System

Abstract

To provide a system that provides quick and highly reliable answers to questions related to childcare.SOLUTION: A system according to an embodiment comprises: a reception unit; a generation unit; a supervision unit; and a provision unit. The reception unit receives a user's question. The generation unit generates a response on the basis of the question received by the reception unit. The supervision unit ensures the reliability of the response generated by the generation unit. The provision unit provides the response generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to get quick and reliable answers to questions raised while raising children.

[0005] The system according to the embodiment aims to provide quick and reliable answers to questions raised during childcare. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a supervision unit, and a provision unit. The reception unit receives a question from a user. The generation unit generates an answer based on the question received by the reception unit. The supervision unit ensures the reliability of the answer generated by the generation unit. The provision unit provides the answer generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide quick and reliable answers to questions raised during parenting. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The childcare support system according to the embodiment of the present invention is a system that can solve questions about childcare, 24 hours a day, anywhere. In this childcare support system, a user inputs questions about childcare, such as "how to ease morning sickness," "how to deal with night crying," and "tip intelligence development for 2-year-olds," and the generation AI immediately provides answers to those questions. The generation AI remembers the user's past questions and tendencies, acquires an ID, and repeatedly learns in order to provide more suitable answers. This solves the problem of having few people close to you to rely on in these days when measures against the declining birthrate are urgently needed, and allows you to get immediate answers while raising your child, who has little free time. For example, a user inputs a question about childcare. For example, a specific question such as "how to ease morning sickness" or "how to deal with night crying" is input. This information is input into the generation AI. Next, the generation AI analyzes the input question and provides an immediate answer. The generation AI generates the optimal answer based on a huge database of childcare. For example, in response to a question such as "how to ease morning sickness," the generation AI provides specific advice such as dietary ideas and relaxation methods. In addition, the generation AI remembers the user's past questions and tendencies, and acquires an ID and learns repeatedly to provide more tailored answers. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation AI can provide more detailed advice based on the previous answer. This realizes a tool that can solve questions during childcare, 24 hours a day, anywhere. Users can solve the problem of having few people close to them to rely on, and get instant answers while raising their children when they have little free time. For example, even if you want to know "how to deal with night crying" in the middle of the night, the generation AI can provide an instant answer, reducing the stress of childcare. This allows the childcare support system to solve questions during childcare, 24 hours a day, anywhere.

[0029] The childcare support system according to the embodiment includes a reception unit, a generation unit, a supervision unit, and a provision unit. The reception unit receives a question from a user. The user's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to such examples. The reception unit receives, for example, a specific question input by the user. The generation unit uses a generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large-scale database on childcare. The generation AI has learned a huge database on childcare and has advanced natural language processing capabilities. The generation unit memorizes the user's past questions and tendencies and repeatedly learns. For example, when a user who has previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. The supervision unit ensures the reliability of the answer generated by the generation unit. The supervision unit ensures the reliability of the answer by, for example, receiving supervision from an expert. The supervision by the expert includes the specific qualifications of the expert and the supervision method. The providing unit provides the answer generated by the generating unit. For example, the providing unit provides the generated answer instantly. The providing unit estimates the user's emotions using the generation AI, and adjusts the method of providing the answer based on the estimated user's emotions. For example, when the user feels anxious, the providing unit provides an answer in gentle language. This enables the childcare support system according to the embodiment to efficiently accept, generate, supervise, and provide questions from users.

[0030] The reception unit receives the user's questions. The user's questions include, for example, questions in text format, voice format, questions about a specific topic, and the like, but are not limited to such examples. The reception unit receives, for example, a specific question input by the user. Specifically, the user can input a question through a dedicated application or website using a smartphone or a personal computer. In the case of voice format, the user uses a microphone to input the question by voice, and the question is converted into text using voice recognition technology. The reception unit processes these inputs in real time and can quickly accept the user's questions. In addition, the reception unit also has a function of classifying the user's questions by category and distributing them to an appropriate processing department. For example, by classifying the questions into various categories such as general questions about childcare, questions about a specific age group, and questions about health, the subsequent processing can be performed efficiently. Furthermore, the reception unit records the user's past question history, and when the same user asks a question again, it can refer to the past history to respond more appropriately. This allows the reception unit to efficiently and quickly accept the user's questions and smoothly hand them over to the next processing step.

[0031] The generation unit uses the generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large-scale database on childcare. The generation AI has learned a huge database on childcare and has advanced natural language processing capabilities. Specifically, the generation AI learns based on data collected from specialized books, papers, websites, forums, etc. on childcare. This allows the generation AI to provide appropriate and detailed answers to the user's questions. The generation unit remembers the user's past questions and trends and repeatedly learns. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. The generation AI analyzes the content of the user's question, quickly searches for related information, and generates the optimal answer. Furthermore, in order to understand the context and intent of the user's question, the generation unit makes full use of natural language processing technology to grasp the background and purpose of the question. This allows the generation unit to provide a more accurate answer to the user's question. In addition, in order to improve the quality of the generated answers, the generator periodically updates the database to incorporate new information and the latest research results, allowing the generator to always provide high-quality answers based on the latest information.

[0032] The supervision unit ensures the reliability of the answers generated by the generation unit. The supervision unit ensures the reliability of the answers, for example, by receiving supervision from an expert. The supervision by the expert includes the specific qualifications of the expert and the supervision method. Specifically, supervision is performed by childcare experts, such as pediatricians, childcare counselors, and psychologists. These experts review the generated answers and confirm the accuracy and appropriateness of the content. The supervision unit can provide reliable information to users by providing only answers that have been supervised by experts. Furthermore, the supervision unit regularly holds meetings with experts, shares the latest childcare information and research results, and updates the database of the generation unit. This allows the supervision unit to always provide reliable answers based on the latest information. In addition, the supervision unit collects feedback from users and continuously improves the quality of the answers. For example, the user evaluates the provided answers and reviews the content of the answers based on the evaluation. This allows the supervision unit to continue providing high-quality answers that meet the needs of users.

[0033] The providing unit provides the answer generated by the generating unit. The providing unit, for example, provides the generated answer instantly. The providing unit uses the generating AI to estimate the user's emotions and adjusts the method of providing the answer based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit provides the answer in gentle language. Specifically, the providing unit uses emotion recognition technology to estimate emotions from the user's input content, tone of voice, facial expression, etc. This makes it possible to understand the user's emotional state and take appropriate action according to that state. For example, if the user is feeling stressed, the providing unit provides language and advice that will help the user relax. The providing unit can also provide individually customized answers based on the user's usage status and past question history. This allows the user to quickly obtain information that is optimal for him or her. Furthermore, the providing unit diversifies the method of providing answers and improves user convenience. For example, not only answers in text format but also answers in audio format and video format can be provided. This allows the user to receive information in a format according to his or her preferences. The providing unit makes full use of these functions to provide the user with prompt and appropriate answers and resolve doubts and anxieties about childcare.

[0034] The generation unit can generate an appropriate answer based on a large-scale database on childcare. The large-scale database includes, for example, a huge amount of information on childcare. For example, the database includes information such as specialized books, papers, websites, and forums on childcare. The generation unit generates an optimal answer based on, for example, a large-scale database on childcare. For example, the generation unit analyzes information in the database and generates an optimal answer to a user's question. As a result, the accuracy of the answer is improved by generating an optimal answer based on a huge database on childcare. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs information in the database to the generation AI and causes the generation AI to generate an optimal answer.

[0035] The generation unit can store the user's past questions or tendencies and repeatedly learn. The generation unit, for example, stores the user's past questions and tendencies. For example, the generation unit stores history data of questions input by the user in the past. The generation unit can also analyze the user's question tendencies and repeatedly learn. For example, the generation unit optimizes the answer to the next question based on the content of questions frequently asked by the user in the past. This allows the user's past questions and tendencies to be stored and repeatedly learned, thereby providing a more suitable answer. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's question history data to the generation AI and cause the generation AI to perform repeated learning.

[0036] The supervision unit can ensure the reliability of the answer by receiving supervision from an expert. The supervision unit, for example, ensures the reliability of the answer by receiving supervision from an expert. Supervision by an expert includes, for example, the specific qualifications of the expert and the supervision method. For example, the supervision unit can receive supervision from a medical expert for questions about medical care. In addition, the supervision unit can also receive supervision from an education expert for questions about education. As a result, the reliability of the answer is improved by receiving supervision from an expert. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can input the generated answer to the generation AI and have the generation AI execute an evaluation of the reliability.

[0037] The providing unit can provide the generated answer instantly. The providing unit, for example, provides the generated answer instantly. Immediately includes, for example, real-time provision and provision within a certain period of time. For example, the providing unit provides the generated answer to the user in real time. The providing unit can also provide the generated answer within a certain period of time. This allows the generated answer to be provided instantly, thereby quickly responding to the user's questions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to the generating AI and cause the generating AI to perform the instantaneous provision.

[0038] The reception unit can receive a specific question input by the user. The reception unit, for example, receives a specific question input by the user. The specific question includes, for example, the level of detail of the question and the topic of the question. For example, the reception unit receives a detailed question input by the user. The reception unit can also receive a question related to a specific topic input by the user. In this way, by receiving a specific question input by the user, an appropriate answer can be provided. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can input a question input by the user to a generation AI and cause the generation AI to receive the question.

[0039] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit, for example, analyzes the user's past question history and selects the optimal reception method. The optimal reception method includes, for example, the timing of reception and the form of reception. For example, the reception unit preferentially receives the content that the user has frequently asked in the past. The reception unit can also analyze the user's past question history to determine the tendency to ask many questions in a specific time period and provide the optimal reception method for that time period. Furthermore, the reception unit can automatically suggest related questions based on the user's past question history. This makes it possible to select the optimal reception method by analyzing the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's question history data to the generation AI and cause the generation AI to select the optimal reception method.

[0040] The reception unit can perform filtering based on the user's current childcare situation and field of interest when receiving a question. For example, the reception unit performs filtering based on the user's current childcare situation and field of interest when receiving a question. Filtering includes, for example, filtering conditions and filtering algorithms. For example, when the user is raising a newborn, the reception unit preferentially receives questions about the newborn. In addition, when the user is interested in a specific childcare method, the reception unit can also preferentially receive questions related to that field. Furthermore, the reception unit can automatically filter related questions based on the user's current childcare situation. As a result, by filtering based on the user's current childcare situation and field of interest, it is possible to preferentially receive highly relevant questions. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's childcare situation data to the generation AI and cause the generation AI to execute filtering.

[0041] The reception unit can preferentially receive questions with high relevance in consideration of the geographical location information of the user when receiving a question. For example, the reception unit preferentially receives questions with high relevance in consideration of the geographical location information of the user when receiving a question. The geographical location information includes, for example, GPS data and the accuracy of the location information. For example, when the user lives in a specific area, the reception unit preferentially receives childcare information related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive childcare information related to the travel destination. Furthermore, the reception unit can automatically filter childcare questions specific to the area based on the geographical location information of the user. In this way, questions specific to the area can be preferentially received by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to execute filtering of questions with high relevance.

[0042] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. For example, the reception unit analyzes the user's social media activity and receives related questions when receiving a question. Social media activity includes, for example, analysis of posted content and analysis of activity frequency. For example, the reception unit preferentially receives related questions based on child-rearing information shared by the user on social media. The reception unit can also analyze child-rearing topics of interest from the user's social media activity and preferentially receive the questions. Furthermore, the reception unit can automatically filter related child-rearing questions based on the user's social media activity history. In this way, by analyzing the user's social media activity, related questions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute filtering of related questions.

[0043] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating the answer. The importance of the question includes, for example, the urgency of the user and the importance of the content of the question. For example, the generation unit generates an answer including detailed information for a question of high importance. In addition, the generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the answer according to the importance of the question. As a result, a detailed answer can be provided for an important question by adjusting the level of detail of the answer based on the importance of the question. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance data of the question to the generation AI and cause the generation AI to adjust the level of detail.

[0044] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of the question when generating an answer. The generation algorithm includes, for example, a machine learning algorithm and a rule-based algorithm. For example, the generation unit applies a generation algorithm based on a medical database to a question about health. The generation unit can also apply a generation algorithm based on an education database to a question about education. Furthermore, the generation unit can apply a generation algorithm based on a childcare database to a question about childcare. In this way, by applying different generation algorithms depending on the category of the question, a more appropriate answer can be provided. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0045] The generation unit can determine the priority of the answers based on the time when the questions were submitted when generating the answers. The generation unit, for example, determines the priority of the answers based on the time when the questions were submitted when generating the answers. The submission time includes, for example, the submission date and time and the frequency of submission. For example, the generation unit generates an answer immediately for a question with high urgency. In addition, the generation unit can also generate an answer with the same priority as other questions for a question with low urgency. Furthermore, the generation unit can dynamically adjust the priority of the answers according to the time when the questions were submitted. In this way, by determining the priority of the answers based on the time when the questions were submitted, it is possible to quickly respond to questions with high urgency. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority.

[0046] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. The generation unit adjusts the order of answers based on the relevance of the questions when generating answers, for example. The relevance includes, for example, the similarity of the question contents and analysis of related topics. For example, the generation unit generates answers preferentially for questions with high relevance. In addition, the generation unit can also generate answers for questions with low relevance in the same order as other questions. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. As a result, by adjusting the order of answers based on the relevance of the questions, it is possible to respond preferentially to questions with high relevance. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order.

[0047] The supervision unit can refer to the opinion of an expert to increase the reliability of the answer during supervision. For example, the supervision unit refers to the opinion of an expert to increase the reliability of the answer during supervision. The expert's opinion includes, for example, the qualifications of the expert and the reliability of the opinion. For example, the supervision unit refers to the opinion of a medical expert for a question about medical care. In addition, the supervision unit can also refer to the opinion of an education expert for a question about education. Furthermore, the supervision unit can also refer to the opinion of a childcare expert for a question about childcare. As a result, the reliability of the answer is improved by referring to the opinion of the expert. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the expert's opinion data to the generation AI and cause the generation AI to perform an evaluation of the reliability.

[0048] The supervision unit can apply different supervision methods to each category of answers during supervision. The supervision unit applies different supervision methods to each category of answers during supervision. The supervision methods include, for example, a review method and a feedback method. For example, the supervision unit applies a supervision method based on a medical database to questions about health. The supervision unit can also apply a supervision method based on an education database to questions about education. Furthermore, the supervision unit can apply a supervision method based on a childcare database to questions about childcare. This allows more appropriate supervision by applying different supervision methods to each category. Part or all of the above-mentioned processing in the supervision unit may be performed using, for example, AI, or may be performed without using AI. For example, the supervision unit can input category data to the generation AI and cause the generation AI to apply the supervision method.

[0049] The supervision unit can perform supervision while taking into consideration the geographical distribution of the answers. For example, the supervision unit performs supervision while taking into consideration the geographical distribution of the answers. The geographical distribution includes, for example, data for each region and geographical characteristics. For example, the supervision unit performs supervision based on childcare information specific to the region. The supervision unit can also preferentially supervise related childcare information based on the geographical distribution. Furthermore, the supervision unit can also apply an optimal supervision method while taking into consideration the geographical distribution. As a result, by taking the geographical distribution into consideration, it is possible to appropriately supervise information specific to the region. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input geographical distribution data to the generation AI and have the generation AI perform supervision.

[0050] The supervision unit can improve the accuracy of supervision by referring to related literature of the answer during supervision. The supervision unit can improve the accuracy of supervision by referring to related literature of the answer during supervision, for example. Related literature includes, for example, the reliability of literature and the relevance of literature. For example, the supervision unit supervises questions about medical care by referring to the latest medical literature. In addition, the supervision unit can also supervise questions about education by referring to the latest educational literature. Furthermore, the supervision unit can supervise questions about childcare by referring to the latest childcare literature. As a result, the accuracy of supervision is improved by referring to related literature. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input related literature data to the generation AI and have the generation AI perform supervision.

[0051] The providing unit can select the optimal providing method by referring to the user's past question history when providing an answer. For example, the providing unit selects the optimal providing method by referring to the user's past question history when providing an answer. The optimal providing method includes, for example, a providing method based on the user's preference and a providing method based on the past history. For example, the providing unit preferentially selects a providing method (text, voice, etc.) that the user has preferred in the past. The providing unit can also select a providing method suitable for a specific time period from the user's past question history. Furthermore, the providing unit can automatically provide related answers based on the user's past question history. In this way, the optimal providing method can be selected by referring to the user's past question history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's question history data to the generating AI and cause the generating AI to select the optimal providing method.

[0052] The providing unit can customize the means of providing based on the user's current childcare situation when providing an answer. For example, the providing unit customizes the means of providing based on the user's current childcare situation when providing an answer. The means of providing includes, for example, email provision and app notification. For example, when the user is raising a newborn, the providing unit provides information about the newborn preferentially. In addition, when the user is interested in a specific childcare method, the providing unit can also provide information related to that field. Furthermore, the providing unit can automatically customize and provide related information based on the user's current childcare situation. As a result, more appropriate information can be provided by customizing the means of providing based on the user's current childcare situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's childcare situation data to the generating AI and cause the generating AI to execute customization of the providing means.

[0053] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing an answer. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing an answer. The geographical location information includes, for example, GPS data and the accuracy of the location information. For example, if the user lives in a specific area, the providing unit can preferentially provide information related to the area. In addition, if the user is traveling, the providing unit can also preferentially provide information related to the travel destination. Furthermore, the providing unit can automatically provide information specific to the area based on the geographical location information of the user. Thereby, by considering the geographical location information of the user, it is possible to appropriately provide information specific to the area. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to select the optimal providing method.

[0054] The providing unit can analyze the user's social media activity and propose a means of providing the answer when providing the answer. For example, the providing unit analyzes the user's social media activity and proposes a means of providing the answer when providing the answer. Social media activity includes, for example, an analysis of the content of posts and an analysis of the frequency of activity. For example, the providing unit provides a related answer based on information shared by the user on social media. The providing unit can also analyze topics of interest from the user's social media activity and provide the information. Furthermore, the providing unit can automatically provide related information based on the user's social media activity history. In this way, the related information can be appropriately provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to execute the proposal of the means of providing.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The childcare support system can further provide advice customized based on the user's childcare style. For example, if the user places importance on natural childcare, the generation unit will provide information about natural childcare preferentially. Also, if the user is a dual-income household, advice that emphasizes time efficiency can be provided. Furthermore, if the user is interested in a particular childcare method (e.g., Montessori education), detailed information about that childcare method can be provided. This allows for more appropriate support by providing advice customized according to the user's childcare style.

[0057] The generation unit can set a child-rearing goal for the user and provide advice based on the goal. For example, if the user sets a goal of "improving the sleep habits of my child," the generation unit can provide information related to the goal with priority. In addition, if the user sets a goal of "improving the balance of my child's meals," specific advice on meals can be provided. Furthermore, if the user has a goal of "improving the sociability of my child," activities and methods for fostering sociability can be suggested. In this way, specific advice according to the user's child-rearing goal can be provided to support goal achievement.

[0058] The generation unit can track the user's progress in childcare and update the advice based on the progress. For example, if the user is working on "child toilet training," the generation unit can suggest the next step based on the progress. Also, if the user is supporting "child language development," the generation unit can suggest a new activity based on the progress. Furthermore, if the user wants to improve "child motor skills," the generation unit can provide an appropriate exercise program based on the progress. This makes it possible to maximize the effectiveness of childcare by providing timely advice based on the user's childcare progress.

[0059] The supervisory department can collect feedback on the user's child-rearing and improve the quality of the answers based on the feedback. For example, when a user provides feedback on a given piece of advice, the supervisory department can improve the answers based on the feedback. In addition, when a user evaluates his or her satisfaction with a particular piece of advice, the supervisory department can improve the reliability of the answers based on the evaluation. Furthermore, when a user raises a new question, a feedback loop can be created to quickly provide an answer to that question. This makes it possible to continuously improve the quality of the answers by utilizing the user's feedback.

[0060] The providing unit can form a community related to child-rearing for users and promote information sharing with other users. For example, if a user is interested in a specific child-rearing topic, a community related to that topic can be provided. Also, if a user wants to share their experiences related to child-rearing, a platform for exchanging information with other users can be provided. Furthermore, a function can be provided that allows a user to ask questions about child-rearing to other users. This can promote information sharing among users and expand knowledge about child-rearing.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives a user's question. The user's question may be, for example, in text format, in voice format, or on a specific topic, but is not limited to such examples. The reception unit may, for example, receive a specific question input by the user. Step 2: The generation unit uses the generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large database on childcare. The generation AI has studied a huge database on childcare and has advanced natural language processing capabilities. The generation unit memorizes the user's past questions and trends and repeatedly learns. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. Step 3: The supervisor ensures the reliability of the answer generated by the generator. For example, the supervisor ensures the reliability of the answer by having an expert supervise the answer. The expert supervision includes the specific qualifications of the expert and the supervision method. Step 4: The providing unit provides the answer generated by the generating unit. For example, the providing unit immediately provides the generated answer. The providing unit estimates the user's emotions using the generation AI, and adjusts the way in which the answer is provided based on the estimated user's emotions. For example, when the user feels anxious, the providing unit provides an answer using gentle language.

[0063] (Example 2) The childcare support system according to the embodiment of the present invention is a system that can solve questions about childcare, 24 hours a day, anywhere. In this childcare support system, a user inputs questions about childcare, such as "how to ease morning sickness," "how to deal with night crying," and "tip intelligence development for 2-year-olds," and the generation AI immediately provides answers to those questions. The generation AI remembers the user's past questions and tendencies, acquires an ID, and repeatedly learns in order to provide more suitable answers. This solves the problem of having few people close to you to rely on in these days when measures against the declining birthrate are urgently needed, and allows you to get immediate answers while raising your child, who has little free time. For example, a user inputs a question about childcare. For example, a specific question such as "how to ease morning sickness" or "how to deal with night crying" is input. This information is input into the generation AI. Next, the generation AI analyzes the input question and provides an immediate answer. The generation AI generates the optimal answer based on a huge database of childcare. For example, in response to a question such as "how to ease morning sickness," the generation AI provides specific advice such as dietary ideas and relaxation methods. In addition, the generation AI remembers the user's past questions and tendencies, and acquires an ID and learns repeatedly to provide more tailored answers. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation AI can provide more detailed advice based on the previous answer. This realizes a tool that can solve questions during childcare, 24 hours a day, anywhere. Users can solve the problem of having few people close to them to rely on, and get instant answers while raising their children when they have little free time. For example, even if you want to know "how to deal with night crying" in the middle of the night, the generation AI can provide an instant answer, reducing the stress of childcare. This allows the childcare support system to solve questions during childcare, 24 hours a day, anywhere.

[0064] The childcare support system according to the embodiment includes a reception unit, a generation unit, a supervision unit, and a provision unit. The reception unit receives a question from a user. The user's question may be, for example, in text format, audio format, or on a specific topic, but is not limited to such examples. The reception unit receives, for example, a specific question input by the user. The generation unit uses a generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large-scale database on childcare. The generation AI has learned a huge database on childcare and has advanced natural language processing capabilities. The generation unit memorizes the user's past questions and tendencies and repeatedly learns. For example, when a user who has previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. The supervision unit ensures the reliability of the answer generated by the generation unit. The supervision unit ensures the reliability of the answer by, for example, receiving supervision from an expert. The supervision by the expert includes the specific qualifications of the expert and the supervision method. The providing unit provides the answer generated by the generating unit. For example, the providing unit provides the generated answer instantly. The providing unit estimates the user's emotions using the generation AI, and adjusts the method of providing the answer based on the estimated user's emotions. For example, when the user feels anxious, the providing unit provides an answer in gentle language. This enables the childcare support system according to the embodiment to efficiently accept, generate, supervise, and provide questions from users.

[0065] The reception unit receives the user's questions. The user's questions include, for example, questions in text format, voice format, questions about a specific topic, and the like, but are not limited to such examples. The reception unit receives, for example, a specific question input by the user. Specifically, the user can input a question through a dedicated application or website using a smartphone or a personal computer. In the case of voice format, the user uses a microphone to input the question by voice, and the question is converted into text using voice recognition technology. The reception unit processes these inputs in real time and can quickly accept the user's questions. In addition, the reception unit also has a function of classifying the user's questions by category and distributing them to an appropriate processing department. For example, by classifying the questions into various categories such as general questions about childcare, questions about a specific age group, and questions about health, the subsequent processing can be performed efficiently. Furthermore, the reception unit records the user's past question history, and when the same user asks a question again, it can refer to the past history to respond more appropriately. This allows the reception unit to efficiently and quickly accept the user's questions and smoothly hand them over to the next processing step.

[0066] The generation unit uses the generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large-scale database on childcare. The generation AI has learned a huge database on childcare and has advanced natural language processing capabilities. Specifically, the generation AI learns based on data collected from specialized books, papers, websites, forums, etc. on childcare. This allows the generation AI to provide appropriate and detailed answers to the user's questions. The generation unit remembers the user's past questions and trends and repeatedly learns. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. The generation AI analyzes the content of the user's question, quickly searches for related information, and generates the optimal answer. Furthermore, in order to understand the context and intent of the user's question, the generation unit makes full use of natural language processing technology to grasp the background and purpose of the question. This allows the generation unit to provide a more accurate answer to the user's question. In addition, in order to improve the quality of the generated answers, the generator periodically updates the database to incorporate new information and the latest research results, allowing the generator to always provide high-quality answers based on the latest information.

[0067] The supervision unit ensures the reliability of the answers generated by the generation unit. The supervision unit ensures the reliability of the answers, for example, by receiving supervision from an expert. The supervision by the expert includes the specific qualifications of the expert and the supervision method. Specifically, supervision is performed by childcare experts, such as pediatricians, childcare counselors, and psychologists. These experts review the generated answers and confirm the accuracy and appropriateness of the content. The supervision unit can provide reliable information to users by providing only answers that have been supervised by experts. Furthermore, the supervision unit regularly holds meetings with experts, shares the latest childcare information and research results, and updates the database of the generation unit. This allows the supervision unit to always provide reliable answers based on the latest information. In addition, the supervision unit collects feedback from users and continuously improves the quality of the answers. For example, the user evaluates the provided answers and reviews the content of the answers based on the evaluation. This allows the supervision unit to continue providing high-quality answers that meet the needs of users.

[0068] The providing unit provides the answer generated by the generating unit. The providing unit, for example, provides the generated answer instantly. The providing unit uses the generating AI to estimate the user's emotions and adjusts the method of providing the answer based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit provides the answer in gentle language. Specifically, the providing unit uses emotion recognition technology to estimate emotions from the user's input content, tone of voice, facial expression, etc. This makes it possible to understand the user's emotional state and take appropriate action according to that state. For example, if the user is feeling stressed, the providing unit provides language and advice that will help the user relax. The providing unit can also provide individually customized answers based on the user's usage status and past question history. This allows the user to quickly obtain information that is optimal for him or her. Furthermore, the providing unit diversifies the method of providing answers and improves user convenience. For example, not only answers in text format but also answers in audio format and video format can be provided. This allows the user to receive information in a format according to his or her preferences. The providing unit makes full use of these functions to provide the user with prompt and appropriate answers and resolve doubts and anxieties about childcare.

[0069] The generation unit can generate an appropriate answer based on a large-scale database on childcare. The large-scale database includes, for example, a huge amount of information on childcare. For example, the database includes information such as specialized books, papers, websites, and forums on childcare. The generation unit generates an optimal answer based on, for example, a large-scale database on childcare. For example, the generation unit analyzes information in the database and generates an optimal answer to a user's question. As a result, the accuracy of the answer is improved by generating an optimal answer based on a huge database on childcare. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs information in the database to the generation AI and causes the generation AI to generate an optimal answer.

[0070] The generation unit can store the user's past questions or tendencies and repeatedly learn. The generation unit, for example, stores the user's past questions and tendencies. For example, the generation unit stores history data of questions input by the user in the past. The generation unit can also analyze the user's question tendencies and repeatedly learn. For example, the generation unit optimizes the answer to the next question based on the content of questions frequently asked by the user in the past. This allows the user's past questions and tendencies to be stored and repeatedly learned, thereby providing a more suitable answer. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's question history data to the generation AI and cause the generation AI to perform repeated learning.

[0071] The supervision unit can ensure the reliability of the answer by receiving supervision from an expert. The supervision unit, for example, ensures the reliability of the answer by receiving supervision from an expert. Supervision by an expert includes, for example, the specific qualifications of the expert and the supervision method. For example, the supervision unit can receive supervision from a medical expert for questions about medical care. In addition, the supervision unit can also receive supervision from an education expert for questions about education. As a result, the reliability of the answer is improved by receiving supervision from an expert. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI or may be performed without using AI. For example, the supervision unit can input the generated answer to the generation AI and have the generation AI execute an evaluation of the reliability.

[0072] The providing unit can provide the generated answer instantly. The providing unit, for example, provides the generated answer instantly. Immediately includes, for example, real-time provision and provision within a certain period of time. For example, the providing unit provides the generated answer to the user in real time. The providing unit can also provide the generated answer within a certain period of time. This allows the generated answer to be provided instantly, thereby quickly responding to the user's questions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to the generating AI and cause the generating AI to perform the instantaneous provision.

[0073] The reception unit can receive a specific question input by the user. The reception unit, for example, receives a specific question input by the user. The specific question includes, for example, the level of detail of the question and the topic of the question. For example, the reception unit receives a detailed question input by the user. The reception unit can also receive a question related to a specific topic input by the user. In this way, by receiving a specific question input by the user, an appropriate answer can be provided. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can input a question input by the user to a generation AI and cause the generation AI to receive the question.

[0074] The reception unit can estimate the user's emotion and adjust the timing of receiving the question based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving the question based on the estimated user's emotion. The estimation of the emotion includes, for example, an emotion analysis algorithm, a type of emotion, and the accuracy of the estimation. For example, when the user is stressed, the reception unit immediately receives the question and responds quickly. Also, when the user is relaxed, the reception unit can delay the reception of the question a little to secure time to collect detailed information. Furthermore, when the user is in a hurry, the reception unit can quickly receive the question with a simple interface. Thereby, by adjusting the timing of receiving the question according to the user's emotion, the question can be received at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0075] The reception unit can analyze the user's past question history and select the optimal reception method. The reception unit, for example, analyzes the user's past question history and selects the optimal reception method. The optimal reception method includes, for example, the timing of reception and the form of reception. For example, the reception unit preferentially receives the content that the user has frequently asked in the past. The reception unit can also analyze the user's past question history to determine the tendency to ask many questions in a specific time period and provide the optimal reception method for that time period. Furthermore, the reception unit can automatically suggest related questions based on the user's past question history. This makes it possible to select the optimal reception method by analyzing the user's past question history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's question history data to the generation AI and cause the generation AI to select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current childcare situation and field of interest when receiving a question. For example, the reception unit performs filtering based on the user's current childcare situation and field of interest when receiving a question. Filtering includes, for example, filtering conditions and filtering algorithms. For example, when the user is raising a newborn, the reception unit preferentially receives questions about the newborn. In addition, when the user is interested in a specific childcare method, the reception unit can also preferentially receive questions related to that field. Furthermore, the reception unit can automatically filter related questions based on the user's current childcare situation. As a result, by filtering based on the user's current childcare situation and field of interest, it is possible to preferentially receive highly relevant questions. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's childcare situation data to the generation AI and cause the generation AI to execute filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the questions to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the questions to be received based on the estimated user's emotion. The determination of the priority includes, for example, a priority evaluation criterion and a priority algorithm. For example, when the user feels anxious, the reception unit receives the question with the highest priority. Also, when the user is relaxed, the reception unit can receive the question with the same priority as other questions. Furthermore, when the user is in a hurry, the reception unit can receive the question with a high priority. In this way, by determining the priority of the questions according to the user's emotion, important questions can be received with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0078] The reception unit can preferentially receive questions with high relevance in consideration of the geographical location information of the user when receiving a question. For example, the reception unit preferentially receives questions with high relevance in consideration of the geographical location information of the user when receiving a question. The geographical location information includes, for example, GPS data and the accuracy of the location information. For example, when the user lives in a specific area, the reception unit preferentially receives childcare information related to the area. In addition, when the user is traveling, the reception unit can also preferentially receive childcare information related to the travel destination. Furthermore, the reception unit can automatically filter childcare questions specific to the area based on the geographical location information of the user. In this way, questions specific to the area can be preferentially received by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to execute filtering of questions with high relevance.

[0079] The reception unit can analyze the user's social media activity and receive related questions when receiving a question. For example, the reception unit analyzes the user's social media activity and receives related questions when receiving a question. Social media activity includes, for example, analysis of posted content and analysis of activity frequency. For example, the reception unit preferentially receives related questions based on child-rearing information shared by the user on social media. The reception unit can also analyze child-rearing topics of interest from the user's social media activity and preferentially receive the questions. Furthermore, the reception unit can automatically filter related child-rearing questions based on the user's social media activity history. In this way, by analyzing the user's social media activity, related questions can be preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute filtering of related questions.

[0080] The generation unit can estimate the user's emotion and adjust the way of expressing the answer based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the way of expressing the answer based on the estimated user's emotion. The way of expressing the answer includes, for example, the wording, the format of the answer, and the level of detail of the answer. For example, the generation unit generates an answer in gentle wording when the user feels anxious. In addition, the generation unit can also generate an answer including detailed information when the user is relaxed. Furthermore, the generation unit can also generate a concise answer that is to the point when the user is in a hurry. This makes it possible to provide a more appropriate answer by adjusting the way of expressing the answer according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data into the generation AI and cause the generation AI to adjust the expression method based on the emotion.

[0081] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating the answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating the answer. The importance of the question includes, for example, the urgency of the user and the importance of the content of the question. For example, the generation unit generates an answer including detailed information for a question of high importance. In addition, the generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the answer according to the importance of the question. As a result, a detailed answer can be provided for an important question by adjusting the level of detail of the answer based on the importance of the question. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the importance data of the question to the generation AI and cause the generation AI to adjust the level of detail.

[0082] The generation unit can apply different generation algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of the question when generating an answer. The generation algorithm includes, for example, a machine learning algorithm and a rule-based algorithm. For example, the generation unit applies a generation algorithm based on a medical database to a question about health. The generation unit can also apply a generation algorithm based on an education database to a question about education. Furthermore, the generation unit can apply a generation algorithm based on a childcare database to a question about childcare. In this way, by applying different generation algorithms depending on the category of the question, a more appropriate answer can be provided. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0083] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the answer based on the estimated user's emotion. The length of the answer includes, for example, the number of characters and the level of detail of the information. For example, when the user is in a hurry, the generation unit generates a short answer that covers the main points. In addition, when the user is relaxed, the generation unit can also generate a longer answer that includes a detailed explanation. Furthermore, when the user is feeling anxious, the generation unit can also generate an answer of a moderate length to give a sense of security. In this way, by adjusting the length of the answer according to the user's emotion, a more appropriate answer can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotion data into the generation AI and cause the generation AI to adjust the length of the answer based on the emotion.

[0084] The generation unit can determine the priority of the answers based on the time when the questions were submitted when generating the answers. The generation unit, for example, determines the priority of the answers based on the time when the questions were submitted when generating the answers. The submission time includes, for example, the submission date and time and the frequency of submission. For example, the generation unit generates an answer immediately for a question with high urgency. In addition, the generation unit can also generate an answer with the same priority as other questions for a question with low urgency. Furthermore, the generation unit can dynamically adjust the priority of the answers according to the time when the questions were submitted. In this way, by determining the priority of the answers based on the time when the questions were submitted, it is possible to quickly respond to questions with high urgency. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the submission time data to the generation AI and cause the generation AI to execute the determination of the priority.

[0085] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. The generation unit adjusts the order of answers based on the relevance of the questions when generating answers, for example. The relevance includes, for example, the similarity of the question contents and analysis of related topics. For example, the generation unit generates answers preferentially for questions with high relevance. In addition, the generation unit can also generate answers for questions with low relevance in the same order as other questions. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. As a result, by adjusting the order of answers based on the relevance of the questions, it is possible to respond preferentially to questions with high relevance. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input question relevance data to the generation AI and cause the generation AI to adjust the order.

[0086] The supervision unit can estimate the user's emotion and adjust the supervision standard based on the estimated user's emotion. The supervision unit, for example, estimates the user's emotion and adjusts the supervision standard based on the estimated user's emotion. The supervision standard includes, for example, the level of detail of supervision and the frequency of supervision. For example, when the user feels anxious, the supervision unit performs supervision with strict standards. In addition, when the user is relaxed, the supervision unit can also perform supervision with normal standards. Furthermore, when the user is in a hurry, the supervision unit can also perform supervision quickly. In this way, by adjusting the supervision standard according to the user's emotion, more appropriate supervision can be performed. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the supervision unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the supervision department can input a user's emotional data into the generation AI and have the generation AI adjust the supervision criteria based on the emotions.

[0087] The supervision unit can refer to the opinion of an expert to increase the reliability of the answer during supervision. For example, the supervision unit refers to the opinion of an expert to increase the reliability of the answer during supervision. The expert's opinion includes, for example, the qualifications of the expert and the reliability of the opinion. For example, the supervision unit refers to the opinion of a medical expert for a question about medical care. In addition, the supervision unit can also refer to the opinion of an education expert for a question about education. Furthermore, the supervision unit can also refer to the opinion of a childcare expert for a question about childcare. As a result, the reliability of the answer is improved by referring to the opinion of the expert. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input the expert's opinion data to the generation AI and cause the generation AI to perform an evaluation of the reliability.

[0088] The supervision unit can apply different supervision methods to each category of answers during supervision. The supervision unit applies different supervision methods to each category of answers during supervision. The supervision methods include, for example, a review method and a feedback method. For example, the supervision unit applies a supervision method based on a medical database to questions about health. The supervision unit can also apply a supervision method based on an education database to questions about education. Furthermore, the supervision unit can apply a supervision method based on a childcare database to questions about childcare. This allows more appropriate supervision by applying different supervision methods to each category. Part or all of the above-mentioned processing in the supervision unit may be performed using, for example, AI, or may be performed without using AI. For example, the supervision unit can input category data to the generation AI and cause the generation AI to apply the supervision method.

[0089] The supervision unit can estimate the user's emotion and adjust the order of displaying the supervision results based on the estimated user's emotion. The supervision unit, for example, estimates the user's emotion and adjusts the order of displaying the supervision results based on the estimated user's emotion. The display order includes, for example, an order based on importance and an order based on relevance. For example, the supervision unit displays the most reliable results first when the user is feeling anxious. In addition, the supervision unit can also display the results in a normal order when the user is relaxed. Furthermore, the supervision unit can also display concise results first when the user is in a hurry. In this way, by adjusting the order of displaying the supervision results according to the user's emotion, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the supervision department can input a user's emotional data into the generation AI and have the generation AI adjust the display order.

[0090] The supervision unit can perform supervision while taking into consideration the geographical distribution of the answers. For example, the supervision unit performs supervision while taking into consideration the geographical distribution of the answers. The geographical distribution includes, for example, data for each region and geographical characteristics. For example, the supervision unit performs supervision based on childcare information specific to the region. The supervision unit can also preferentially supervise related childcare information based on the geographical distribution. Furthermore, the supervision unit can also apply an optimal supervision method while taking into consideration the geographical distribution. As a result, by taking the geographical distribution into consideration, it is possible to appropriately supervise information specific to the region. A part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input geographical distribution data to the generation AI and have the generation AI perform supervision.

[0091] The supervision unit can improve the accuracy of supervision by referring to related literature of the answer during supervision. The supervision unit can improve the accuracy of supervision by referring to related literature of the answer during supervision, for example. Related literature includes, for example, the reliability of literature and the relevance of literature. For example, the supervision unit supervises questions about medical care by referring to the latest medical literature. In addition, the supervision unit can also supervise questions about education by referring to the latest educational literature. Furthermore, the supervision unit can supervise questions about childcare by referring to the latest childcare literature. As a result, the accuracy of supervision is improved by referring to related literature. Part or all of the above-mentioned processing in the supervision unit may be performed, for example, using AI, or may be performed without using AI. For example, the supervision unit can input related literature data to the generation AI and have the generation AI perform supervision.

[0092] The providing unit can estimate the user's emotion and adjust the answer providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the answer providing method based on the estimated user's emotion. The providing method includes, for example, the format of the answer and the timing of the answer. For example, when the user feels anxious, the providing unit provides an answer in gentle language. Also, when the user is relaxed, the providing unit can provide an answer including detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a concise answer that is to the point. This allows a more appropriate answer to be provided by adjusting the answer providing method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing method based on the emotion.

[0093] The providing unit can select the optimal providing method by referring to the user's past question history when providing an answer. For example, the providing unit selects the optimal providing method by referring to the user's past question history when providing an answer. The optimal providing method includes, for example, a providing method based on the user's preference and a providing method based on the past history. For example, the providing unit preferentially selects a providing method (text, voice, etc.) that the user has preferred in the past. The providing unit can also select a providing method suitable for a specific time period from the user's past question history. Furthermore, the providing unit can automatically provide related answers based on the user's past question history. In this way, the optimal providing method can be selected by referring to the user's past question history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's question history data to the generating AI and cause the generating AI to select the optimal providing method.

[0094] The providing unit can customize the means of providing based on the user's current childcare situation when providing an answer. For example, the providing unit customizes the means of providing based on the user's current childcare situation when providing an answer. The means of providing includes, for example, email provision and app notification. For example, when the user is raising a newborn, the providing unit provides information about the newborn preferentially. In addition, when the user is interested in a specific childcare method, the providing unit can also provide information related to that field. Furthermore, the providing unit can automatically customize and provide related information based on the user's current childcare situation. As a result, more appropriate information can be provided by customizing the means of providing based on the user's current childcare situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's childcare situation data to the generating AI and cause the generating AI to execute customization of the providing means.

[0095] The providing unit can estimate the user's emotion and determine the priority of answer provision based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of answer provision based on the estimated user's emotion. The priority of answer provision includes, for example, priority based on importance and priority based on urgency. For example, when the user feels anxious, the providing unit provides the answer with the highest priority. Also, when the user is relaxed, the providing unit can provide the answer with the same priority as other answers. Furthermore, when the user is in a hurry, the providing unit can provide the answer with high priority. In this way, by determining the priority of answer provision according to the user's emotion, important answers can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the provision priority order based on the emotion.

[0096] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing an answer. For example, the providing unit selects the optimal providing method in consideration of the geographical location information of the user when providing an answer. The geographical location information includes, for example, GPS data and the accuracy of the location information. For example, if the user lives in a specific area, the providing unit can preferentially provide information related to the area. In addition, if the user is traveling, the providing unit can also preferentially provide information related to the travel destination. Furthermore, the providing unit can automatically provide information specific to the area based on the geographical location information of the user. Thereby, by considering the geographical location information of the user, it is possible to appropriately provide information specific to the area. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to select the optimal providing method.

[0097] The providing unit can analyze the user's social media activity and propose a means of providing the answer when providing the answer. For example, the providing unit analyzes the user's social media activity and proposes a means of providing the answer when providing the answer. Social media activity includes, for example, an analysis of the content of posts and an analysis of the frequency of activity. For example, the providing unit provides a related answer based on information shared by the user on social media. The providing unit can also analyze topics of interest from the user's social media activity and provide the information. Furthermore, the providing unit can automatically provide related information based on the user's social media activity history. In this way, the related information can be appropriately provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to execute the proposal of the means of providing.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The childcare support system can further provide advice customized based on the user's childcare style. For example, if the user places importance on natural childcare, the generation unit will provide information about natural childcare preferentially. Also, if the user is a dual-income household, advice that emphasizes time efficiency can be provided. Furthermore, if the user is interested in a particular childcare method (e.g., Montessori education), detailed information about that childcare method can be provided. This allows for more appropriate support by providing advice customized according to the user's childcare style.

[0100] The generation unit can set a child-rearing goal for the user and provide advice based on the goal. For example, if the user sets a goal of "improving the sleep habits of my child," the generation unit can provide information related to the goal with priority. In addition, if the user sets a goal of "improving the balance of my child's meals," specific advice on meals can be provided. Furthermore, if the user has a goal of "improving the sociability of my child," activities and methods for fostering sociability can be suggested. In this way, specific advice according to the user's child-rearing goal can be provided to support goal achievement.

[0101] The generation unit can track the user's progress in childcare and update the advice based on the progress. For example, if the user is working on "child toilet training," the generation unit can suggest the next step based on the progress. Also, if the user is supporting "child language development," the generation unit can suggest a new activity based on the progress. Furthermore, if the user wants to improve "child motor skills," the generation unit can provide an appropriate exercise program based on the progress. This makes it possible to maximize the effectiveness of childcare by providing timely advice based on the user's childcare progress.

[0102] The supervisory department can collect feedback on the user's child-rearing and improve the quality of the answers based on the feedback. For example, when a user provides feedback on a given piece of advice, the supervisory department can improve the answers based on the feedback. In addition, when a user evaluates his or her satisfaction with a particular piece of advice, the supervisory department can improve the reliability of the answers based on the evaluation. Furthermore, when a user raises a new question, a feedback loop can be created to quickly provide an answer to that question. This makes it possible to continuously improve the quality of the answers by utilizing the user's feedback.

[0103] The providing unit can form a community related to child-rearing for users and promote information sharing with other users. For example, if a user is interested in a specific child-rearing topic, a community related to that topic can be provided. Also, if a user wants to share their experiences related to child-rearing, a platform for exchanging information with other users can be provided. Furthermore, a function can be provided that allows a user to ask questions about child-rearing to other users. This can promote information sharing among users and expand knowledge about child-rearing.

[0104] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and quick reception method can be provided. Also, if the user is feeling relaxed, an interface for collecting detailed information can be provided. Furthermore, if the user is feeling anxious, gentle language can be used to reassure the user. In this way, by providing an appropriate reception method according to the user's emotions, user satisfaction can be improved.

[0105] The generation unit can estimate the user's emotion and adjust the tone of the answer based on the estimated user's emotion. For example, if the user feels anxious, the answer can be provided in a gentle tone. If the user feels relaxed, the answer can be provided in a friendly tone. Furthermore, if the user is in a hurry, the answer can be provided in a concise and to the point tone. In this way, the user's satisfaction can be improved by providing an answer in an appropriate tone according to the user's emotion.

[0106] The providing unit can estimate the user's emotions and adjust the timing of providing the answer based on the estimated user's emotions. For example, if the user is in a hurry, the answer can be provided immediately. Also, if the user is relaxed, the answer can be provided including detailed information. Furthermore, if the user is feeling anxious, the answer can be provided quickly to give the user a sense of security. In this way, the answer can be provided at an appropriate timing according to the user's emotions, thereby improving the user's satisfaction.

[0107] The supervision unit can estimate the user's emotions and adjust the frequency of supervision based on the estimated user's emotions. For example, if the user feels anxious, supervision can be performed frequently to provide a reliable answer. If the user feels relaxed, supervision can be performed at a normal frequency. Furthermore, if the user is in a hurry, supervision can be performed quickly to provide an answer immediately. In this way, by performing supervision at an appropriate frequency according to the user's emotions, user satisfaction can be improved.

[0108] The providing unit can estimate the user's emotions and adjust the format of the answer provided based on the estimated user's emotions. For example, if the user feels anxious, a text-format answer using gentle language can be provided. If the user feels relaxed, a voice-format answer including detailed information can be provided. Furthermore, if the user is in a hurry, a concise, to-the-point text-format answer can be provided. This makes it possible to improve user satisfaction by providing an answer in an appropriate format according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives a user's question. The user's question may be, for example, in text format, in voice format, or on a specific topic, but is not limited to such examples. The reception unit may, for example, receive a specific question input by the user. Step 2: The generation unit uses the generation AI to generate an answer based on the question received by the reception unit. The generation unit generates an appropriate answer based on, for example, a large database on childcare. The generation AI has studied a huge database on childcare and has advanced natural language processing capabilities. The generation unit memorizes the user's past questions and trends and repeatedly learns. For example, if a user who previously asked "how to deal with night crying" asks the same question again, the generation unit can provide more detailed advice based on the previous answer. Step 3: The supervisor ensures the reliability of the answer generated by the generator. For example, the supervisor ensures the reliability of the answer by having an expert supervise the answer. The expert supervision includes the specific qualifications of the expert and the supervision method. Step 4: The providing unit provides the answer generated by the generating unit. For example, the providing unit immediately provides the generated answer. The providing unit estimates the user's emotions using the generation AI, and adjusts the way in which the answer is provided based on the estimated user's emotions. For example, when the user feels anxious, the providing unit provides an answer using gentle language.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, generation unit, supervision unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives a user's question in text format or voice format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate answer based on a large-scale database on childcare. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and ensures the reliability of the answer by receiving supervision from an expert. The provision unit is realized, for example, by the output device 40 of the smart device 14, and immediately provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, generation unit, supervision unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the user's question in voice format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on a large-scale database related to childcare. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the answer by receiving supervision from an expert. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and immediately provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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, the RAM 30, and the 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, generation unit, supervision unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives the user's question in the form of voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an appropriate answer based on a large-scale database on childcare. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and ensures the reliability of the answer by receiving supervision from an expert. The provision unit is realized, for example, by the display 343 of the headset type terminal 314, and immediately provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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, the RAM 30, and the 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0163] Each of the multiple elements including the above-mentioned reception unit, generation unit, supervision unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's question in the form of voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate answer based on a large-scale database related to childcare. The supervision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the answer by receiving supervision from an expert. The provision unit is realized, for example, by the speaker 240 of the robot 414 and immediately provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a reception unit for receiving questions from users; a generator for generating an answer based on the question received by the receiver; A supervisory unit that ensures the reliability of the answers generated by the generator; a providing unit that provides the answer generated by the generating unit; A system characterized by: (Appendix 2) The generation unit is Generate relevant answers based on a large database of parenting information 2. The system of claim 1, (Appendix 3) The generation unit is Remembers the user's past questions or tendencies and learns from them 2. The system of claim 1, (Appendix 4) The supervisory department: The reliability of the answers is guaranteed through expert supervision 2. The system of claim 1, (Appendix 5) The providing unit is Providing instant generated answers 2. The system of claim 1, (Appendix 6) The reception unit is Accept specific questions typed in by the user 2. The system of claim 1, (Appendix 7) The reception unit is Estimate user emotions and adjust the timing of accepting questions based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past question history and select the optimal reception method 2. The system of claim 1, (Appendix 9) The reception unit is When questions are submitted, they are filtered based on the user's current parenting situation or areas of interest. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize questions based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting questions, the system takes into account the user's geographic location information to prioritize questions that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The reception unit is When a question is submitted, we analyze the user's social media activity and submit related questions. 2. The system of claim 1, (Appendix 13) The generation unit is Infer user sentiment and adjust answer expression based on the inferred sentiment 2. The system of claim 1, (Appendix 14) The generation unit is When generating answers, adjust the detail of the answer based on the importance of the question 2. The system of claim 1, (Appendix 15) The generation unit is When generating answers, different generation algorithms are applied depending on the question category. 2. The system of claim 1, (Appendix 16) The generation unit is Infer user sentiment and adjust the length of answers based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The generation unit is When generating answers, prioritize answers based on when the question was asked 2. The system of claim 1, (Appendix 18) The generation unit is During answer generation, adjust the order of answers based on the relevance of the question 2. The system of claim 1, (Appendix 19) The supervisory department: Estimate user's emotions and adjust the standards of supervision based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The supervisory department: During supervision, refer to expert opinions to increase the reliability of answers. 2. The system of claim 1, (Appendix 21) The supervisory department: During review, apply different review methods to different categories of answers. 2. The system of claim 1, (Appendix 22) The supervisory department: Infer user emotions and adjust the order in which the results of supervision are displayed based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The supervisory department: When reviewing, take into account the geographical distribution of responses. 2. The system of claim 1, (Appendix 24) The supervisory department: When reviewing, refer to the relevant literature for the answers to improve the accuracy of the review. 2. The system of claim 1, (Appendix 25) The providing unit is Inferring user sentiment and adjusting answer delivery based on the inferred user sentiment 2. The system of claim 1, (Appendix 26) The providing unit is When providing an answer, the system selects the most appropriate method by referring to the user's past question history. 2. The system of claim 1, (Appendix 27) The providing unit is When providing answers, customize the delivery method based on the user's current parenting situation 2. The system of claim 1, (Appendix 28) The providing unit is A method for predicting a user's emotions and determining the priority of providing answers based on the predicted user's emotions 2. The system of claim 1, (Appendix 29) The providing unit is When providing answers, the optimal method of providing them is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The providing unit is When providing answers, we analyze users' social media activity to suggest ways to provide them. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 reception unit for receiving questions from users; a generator for generating an answer based on the question received by the receiver; A supervisory unit that ensures the reliability of the answers generated by the generator; a providing unit that provides the answer generated by the generating unit; A system characterized by:

2. The generation unit is Generate relevant answers based on a large database of parenting information 2. The system of claim 1 .

3. The generation unit is Remembers the user's past questions or tendencies and learns from them 2. The system of claim 1 .

4. The supervisory department: The reliability of the answers is guaranteed through expert supervision 2. The system of claim 1 .

5. The reception unit is Estimate user emotions and adjust the timing of accepting questions based on the estimated user emotions 2. The system of claim 1 .

6. The reception unit is Analyze the user's past question history and select the optimal reception method 2. The system of claim 1 .

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