System

Abstract

An object of a system according to an embodiment is to provide quick and appropriate information for worries and questions about childcare.SOLUTION: A system includes a question analysis unit, an information collection unit, a summary generation unit, and an answer provision unit. The question analysis unit analyzes worries and questions about childcare from the user. The information collection unit collects related information based on the content analyzed by the question analysis unit. The summary generation unit summarizes the information collected by the information collection unit. The answer provider may provide the summary generated by the summary generator to the user.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 technologies have had the problem of making it difficult to efficiently collect vast amounts of information related to child-rearing and provide it to users in an appropriate format.

[0005] The system according to the embodiment aims to provide prompt and appropriate information in response to worries and questions about child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a question analysis unit, an information collection unit, a summary generation unit, and an answer provision unit. The question analysis unit analyzes worries and questions about child-rearing from a user. The information collection unit collects related information based on the content analyzed by the question analysis unit. The summary generation unit summarizes the information collected by the information collection unit. The answer provision unit provides the user with the summary generated by the summary generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate information in response to worries and questions about child-rearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​app for supporting parents according to the embodiment of the present invention is an AI system that responds instantly to all worries and questions about child rearing, such as children's growth and health, illness, child rearing consultations, choosing a nursery school or school, how to interact with parents, study methods, etc. As a result, the AI ​​app for supporting parents can provide users with prompt and appropriate information in response to their worries and questions about child rearing.

[0029] The parent support AI app according to the embodiment includes a question analysis unit, an information collection unit, a summary generation unit, and an answer provision unit. The question analysis unit analyzes parenting-related concerns and questions from a user. For example, the question analysis unit analyzes the user's question using natural language processing technology and extracts keywords. The question analysis unit can also understand the intent of the question based on the user's input. The information collection unit collects related information based on the content analyzed by the question analysis unit. For example, the information collection unit performs a database search to collect related articles and research papers. The information collection unit can also collect information on the Internet using web scraping technology. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit concisely summarizes information based on the length of the sentence and the importance of the information to be summarized. The summary generation unit can also summarize the collected information using a generation AI. The answer provision unit provides the summary generated by the summary generation unit to the user. For example, the answer provision unit displays the summary in text format. The answer provision unit can also provide the summary in audio format or visual format. As a result, the AI ​​app for supporting parents according to the embodiment can respond immediately to users' concerns and questions about child-rearing and provide appropriate information.

[0030] The question analysis unit can analyze a user's past question history and generate personalized answers based on individual trends. For example, the question analysis unit uses a generation AI to analyze a user's past question history and extract specific patterns and trends. For example, it identifies themes and keywords that the user frequently asks about and generates personalized answers based on them. The question analysis unit also allows the generation AI to learn individual trends based on the user's past question history and provide more appropriate answers. For example, in response to a question similar to one the user has asked in the past, it generates an answer that references the past answers. The question analysis unit also allows the generation AI to analyze a user's past question history and provide personalized advice based on the user's interests and concerns. For example, if a user is interested in a specific topic, it prioritizes providing information related to that topic. This makes it possible to provide more appropriate answers based on the user's past question history.

[0031] The question analysis unit can provide parenting information and advice specific to the user's region by taking into account the user's region and cultural background. For example, the generation AI in the question analysis unit provides parenting information specific to the user's region based on the user's region information. For example, it can provide information on local nurseries and schools, and local parenting support services. The question analysis unit can also provide culture-specific parenting advice by taking into account the user's cultural background. For example, it can provide advice that reflects the parenting customs and traditions of a particular culture. The question analysis unit can also provide personalized parenting information by taking into account the user's region and cultural background. For example, it can provide information on local events and activities, and cultural events. This allows the generation AI to provide more appropriate parenting information and advice based on the user's region and cultural background.

[0032] The question analysis unit can analyze a user's voice input using voice input and generate an answer instantly using voice recognition technology. In the question analysis unit, for example, a generation AI analyzes a user's voice input using voice recognition technology and generates an answer instantly. For example, when a user inputs a question by voice, the generation AI analyzes the content and provides an appropriate answer. In addition, the question analysis unit converts the user's voice input into text using voice recognition technology, and the generation AI generates an answer based on the text. For example, when a user inputs a question by voice, the generation AI converts the content into text and generates an answer. In addition, the question analysis unit can analyze a user's voice input in real time using voice recognition technology and provide an answer instantly. For example, when a user inputs a question by voice, the generation AI analyzes the content in real time and generates an answer. This allows answers to be generated instantly based on the user's voice input.

[0033] The question analysis unit can provide answers to user questions using visual content. In the question analysis unit, for example, the generation AI provides visual content related to the user's question. For example, in response to a question about a child's growth, videos and illustrations showing the growth process are provided. In addition, the question analysis unit causes the generation AI to generate visual content according to the content of the user's question and provide it as an answer. For example, in response to a question about a child's study methods, illustrations and videos showing study methods are provided. In addition, the question analysis unit causes the generation AI to visually provide answers to user questions using visual content. For example, in response to a question about a child's health, illustrations and videos showing key points for health management are provided. In this way, answers using visual content can be provided to user questions.

[0034] The information collection unit can integrate data collected from multiple sources and generate a summary that eliminates duplication and contradictions. For example, the information collection unit allows the generation AI to collect data from multiple sources and generate a summary that eliminates duplication and contradictions. For example, the information collection unit analyzes multiple articles on the same topic and provides a summary that eliminates duplicate information. The information collection unit also allows the generation AI to integrate data collected from different sources and analyze contradictory information to provide the most reliable information as a summary. For example, the information collection unit compares different research results and selects reliable information to generate a summary. The information collection unit also analyzes data collected by the generation AI from multiple sources and provides a coherent summary that eliminates duplication and contradictions. For example, the information collection unit integrates information from different perspectives and generates a balanced summary. This makes it possible to integrate data collected from multiple sources and provide a summary that eliminates duplication and contradictions.

[0035] The information collection unit can prioritize and summarize and provide related information based on the user's areas of interest. For example, the generation AI analyzes the user's areas of interest and prioritizes and provides information related to those areas. For example, if the user is interested in children's health, the latest health information is summarized and provided. The information collection unit also collects related information based on the user's areas of interest and generates summaries. For example, if the user is interested in education, research results and articles on education are summarized and provided. The information collection unit also learns the user's areas of interest and prioritizes and provides information related to those areas. For example, if the user is interested in a particular topic, information related to that topic is summarized and provided. This allows related information to be prioritized and provided as summaries based on the user's areas of interest.

[0036] The summary generation unit can visually provide the summarized information as an infographic. For example, the summary generation unit generates an infographic that visually summarizes the information generated by a generation AI. For example, the summary generation unit generates an infographic that shows information about a child's growth in graphs and charts. The summary generation unit also generates an infographic by the generation AI to provide the summarized information in a visually easy-to-understand format. For example, the summary generation unit provides an infographic that shows key points in child-rearing using diagrams and icons. The summary generation unit also provides the summarized information by the generation AI as an infographic, allowing the user to visually understand the information. For example, the summary generation unit generates an infographic that shows information about child health care in diagrams and charts. This allows the summarized information to be visually provided as an infographic.

[0037] The summary generation unit can automatically translate the summarized information into different languages ​​and provide information in multiple languages. For example, the summary generation unit uses a generation AI to automatically translate summarized information into different languages ​​and provide information in multiple languages. For example, the summary generation unit translates the summarized information into multiple languages, such as English, French, and Chinese, and provides it. The summary generation unit can also automatically translate the summarized information and provide it in different languages ​​to accommodate international users. For example, it provides summarized information in the language selected by the user. The summary generation unit can also build a system in which the generation AI translates the summarized information into different languages ​​and provides information in multiple languages. For example, it provides summarized information in the language desired by the user. This allows the summarized information to be automatically translated into different languages ​​and provide information in multiple languages.

[0038] The answer providing unit can learn the user's past responses and identify and provide the most preferred casual style of content. For example, the answer providing unit has the generation AI analyze the user's past responses and identify the most preferred casual style of content. For example, the answer providing unit learns the style in which the user has responded favorably in the past and generates an answer in that style. The answer providing unit also has the generation AI identify the most preferred casual style of content based on the user's past response data and provide a personalized answer. For example, if the user prefers the tone of a particular character, the answer providing unit generates an answer in that tone of voice. The answer providing unit also has the generation AI learn the user's past responses and identify and provide the most preferred casual style of content. For example, the answer providing unit generates a humorous answer based on a style in which the user laughed or enjoyed themselves in the past. This makes it possible to provide the most preferred casual style of content based on the user's past responses.

[0039] The answer providing unit can imitate the tone and style of speech of different characters and generate an answer according to the user's selection. For example, the generation AI of the answer providing unit learns the tone and style of speech of different characters and generates an answer according to the user's selection. For example, if the user desires a comedian-style answer, the answer providing unit provides a humorous answer in that tone. The answer providing unit also imitates the tone and style of speech of a character selected by the user, and the generation AI generates an answer. For example, the answer providing unit generates an answer in the tone of speech of a historical figure and provides it to the user. The answer providing unit also learns the style of different characters and provides a personalized answer according to the user's selection. For example, the answer providing unit generates an answer in the tone of speech of a famous person and provides it to the user. This makes it possible to imitate the tone and style of speech of different characters and provide an answer according to the user's selection.

[0040] The answer providing unit can provide humorous answers to user questions using animations or GIFs. For example, the generation AI in the answer providing unit provides humorous answers to user questions using animations or GIFs. For example, in response to a question about children's study methods, an animation that makes studying fun is provided. In addition, the answer providing unit generates animations or GIFs according to the content of the user's question and provides them as answers. For example, in response to a question about a child's growth, an animation showing the growth process is provided. In addition, the answer providing unit visually provides answers to user questions using animations or GIFs. For example, in response to a question about a child's health, an animation showing key points for health management is provided. In this way, humorous answers using animations or GIFs can be provided to user questions.

[0041] The answer providing unit can use voice synthesis technology to provide answers to user questions in the voice of a selected character. For example, the generation AI uses voice synthesis technology to provide answers to user questions in the voice of a selected character. For example, if a comedian-style answer is desired, a humorous answer is provided in that voice. The answer providing unit also imitates the voice of a character selected by the user, and the generation AI generates an answer using voice synthesis technology. For example, an answer is generated in the voice of a historical figure and provided to the user. The answer providing unit also uses voice synthesis technology to provide personalized answers in the voice of a character selected by the user. For example, an answer is generated in the voice of a famous person and provided to the user. This allows answers to user questions to be provided in the voice of a selected character using voice synthesis technology.

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

[0043] The question analysis unit can also provide related video content based on the content of the user's question. For example, in response to a question about a child's growth, a video showing the growth process is provided. The question analysis unit can also provide educational videos or animations in response to the content of the user's question. For example, in response to a question about a child's study methods, a video showing effective study methods is provided. The question analysis unit can also provide videos related to health based on the content of the user's question. For example, in response to a question about a child's health care, a video showing key points for health care is provided. This makes it possible to provide video content that is visually easy to understand in response to the user's question.

[0044] The question analysis unit can also provide related audio content based on the content of the user's question. For example, in response to a question about a child's growth, an audio guide explaining the growth process is provided. The question analysis unit can also provide educational podcasts or audio books based on the content of the user's question. For example, in response to a question about a child's study methods, audio content explaining effective study methods is provided. The question analysis unit can also provide audio guidance related to health based on the content of the user's question. For example, in response to a question about a child's health care, an audio guide explaining key points of health care is provided. This makes it possible to provide audio content that is easy to understand auditorily in response to the user's question.

[0045] The question analysis unit can also provide related interactive content based on the content of the user's question. For example, in response to a question about a child's growth, an interactive app that simulates the growth process can be provided. The question analysis unit can also provide educational games or quizzes based on the content of the user's question. For example, in response to a question about a child's study methods, an interactive game for learning effective study methods can be provided. The question analysis unit can also provide an interactive guide about health based on the content of the user's question. For example, in response to a question about a child's health care, an interactive app for learning key points about health care can be provided. In this way, interactive learning content can be provided in response to the user's question.

[0046] The information collecting unit can also provide relevant expert opinions and advice based on the content of the user's question. For example, in response to a question about child development, the information collecting unit can provide the opinions of a pediatrician or a childcare expert. The information collecting unit can also provide advice from an education expert or a psychologist based on the content of the user's question. For example, in response to a question about a child's study method, the information collecting unit can provide advice from an education expert. The information collecting unit can also provide opinions from health experts or nutritionists based on the content of the user's question. For example, in response to a question about child health management, the information collecting unit can provide the opinion of a health expert. This makes it possible to provide expert opinions and advice in response to the user's question.

[0047] The information collecting unit can also provide information on related communities and support groups based on the content of the user's question. For example, in response to a question about child development, information on local childcare support groups is provided. The information collecting unit can also provide information on online forums and SNS groups based on the content of the user's question. For example, in response to a question about a child's study methods, information on online forums related to education is provided. The information collecting unit can also provide information on health support groups based on the content of the user's question. For example, in response to a question about child health management, information on health support groups is provided. This makes it possible to provide information on related communities and support groups in response to the user's question.

[0048] The information collecting unit can also provide information on related books and literature based on the content of the user's question. For example, in response to a question about child development, information on books and literature related to the developmental process is provided. The information collecting unit can also provide information on books and research papers related to education based on the content of the user's question. For example, in response to a question about children's study methods, information on books and research papers on effective study methods is provided. The information collecting unit can also provide information on books and literature related to health based on the content of the user's question. For example, in response to a question about children's health management, information on books and literature showing key points for health management is provided. This makes it possible to provide information on related books and literature in response to the user's question.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The question analysis unit analyzes the user's concerns and questions about child-rearing. For example, the question analysis unit uses natural language processing technology to analyze the user's question and extract keywords. The question analysis unit can also understand the intent of the question based on the user's input. Step 2: The information gathering unit collects relevant information based on the content analyzed by the question analysis unit. For example, the information gathering unit may perform a database search to collect relevant articles and research papers. The information gathering unit may also use web scraping technology to collect information from the Internet. Step 3: The summary generator summarizes the information collected by the information collector. For example, the summary generator may summarize the information succinctly based on the length of the sentence and the importance of the information being summarized. The summary generator may also use a generation AI to summarize the collected information. Step 4: The answer providing unit provides the summary generated by the summary generating unit to the user. For example, the answer providing unit displays the summary in text format. The answer providing unit can also provide the summary in audio or visual format.

[0051] (Example 2) The AI ​​app for supporting parents according to the embodiment of the present invention is an AI system that responds instantly to all worries and questions about child rearing, such as children's growth and health, illness, child rearing consultations, choosing a nursery school or school, how to interact with parents, study methods, etc. As a result, the AI ​​app for supporting parents can provide users with prompt and appropriate information in response to their worries and questions about child rearing.

[0052] The parent support AI app according to the embodiment includes a question analysis unit, an information collection unit, a summary generation unit, and an answer provision unit. The question analysis unit analyzes parenting-related concerns and questions from a user. For example, the question analysis unit analyzes the user's question using natural language processing technology and extracts keywords. The question analysis unit can also understand the intent of the question based on the user's input. The information collection unit collects related information based on the content analyzed by the question analysis unit. For example, the information collection unit performs a database search to collect related articles and research papers. The information collection unit can also collect information on the Internet using web scraping technology. The summary generation unit summarizes the information collected by the information collection unit. For example, the summary generation unit concisely summarizes information based on the length of the sentence and the importance of the information to be summarized. The summary generation unit can also summarize the collected information using a generation AI. The answer provision unit provides the summary generated by the summary generation unit to the user. For example, the answer provision unit displays the summary in text format. The answer provision unit can also provide the summary in audio format or visual format. As a result, the AI ​​app for supporting parents according to the embodiment can respond immediately to users' concerns and questions about child-rearing and provide appropriate information.

[0053] The question analysis unit can analyze a user's past question history and generate personalized answers based on individual trends. For example, the question analysis unit uses a generation AI to analyze a user's past question history and extract specific patterns and trends. For example, it identifies themes and keywords that the user frequently asks about and generates personalized answers based on them. The question analysis unit also allows the generation AI to learn individual trends based on the user's past question history and provide more appropriate answers. For example, in response to a question similar to one the user has asked in the past, it generates an answer that references the past answers. The question analysis unit also allows the generation AI to analyze a user's past question history and provide personalized advice based on the user's interests and concerns. For example, if a user is interested in a specific topic, it prioritizes providing information related to that topic. This makes it possible to provide more appropriate answers based on the user's past question history.

[0054] The question analysis unit can provide parenting information and advice specific to the user's region by taking into account the user's region and cultural background. For example, the generation AI in the question analysis unit provides parenting information specific to the user's region based on the user's region information. For example, it can provide information on local nurseries and schools, and local parenting support services. The question analysis unit can also provide culture-specific parenting advice by taking into account the user's cultural background. For example, it can provide advice that reflects the parenting customs and traditions of a particular culture. The question analysis unit can also provide personalized parenting information by taking into account the user's region and cultural background. For example, it can provide information on local events and activities, and cultural events. This allows the generation AI to provide more appropriate parenting information and advice based on the user's region and cultural background.

[0055] The question analysis unit can use the emotion estimation function to analyze the emotional state of the user and generate an answer that is in tune with the emotion. For example, the question analysis unit uses the emotion estimation function to analyze the emotional state from the user's input content and generate an answer that is in tune with the emotion. For example, if the user is feeling stressed, an answer including words of encouragement and comfort is provided. The question analysis unit also analyzes the user's emotional state in real time and generates an appropriate answer based on the results. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The question analysis unit also uses the emotion estimation function to provide personalized advice according to the user's emotions. For example, if the user is feeling happy, a positive answer that further enhances that emotion is provided. This makes it possible to provide an answer that is in tune with the user's emotional state.

[0056] The question analysis unit can analyze a user's voice input using voice input and generate an answer instantly using voice recognition technology. In the question analysis unit, for example, a generation AI analyzes a user's voice input using voice recognition technology and generates an answer instantly. For example, when a user inputs a question by voice, the generation AI analyzes the content and provides an appropriate answer. In addition, the question analysis unit converts the user's voice input into text using voice recognition technology, and the generation AI generates an answer based on the text. For example, when a user inputs a question by voice, the generation AI converts the content into text and generates an answer. In addition, the question analysis unit can analyze a user's voice input in real time using voice recognition technology and provide an answer instantly. For example, when a user inputs a question by voice, the generation AI analyzes the content in real time and generates an answer. This allows answers to be generated instantly based on the user's voice input.

[0057] The question analysis unit can provide answers to user questions using visual content. In the question analysis unit, for example, the generation AI provides visual content related to the user's question. For example, in response to a question about a child's growth, videos and illustrations showing the growth process are provided. In addition, the question analysis unit causes the generation AI to generate visual content according to the content of the user's question and provide it as an answer. For example, in response to a question about a child's study methods, illustrations and videos showing study methods are provided. In addition, the question analysis unit causes the generation AI to visually provide answers to user questions using visual content. For example, in response to a question about a child's health, illustrations and videos showing key points for health management are provided. In this way, answers using visual content can be provided to user questions.

[0058] The question analysis unit can use the emotion estimation function to generate an answer in a voice tone that corresponds to the user's emotion. For example, the question analysis unit uses the emotion estimation function to analyze the user's emotional state and generate an answer in a voice tone that corresponds to the emotion. For example, if the user is depressed, the question analysis unit provides encouraging words in a gentle tone. The question analysis unit also analyzes the user's emotional state in real time and generates an answer in an appropriate voice tone based on the results. For example, if the user is excited, the question analysis unit provides an answer in a calm tone. The question analysis unit also uses the emotion estimation function to provide an answer in a voice tone that corresponds to the user's emotion. For example, if the user is happy, the question analysis unit provides an answer in a bright tone that further enhances the user's emotion. This makes it possible to provide an answer in a voice tone that corresponds to the user's emotion.

[0059] The information collection unit can integrate data collected from multiple sources and generate a summary that eliminates duplication and contradictions. For example, the information collection unit allows the generation AI to collect data from multiple sources and generate a summary that eliminates duplication and contradictions. For example, the information collection unit analyzes multiple articles on the same topic and provides a summary that eliminates duplicate information. The information collection unit also allows the generation AI to integrate data collected from different sources and analyze contradictory information to provide the most reliable information as a summary. For example, the information collection unit compares different research results and selects reliable information to generate a summary. The information collection unit also analyzes data collected by the generation AI from multiple sources and provides a coherent summary that eliminates duplication and contradictions. For example, the information collection unit integrates information from different perspectives and generates a balanced summary. This makes it possible to integrate data collected from multiple sources and provide a summary that eliminates duplication and contradictions.

[0060] The information collection unit can prioritize and summarize and provide related information based on the user's areas of interest. For example, the generation AI analyzes the user's areas of interest and prioritizes and provides information related to those areas. For example, if the user is interested in children's health, the latest health information is summarized and provided. The information collection unit also collects related information based on the user's areas of interest and generates summaries. For example, if the user is interested in education, research results and articles on education are summarized and provided. The information collection unit also learns the user's areas of interest and prioritizes and provides information related to those areas. For example, if the user is interested in a particular topic, information related to that topic is summarized and provided. This allows related information to be prioritized and provided as summaries based on the user's areas of interest.

[0061] The summary generation unit can visually provide the summarized information as an infographic. For example, the summary generation unit generates an infographic that visually summarizes the information generated by a generation AI. For example, the summary generation unit generates an infographic that shows information about a child's growth in graphs and charts. The summary generation unit also generates an infographic by the generation AI to provide the summarized information in a visually easy-to-understand format. For example, the summary generation unit provides an infographic that shows key points in child-rearing using diagrams and icons. The summary generation unit also provides the summarized information by the generation AI as an infographic, allowing the user to visually understand the information. For example, the summary generation unit generates an infographic that shows information about child health care in diagrams and charts. This allows the summarized information to be visually provided as an infographic.

[0062] The summary generation unit can automatically translate the summarized information into different languages ​​and provide information in multiple languages. For example, the summary generation unit uses a generation AI to automatically translate summarized information into different languages ​​and provide information in multiple languages. For example, the summary generation unit translates the summarized information into multiple languages, such as English, French, and Chinese, and provides it. The summary generation unit can also automatically translate the summarized information and provide it in different languages ​​to accommodate international users. For example, it provides summarized information in the language selected by the user. The summary generation unit can also build a system in which the generation AI translates the summarized information into different languages ​​and provides information in multiple languages. For example, it provides summarized information in the language desired by the user. This allows the summarized information to be automatically translated into different languages ​​and provide information in multiple languages.

[0063] The summary generation unit can use the emotion estimation function to select a summary style according to the user's emotion. For example, the summary generation unit uses the emotion estimation function to select a summary style according to the user's emotional state. For example, if the user is depressed, the summary is provided in a positive tone. The summary generation unit also analyzes the user's emotional state and selects an appropriate summary style based on the results. For example, if the user is feeling stressed, the summary is provided in a humorous tone. The summary generation unit also uses the emotion estimation function to select a summary style that is in line with the user's emotion. For example, if the user is happy, the summary is provided in a positive tone that further enhances the user's emotion. In this way, a summary style according to the user's emotion can be selected.

[0064] The answer providing unit can learn the user's past responses and identify and provide the most preferred casual style of content. For example, the answer providing unit has the generation AI analyze the user's past responses and identify the most preferred casual style of content. For example, the answer providing unit learns the style in which the user has responded favorably in the past and generates an answer in that style. The answer providing unit also has the generation AI identify the most preferred casual style of content based on the user's past response data and provide a personalized answer. For example, if the user prefers the tone of a particular character, the answer providing unit generates an answer in that tone of voice. The answer providing unit also has the generation AI learn the user's past responses and identify and provide the most preferred casual style of content. For example, the answer providing unit generates a humorous answer based on a style in which the user laughed or enjoyed themselves in the past. This makes it possible to provide the most preferred casual style of content based on the user's past responses.

[0065] The answer providing unit can imitate the tone and style of speech of different characters and generate an answer according to the user's selection. For example, the generation AI of the answer providing unit learns the tone and style of speech of different characters and generates an answer according to the user's selection. For example, if the user desires a comedian-style answer, the answer providing unit provides a humorous answer in that tone. The answer providing unit also imitates the tone and style of speech of a character selected by the user, and the generation AI generates an answer. For example, the answer providing unit generates an answer in the tone of speech of a historical figure and provides it to the user. The answer providing unit also learns the style of different characters and provides a personalized answer according to the user's selection. For example, the answer providing unit generates an answer in the tone of speech of a famous person and provides it to the user. This makes it possible to imitate the tone and style of speech of different characters and provide an answer according to the user's selection.

[0066] The answer providing unit can use the emotion estimation function to select the optimal character according to the user's emotional state and generate an answer in the style of that character. The answer providing unit, for example, uses the emotion estimation function to analyze the user's emotional state and selects the optimal character according to that emotion to generate an answer. For example, if the user is feeling down, a character that offers words of encouragement is selected. The answer providing unit can also analyze the user's emotional state in real time, select an appropriate character based on the results, and generate an answer in that character's style. For example, if the user is happy, a character that further enhances that emotion is selected. The answer providing unit can also use the emotion estimation function to select a character that is in tune with the user's emotions and provide an answer in the style of that character. For example, if the user is feeling anxious, a character that gives a sense of security is selected. This makes it possible to select the optimal character according to the user's emotional state and provide an answer in the style of that character.

[0067] The answer providing unit can provide humorous answers to user questions using animations or GIFs. For example, the generation AI in the answer providing unit provides humorous answers to user questions using animations or GIFs. For example, in response to a question about children's study methods, an animation that makes studying fun is provided. In addition, the answer providing unit generates animations or GIFs according to the content of the user's question and provides them as answers. For example, in response to a question about a child's growth, an animation showing the growth process is provided. In addition, the answer providing unit visually provides answers to user questions using animations or GIFs. For example, in response to a question about a child's health, an animation showing key points for health management is provided. In this way, humorous answers using animations or GIFs can be provided to user questions.

[0068] The answer providing unit can use voice synthesis technology to provide answers to user questions in the voice of a selected character. For example, the generation AI uses voice synthesis technology to provide answers to user questions in the voice of a selected character. For example, if a comedian-style answer is desired, a humorous answer is provided in that voice. The answer providing unit also imitates the voice of a character selected by the user, and the generation AI generates an answer using voice synthesis technology. For example, an answer is generated in the voice of a historical figure and provided to the user. The answer providing unit also uses voice synthesis technology to provide personalized answers in the voice of a character selected by the user. For example, an answer is generated in the voice of a famous person and provided to the user. This allows answers to user questions to be provided in the voice of a selected character using voice synthesis technology.

[0069] The answer providing unit can use the emotion estimation function to select a character according to the user's emotion and generate an answer in the style of that character. The answer providing unit, for example, uses the emotion estimation function to analyze the user's emotional state and select a character according to that emotion to generate an answer. For example, if the user is feeling down, a character that offers words of encouragement is selected. The answer providing unit can also analyze the user's emotional state in real time, select an appropriate character based on the results, and generate an answer in that character's style. For example, if the user is happy, a character that further enhances that emotion is selected. The answer providing unit can also use the emotion estimation function to select a character that is in tune with the user's emotion and provide an answer in the style of that character. For example, if the user is feeling anxious, a character that gives a sense of security is selected. This makes it possible to select a character according to the user's emotion and provide an answer in the style of that character.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The question analysis unit can also provide related video content based on the content of the user's question. For example, in response to a question about a child's growth, a video showing the growth process is provided. The question analysis unit can also provide educational videos or animations in response to the content of the user's question. For example, in response to a question about a child's study methods, a video showing effective study methods is provided. The question analysis unit can also provide videos related to health based on the content of the user's question. For example, in response to a question about a child's health care, a video showing key points for health care is provided. This makes it possible to provide video content that is visually easy to understand in response to the user's question.

[0072] The question analysis unit can also provide related audio content based on the content of the user's question. For example, in response to a question about a child's growth, an audio guide explaining the growth process is provided. The question analysis unit can also provide educational podcasts or audio books based on the content of the user's question. For example, in response to a question about a child's study methods, audio content explaining effective study methods is provided. The question analysis unit can also provide audio guidance related to health based on the content of the user's question. For example, in response to a question about a child's health care, an audio guide explaining key points of health care is provided. This makes it possible to provide audio content that is easy to understand auditorily in response to the user's question.

[0073] The question analysis unit can also provide related interactive content based on the content of the user's question. For example, in response to a question about a child's growth, an interactive app that simulates the growth process can be provided. The question analysis unit can also provide educational games or quizzes based on the content of the user's question. For example, in response to a question about a child's study methods, an interactive game for learning effective study methods can be provided. The question analysis unit can also provide an interactive guide about health based on the content of the user's question. For example, in response to a question about a child's health care, an interactive app for learning key points about health care can be provided. In this way, interactive learning content can be provided in response to the user's question.

[0074] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest relaxation and stress relief methods according to the emotion. For example, if the user is feeling stressed, it can suggest breathing techniques or meditation methods to help them relax. The question analysis unit can also analyze the user's emotional state in real time and suggest appropriate relaxation methods based on the results. For example, if the user is feeling anxious, it can suggest music or aromatherapy methods to relieve anxiety. The question analysis unit can also use the emotion estimation function to suggest stress relief methods according to the user's emotion. For example, if the user is tired, it can suggest simple exercises or stretching methods to refresh them. This makes it possible to provide relaxation and stress relief methods according to the user's emotional state.

[0075] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and provide dietary and nutritional advice according to the emotion. For example, if the user is feeling stressed, dietary and nutritional advice to reduce the stress is provided. The question analysis unit can also analyze the user's emotional state in real time and provide appropriate dietary and nutritional advice based on the results. For example, if the user is tired, dietary and nutritional advice to restore energy is provided. The question analysis unit can also use the emotion estimation function to provide dietary and nutritional advice according to the user's emotion. For example, if the user is feeling anxious, dietary and nutritional advice to relieve the anxiety is provided. This makes it possible to provide dietary and nutritional advice according to the user's emotional state.

[0076] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and provide exercise or fitness advice according to the emotion. For example, if the user is feeling stressed, exercise or fitness advice to relieve stress is provided. The question analysis unit can also analyze the user's emotional state in real time and provide appropriate exercise or fitness advice based on the results. For example, if the user is tired, exercise or fitness advice to restore energy is provided. The question analysis unit can also use the emotion estimation function to provide exercise or fitness advice according to the user's emotion. For example, if the user is feeling anxious, exercise or fitness advice to relieve anxiety is provided. This makes it possible to provide exercise or fitness advice according to the user's emotional state.

[0077] The question analysis unit can use the emotion estimation function to analyze the user's emotional state and provide relaxation music or podcasts according to the emotion. For example, if the user is feeling stressed, music or podcasts for relaxation can be provided. The question analysis unit can also analyze the user's emotional state in real time and provide appropriate relaxation music or podcasts based on the results of the analysis. For example, if the user is feeling anxious, music or podcasts for relieving anxiety can be provided. The question analysis unit can also use the emotion estimation function to provide relaxation music or podcasts according to the user's emotion. For example, if the user is tired, music or podcasts for restoring energy can be provided. In this way, relaxation music or podcasts according to the user's emotional state can be provided.

[0078] The information collecting unit can also provide relevant expert opinions and advice based on the content of the user's question. For example, in response to a question about child development, the information collecting unit can provide the opinions of a pediatrician or a childcare expert. The information collecting unit can also provide advice from an education expert or a psychologist based on the content of the user's question. For example, in response to a question about a child's study method, the information collecting unit can provide advice from an education expert. The information collecting unit can also provide opinions from health experts or nutritionists based on the content of the user's question. For example, in response to a question about child health management, the information collecting unit can provide the opinion of a health expert. This makes it possible to provide expert opinions and advice in response to the user's question.

[0079] The information collecting unit can also provide information on related communities and support groups based on the content of the user's question. For example, in response to a question about child development, information on local childcare support groups is provided. The information collecting unit can also provide information on online forums and SNS groups based on the content of the user's question. For example, in response to a question about a child's study methods, information on online forums related to education is provided. The information collecting unit can also provide information on health support groups based on the content of the user's question. For example, in response to a question about child health management, information on health support groups is provided. This makes it possible to provide information on related communities and support groups in response to the user's question.

[0080] The information collecting unit can also provide information on related books and literature based on the content of the user's question. For example, in response to a question about child development, information on books and literature related to the developmental process is provided. The information collecting unit can also provide information on books and research papers related to education based on the content of the user's question. For example, in response to a question about children's study methods, information on books and research papers on effective study methods is provided. The information collecting unit can also provide information on books and literature related to health based on the content of the user's question. For example, in response to a question about children's health management, information on books and literature showing key points for health management is provided. This makes it possible to provide information on related books and literature in response to the user's question.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The question analysis unit analyzes the user's concerns and questions about child-rearing. For example, the question analysis unit uses natural language processing technology to analyze the user's question and extract keywords. The question analysis unit can also understand the intent of the question based on the user's input. Step 2: The information gathering unit collects relevant information based on the content analyzed by the question analysis unit. For example, the information gathering unit may perform a database search to collect relevant articles and research papers. The information gathering unit may also use web scraping technology to collect information from the Internet. Step 3: The summary generator summarizes the information collected by the information collector. For example, the summary generator may summarize the information succinctly based on the length of the sentence and the importance of the information being summarized. The summary generator may also use a generation AI to summarize the collected information. Step 4: The answer providing unit provides the summary generated by the summary generating unit to the user. For example, the answer providing unit displays the summary in text format. The answer providing unit can also provide the summary in audio or visual format.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 analysis section that analyzes user concerns and questions about child-rearing, and an information collection unit that collects related information based on the content analyzed by the question analysis unit; a summary generation unit that summarizes the information collected by the information collection unit; an answer providing unit that provides the summary generated by the summary generating unit to the user; A system characterized by:

2. The question analysis unit Analyzing the user's past question history and generating personalized answers based on individual trends 2. The system of claim 1.

3. The question analysis unit Provide the user with localized parenting information and advice, taking into account their region and cultural background.

2. The system of claim 1.

4. The question analysis unit Analyzing the emotional state of the user and generating an answer that is in tune with the user's emotions 2. The system of claim 1.

5. The question analysis unit Analyzing the user's voice input and generating an immediate response using speech recognition technology 2. The system of claim 1.

6. The question analysis unit Providing answers to the user's questions using visual content 2. The system of claim 1.

Images