing parents to intuitively understand. The data analysis unit, for example, builds a system that generates graphs and charts for visually displaying the data analysis results. For example, a child's growth curve is displayed in a graph. The visual display system displays the data analysis results in graphs and charts, allowing parents to intuitively understand. For example, the data is visually displayed using bar graphs and line graphs. This allows the data analysis results to be displayed in graphs and charts that are visually easy to understand, allowing parents to intuitively understand.

[0063] The action suggestion unit can monitor the effects of the proposed action in real time and update the advice as necessary. The action suggestion unit, for example, builds a system that monitors the effects of the proposed action in real time. For example, it monitors the child's reactions with a camera and provides new advice if the action is ineffective. The monitoring system monitors the effects of the proposed action in real time and updates the advice as necessary. For example, it uses a sensor to collect the child's reactions as data, which is then analyzed by the generation AI. This makes it possible to monitor the effects of the proposed action in real time and update the advice as necessary.

[0064] The action suggestion unit collects parental feedback on the proposed actions, allowing the generation AI to continuously learn and improve the accuracy of the suggestions. The action suggestion unit, for example, collects parental feedback on the proposed actions, building a system in which the generation AI continuously learns. For example, a questionnaire is provided in which parents evaluate the effectiveness of actions. The feedback collection system collects parental feedback, and the generation AI learns based on that data. For example, a questionnaire is provided in which parents evaluate the effectiveness of actions, and the generation AI analyzes the results. This allows parental feedback on the proposed actions to be collected, allowing the generation AI to continuously learn and improve the accuracy of the suggestions.

[0065] The action suggestion unit can use the emotion estimation function to evaluate the impact of proposed actions on the parent's emotions and prioritize emotionally positive suggestions. The action suggestion unit, for example, uses the emotion estimation function to build a system that evaluates the impact of proposed actions on the parent's emotions. For example, it evaluates whether the proposed actions will reduce the parent's stress. The emotion estimation function analyzes the parent's emotions and prioritizes positive suggestions. For example, it suggests actions that have a positive impact on the parent's emotions based on the emotion evaluation score. This makes it possible to evaluate the impact of proposed actions on the parent's emotions and prioritize emotionally positive suggestions.

[0066] The action suggestion unit can share the suggested actions with other parents and experts, thereby promoting community-based support. The action suggestion unit, for example, builds a system for sharing the suggested actions with other parents and experts. For example, the suggestions are shared in an online forum and opinions are exchanged. The sharing platform shares the suggested actions with other parents and experts, thereby promoting community-based support. For example, information is shared using a data sharing protocol. This allows the suggested actions to be shared with other parents and experts, thereby promoting community-based support.

[0067] The action suggestion unit can present the suggested action in the form of a visual guide or video, making it easier for parents to carry out the action. The action suggestion unit, for example, builds a system that presents the suggested action in the form of a visual guide or video. For example, specific steps are explained using illustrations or video. The visual guide or video visually shows the suggested action, making it easier for parents to carry out. For example, the format of the video or the content of the guide can be devised so that parents can intuitively understand. In this way, the suggested action can be presented in the form of a visual guide or video, making it easier for parents to carry out the action.

[0068] The action suggestion unit can use the emotion estimation function to evaluate the impact of proposed actions on the parent's emotions and prioritize emotionally positive suggestions. The action suggestion unit, for example, uses the emotion estimation function to build a system that evaluates the impact of proposed actions on the parent's emotions. For example, it evaluates whether the proposed actions will reduce the parent's stress. The emotion estimation function analyzes the parent's emotions and prioritizes positive suggestions. For example, it suggests actions that have a positive impact on the parent's emotions based on the emotion evaluation score. This makes it possible to evaluate the impact of proposed actions on the parent's emotions and prioritize emotionally positive suggestions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The child-rearing support system can also be equipped with a health management unit that monitors the parent's health condition. The health management unit uses sensors to monitor the parent's body temperature, heart rate, sleep patterns, etc., and evaluates their health condition. For example, if the parent is not getting enough sleep, the unit will advise them to get some rest. It can also suggest appropriate nutritional intake and exercise based on the parent's health condition. This helps parents maintain their health and reduces the burden of child-rearing.

[0071] The child-rearing support system can further include a relaxation suggestion unit that estimates the parent's emotions and suggests relaxation methods based on the estimated emotions. The relaxation suggestion unit analyzes the parent's emotions and suggests relaxation methods if the parent is under high stress. For example, it can suggest specific methods such as deep breathing, meditation, or light exercise. It can also suggest music or aromatherapy that will help the parent relax. This can reduce stress for parents and lighten the burden of child-rearing.

[0072] The parenting support system can also be equipped with a learning support unit that monitors a child's learning status and provides appropriate learning advice. The learning support unit monitors a child's learning status using cameras and sensors, and the generation AI automatically analyzes the learning status. For example, it can evaluate a child's level of concentration and understanding when working on a specific task and suggest appropriate learning methods. It can also customize learning content based on the child's interests. This can improve children's learning effectiveness and reduce the burden on parents.

[0073] The child-rearing support system can further include a communication suggestion unit that estimates the parent's emotions and suggests communication methods based on the estimated emotions. The communication suggestion unit analyzes the parent's emotions and suggests emotionally appropriate communication methods. For example, if the parent is irritated, it suggests ways to speak calmly and ways to control emotions. It can also provide specific advice to facilitate smooth parent-child communication. This can improve parent-child communication and reduce the burden of child-rearing.

[0074] The child-rearing support system can further include a support suggestion unit that estimates the parent's emotions and provides appropriate support based on the estimated emotions. The support suggestion unit analyzes the parent's emotions and suggests emotionally appropriate support. For example, if the parent is tired, it can advise the parent to take a rest. Also, if the parent feels lonely, it can suggest encouraging communication with friends and family. This can support the parent's emotions and reduce the burden of child-rearing.

[0075] The child-rearing support system can also be equipped with a play suggestion unit that monitors children's play and suggests appropriate play activities. The play suggestion unit monitors children's play activities using cameras and sensors, and the generation AI automatically analyzes the play situation. For example, if a child is bored with a particular game, it can suggest a new game. It can also customize appropriate play content based on the child's age and interests. This improves the quality of children's play and reduces the burden on parents.

[0076] The child-rearing support system can further include an information provision unit that estimates the parent's emotions and provides appropriate information based on the estimated emotions. The information provision unit analyzes the parent's emotions and provides emotionally appropriate information. For example, if the parent is feeling anxious, it can provide information that gives a sense of security. It can also provide specific solutions or reference information if the parent has questions. This supports the parent's emotions and reduces the burden of child-rearing.

[0077] The child-rearing support system can also be equipped with a meal support unit that monitors children's eating habits and provides appropriate dietary advice. The meal support unit monitors children's eating habits using cameras and sensors, and the generation AI automatically analyzes the eating habits. For example, if a child dislikes a particular ingredient, it can suggest alternative ingredients. It can also suggest meal menus that take children's nutritional balance into consideration. This can improve the quality of children's meals and reduce the burden on parents.

[0078] The child-rearing support system can further include a refreshment suggestion unit that estimates the parent's emotions and suggests appropriate refreshment methods based on the estimated emotions. The refreshment suggestion unit analyzes the parent's emotions and suggests emotionally appropriate refreshment methods. For example, if the parent is tired, it can suggest a short break or a way to refresh. Also, if the parent is feeling stressed, it can suggest specific methods for relieving stress. This supports the parent's emotions and reduces the burden of child-rearing.

[0079] The parenting support system can also be equipped with a sleep support unit that monitors a child's sleep status and provides appropriate sleep advice. The sleep support unit monitors a child's sleep status using cameras and sensors, and the AI ​​generation system automatically analyzes the sleep status. For example, if a child is crying at night, it will identify the cause and suggest appropriate measures. It can also suggest an appropriate sleeping environment based on the child's age and daily routine. This can improve the quality of a child's sleep and reduce the burden on parents.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The situation input unit inputs the current situation. For example, a parent inputs specific situations such as the baby crying, refusing to eat, or constant crying at night. Step 2: The data analysis unit analyzes the current situation input by the situation input unit. For example, the generation AI analyzes a huge amount of past child-rearing performance data and finds the most appropriate response to the current situation. Step 3: The action suggestion unit suggests the next best action based on the results of the analysis by the data analysis unit. For example, it provides specific advice such as, "If the baby is crying, first check the diaper, then try giving him milk."

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a situation input section for inputting a current situation; a data analysis unit that analyzes the current situation input by the situation input unit; an action suggestion unit that suggests the next optimal action to be taken based on the results of the analysis by the data analysis unit; A system characterized by:

2. The situation input unit The child's behavior and condition are monitored using cameras and sensors, and the AI ​​automatically recognizes and inputs the situation.

2. The system of claim 1.

3. The situation input unit Automatically collects children's health and growth records and manages their health in cooperation with medical institutions 2. The system of claim 1.

4. The data analysis unit Learns your child's individual characteristics and patterns based on past performance data to provide more personalized advice 2. The system of claim 1.

5. The action suggestion unit Evaluating the emotional impact of the proposed actions on the parent and prioritizing the emotionally positive suggestions.

2. The system of claim 1.

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