System

Abstract

An object of a system according to an embodiment is to eliminate anxiety and worries about childcare and provide an individual child care document.SOLUTION: A system according to an embodiment includes a question and answer part, a childcare note generation part, a consultation part, and a voice support part. The question answering unit answers a question of the user. A child care document generation part generates an individual child care document on the basis of the information obtained by the question answering part. The consultation unit receives a 24-hour consultation. The voice support unit provides support by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not provided sufficient support to alleviate concerns and worries about child-rearing, and it has been difficult to provide individual child-rearing books.

[0005] The system according to the embodiment aims to alleviate concerns and worries about child-rearing and to provide individualized child-rearing books. [Means for solving the problem]

[0006] The system according to the embodiment includes a question and answer unit, a parenting book generation unit, a consultation unit, and an audio support unit. The question and answer unit responds to questions from users. The parenting book generation unit generates an individual parenting book based on information obtained by the question and answer unit. The consultation unit accepts consultations 24 hours a day. The audio support unit provides support via audio. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate concerns and worries about child-rearing and provide individualized child-rearing books. [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 AI system according to an embodiment of the present invention is a system that supports the anxieties and worries of the parenting generation. This system provides various support related to child-rearing by linking a smart speaker with an app. This allows the child-rearing support AI system to comprehensively support the anxieties and worries of the parenting generation.

[0029] The parenting support AI system according to the embodiment includes a question-answering unit, a parenting book generation unit, a consultation unit, and a voice support unit. The question-answering unit responds to user questions. For example, the generation AI responds to questions such as "Why do babies cry at night?" based on parenting books and expert knowledge. The question-answering unit can also analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user has asked many questions about "babies crying at night," the system can suggest related predictive questions. The parenting book generation unit generates individual parenting books based on information obtained by the question-answering unit. For example, the generation AI accumulates data provided by the user and collects data such as a child's growth record, developmental speed, and health condition, and generates an individually customized parenting book based on that data. The parenting book generation unit can also learn a parent's parenting style and values ​​and generate a customized parenting book based on that. For example, the system can suggest parenting methods that match the parent's values. The consultation unit accepts consultations 24 hours a day. For example, the generation AI can act as a mom / dad friend that parents can talk to freely and consult with 24 hours a day. The consultation unit can also analyze a parent's past conversation history and suggest conversations based on individual interests and concerns. For example, if a parent has talked a lot about "children's meals" in the past, the AI ​​can suggest related conversations. The voice support unit provides voice support. For example, in response to a request such as "Teach me how to change my child's diaper" via a smart speaker, the AI ​​can explain the procedure aloud. The voice support unit can also analyze a parent's tone of voice and speaking style to provide appropriate solutions based on the level of urgency. For example, if the parent's voice becomes higher or the speaking speed increases, the AI ​​can determine that the level of urgency is high. This allows the parenting support AI system according to the embodiment to comprehensively support the anxieties and worries of parenting generations. For example, even parents who do not have time to read parenting books can quickly obtain the necessary information through the generation AI. Furthermore, concerns about their child's growth and development speed can be alleviated through specialized parenting books provided by the generation AI. Furthermore, even parents who have no one to talk to or consult with can reduce their sense of loneliness by having the generative AI available 24 hours a day.

[0030] The question answering unit can analyze the user's past question history and suggest predictive questions based on individual trends. For example, in the question answering unit, the generation AI analyzes the user's past question history to identify frequently asked questions and interests. For example, if the user has asked many questions about "baby crying at night," the generation AI can suggest related predictive questions. The question answering unit can also identify individual trends based on the user's past question history and provide related information. For example, it can use frequency analysis or pattern recognition technology to identify the user's interests and suggest predictive questions based on them. This makes it possible to provide information that meets the user's interests by analyzing the user's past question history and suggesting predictive questions based on individual trends.

[0031] The question-answering unit can analyze the user's tone of voice or speaking style, estimate their stress or fatigue level, and provide appropriate advice. For example, the generation AI in the question-answering unit analyzes the user's tone of voice and speaking style in real time to estimate their stress or fatigue level. For example, if their voice gets lower or their speaking style becomes slower, it can be determined that their stress level is increasing. The generation AI in the question-answering unit can also analyze the user's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, it can use voice frequency analysis to evaluate the pitch and intensity of their voice and estimate their stress or fatigue level. It can also use emotion recognition technology to analyze the user's speaking pattern and estimate their stress or fatigue level. This supports the user's health management by analyzing the user's tone of voice and speaking style, estimating their stress or fatigue level, and providing appropriate advice.

[0032] The parenting book generation unit can analyze a child's growth data and provide a prediction of future development. For example, the parenting book generation unit builds a system in which a generation AI analyzes a child's growth data and provides a prediction of future development. For example, a future growth curve is predicted based on height and weight data. The parenting book generation unit can also analyze a child's growth data using a statistical model or machine learning algorithm. For example, a statistical model is used to predict future growth from past data. A machine learning algorithm can also be used to learn a child's growth patterns and predict future development. This makes it easier for parents to understand their child's growth by analyzing a child's growth data and providing a prediction of future development.

[0033] The parenting book generation unit can learn parents' parenting styles or values ​​and generate a customized parenting book based on them. For example, the parenting book generation unit builds a system in which a generation AI learns parents' parenting styles and values ​​and generates a customized parenting book based on them. For example, it suggests a parenting method that matches the parents' values. The parenting book generation unit can also collect data through questionnaires and interviews so that the generation AI can learn parents' parenting styles and values. For example, it can collect information about parents' parenting styles and values ​​through questionnaires and generate a customized parenting book based on that information. It can also understand parents' parenting styles and values ​​in detail through interviews and customize the parenting book based on that information. In this way, the parenting style and values ​​of parents can be learned and a customized parenting book based on that information can be generated, providing the optimal parenting method for parents.

[0034] The consultation unit can analyze the parent's past conversation history and suggest conversations based on their individual interests or concerns. For example, the generation AI analyzes the parent's past conversation history to identify their individual interests or concerns. For example, if the parent has talked a lot about "children's meals" in the past, the consultation unit can suggest related conversations. The generation AI can also use a text database or voice database to store the parent's past conversation history and perform analysis based on that. For example, the conversation history stored in the text database can be analyzed using text mining technology to identify the parent's interests and concerns. The conversation history stored in the voice database can also be analyzed using voice analysis technology to identify the parent's interests and concerns. In this way, the system can analyze the parent's past conversation history and suggest conversations based on their individual interests and concerns, thereby providing support that meets the parent's needs.

[0035] The consultation unit can analyze the parent's tone of voice or speaking style and provide an appropriate response according to their emotional state. For example, the generation AI in the consultation unit analyzes the parent's tone of voice and speaking style in real time to estimate their emotional state. For example, if their voice gets lower or their speech becomes slower, it can be determined that their stress is increasing. The consultation unit can also analyze the parent's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, voice frequency analysis can be used to evaluate the pitch and intensity of the voice to estimate their emotional state. Emotion recognition technology can also be used to analyze the parent's speaking patterns to estimate their emotional state. In this way, the system can analyze the parent's tone of voice and speaking style and provide an appropriate response according to their emotional state, thereby providing psychological support to the parent.

[0036] The voice support unit can analyze the parent's past request history and provide immediate responses to frequently asked questions. For example, the generation AI analyzes the parent's past request history and identifies frequently asked questions. For example, if the parent has made many requests in the past about "how to change diapers," the voice support unit can provide relevant immediate responses. The generation AI can also analyze the request history stored in the database using text mining technology to identify frequently asked questions. For example, the voice support unit can extract frequently asked questions based on past request data and provide immediate responses to them. This allows the system to quickly respond to the parent's needs by analyzing the parent's past request history and providing immediate responses to frequently asked questions.

[0037] The voice support unit can analyze the parent's tone of voice or speaking style and provide appropriate countermeasures according to the level of urgency. For example, the generation AI of the voice support unit analyzes the parent's tone of voice and speaking style in real time to estimate the level of urgency. For example, if the parent's voice gets higher or the speaking speed increases, it determines that the level of urgency is high. The voice support unit can also analyze the parent's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, the voice frequency analysis can be used to evaluate the pitch and intensity of the voice to estimate the level of urgency. Emotion recognition technology can also be used to analyze the parent's speaking pattern to estimate the level of urgency. In this way, the system supports parents in responding to emergencies by analyzing the parent's tone of voice and speaking style and providing appropriate countermeasures according to the level of urgency.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The question and answering unit can also provide the latest research results and statistical data on child-rearing in response to user questions. For example, by providing information based on the latest child-rearing research papers, users can select child-rearing methods that are based on scientific evidence. The question and answering unit can also aggregate and provide other parents' experiences and community opinions in response to user questions. For example, by referring to the opinions and advice of parents with the same concerns, users can solve problems from multiple perspectives. Furthermore, the question and answering unit can also provide links to video tutorials and online seminars on child-rearing in response to user questions. For example, users can gain deeper knowledge by watching videos of child-rearing seminars led by experts.

[0040] The question answering unit can analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user is interested in a specific child-rearing topic, it can suggest new research results or topics related to that topic. The question answering unit can also suggest predictive questions based on the season or event, based on the user's question history. For example, in the summer, it suggests questions such as "How to prevent heatstroke in babies" and "Things to be careful about when playing outside in the summer." Furthermore, the question answering unit can also suggest predictive questions based on the user's question history, based on the child's developmental stage. For example, when it is time for a baby to start solid food, it suggests questions such as "How to introduce solid food" and "How to deal with allergies."

[0041] The parenting book generator can analyze a child's growth data and provide a prediction of future development. For example, it can predict a future growth curve based on height and weight data. The parenting book generator can also provide appropriate parenting advice according to a child's developmental stage. For example, when a baby starts crawling, it can provide advice on creating a safe environment and how to play. Furthermore, the parenting book generator can provide an individually customized parenting plan based on the child's health condition and developmental speed. For example, it can suggest parenting methods that address a child's allergies or specific health issues.

[0042] The parenting book generation unit can learn the parenting style or values ​​of the parents and generate a customized parenting book based on that. For example, it can suggest a parenting method that matches the parents' values. The parenting book generation unit can also collect data through questionnaires or interviews to learn the parents' parenting style and values. For example, it can collect information about the parents' parenting style and values ​​through questionnaires and generate a customized parenting book based on that information. It can also understand the parents' parenting style and values ​​in detail through interviews and customize the parenting book based on that information. In this way, the parenting style and values ​​of the parents can be learned and a customized parenting book based on that information can be generated, providing the optimal parenting method for the parents.

[0043] The consultation unit can analyze the parent's past conversation history and suggest conversations based on individual interests or concerns. For example, if the parent has talked a lot about "children's meals" in the past, related conversations will be suggested. The consultation unit can also use the generation AI to store the parent's past conversation history using a text database or voice database, and perform analysis based on this. For example, the conversation history stored in the text database can be analyzed using text mining technology to identify the parent's interests and concerns. The conversation history stored in the voice database can also be analyzed using voice analysis technology to identify the parent's interests and concerns. This allows the system to analyze the parent's past conversation history and suggest conversations based on the parent's individual interests and concerns, thereby providing support that meets the parent's needs.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The question answering unit responds to the user's question. For example, the generation AI will answer a question such as "Why do babies cry at night?" based on parenting books and expert knowledge. The question answering unit can also analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user has asked many questions about "babies crying at night" in the past, the generation AI will suggest related predictive questions. Step 2: The parenting book generator generates an individual parenting book based on the information obtained by the question and answer unit. For example, the generation AI accumulates data provided by the user, collecting data such as the child's growth record, developmental rate, and health condition, and generates an individually customized parenting book based on that data. The parenting book generator can also learn the parenting style and values ​​of the parents and generate a customized parenting book based on that. For example, it can suggest parenting methods that match the parents' values. Step 3: The consultation section accepts consultations 24 hours a day. For example, the generation AI can act as a mommy (dad) friend that parents can talk to freely and talk to about anything, 24 hours a day. The consultation section can also analyze the parent's past conversation history and suggest conversations based on individual interests and worries. For example, if there has been a lot of talk about "children's meals" in the past, it will suggest related conversations. Step 4: The voice support unit provides support via voice. For example, the generation AI responds to requests such as "Teach me how to change a diaper" through a smart speaker by explaining the procedure via voice. The voice support unit can also analyze the parent's tone of voice and speaking style to provide appropriate measures depending on the level of urgency. For example, if the parent's voice gets higher or the speaking speed increases, it will determine that the level of urgency is high.

[0046] (Example 2) The child-rearing support AI system according to an embodiment of the present invention is a system that supports the anxieties and worries of the parenting generation. This system provides various support related to child-rearing by linking a smart speaker with an app. This allows the child-rearing support AI system to comprehensively support the anxieties and worries of the parenting generation.

[0047] The parenting support AI system according to the embodiment includes a question-answering unit, a parenting book generation unit, a consultation unit, and a voice support unit. The question-answering unit responds to user questions. For example, the generation AI responds to questions such as "Why do babies cry at night?" based on parenting books and expert knowledge. The question-answering unit can also analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user has asked many questions about "babies crying at night," the system can suggest related predictive questions. The parenting book generation unit generates individual parenting books based on information obtained by the question-answering unit. For example, the generation AI accumulates data provided by the user and collects data such as a child's growth record, developmental speed, and health condition, and generates an individually customized parenting book based on that data. The parenting book generation unit can also learn a parent's parenting style and values ​​and generate a customized parenting book based on that. For example, the system can suggest parenting methods that match the parent's values. The consultation unit accepts consultations 24 hours a day. For example, the generation AI can act as a mom / dad friend that parents can talk to freely and consult with 24 hours a day. The consultation unit can also analyze a parent's past conversation history and suggest conversations based on individual interests and concerns. For example, if a parent has talked a lot about "children's meals" in the past, the AI ​​can suggest related conversations. The voice support unit provides voice support. For example, in response to a request such as "Teach me how to change my child's diaper" via a smart speaker, the AI ​​can explain the procedure aloud. The voice support unit can also analyze a parent's tone of voice and speaking style to provide appropriate solutions based on the level of urgency. For example, if the parent's voice becomes higher or the speaking speed increases, the AI ​​can determine that the level of urgency is high. This allows the parenting support AI system according to the embodiment to comprehensively support the anxieties and worries of parenting generations. For example, even parents who do not have time to read parenting books can quickly obtain the necessary information through the generation AI. Furthermore, concerns about their child's growth and development speed can be alleviated through specialized parenting books provided by the generation AI. Furthermore, even parents who have no one to talk to or consult with can reduce their sense of loneliness by having the generative AI available 24 hours a day.

[0048] The question answering unit can analyze the user's past question history and suggest predictive questions based on individual trends. For example, in the question answering unit, the generation AI analyzes the user's past question history to identify frequently asked questions and interests. For example, if the user has asked many questions about "baby crying at night," the generation AI can suggest related predictive questions. The question answering unit can also identify individual trends based on the user's past question history and provide related information. For example, it can use frequency analysis or pattern recognition technology to identify the user's interests and suggest predictive questions based on them. This makes it possible to provide information that meets the user's interests by analyzing the user's past question history and suggesting predictive questions based on individual trends.

[0049] The question-answering unit can analyze the user's tone of voice or speaking style, estimate their stress or fatigue level, and provide appropriate advice. For example, the generation AI in the question-answering unit analyzes the user's tone of voice and speaking style in real time to estimate their stress or fatigue level. For example, if their voice gets lower or their speaking style becomes slower, it can be determined that their stress level is increasing. The generation AI in the question-answering unit can also analyze the user's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, it can use voice frequency analysis to evaluate the pitch and intensity of their voice and estimate their stress or fatigue level. It can also use emotion recognition technology to analyze the user's speaking pattern and estimate their stress or fatigue level. This supports the user's health management by analyzing the user's tone of voice and speaking style, estimating their stress or fatigue level, and providing appropriate advice.

[0050] The question answering unit uses the emotion estimation function to generate answers according to the user's emotional state, thereby reducing parental anxiety. The question answering unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate appropriate answers. For example, if the user is feeling anxious, it provides an answer that gives a sense of security. The question answering unit can also analyze the user's emotional state using voice analysis and facial expression recognition technology. For example, it can use voice analysis to evaluate the tone of voice and speaking style to estimate the emotional state. It can also use facial expression recognition technology to analyze the user's facial expression and estimate the emotional state. In this way, the emotion estimation function can be used to generate answers according to the user's emotional state, reducing parental anxiety and providing psychological support to the user.

[0051] The parenting book generation unit can analyze a child's growth data and provide a prediction of future development. For example, the parenting book generation unit builds a system in which a generation AI analyzes a child's growth data and provides a prediction of future development. For example, a future growth curve is predicted based on height and weight data. The parenting book generation unit can also analyze a child's growth data using a statistical model or machine learning algorithm. For example, a statistical model is used to predict future growth from past data. A machine learning algorithm can also be used to learn a child's growth patterns and predict future development. This makes it easier for parents to understand their child's growth by analyzing a child's growth data and providing a prediction of future development.

[0052] The parenting book generation unit can learn parents' parenting styles or values ​​and generate a customized parenting book based on them. For example, the parenting book generation unit builds a system in which a generation AI learns parents' parenting styles and values ​​and generates a customized parenting book based on them. For example, it suggests a parenting method that matches the parents' values. The parenting book generation unit can also collect data through questionnaires and interviews so that the generation AI can learn parents' parenting styles and values. For example, it can collect information about parents' parenting styles and values ​​through questionnaires and generate a customized parenting book based on that information. It can also understand parents' parenting styles and values ​​in detail through interviews and customize the parenting book based on that information. In this way, the parenting style and values ​​of parents can be learned and a customized parenting book based on that information can be generated, providing the optimal parenting method for parents.

[0053] The parenting book generation unit can use the emotion estimation function to incorporate parenting advice into the parenting book according to the parent's emotional state. For example, the parenting book generation unit uses the emotion estimation function to analyze the parent's emotional state and builds a system that incorporates parenting advice into the parenting book based on that data. For example, if the parent is feeling stressed, the generation AI in the parenting book generation unit can also analyze the parent's emotional state using voice analysis and facial expression recognition technology. For example, voice analysis can be used to evaluate the tone of voice and speaking style to estimate the emotional state. Facial expression recognition technology can also be used to analyze the parent's facial expressions to estimate the emotional state. In this way, the emotion estimation function can be used to incorporate parenting advice into the parenting book according to the parent's emotional state, providing psychological support to the parent.

[0054] The consultation unit can analyze the parent's past conversation history and suggest conversations based on their individual interests or concerns. For example, the generation AI analyzes the parent's past conversation history to identify their individual interests or concerns. For example, if the parent has talked a lot about "children's meals" in the past, the consultation unit can suggest related conversations. The generation AI can also use a text database or voice database to store the parent's past conversation history and perform analysis based on that. For example, the conversation history stored in the text database can be analyzed using text mining technology to identify the parent's interests and concerns. The conversation history stored in the voice database can also be analyzed using voice analysis technology to identify the parent's interests and concerns. In this way, the system can analyze the parent's past conversation history and suggest conversations based on their individual interests and concerns, thereby providing support that meets the parent's needs.

[0055] The consultation unit can analyze the parent's tone of voice or speaking style and provide an appropriate response according to their emotional state. For example, the generation AI in the consultation unit analyzes the parent's tone of voice and speaking style in real time to estimate their emotional state. For example, if their voice gets lower or their speech becomes slower, it can be determined that their stress is increasing. The consultation unit can also analyze the parent's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, voice frequency analysis can be used to evaluate the pitch and intensity of the voice to estimate their emotional state. Emotion recognition technology can also be used to analyze the parent's speaking patterns to estimate their emotional state. In this way, the system can analyze the parent's tone of voice and speaking style and provide an appropriate response according to their emotional state, thereby providing psychological support to the parent.

[0056] The counseling unit can use the emotion estimation function to provide words of encouragement or comfort according to the parent's emotional state. For example, the counseling unit uses the emotion estimation function to analyze the parent's emotional state in real time and provide appropriate words of encouragement or comfort. For example, if the parent is feeling anxious, the counseling unit provides words that give a sense of security. The counseling unit can also use the generation AI to analyze the parent's emotional state using voice analysis and facial expression recognition technology. For example, voice analysis can be used to evaluate the tone of voice and speaking style to estimate the emotional state. Facial expression recognition technology can also be used to analyze the parent's facial expressions to estimate the emotional state. In this way, the emotion estimation function can be used to provide words of encouragement or comfort according to the parent's emotional state, thereby providing psychological support to the parent.

[0057] The voice support unit can analyze the parent's past request history and provide immediate responses to frequently asked questions. For example, the generation AI analyzes the parent's past request history and identifies frequently asked questions. For example, if the parent has made many requests in the past about "how to change diapers," the voice support unit can provide relevant immediate responses. The generation AI can also analyze the request history stored in the database using text mining technology to identify frequently asked questions. For example, the voice support unit can extract frequently asked questions based on past request data and provide immediate responses to them. This allows the system to quickly respond to the parent's needs by analyzing the parent's past request history and providing immediate responses to frequently asked questions.

[0058] The voice support unit can analyze the parent's tone of voice or speaking style and provide appropriate countermeasures according to the level of urgency. For example, the generation AI of the voice support unit analyzes the parent's tone of voice and speaking style in real time to estimate the level of urgency. For example, if the parent's voice gets higher or the speaking speed increases, it determines that the level of urgency is high. The voice support unit can also analyze the parent's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, the voice frequency analysis can be used to evaluate the pitch and intensity of the voice to estimate the level of urgency. Emotion recognition technology can also be used to analyze the parent's speaking pattern to estimate the level of urgency. In this way, the system supports parents in responding to emergencies by analyzing the parent's tone of voice and speaking style and providing appropriate countermeasures according to the level of urgency.

[0059] The audio support unit can use the emotion estimation function to provide audio guidance that gives a sense of security according to the parent's emotional state. For example, the audio support unit can use the emotion estimation function to analyze the parent's emotional state in real time and provide audio guidance that gives an appropriate sense of security. For example, if the parent is feeling anxious, the audio support unit can provide audio guidance that gives a sense of security. The audio support unit can also use the generation AI to analyze the parent's emotional state using voice analysis and facial expression recognition technology. For example, the voice analysis can be used to evaluate the tone of voice and speaking style to estimate the emotional state. The facial expression recognition technology can also be used to analyze the parent's facial expression to estimate the emotional state. In this way, the emotion estimation function can be used to provide audio guidance that gives a sense of security according to the parent's emotional state, thereby providing psychological support to the parent.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The question and answering unit can also provide the latest research results and statistical data on child-rearing in response to user questions. For example, by providing information based on the latest child-rearing research papers, users can select child-rearing methods that are based on scientific evidence. The question and answering unit can also aggregate and provide other parents' experiences and community opinions in response to user questions. For example, by referring to the opinions and advice of parents with the same concerns, users can solve problems from multiple perspectives. Furthermore, the question and answering unit can also provide links to video tutorials and online seminars on child-rearing in response to user questions. For example, users can gain deeper knowledge by watching videos of child-rearing seminars led by experts.

[0062] The question answering unit can analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user is interested in a specific child-rearing topic, it can suggest new research results or topics related to that topic. The question answering unit can also suggest predictive questions based on the season or event, based on the user's question history. For example, in the summer, it suggests questions such as "How to prevent heatstroke in babies" and "Things to be careful about when playing outside in the summer." Furthermore, the question answering unit can also suggest predictive questions based on the user's question history, based on the child's developmental stage. For example, when it is time for a baby to start solid food, it suggests questions such as "How to introduce solid food" and "How to deal with allergies."

[0063] The question and answer unit can analyze the user's tone of voice or speaking style to estimate the user's stress or fatigue level and provide appropriate advice. For example, if it is estimated that the user is tired, it can provide advice on relaxation methods and the importance of rest. The question and answer unit can also analyze the user's tone of voice or speaking style to estimate that the user is under stress and provide information on stress relief methods and mental health. Furthermore, if the question and answer unit analyzes the user's tone of voice or speaking style and estimates that the user is highly fatigued, it can introduce support services and resources to reduce the burden of childcare. For example, it can introduce local childcare support groups or online counseling services.

[0064] The question answering unit can use the emotion estimation function to generate answers according to the user's emotional state and alleviate the parent's anxiety. For example, if the user is feeling anxious, it can provide an answer that gives a sense of security. The question answering unit can also analyze the user's emotional state and provide words of encouragement or comfort. For example, if the user is feeling depressed, it can provide words of encouragement or positive messages. Furthermore, the question answering unit can analyze the user's emotional state and suggest relaxing music or a meditation guide. For example, if the user is feeling stressed, it can provide a link to relaxing music or a meditation guide.

[0065] The parenting book generator can analyze a child's growth data and provide a prediction of future development. For example, it can predict a future growth curve based on height and weight data. The parenting book generator can also provide appropriate parenting advice according to a child's developmental stage. For example, when a baby starts crawling, it can provide advice on creating a safe environment and how to play. Furthermore, the parenting book generator can provide an individually customized parenting plan based on the child's health condition and developmental speed. For example, it can suggest parenting methods that address a child's allergies or specific health issues.

[0066] The parenting book generation unit can learn the parenting style or values ​​of the parents and generate a customized parenting book based on that. For example, it can suggest a parenting method that matches the parents' values. The parenting book generation unit can also collect data through questionnaires or interviews to learn the parents' parenting style and values. For example, it can collect information about the parents' parenting style and values ​​through questionnaires and generate a customized parenting book based on that information. It can also understand the parents' parenting style and values ​​in detail through interviews and customize the parenting book based on that information. In this way, the parenting style and values ​​of the parents can be learned and a customized parenting book based on that information can be generated, providing the optimal parenting method for the parents.

[0067] The parenting book generation unit can use the emotion estimation function to incorporate parenting advice into the parenting book according to the parent's emotional state. For example, if the parent is feeling stressed, it can suggest ways to relax. The parenting book generation unit can also use the generation AI to analyze the parent's emotional state using voice analysis and facial expression recognition technology. For example, voice analysis can be used to evaluate the tone of voice and speaking style to estimate the parent's emotional state. Facial expression recognition technology can also be used to analyze the parent's facial expressions to estimate the parent's emotional state. In this way, the emotion estimation function can be used to incorporate parenting advice into the parenting book according to the parent's emotional state, providing psychological support to the parent.

[0068] The consultation unit can analyze the parent's past conversation history and suggest conversations based on individual interests or concerns. For example, if the parent has talked a lot about "children's meals" in the past, related conversations will be suggested. The consultation unit can also use the generation AI to store the parent's past conversation history using a text database or voice database, and perform analysis based on this. For example, the conversation history stored in the text database can be analyzed using text mining technology to identify the parent's interests and concerns. The conversation history stored in the voice database can also be analyzed using voice analysis technology to identify the parent's interests and concerns. This allows the system to analyze the parent's past conversation history and suggest conversations based on the parent's individual interests and concerns, thereby providing support that meets the parent's needs.

[0069] The counseling unit can analyze the parent's tone of voice or speaking style and provide an appropriate response according to their emotional state. For example, if their voice gets lower or their speech becomes slower, it can be determined that stress is increasing. The counseling unit can also analyze the parent's tone of voice and speaking style using voice frequency analysis and emotion recognition technology. For example, voice frequency analysis can be used to evaluate the pitch and intensity of the voice and estimate their emotional state. Emotion recognition technology can also be used to analyze the parent's speaking patterns and estimate their emotional state. This allows the system to analyze the parent's tone of voice and speaking style and provide an appropriate response according to their emotional state, thereby providing psychological support to the parent.

[0070] The counseling unit can use the emotion estimation function to provide words of encouragement or comfort according to the parent's emotional state. For example, if the parent is feeling anxious, it can provide words that give a sense of security. The counseling unit can also use the generation AI to analyze the parent's emotional state using voice analysis and facial expression recognition technology. For example, it can use voice analysis to evaluate the tone of voice and speaking style to estimate the emotional state. It can also use facial expression recognition technology to analyze the parent's facial expressions to estimate the emotional state. As a result, the emotion estimation function can be used to provide words of encouragement or comfort according to the parent's emotional state, thereby providing psychological support to the parent.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The question answering unit responds to the user's question. For example, the generation AI will answer a question such as "Why do babies cry at night?" based on parenting books and expert knowledge. The question answering unit can also analyze the user's past question history and suggest predictive questions based on individual trends. For example, if the user has asked many questions about "babies crying at night" in the past, the generation AI will suggest related predictive questions. Step 2: The parenting book generator generates an individual parenting book based on the information obtained by the question and answer unit. For example, the generation AI accumulates data provided by the user, collecting data such as the child's growth record, developmental rate, and health condition, and generates an individually customized parenting book based on that data. The parenting book generator can also learn the parenting style and values ​​of the parents and generate a customized parenting book based on that. For example, it can suggest parenting methods that match the parents' values. Step 3: The consultation section accepts consultations 24 hours a day. For example, the generation AI can act as a mommy (dad) friend that parents can talk to freely and talk to about anything, 24 hours a day. The consultation section can also analyze the parent's past conversation history and suggest conversations based on individual interests and worries. For example, if there has been a lot of talk about "children's meals" in the past, it will suggest related conversations. Step 4: The voice support unit provides support via voice. For example, the generation AI responds to requests such as "Teach me how to change a diaper" through a smart speaker by explaining the procedure via voice. The voice support unit can also analyze the parent's tone of voice and speaking style to provide appropriate measures depending on the level of urgency. For example, if the parent's voice gets higher or the speaking speed increases, it will determine that the level of urgency is high.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question answering unit that answers questions from a user; a parenting book generation unit that generates an individual parenting book based on the information obtained by the question and answer unit; A consultation department that accepts consultations 24 hours a day, and a voice support unit that provides voice support. A system characterized by:

2. The question answering unit Analyzing the user's tone of voice or speaking style to estimate stress or fatigue level and provide appropriate advice 2. The system of claim 1.

3. The parenting book creation unit Analyzing children's growth data and providing future developmental predictions 2. The system of claim 1.

4. The consultation department: Analyzing the parent's past conversation history and suggesting conversations based on individual interests or concerns 2. The system of claim 1.

5. The audio support unit Analyzing the parent's tone of voice or manner of speaking and providing appropriate measures depending on the level of urgency 2. The system of claim 1.

6. The question answering unit Generating answers according to the user's emotional state to reduce parental anxiety.

2. The system of claim 1.

7. The parenting book creation unit Incorporating parenting advice tailored to the emotional state of the parents into parenting books 2. The system of claim 1.

8. The consultation department: Offer words of encouragement or comfort that correspond to the parent's emotional state 2. The system of claim 1.

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