System

Abstract

The system according to the embodiment is intended to comprehensively support the growth and safety of children.SOLUTION: A system includes an action guidance unit, a danger information providing unit, a play suggestion unit, a question answering unit, a physical condition support unit, and a dream support unit. The action instructor uses the generated AI to instruct an action based on the age of the child. The danger information providing unit provides danger information based on the position information of the child. The play suggester acts as a play partner for the child when the parent is doing housework. The question answering unit provides an appropriate answer to the child's difficulty in hearing the parent. The physical condition support unit provides appropriate support when the child's physical condition is poor. The dream support unit introduces an appropriate route based on the dream of the child.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 does not adequately provide a system that comprehensively supports children's growth and safety, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively support the growth and safety of children. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior guidance unit, a danger information provision unit, a play suggestion unit, a question and answer unit, a physical condition support unit, and a dream support unit. The behavior guidance unit uses a generation AI to provide guidance on behavior based on the child's age. The danger information provision unit provides danger information based on the child's location information. The play suggestion unit acts as a playmate for the child while the parent is doing housework. The question and answer unit provides appropriate answers to questions that the child finds difficult to ask their parent. The physical condition support unit provides appropriate support when the child is feeling unwell. The dream support unit introduces appropriate routes based on the child's dreams. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively support the growth and safety of children. [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 provide multifaceted support for children's safety and growth. Based on the child's age, location, and dreams, the child-rearing support system can provide appropriate behavior and danger information, playmates, respond to questions that are difficult to ask parents, and provide support when the child is unwell.

[0029] A child-rearing support system according to an embodiment includes a behavior guidance unit, a danger information provision unit, a play suggestion unit, a question and answer unit, a physical condition support unit, and a dream support unit. The behavior guidance unit provides guidance on behavior based on a child's age. For example, the behavior guidance unit may instruct a 5-year-old child, "Don't play in the street." The behavior guidance unit may instruct a 10-year-old child, "Finish your homework before playing." The behavior guidance unit also uses a generative AI to provide guidance on appropriate behavior according to the child's age. The danger information provision unit provides danger information based on the child's location information. For example, if a child is in a park, the danger information provision unit may provide information such as, "Be careful when playing on the playground equipment." The danger information provision unit also works with police and local governments to collect local danger information and provide it to the child. For example, the information may provide information such as, "Be careful, there have been suspicious people in this area recently." The play suggestion unit acts as a playmate for the child while the parent is doing housework. For example, the play suggestion unit may suggest to the child, "Let's draw a picture together," to help the child have fun. The play suggestion unit uses a generation AI to suggest play activities based on the child's interests and activities. The question answering unit provides appropriate answers to questions that children find difficult to ask their parents. For example, the question answering unit provides a scientific explanation for a question such as, "Why is the sky blue?" The question answering unit uses a generation AI to generate appropriate answers based on the child's questions. The health support unit provides appropriate support when a child is feeling unwell. For example, the health support unit advises on what to do if the child says, "I have a stomachache." The health support unit also uses a generation AI to provide appropriate advice based on the child's health. The dream support unit suggests appropriate routes based on the child's dreams. For example, the dream support unit suggests what kind of studies and experiences are needed to achieve a dream such as "I want to be a doctor in the future." The dream support unit also uses a generation AI to suggest appropriate routes based on the child's dreams. As a result, the parenting support system according to the embodiment can support children's safety and growth in a multifaceted way. For example, children can grow up while playing safely and take appropriate steps toward their future dreams.It also provides an environment where parents can feel at ease and concentrate on housework and work.

[0030] The behavior guidance unit can analyze a child's behavior history and provide individually customized behavior guidance based on past behavior patterns. The behavior guidance unit, for example, collects a child's behavior history and analyzes past behavior patterns. For example, if a child tends to repeat a certain behavior at a certain time of day, it provides specific guidance to improve that behavior. The behavior guidance unit also uses generative AI to provide individually customized behavior guidance based on the child's behavior history. This makes it possible to provide individually customized behavior guidance.

[0031] The behavior guidance unit can provide behavior guidance that takes into account social influences, taking into account the child's friendships and home environment. For example, the behavior guidance unit analyzes the child's friendships and provides behavior guidance that takes into account the influence of friends. For example, if a friend is having a negative influence, the unit provides guidance on how to interact with that friend. The behavior guidance unit also takes into account the home environment and provides behavior guidance based on home rules and family structure. For example, the unit provides guidance to act in accordance with home rules. This makes it possible to provide behavior guidance that takes into account social influences.

[0032] The danger information provision unit can analyze past accident data based on location information, predict future dangers, and issue warnings. The danger information provision unit, for example, collects and analyzes past accident data based on location information. For example, it identifies patterns of accidents that occur frequently in specific locations and issues a warning when approaching those locations. The danger information provision unit also uses generation AI to predict future dangers based on past accident data and issue a warning. This makes it possible to predict future dangers and issue warnings.

[0033] The danger information provision unit can analyze the child's moving speed and direction in real time and issue a warning immediately when danger approaches. The danger information provision unit can, for example, build a system that analyzes the child's moving speed and direction in real time and issues a warning immediately when danger approaches. For example, it can issue a warning if the child suddenly starts running. The danger information provision unit also uses generation AI to predict danger based on the child's moving speed and direction and issue a warning. This makes it possible to detect danger in real time and issue a warning immediately.

[0034] The danger information providing unit can also notify parents' smartphones of danger information based on location information in real time. The danger information providing unit builds a system that notifies parents' smartphones of danger information based on, for example, a child's location information in real time. For example, it notifies parents when a child approaches a dangerous location. The danger information providing unit also uses a generation AI to generate danger information based on the child's location information and notifies the parents. This allows parents to receive danger information in real time.

[0035] The danger information providing unit can provide a map app that visually displays danger information, allowing children to avoid danger on their own. The danger information providing unit develops, for example, a map app that visually displays danger information based on location information. For example, dangerous places can be displayed in red, allowing children to avoid danger on their own. The danger information providing unit also uses a generation AI to generate danger information based on the child's location information and display it on the map app. This allows children to avoid danger on their own.

[0036] The play suggestion unit can suggest individually customized play activities based on the child's interests and hobbies. The play suggestion unit, for example, analyzes the child's interests and hobbies and suggests individually customized play activities based on the analysis. For example, it suggests games using characters that the child likes. The play suggestion unit also uses a generation AI to suggest games based on the child's interests and hobbies. This makes it possible to suggest games based on the child's interests and hobbies.

[0037] The play suggestion unit can periodically update the content of the play so that children do not get bored. The play suggestion unit, for example, periodically updates the content of the play to build a system that prevents children from getting bored. For example, new play ideas are periodically added. The play suggestion unit also periodically updates the content of the play using a generation AI. This allows children to continue playing without getting bored.

[0038] The question answering unit can analyze a child's question history and predict and answer new related questions based on past questions. The question answering unit, for example, analyzes a child's question history and builds a system that predicts new related questions based on past questions. For example, if there have been many science-related questions in the past, it predicts new related questions. The question answering unit also uses a generative AI to provide appropriate answers based on the child's question history. This makes it possible to predict and answer new related questions.

[0039] The question answering unit can provide an answer that incorporates the opinions of experts depending on the content of the question. For example, the question answering unit builds a system that provides an answer that incorporates the opinions of experts depending on the content of the question. For example, an answer that incorporates the opinions of scientists is provided for a question about science. The question answering unit also uses a generation AI to provide an answer that incorporates the opinions of experts based on the content of the question. This makes it possible to provide an answer that incorporates the opinions of experts.

[0040] The health support unit can analyze a child's health history and suggest preventive measures based on past patterns of poor health. For example, the health support unit can build a system that analyzes a child's health history and suggests preventive measures based on past patterns of poor health. For example, if a child is prone to getting sick during a particular season, preventive measures for that season can be suggested. The health support unit also uses generative AI to suggest appropriate preventive measures based on the child's health history. This makes it possible to suggest preventive measures based on past patterns of poor health.

[0041] The health support unit can provide advice that incorporates the opinion of a doctor, depending on the symptoms of poor health. The health support unit will build a system that provides advice that incorporates the opinion of a doctor, depending on the symptoms of poor health. For example, in the case of a fever, the system will suggest an appropriate way to deal with the condition based on the doctor's opinion. The health support unit will also use generative AI to provide advice that incorporates the opinion of a doctor, based on the symptoms of poor health. This makes it possible to provide advice that incorporates the opinion of a doctor.

[0042] The Dream Support Department can suggest specific goal setting and methods for achieving it based on a child's dreams. The Dream Support Department builds a system that suggests specific goal setting and methods for achieving it based on a child's dreams. For example, for a child who wants to become a doctor in the future, it suggests the necessary studies and experiences. The Dream Support Department also uses generative AI to suggest appropriate goal setting and methods for achieving it based on the child's dreams. This makes it possible to suggest specific goal setting and methods for achieving it.

[0043] The Dream Support Department can increase children's motivation by introducing success stories and role models related to their dreams. The Dream Support Department will build a system that introduces success stories and role models related to their dreams to increase children's motivation. For example, it will introduce the case of a successful doctor. The Dream Support Department will also use generative AI to introduce success stories and role models based on children's dreams. This will increase children's motivation by introducing success stories and role models.

[0044] The dream support unit can share support for dreams with parents, enabling them to support their children's dreams. The dream support unit, for example, builds a system that shares support for dreams with parents, enabling parents to support their children's dreams. For example, it notifies parents of information about their children's dreams. The dream support unit also uses generative AI to provide parents with appropriate information based on their children's dreams. This enables parents to support their children's dreams.

[0045] The dream support unit can provide dream-related learning resources and event information, enabling children to grow toward their dreams. For example, the dream support unit builds a system that provides dream-related learning resources and event information, enabling children to grow toward their dreams. For example, a child who wants to become a doctor is provided with medical learning resources. The dream support unit also uses generative AI to provide appropriate learning resources and event information based on the child's dream. This allows children to grow toward their dreams.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The child-rearing support system can further include a nutrition management unit. The nutrition management unit analyzes a child's dietary history and suggests balanced meals. For example, if a child does not eat many vegetables, the nutrition management unit will suggest recipes that include a lot of vegetables. The nutrition management unit also uses generative AI to provide meal plans that take into account the nutrients necessary for the child's growth. This can support the child's health.

[0048] The child-rearing support system can further include a learning support unit. The learning support unit analyzes a child's learning history and provides an individually customized learning plan. For example, the learning support unit may suggest supplementary lessons for subjects in which the child is weak. The learning support unit also uses generative AI to provide appropriate learning resources based on the child's learning progress. This can improve the child's learning effectiveness.

[0049] The child-rearing support system can further include a sleep management unit. The sleep management unit analyzes a child's sleep patterns and suggests an appropriate sleeping environment. For example, if a child tends to stay up late, the sleep management unit may suggest the habit of getting up early and going to bed early. The sleep management unit also uses generative AI to provide appropriate advice based on the child's sleep data. This helps support healthy sleep for children.

[0050] The child-rearing support system can further include an exercise suggestion unit. The exercise suggestion unit analyzes a child's exercise history and proposes an appropriate exercise plan. For example, if a child is not getting enough exercise, the exercise suggestion unit suggests appropriate exercises. The exercise suggestion unit also uses a generative AI to provide an appropriate exercise plan based on the child's exercise data. This can support the child's health.

[0051] The child-rearing support system can further include a housework support unit. The housework support unit makes suggestions to reduce the housework burden on parents. For example, the housework support unit suggests efficient ways to perform housework. The housework support unit also uses generative AI to provide appropriate advice to reduce the housework burden on parents. This can provide an environment where parents can concentrate on raising their children.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The behavioral guidance module guides children based on their age. For example, a 5-year-old child might be told "Don't play in the street," while a 10-year-old child might be told "Finish your homework before playing." Generative AI is used to guide children to appropriate behavior based on their age. Step 2: The danger information provision unit provides danger information based on the child's location information. For example, if a child is in a park, it will provide information such as "Be careful when playing on the playground equipment." It also works with the police and local governments to collect local danger information and provide it to the child. For example, it may provide information such as "Be careful, as suspicious people have been spotted in this area recently." Step 3: The play suggestion module acts as a playmate for the child while the parent is doing housework. For example, it might suggest to the child, "Let's draw a picture together," helping the child have fun. Using generative AI, it makes play suggestions based on the child's interests and activities. Step 4: The question answering section provides appropriate answers to questions that children find difficult to ask their parents. For example, it provides a scientific explanation for questions like, "Why is the sky blue?". Generative AI is used to generate appropriate answers based on the child's question. Step 5: The health support section provides appropriate support when a child is unwell. For example, if a child says, "I have a stomachache," it provides advice on what to do. Generative AI is used to provide appropriate advice based on the child's physical condition. Step 6: The Dream Support Department recommends an appropriate route based on the child's dream. For example, if a child dreams of becoming a doctor in the future, it suggests what kind of studies and experiences are necessary. Using generative AI, it recommends an appropriate route based on the child's dream.

[0054] (Example 2) The child-rearing support system according to an embodiment of the present invention is a system that uses generative AI to provide multifaceted support for children's safety and growth. Based on the child's age, location, and dreams, the child-rearing support system can provide appropriate behavior and danger information, playmates, respond to questions that are difficult to ask parents, and provide support when the child is unwell.

[0055] A child-rearing support system according to an embodiment includes a behavior guidance unit, a danger information provision unit, a play suggestion unit, a question and answer unit, a physical condition support unit, and a dream support unit. The behavior guidance unit provides guidance on behavior based on a child's age. For example, the behavior guidance unit may instruct a 5-year-old child, "Don't play in the street." The behavior guidance unit may instruct a 10-year-old child, "Finish your homework before playing." The behavior guidance unit also uses a generative AI to provide guidance on appropriate behavior according to the child's age. The danger information provision unit provides danger information based on the child's location information. For example, if a child is in a park, the danger information provision unit may provide information such as, "Be careful when playing on the playground equipment." The danger information provision unit also works with police and local governments to collect local danger information and provide it to the child. For example, the information may provide information such as, "Be careful, there have been suspicious people in this area recently." The play suggestion unit acts as a playmate for the child while the parent is doing housework. For example, the play suggestion unit may suggest to the child, "Let's draw a picture together," to help the child have fun. The play suggestion unit uses a generation AI to suggest play activities based on the child's interests and activities. The question answering unit provides appropriate answers to questions that children find difficult to ask their parents. For example, the question answering unit provides a scientific explanation for a question such as, "Why is the sky blue?" The question answering unit uses a generation AI to generate appropriate answers based on the child's questions. The health support unit provides appropriate support when a child is feeling unwell. For example, the health support unit advises on what to do if the child says, "I have a stomachache." The health support unit also uses a generation AI to provide appropriate advice based on the child's health. The dream support unit suggests appropriate routes based on the child's dreams. For example, the dream support unit suggests what kind of studies and experiences are needed to achieve a dream such as "I want to be a doctor in the future." The dream support unit also uses a generation AI to suggest appropriate routes based on the child's dreams. As a result, the parenting support system according to the embodiment can support children's safety and growth in a multifaceted way. For example, children can grow up while playing safely and take appropriate steps toward their future dreams.It also provides an environment where parents can feel at ease and concentrate on housework and work.

[0056] The behavior guidance unit can analyze a child's behavior history and provide individually customized behavior guidance based on past behavior patterns. The behavior guidance unit, for example, collects a child's behavior history and analyzes past behavior patterns. For example, if a child tends to repeat a certain behavior at a certain time of day, it provides specific guidance to improve that behavior. The behavior guidance unit also uses generative AI to provide individually customized behavior guidance based on the child's behavior history. This makes it possible to provide individually customized behavior guidance.

[0057] The behavior guidance unit can provide behavior guidance that takes into account social influences, taking into account the child's friendships and home environment. For example, the behavior guidance unit analyzes the child's friendships and provides behavior guidance that takes into account the influence of friends. For example, if a friend is having a negative influence, the unit provides guidance on how to interact with that friend. The behavior guidance unit also takes into account the home environment and provides behavior guidance based on home rules and family structure. For example, the unit provides guidance to act in accordance with home rules. This makes it possible to provide behavior guidance that takes into account social influences.

[0058] The behavioral guidance unit uses the emotion estimation function to provide behavioral guidance according to the child's emotional state, thereby reducing stress. The behavioral guidance unit, for example, uses the emotion estimation function to analyze the child's emotional state in real time and provide behavioral guidance according to that emotion. For example, if stress is high, the behavioral guidance unit provides guidance on how to relax. The behavioral guidance unit also uses a generative AI to provide appropriate behavioral guidance based on the child's emotional state. This makes it possible to provide behavioral guidance according to the child's emotional state, thereby reducing stress.

[0059] The danger information provision unit can analyze past accident data based on location information, predict future dangers, and issue warnings. The danger information provision unit, for example, collects and analyzes past accident data based on location information. For example, it identifies patterns of accidents that occur frequently in specific locations and issues a warning when approaching those locations. The danger information provision unit also uses generation AI to predict future dangers based on past accident data and issue a warning. This makes it possible to predict future dangers and issue warnings.

[0060] The danger information provision unit can analyze the child's moving speed and direction in real time and issue a warning immediately when danger approaches. The danger information provision unit can, for example, build a system that analyzes the child's moving speed and direction in real time and issues a warning immediately when danger approaches. For example, it can issue a warning if the child suddenly starts running. The danger information provision unit also uses generation AI to predict danger based on the child's moving speed and direction and issue a warning. This makes it possible to detect danger in real time and issue a warning immediately.

[0061] The danger information providing unit uses the emotion estimation function to collect emotion data when a child senses danger, thereby improving the accuracy of the danger information. The danger information providing unit, for example, uses the emotion estimation function to collect emotion data when a child senses danger. For example, it analyzes the child's facial expression or voice when they feel fear. The danger information providing unit also uses a generation AI to improve the accuracy of the danger information based on the child's emotion data. This makes it possible to improve the accuracy of the danger information using emotion data.

[0062] The danger information providing unit can also notify parents' smartphones of danger information based on location information in real time. The danger information providing unit builds a system that notifies parents' smartphones of danger information based on, for example, a child's location information in real time. For example, it notifies parents when a child approaches a dangerous location. The danger information providing unit also uses a generation AI to generate danger information based on the child's location information and notifies the parents. This allows parents to receive danger information in real time.

[0063] The danger information providing unit can provide a map app that visually displays danger information, allowing children to avoid danger on their own. The danger information providing unit develops, for example, a map app that visually displays danger information based on location information. For example, dangerous places can be displayed in red, allowing children to avoid danger on their own. The danger information providing unit also uses a generation AI to generate danger information based on the child's location information and display it on the map app. This allows children to avoid danger on their own.

[0064] The danger information provision unit can use the emotion estimation function to share emotional data when a child senses danger with parents, allowing the parents to take appropriate action. For example, the danger information provision unit uses the emotion estimation function to build a system that shares emotional data when a child senses danger with parents in real time. For example, it notifies parents of data when a child feels fear. In addition, the danger information provision unit uses a generation AI to generate danger information based on the child's emotional data and notify the parent. This allows parents to understand their child's emotional state and take appropriate action.

[0065] The play suggestion unit can suggest individually customized play activities based on the child's interests and hobbies. The play suggestion unit, for example, analyzes the child's interests and hobbies and suggests individually customized play activities based on the analysis. For example, it suggests games using characters that the child likes. The play suggestion unit also uses a generation AI to suggest games based on the child's interests and hobbies. This makes it possible to suggest games based on the child's interests and hobbies.

[0066] The play suggestion unit can periodically update the content of the play so that children do not get bored. The play suggestion unit, for example, periodically updates the content of the play to build a system that prevents children from getting bored. For example, new play ideas are periodically added. The play suggestion unit also periodically updates the content of the play using a generation AI. This allows children to continue playing without getting bored.

[0067] The play suggestion unit uses the emotion estimation function to suggest games that correspond to the child's emotional state, thereby reducing stress. The play suggestion unit, for example, uses the emotion estimation function to analyze the child's emotional state and suggest games that correspond to that emotion. For example, if stress is high, it will suggest games that will help relax. The play suggestion unit also uses a generation AI to suggest appropriate games based on the child's emotional state. This makes it possible to suggest games that correspond to the child's emotional state, thereby reducing stress.

[0068] The question answering unit can analyze a child's question history and predict and answer new related questions based on past questions. The question answering unit, for example, analyzes a child's question history and builds a system that predicts new related questions based on past questions. For example, if there have been many science-related questions in the past, it predicts new related questions. The question answering unit also uses a generative AI to provide appropriate answers based on the child's question history. This makes it possible to predict and answer new related questions.

[0069] The question answering unit can provide an answer that incorporates the opinions of experts depending on the content of the question. For example, the question answering unit builds a system that provides an answer that incorporates the opinions of experts depending on the content of the question. For example, an answer that incorporates the opinions of scientists is provided for a question about science. The question answering unit also uses a generation AI to provide an answer that incorporates the opinions of experts based on the content of the question. This makes it possible to provide an answer that incorporates the opinions of experts.

[0070] The question answering unit can use the emotion estimation function to analyze the emotional state of a child when they ask a question and provide an appropriate answer. For example, the question answering unit uses the emotion estimation function to analyze the emotional state of a child when they ask a question, and builds a system that provides an appropriate answer according to that emotion. For example, if a child is feeling anxious, it provides a reassuring answer. The question answering unit also uses a generation AI to provide an appropriate answer based on the child's emotional state. This makes it possible to provide an appropriate answer according to the emotional state.

[0071] The health support unit can analyze a child's health history and suggest preventive measures based on past patterns of poor health. For example, the health support unit can build a system that analyzes a child's health history and suggests preventive measures based on past patterns of poor health. For example, if a child is prone to getting sick during a particular season, preventive measures for that season can be suggested. The health support unit also uses generative AI to suggest appropriate preventive measures based on the child's health history. This makes it possible to suggest preventive measures based on past patterns of poor health.

[0072] The health support unit can provide advice that incorporates the opinion of a doctor, depending on the symptoms of poor health. The health support unit will build a system that provides advice that incorporates the opinion of a doctor, depending on the symptoms of poor health. For example, in the case of a fever, the system will suggest an appropriate way to deal with the condition based on the doctor's opinion. The health support unit will also use generative AI to provide advice that incorporates the opinion of a doctor, based on the symptoms of poor health. This makes it possible to provide advice that incorporates the opinion of a doctor.

[0073] The physical condition support unit can use the emotion estimation function to analyze the emotional state of a child when they are unwell and provide appropriate support. For example, the physical condition support unit uses the emotion estimation function to analyze the emotional state of a child when they are unwell and builds a system that provides appropriate support according to that emotion. For example, if a child is feeling anxious, it provides reassuring support. The physical condition support unit also uses a generation AI to provide appropriate support based on the child's emotional state. This makes it possible to provide appropriate support according to the emotional state.

[0074] The Dream Support Department can suggest specific goal setting and methods for achieving it based on a child's dreams. The Dream Support Department builds a system that suggests specific goal setting and methods for achieving it based on a child's dreams. For example, for a child who wants to become a doctor in the future, it suggests the necessary studies and experiences. The Dream Support Department also uses generative AI to suggest appropriate goal setting and methods for achieving it based on the child's dreams. This makes it possible to suggest specific goal setting and methods for achieving it.

[0075] The Dream Support Department can increase children's motivation by introducing success stories and role models related to their dreams. The Dream Support Department will build a system that introduces success stories and role models related to their dreams to increase children's motivation. For example, it will introduce the case of a successful doctor. The Dream Support Department will also use generative AI to introduce success stories and role models based on children's dreams. This will increase children's motivation by introducing success stories and role models.

[0076] The dream support unit can use the emotion estimation function to analyze a child's emotional state regarding their dream and provide appropriate support. For example, the dream support unit uses the emotion estimation function to analyze a child's emotional state regarding their dream and builds a system that provides appropriate support according to that emotion. For example, if a child feels anxious about their dream, it provides reassuring support. The dream support unit also uses a generation AI to provide appropriate support based on the child's emotional state. This makes it possible to provide appropriate support according to the emotional state.

[0077] The dream support unit can share support for dreams with parents, enabling them to support their children's dreams. The dream support unit, for example, builds a system that shares support for dreams with parents, enabling parents to support their children's dreams. For example, it notifies parents of information about their children's dreams. The dream support unit also uses generative AI to provide parents with appropriate information based on their children's dreams. This enables parents to support their children's dreams.

[0078] The dream support unit can provide dream-related learning resources and event information, enabling children to grow toward their dreams. For example, the dream support unit builds a system that provides dream-related learning resources and event information, enabling children to grow toward their dreams. For example, a child who wants to become a doctor is provided with medical learning resources. The dream support unit also uses generative AI to provide appropriate learning resources and event information based on the child's dream. This allows children to grow toward their dreams.

[0079] The dream support unit can use the emotion estimation function to share the emotional state of the child regarding the dream with the parent, allowing the parent to provide appropriate support. For example, the dream support unit uses the emotion estimation function to build a system that shares the emotional state of the child regarding the dream with the parent in real time. For example, if the child feels anxious about the dream, the dream support unit notifies the parent. The dream support unit also uses a generative AI to provide appropriate information to the parent based on the child's emotional state. This allows the parent to understand the emotional state of the child regarding the dream and provide appropriate support.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The child-rearing support system can further include a nutrition management unit. The nutrition management unit analyzes a child's dietary history and suggests balanced meals. For example, if a child does not eat many vegetables, the nutrition management unit will suggest recipes that include a lot of vegetables. The nutrition management unit also uses generative AI to provide meal plans that take into account the nutrients necessary for the child's growth. This can support the child's health.

[0082] The child-rearing support system can further include a learning support unit. The learning support unit analyzes a child's learning history and provides an individually customized learning plan. For example, the learning support unit may suggest supplementary lessons for subjects in which the child is weak. The learning support unit also uses generative AI to provide appropriate learning resources based on the child's learning progress. This can improve the child's learning effectiveness.

[0083] The child-rearing support system can further include a sleep management unit. The sleep management unit analyzes a child's sleep patterns and suggests an appropriate sleeping environment. For example, if a child tends to stay up late, the sleep management unit may suggest the habit of getting up early and going to bed early. The sleep management unit also uses generative AI to provide appropriate advice based on the child's sleep data. This helps support healthy sleep for children.

[0084] The parenting support system can also use an emotion estimation function to provide learning support based on a child's emotional state. For example, the learning support unit can suggest ways to relax if a child is feeling stressed. The learning support unit also uses generative AI to provide appropriate learning resources based on the child's emotional state. This enables learning support tailored to the child's emotional state, improving learning effectiveness.

[0085] The parenting support system can also use emotion estimation functions to manage nutrition based on a child's emotional state. For example, if a child is feeling stressed, the nutrition management unit can suggest a relaxing meal. The nutrition management unit also uses generative AI to provide an appropriate meal plan based on the child's emotional state. This enables nutrition management according to emotional states and supports the child's health.

[0086] The parenting support system can also use emotion estimation functions to manage sleep based on a child's emotional state. For example, the sleep management unit can suggest ways to reassure a child if they are feeling anxious. The sleep management unit also uses generative AI to provide an appropriate sleeping environment based on the child's emotional state. This enables sleep management according to the child's emotional state, supporting healthy sleep for children.

[0087] The parenting support system can also use the emotion estimation function to suggest games based on a child's emotional state. For example, if a child is feeling stressed, the game suggestion unit will suggest games that will help them relax. The game suggestion unit also uses a generation AI to suggest appropriate games based on the child's emotional state. This makes it possible to suggest games that are appropriate for the child's emotional state, helping to reduce stress.

[0088] The child-rearing support system can also use the emotion estimation function to provide physical condition support based on the child's emotional state. For example, the physical condition support unit can provide reassuring support if the child is feeling anxious. The physical condition support unit also uses generative AI to provide appropriate support based on the child's emotional state. This makes it possible to provide physical condition support according to the child's emotional state, thereby supporting the child's health.

[0089] The child-rearing support system can further include an exercise suggestion unit. The exercise suggestion unit analyzes a child's exercise history and proposes an appropriate exercise plan. For example, if a child is not getting enough exercise, the exercise suggestion unit suggests appropriate exercises. The exercise suggestion unit also uses a generative AI to provide an appropriate exercise plan based on the child's exercise data. This can support the child's health.

[0090] The child-rearing support system can further include a housework support unit. The housework support unit makes suggestions to reduce the housework burden on parents. For example, the housework support unit suggests efficient ways to perform housework. The housework support unit also uses generative AI to provide appropriate advice to reduce the housework burden on parents. This can provide an environment where parents can concentrate on raising their children.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The behavioral guidance module guides children based on their age. For example, a 5-year-old child might be told "Don't play in the street," while a 10-year-old child might be told "Finish your homework before playing." Generative AI is used to guide children to appropriate behavior based on their age. Step 2: The danger information provision unit provides danger information based on the child's location information. For example, if a child is in a park, it will provide information such as "Be careful when playing on the playground equipment." It also works with the police and local governments to collect local danger information and provide it to the child. For example, it may provide information such as "Be careful, as suspicious people have been spotted in this area recently." Step 3: The play suggestion module acts as a playmate for the child while the parent is doing housework. For example, it might suggest to the child, "Let's draw a picture together," helping the child have fun. Using generative AI, it makes play suggestions based on the child's interests and activities. Step 4: The question answering section provides appropriate answers to questions that children find difficult to ask their parents. For example, it provides a scientific explanation for questions like, "Why is the sky blue?". Generative AI is used to generate appropriate answers based on the child's question. Step 5: The health support section provides appropriate support when a child is unwell. For example, if a child says, "I have a stomachache," it provides advice on what to do. Generative AI is used to provide appropriate advice based on the child's physical condition. Step 6: The Dream Support Department recommends an appropriate route based on the child's dream. For example, if a child dreams of becoming a doctor in the future, it suggests what kind of studies and experiences are necessary. Using generative AI, it recommends an appropriate route based on the child's dream.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. Using generative AI, A behavioral guidance department that provides guidance on behavior based on the child's age; a danger information providing unit that provides danger information based on location information of a child; A play suggestion section that acts as a play partner for children while parents are doing housework, a question-answering section that provides appropriate answers to questions that children find difficult to ask their parents; A health support department that provides appropriate support when children are unwell, and A dream support department that introduces appropriate routes based on children's dreams. A system characterized by:

2. The behavioral guidance department Analyzing the child's behavioral history and providing individually customized behavioral guidance based on past behavioral patterns 2. The system of claim 1.

3. The danger information providing unit Analyze past accident data based on the location information, predict future dangers, and issue warnings.

2. The system of claim 1.

4. The play suggestion unit Providing personalized play suggestions based on the child's interests and hobbies 2. The system of claim 1.

5. The question answering unit Analyzing the child's question history and predicting and answering relevant new questions based on past questions 2. The system of claim 1.

6. The physical condition support unit includes: Analyze the child's health history and suggest preventative measures based on past patterns of poor health 2. The system of claim 1.

7. The Dream Support Department: Based on the child's dreams, we suggest specific goals and how to achieve them.

2. The system of claim 1.

8. The behavioral guidance department Provide behavioral guidance according to the child's emotional state to reduce stress 2. The system of claim 1.

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