System

Abstract

An object of a system according to an embodiment is to efficiently collect information related to child-raising and share the information with a partner.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a sharing unit. The reception unit receives a question from a user. The generation unit analyzes the question received by the reception unit and generates an answer. The providing unit provides the answer generated by the generating unit to the user. The sharing unit shares the answer provided by the providing unit with the partner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently collect information about child-rearing and share it with partners.

[0005] The system according to the embodiment aims to efficiently collect information related to child-rearing and share it with partners. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a sharing unit. The reception unit receives questions from users. The generation unit analyzes the questions received by the reception unit and generates answers. The provision unit provides the answers generated by the generation unit to the users. The sharing unit shares the answers provided by the provision unit with partners. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information about child-rearing and share it with partners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A child-rearing support system according to an embodiment of the present invention is a system that allows users to consult an AI about child-rearing information, receive answers, and share them with their partners. In this child-rearing support system, a user inputs a question about child-rearing into the AI, which generates an appropriate answer to the question and provides the answer to the user, who also shares the answer with their partner. For example, if a user inputs a question such as, "What causes a baby to cry at night?", the AI ​​generates an answer such as, "Possible reasons include hunger, a wet diaper, or poor health," and provides the answer to the user and their partner. This mechanism allows for efficient acquisition of child-rearing information and sharing with partners. This allows the child-rearing support system to efficiently accept and analyze user questions, generate answers, and provide and share them.

[0029] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a sharing unit. The reception unit receives questions from a user. For example, the user inputs a question such as, "What causes babies to cry at night?" The generation unit analyzes the received question and generates an answer. For example, the generation unit generates an answer to the question based on previously learned knowledge about child-rearing. The generation unit generates an answer to the question using, for example, a generation AI (for example, a text generation AI or a multimodal generation AI). The provision unit provides the generated answer to the user. For example, the provision unit displays the generated answer on the user's smartphone. The sharing unit shares the provided answer with the partner. For example, the sharing unit notifies the partner's smartphone of the generated answer. This allows the child-rearing support system according to an embodiment to efficiently receive and analyze user questions, generate, provide, and share answers.

[0030] The generation unit can generate answers to questions based on learned knowledge about child-rearing. The generation unit generates answers to questions based on, for example, previously learned knowledge about child-rearing. For example, the generation unit generates answers to questions based on knowledge about child-rearing methods and child development stages. The generation unit can also generate answers to questions using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the generation unit inputs the question, "What causes babies to cry at night?" into the generation AI, and the generation AI generates the answer, "Possible reasons include hunger, a wet diaper, or feeling unwell." This makes it possible to generate more appropriate answers based on previously learned knowledge.

[0031] The sharing unit can notify the partner of the generated answer to their mobile phone. For example, the sharing unit notifies the partner of the generated answer to their smartphone. For example, the sharing unit notifies the partner of the generated answer by a push notification, an SMS notification, or the like. This allows the partner to be promptly notified of the generated answer.

[0032] The providing unit can display the generated answer on the user's mobile phone. For example, the providing unit displays the generated answer on the user's smartphone. For example, the providing unit provides the generated answer to the user in a pop-up display, a list display, or the like. This allows the generated answer to be quickly provided to the user.

[0033] The reception unit can input questions from users to the artificial intelligence. The reception unit, for example, inputs questions from users to the AI. For example, the reception unit inputs text questions entered by the user to the AI. The reception unit can also input questions in voice or image format to the AI. For example, the reception unit converts voice questions into text format using voice recognition technology and inputs the text to the AI. This allows questions from users to be input efficiently to the AI.

[0034] The generation unit may include a database that is referenced when generating an answer to a question. The generation unit may include, for example, a database that is referenced when generating an answer to a question. For example, the generation unit may reference a database such as an SQL database or a NoSQL database to generate an answer to a question. In this way, by referencing the database, a more accurate answer can be generated.

[0035] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit, for example, analyzes the user's past question history and selects an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest question formats to be used in specific time periods based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the user with the optimal reception method.

[0036] The reception unit can filter questions based on the user's current living situation or areas of interest when receiving the questions. For example, the reception unit filters questions based on the user's current living situation or areas of interest when receiving the questions. For example, the reception unit preferentially receives questions related to the user's current living situation (e.g., on parental leave, at work, etc.). The reception unit can also filter related questions based on the user's areas of interest (e.g., sleep, diet, health, etc.). The reception unit can also determine the priority of questions based on the user's current living situation or areas of interest. By filtering questions based on the user's living situation or areas of interest, more relevant questions can be received.

[0037] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a question. For example, when the user inputs a question by voice, the reception unit may use voice recognition technology to receive the question. Furthermore, when the user inputs a question in text, the reception unit may also use text analysis technology to receive the question. Furthermore, when the user inputs a question in image form, the reception unit may also use image recognition technology to receive the question. In this way, by selecting the optimal reception means according to the user's input method, the reception of questions is made more efficient.

[0038] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving highly relevant questions by taking into account the user's geographical location information when receiving a question. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to child-rearing at home. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0039] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity when receiving a question and receive related questions. For example, the reception unit receives related questions based on posts about child-rearing shared by the user on social media. The reception unit can also analyze the user's social media activity and receive questions related to topics of interest. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, related questions can be efficiently received by analyzing the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the question reception interface. In this way, a more appropriate reception method can be provided by reflecting the user's past feedback.

[0041] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also dynamically adjust the level of detail of the answer depending on the importance of the question. In this way, by adjusting the level of detail of the answer depending on the importance of the question, a more appropriate answer can be provided.

[0042] The generation unit can apply different answering algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different answering algorithms depending on the category of the question when generating an answer. For example, the generation unit applies an answering algorithm based on medical knowledge to a question about health. The generation unit can also apply an answering algorithm based on educational theory to a question about education. The generation unit can also select and apply the optimal answering algorithm depending on the category of the question. In this way, by applying the optimal answering algorithm depending on the category of the question, a more appropriate answer can be provided.

[0043] The generation unit can improve the accuracy of the answer by referring to the user's past question results when generating an answer. For example, the generation unit improves the accuracy of the answer by referring to the user's past question results when generating an answer. For example, the generation unit generates a more accurate answer based on answers the user has received in the past. The generation unit can also analyze the user's past question results to improve the accuracy of the answer. The generation unit can also customize the content of the answer by referring to the user's past question results. In this way, the accuracy of the answer can be improved by referring to the user's past question results.

[0044] The generation unit can determine the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted. The generation unit can also generate answers with priority if the question was submitted earlier. The generation unit can also dynamically adjust the priority of answers depending on the time when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, answers can be provided more quickly.

[0045] The generation unit can adjust the order of answers based on the relevance of the question when generating an answer. For example, the generation unit adjusts the order of answers based on the relevance of the question when generating an answer. For example, if the relevance of the question is high, the generation unit generates an answer preferentially. Also, if the relevance of the question is low, the generation unit can also generate an answer later. Also, the generation unit can dynamically adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, it is possible to provide more relevant answers preferentially.

[0046] The generation unit can adjust the use of technical terms in the answer according to the user's level of expertise when generating an answer. For example, the generation unit adjusts the use of technical terms in the answer according to the user's level of expertise when generating an answer. For example, if the user's level of expertise is high, the generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the generation unit can also generate an easy-to-understand answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer according to the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, it is possible to provide an answer that is easier to understand.

[0047] The providing unit can select the optimal display method by referring to the user's past operation history when providing an answer. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing an answer. For example, the providing unit provides the optimal display method based on a display method that the user has preferred in the past. The providing unit can also select a display method with high visibility from the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, a more appropriate display method can be provided by referring to the user's past operation history.

[0048] The providing unit can customize the display content according to the user's current task when providing an answer. For example, the providing unit customizes the display content according to the user's current task when providing an answer. For example, the providing unit preferentially displays related information based on the user's current task (at work, raising children, etc.). The providing unit can also customize the display content according to the user's current task. The providing unit can also provide an optimal display method taking into account the user's current task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0049] The providing unit can improve the display method by reflecting user feedback when providing an answer. For example, the providing unit improves the display method by reflecting user feedback when providing an answer. For example, the providing unit improves the display method based on feedback provided by the user. The providing unit can also select a display method with high visibility based on user feedback. The providing unit can also analyze user feedback and customize the display method. In this way, a more appropriate display method can be provided by reflecting user feedback.

[0050] The providing unit can select the optimal display method by taking into consideration the user's device information when providing an answer. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing an answer. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, a more appropriate display method can be provided by taking into consideration the user's device information.

[0051] The providing unit can make the display content multilingual in accordance with the user's language setting when providing an answer. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing an answer. For example, the providing unit can automatically set the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the answer in that language. This makes it possible to accommodate a larger number of users by making the display content multilingual in accordance with the user's language setting.

[0052] The providing unit can customize the display method according to the visual and auditory characteristics of the user when providing an answer. For example, the providing unit customizes the display method according to the visual and auditory characteristics of the user when providing an answer. For example, if the user is visually impaired, the providing unit can provide the answer by voice. Also, if the user is hearing impaired, the providing unit can provide the answer by text. Also, the providing unit can customize the display method according to the visual and auditory characteristics of the user. In this way, by customizing the display method according to the visual and auditory characteristics of the user, a more appropriate display method can be provided.

[0053] The sharing unit can select the optimal sharing method by referring to the partner's past reaction history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the partner's past reaction history when sharing. For example, the sharing unit provides the optimal sharing method based on the sharing methods that the partner has preferred in the past. The sharing unit can also select a sharing method with high visibility from the partner's past reaction history. The sharing unit can also analyze the partner's past reaction history and customize the sharing method. In this way, by referring to the partner's past reaction history, a more appropriate sharing method can be provided.

[0054] The sharing unit can customize the shared content according to the partner's current situation when sharing. For example, the sharing unit customizes the shared content according to the partner's current situation when sharing. For example, the sharing unit prioritizes sharing relevant information based on the partner's current situation (at work, raising children, etc.). The sharing unit can also customize the shared content according to the partner's current situation. The sharing unit can also provide the optimal sharing method taking into account the partner's current situation. In this way, by customizing the shared content according to the partner's current situation, more appropriate information can be provided.

[0055] The sharing unit can improve the sharing method by reflecting the partner's feedback when sharing. For example, the sharing unit improves the sharing method by reflecting the partner's feedback when sharing. For example, the sharing unit improves the sharing method based on the feedback provided by the partner. The sharing unit can also select a sharing method with high visibility based on the partner's feedback. The sharing unit can also analyze the partner's feedback and customize the sharing method. In this way, a more appropriate sharing method can be provided by reflecting the partner's feedback.

[0056] The sharing unit can select the optimal sharing method by taking into account the geographical location information of the partner when sharing. For example, when sharing, the sharing unit selects the optimal sharing method by taking into account the geographical location information of the partner. For example, if the partner is in a specific area, the sharing unit can prioritize sharing information related to that area. Also, if the partner is traveling, the sharing unit can prioritize sharing information related to the travel destination. Also, if the partner is at home, the sharing unit can prioritize sharing information related to child-rearing at home. In this way, by taking into account the geographical location information of the partner, a more appropriate sharing method can be provided.

[0057] The sharing unit can analyze the partner's social media activity at the time of sharing to customize the shared content. For example, the sharing unit analyzes the partner's social media activity at the time of sharing to customize the shared content. For example, the sharing unit can share relevant information based on posts about child-rearing that the partner has shared on social media. The sharing unit can also analyze the partner's social media activity to share information related to topics of interest. The sharing unit can also share relevant information based on the activity of the partner's friends on social media. In this way, more relevant information can be shared by analyzing the partner's social media activity.

[0058] The sharing unit can customize the sharing method by reflecting the partner's past feedback when sharing. For example, the sharing unit customizes the sharing method by reflecting the partner's past feedback when sharing. For example, the sharing unit can suggest an optimal sharing method based on feedback provided by the partner in the past. The sharing unit can also preferentially suggest a specific sharing format based on the partner's past feedback. The sharing unit can also analyze the partner's past feedback and customize the sharing method. In this way, a more appropriate sharing method can be provided by reflecting the partner's past feedback.

[0059] The database unit can select the optimal update method by referring to past data when updating the database. For example, the database unit selects the optimal update method by referring to past data when updating the database. For example, the database unit selects the optimal update method based on past data. The database unit can also adjust the frequency of database updates by referring to past data. The database unit can also analyze past data and improve the database update method. In this way, by referring to past data, a more appropriate update method can be provided.

[0060] The database unit can improve the contents of the database by reflecting user feedback when updating the database. For example, the database unit improves the contents of the database by reflecting user feedback when updating the database. For example, the database unit improves the contents of the database based on feedback provided by the user. The database unit can also prioritize updating important information based on user feedback. The database unit can also analyze user feedback and customize the contents of the database. In this way, more appropriate database content can be provided by reflecting user feedback.

[0061] The database unit can customize the update content according to the user's current situation when updating the database. For example, the database unit customizes the update content according to the user's current situation when updating the database. For example, the database unit prioritizes updating related information based on the user's current situation (at work, raising children, etc.). The database unit can also customize the update content of the database according to the user's current situation. The database unit can also provide an optimal update method taking the user's current situation into consideration. In this way, by customizing the update content according to the user's current situation, more appropriate information can be provided.

[0062] The database unit can select the optimal update method when updating the database, taking into account the user's geographical location information. For example, when updating the database, the database unit selects the optimal update method by taking into account the user's geographical location information. For example, when the user is in a specific area, the database unit can prioritize updating information related to that area. Furthermore, when the user is traveling, the database unit can also prioritize updating information related to the travel destination. Furthermore, when the user is at home, the database unit can prioritize updating information related to child-rearing at home. In this way, a more appropriate update method can be provided by taking into account the user's geographical location information.

[0063] The database unit can analyze the user's social media activity and customize the update content when updating the database. For example, the database unit analyzes the user's social media activity and customizes the update content when updating the database. For example, the database unit updates related information based on posts about child-rearing shared by the user on social media. The database unit can also analyze the user's social media activity and update information related to topics of interest. The database unit can also update related information based on the activity of the user's friends on social media. In this way, more relevant information can be provided by analyzing the user's social media activity.

[0064] The database unit can customize the update method by reflecting the user's past feedback when updating the database. For example, the database unit customizes the update method by reflecting the user's past feedback when updating the database. For example, the database unit suggests an optimal update method based on feedback provided by the user in the past. The database unit can also preferentially suggest a specific update format based on the user's past feedback. The database unit can also analyze the user's past feedback and customize the update method. In this way, a more appropriate update method can be provided by reflecting the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When accepting a user's question, the acceptance unit refers to the user's past question history, and if a similar question already exists, it can provide that answer preferentially. For example, if a user asks, "What causes babies to cry at night?", if a similar question has been asked in the past, the acceptance unit can immediately provide the answer. The acceptance unit can also analyze the user's past question history and automatically display frequently asked questions as candidates. Furthermore, the acceptance unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. This makes it possible to accept questions more efficiently by utilizing the user's past question history.

[0067] When sharing the generated answer with a partner, the sharing unit can adjust the sharing method based on the partner's current situation (at work, raising children, etc.). For example, if the partner is at work, a simple notification can be sent, allowing the partner to check detailed information later. Alternatively, if the partner is raising children, a notification containing detailed information can be sent. Furthermore, the sharing unit can adjust the timing of sharing according to the partner's current situation. This makes it possible to provide the optimal sharing method according to the partner's situation.

[0068] When providing the generated answer to the user, the providing unit can customize the display content based on the user's current task (e.g., at work, raising children, etc.). For example, when the user is at work, concise information can be displayed preferentially, and detailed information can be checked later. Also, when the user is raising children, a display including detailed information can be provided. Furthermore, the providing unit can adjust the display method according to the user's current task. This makes it possible to provide the optimal display content according to the user's current task.

[0069] When generating an answer to a question, the generation unit can customize the answer by taking into account the user's current geographical location information. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is traveling, information related to the travel destination can be provided. Furthermore, if the user is at home, information related to child-rearing at home can be provided. In this way, by taking into account the user's geographical location information, more relevant answers can be provided.

[0070] When sharing the generated answer with a partner, the sharing unit can select the optimal sharing method by referring to the partner's past response history. For example, the optimal sharing method can be provided based on the partner's preferred sharing method in the past. It can also select a sharing method with high visibility from the partner's past response history. Furthermore, it can analyze the partner's past response history and customize the sharing method. In this way, it is possible to provide a more appropriate sharing method by referring to the partner's past response history.

[0071] When providing the generated answer to the user, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, by taking into account the user's device information, a more appropriate display method can be provided.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives a question from a user. For example, the user inputs a question such as, "What causes my baby to cry at night?" Step 2: The generation unit analyzes the received question and generates an answer. For example, the generation unit generates an answer to the question based on previously learned knowledge about child-rearing. The generation unit generates an answer to the question using, for example, a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The providing unit provides the generated answer to the user. For example, the providing unit displays the generated answer on the user's smartphone. Step 4: The sharing unit shares the provided answer with the partner. For example, the sharing unit notifies the partner's smartphone of the generated answer.

[0074] (Example 2) A child-rearing support system according to an embodiment of the present invention is a system that allows users to consult an AI about child-rearing information, receive answers, and share them with their partners. In this child-rearing support system, a user inputs a question about child-rearing into the AI, which generates an appropriate answer to the question and provides the answer to the user, who also shares the answer with their partner. For example, if a user inputs a question such as, "What causes a baby to cry at night?", the AI ​​generates an answer such as, "Possible reasons include hunger, a wet diaper, or poor health," and provides the answer to the user and their partner. This mechanism allows for efficient acquisition of child-rearing information and sharing with partners. This allows the child-rearing support system to efficiently accept and analyze user questions, generate answers, and provide and share them.

[0075] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a sharing unit. The reception unit receives questions from a user. For example, the user inputs a question such as, "What causes babies to cry at night?" The generation unit analyzes the received question and generates an answer. For example, the generation unit generates an answer to the question based on previously learned knowledge about child-rearing. The generation unit generates an answer to the question using, for example, a generation AI (for example, a text generation AI or a multimodal generation AI). The provision unit provides the generated answer to the user. For example, the provision unit displays the generated answer on the user's smartphone. The sharing unit shares the provided answer with the partner. For example, the sharing unit notifies the partner's smartphone of the generated answer. This allows the child-rearing support system according to an embodiment to efficiently receive and analyze user questions, generate, provide, and share answers.

[0076] The generation unit can generate answers to questions based on learned knowledge about child-rearing. The generation unit generates answers to questions based on, for example, previously learned knowledge about child-rearing. For example, the generation unit generates answers to questions based on knowledge about child-rearing methods and child development stages. The generation unit can also generate answers to questions using a generation AI (for example, a text generation AI or a multimodal generation AI). For example, the generation unit inputs the question, "What causes babies to cry at night?" into the generation AI, and the generation AI generates the answer, "Possible reasons include hunger, a wet diaper, or feeling unwell." This makes it possible to generate more appropriate answers based on previously learned knowledge.

[0077] The sharing unit can notify the partner of the generated answer to their mobile phone. For example, the sharing unit notifies the partner of the generated answer to their smartphone. For example, the sharing unit notifies the partner of the generated answer by a push notification, an SMS notification, or the like. This allows the partner to be promptly notified of the generated answer.

[0078] The providing unit can display the generated answer on the user's mobile phone. For example, the providing unit displays the generated answer on the user's smartphone. For example, the providing unit provides the generated answer to the user in a pop-up display, a list display, or the like. This allows the generated answer to be quickly provided to the user.

[0079] The reception unit can input questions from users to the artificial intelligence. The reception unit, for example, inputs questions from users to the AI. For example, the reception unit inputs text questions entered by the user to the AI. The reception unit can also input questions in voice or image format to the AI. For example, the reception unit converts voice questions into text format using voice recognition technology and inputs the text to the AI. This allows questions from users to be input efficiently to the AI.

[0080] The generation unit may include a database that is referenced when generating an answer to a question. The generation unit may include, for example, a database that is referenced when generating an answer to a question. For example, the generation unit may reference a database such as an SQL database or a NoSQL database to generate an answer to a question. In this way, by referencing the database, a more accurate answer can be generated.

[0081] The reception unit can analyze the user's emotions and adjust the timing of question reception based on the analyzed user emotions. The reception unit, for example, analyzes the user's emotions and adjusts the timing of question reception based on the analyzed user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions to allow the user to receive an answer immediately. Furthermore, if the user is relaxed, the reception unit can slowly receive questions to allow the user to enter detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question input interface and quickly receive questions. This allows the timing of question reception to be adjusted according to the user's emotions, thereby allowing questions to be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit, for example, analyzes the user's past question history and selects an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest question formats to be used in specific time periods based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the user with the optimal reception method.

[0083] The reception unit can filter questions based on the user's current living situation or areas of interest when receiving the questions. For example, the reception unit filters questions based on the user's current living situation or areas of interest when receiving the questions. For example, the reception unit preferentially receives questions related to the user's current living situation (e.g., on parental leave, at work, etc.). The reception unit can also filter related questions based on the user's areas of interest (e.g., sleep, diet, health, etc.). The reception unit can also determine the priority of questions based on the user's current living situation or areas of interest. By filtering questions based on the user's living situation or areas of interest, more relevant questions can be received.

[0084] The reception unit can select an appropriate reception means according to the user's input method when receiving a question. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving a question. For example, when the user inputs a question by voice, the reception unit may use voice recognition technology to receive the question. Furthermore, when the user inputs a question in text, the reception unit may also use text analysis technology to receive the question. Furthermore, when the user inputs a question in image form, the reception unit may also use image recognition technology to receive the question. In this way, by selecting the optimal reception means according to the user's input method, the reception of questions is made more efficient.

[0085] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of questions to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving urgent questions. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving concise questions. In this way, by determining the priority of questions according to the user's emotions, more important questions can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information when receiving a question. For example, the reception unit prioritizes receiving highly relevant questions by taking into account the user's geographical location information when receiving a question. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving questions related to child-rearing at home. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0087] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the user's social media activity when receiving a question and receive related questions. For example, the reception unit receives related questions based on posts about child-rearing shared by the user on social media. The reception unit can also analyze the user's social media activity and receive questions related to topics of interest. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. In this way, related questions can be efficiently received by analyzing the user's social media activity.

[0088] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit suggests an optimal question reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the question reception interface. In this way, a more appropriate reception method can be provided by reflecting the user's past feedback.

[0089] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a concise and easy-to-understand answer. If the user is relaxed, the generation unit can also generate an answer that includes a detailed explanation. If the user is in a hurry, the generation unit can also generate a short answer that gets to the point. This allows for adjusting the way the answer is expressed based on the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. The generation unit can also dynamically adjust the level of detail of the answer depending on the importance of the question. In this way, by adjusting the level of detail of the answer depending on the importance of the question, a more appropriate answer can be provided.

[0091] The generation unit can apply different answering algorithms depending on the category of the question when generating an answer. For example, the generation unit applies different answering algorithms depending on the category of the question when generating an answer. For example, the generation unit applies an answering algorithm based on medical knowledge to a question about health. The generation unit can also apply an answering algorithm based on educational theory to a question about education. The generation unit can also select and apply the optimal answering algorithm depending on the category of the question. In this way, by applying the optimal answering algorithm depending on the category of the question, a more appropriate answer can be provided.

[0092] The generation unit can improve the accuracy of the answer by referring to the user's past question results when generating an answer. For example, the generation unit improves the accuracy of the answer by referring to the user's past question results when generating an answer. For example, the generation unit generates a more accurate answer based on answers the user has received in the past. The generation unit can also analyze the user's past question results to improve the accuracy of the answer. The generation unit can also customize the content of the answer by referring to the user's past question results. In this way, the accuracy of the answer can be improved by referring to the user's past question results.

[0093] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the answer based on the estimated user emotions. For example, if the user is stressed, the generation unit generates a short, to-the-point answer. If the user is relaxed, the generation unit can also generate a longer answer with detailed explanations. If the user is in a hurry, the generation unit can also generate a concise, quick answer. This allows for adjusting the length of the answer according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The generation unit can determine the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted. The generation unit can also generate answers with priority if the question was submitted earlier. The generation unit can also dynamically adjust the priority of answers depending on the time when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, answers can be provided more quickly.

[0095] The generation unit can adjust the order of answers based on the relevance of the question when generating an answer. For example, the generation unit adjusts the order of answers based on the relevance of the question when generating an answer. For example, if the relevance of the question is high, the generation unit generates an answer preferentially. Also, if the relevance of the question is low, the generation unit can also generate an answer later. Also, the generation unit can dynamically adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, it is possible to provide more relevant answers preferentially.

[0096] The generation unit can adjust the use of technical terms in the answer according to the user's level of expertise when generating an answer. For example, the generation unit adjusts the use of technical terms in the answer according to the user's level of expertise when generating an answer. For example, if the user's level of expertise is high, the generation unit generates an answer that uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the generation unit can also generate an easy-to-understand answer that avoids technical terms. Furthermore, the generation unit can dynamically adjust the use of technical terms in the answer according to the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, it is possible to provide an answer that is easier to understand.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the answer based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the display method of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the answer according to the user's emotions and provide a more appropriate display method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The providing unit can select the optimal display method by referring to the user's past operation history when providing an answer. For example, the providing unit selects the optimal display method by referring to the user's past operation history when providing an answer. For example, the providing unit provides the optimal display method based on a display method that the user has preferred in the past. The providing unit can also select a display method with high visibility from the user's past operation history. The providing unit can also analyze the user's past operation history and customize the display method. In this way, a more appropriate display method can be provided by referring to the user's past operation history.

[0099] The providing unit can customize the display content according to the user's current task when providing an answer. For example, the providing unit customizes the display content according to the user's current task when providing an answer. For example, the providing unit preferentially displays related information based on the user's current task (at work, raising children, etc.). The providing unit can also customize the display content according to the user's current task. The providing unit can also provide an optimal display method taking into account the user's current task. In this way, by customizing the display content according to the user's current task, more appropriate information can be provided.

[0100] The providing unit can improve the display method by reflecting user feedback when providing an answer. For example, the providing unit improves the display method by reflecting user feedback when providing an answer. For example, the providing unit improves the display method based on feedback provided by the user. The providing unit can also select a display method with high visibility based on user feedback. The providing unit can also analyze user feedback and customize the display method. In this way, a more appropriate display method can be provided by reflecting user feedback.

[0101] The providing unit can estimate the user's emotions and adjust the display order of answers based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the display order of answers based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize displaying important information. Furthermore, when the user is relaxed, the providing unit can also display detailed information in an orderly manner. Furthermore, when the user is in a hurry, the providing unit can also display information that emphasizes the main points first. In this way, by adjusting the display order of answers according to the user's emotions, more important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The providing unit can select the optimal display method by taking into consideration the user's device information when providing an answer. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing an answer. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, a more appropriate display method can be provided by taking into consideration the user's device information.

[0103] The providing unit can make the display content multilingual in accordance with the user's language setting when providing an answer. For example, the providing unit can make the display content multilingual in accordance with the user's language setting when providing an answer. For example, the providing unit can automatically set the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the answer in that language. This makes it possible to accommodate a larger number of users by making the display content multilingual in accordance with the user's language setting.

[0104] The providing unit can customize the display method according to the visual and auditory characteristics of the user when providing an answer. For example, the providing unit customizes the display method according to the visual and auditory characteristics of the user when providing an answer. For example, if the user is visually impaired, the providing unit can provide the answer by voice. Also, if the user is hearing impaired, the providing unit can provide the answer by text. Also, the providing unit can customize the display method according to the visual and auditory characteristics of the user. In this way, by customizing the display method according to the visual and auditory characteristics of the user, a more appropriate display method can be provided.

[0105] The sharing unit can estimate the user's emotions and adjust the timing of sharing based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and adjusts the timing of sharing based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit immediately shares with the partner. Also, if the user is relaxed, the sharing unit can share with the partner at an appropriate time. Also, if the user is in a hurry, the sharing unit can quickly share with the partner. In this way, by adjusting the timing of sharing according to the user's emotions, information can be shared at a more appropriate time. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The sharing unit can select the optimal sharing method by referring to the partner's past reaction history when sharing. For example, the sharing unit selects the optimal sharing method by referring to the partner's past reaction history when sharing. For example, the sharing unit provides the optimal sharing method based on the sharing methods that the partner has preferred in the past. The sharing unit can also select a sharing method with high visibility from the partner's past reaction history. The sharing unit can also analyze the partner's past reaction history and customize the sharing method. In this way, by referring to the partner's past reaction history, a more appropriate sharing method can be provided.

[0107] The sharing unit can customize the shared content according to the partner's current situation when sharing. For example, the sharing unit customizes the shared content according to the partner's current situation when sharing. For example, the sharing unit prioritizes sharing relevant information based on the partner's current situation (at work, raising children, etc.). The sharing unit can also customize the shared content according to the partner's current situation. The sharing unit can also provide the optimal sharing method taking into account the partner's current situation. In this way, by customizing the shared content according to the partner's current situation, more appropriate information can be provided.

[0108] The sharing unit can improve the sharing method by reflecting the partner's feedback when sharing. For example, the sharing unit improves the sharing method by reflecting the partner's feedback when sharing. For example, the sharing unit improves the sharing method based on the feedback provided by the partner. The sharing unit can also select a sharing method with high visibility based on the partner's feedback. The sharing unit can also analyze the partner's feedback and customize the sharing method. In this way, a more appropriate sharing method can be provided by reflecting the partner's feedback.

[0109] The sharing unit can estimate the user's emotions and determine the priority of sharing based on the estimated user emotions. The sharing unit, for example, estimates the user's emotions and determines the priority of sharing based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit prioritizes sharing of important information. Furthermore, if the user is relaxed, the sharing unit can share detailed information in an orderly manner. Furthermore, if the user is in a hurry, the sharing unit can share information that emphasizes the main points first. In this way, by determining the priority of sharing according to the user's emotions, more important information can be shared preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The sharing unit can select the optimal sharing method by taking into account the geographical location information of the partner when sharing. For example, when sharing, the sharing unit selects the optimal sharing method by taking into account the geographical location information of the partner. For example, if the partner is in a specific area, the sharing unit can prioritize sharing information related to that area. Also, if the partner is traveling, the sharing unit can prioritize sharing information related to the travel destination. Also, if the partner is at home, the sharing unit can prioritize sharing information related to child-rearing at home. In this way, by taking into account the geographical location information of the partner, a more appropriate sharing method can be provided.

[0111] The sharing unit can analyze the partner's social media activity at the time of sharing to customize the shared content. For example, the sharing unit analyzes the partner's social media activity at the time of sharing to customize the shared content. For example, the sharing unit can share relevant information based on posts about child-rearing that the partner has shared on social media. The sharing unit can also analyze the partner's social media activity to share information related to topics of interest. The sharing unit can also share relevant information based on the activity of the partner's friends on social media. In this way, more relevant information can be shared by analyzing the partner's social media activity.

[0112] The sharing unit can customize the sharing method by reflecting the partner's past feedback when sharing. For example, the sharing unit customizes the sharing method by reflecting the partner's past feedback when sharing. For example, the sharing unit can suggest an optimal sharing method based on feedback provided by the partner in the past. The sharing unit can also preferentially suggest a specific sharing format based on the partner's past feedback. The sharing unit can also analyze the partner's past feedback and customize the sharing method. In this way, a more appropriate sharing method can be provided by reflecting the partner's past feedback.

[0113] The database unit can estimate the user's emotions and adjust the database update frequency based on the estimated user emotions. For example, the database unit can estimate the user's emotions and adjust the database update frequency based on the estimated user emotions. For example, if the user is feeling stressed, the database unit can increase the database update frequency to provide the latest information. Also, if the user is relaxed, the database unit can maintain the database update frequency at a normal level. Also, if the user is in a hurry, the database unit can prioritize updating important information. In this way, by adjusting the database update frequency according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] The database unit can select the optimal update method by referring to past data when updating the database. For example, the database unit selects the optimal update method by referring to past data when updating the database. For example, the database unit selects the optimal update method based on past data. The database unit can also adjust the frequency of database updates by referring to past data. The database unit can also analyze past data and improve the database update method. In this way, by referring to past data, a more appropriate update method can be provided.

[0115] The database unit can improve the contents of the database by reflecting user feedback when updating the database. For example, the database unit improves the contents of the database by reflecting user feedback when updating the database. For example, the database unit improves the contents of the database based on feedback provided by the user. The database unit can also prioritize updating important information based on user feedback. The database unit can also analyze user feedback and customize the contents of the database. In this way, more appropriate database content can be provided by reflecting user feedback.

[0116] The database unit can customize the update content according to the user's current situation when updating the database. For example, the database unit customizes the update content according to the user's current situation when updating the database. For example, the database unit prioritizes updating related information based on the user's current situation (at work, raising children, etc.). The database unit can also customize the update content of the database according to the user's current situation. The database unit can also provide an optimal update method taking the user's current situation into consideration. In this way, by customizing the update content according to the user's current situation, more appropriate information can be provided.

[0117] The database unit can estimate the user's emotions and determine the priority of the database based on the estimated user emotions. The database unit, for example, estimates the user's emotions and determines the priority of the database based on the estimated user emotions. For example, if the user is feeling stressed, the database unit prioritizes updating important information. Also, if the user is relaxed, the database unit can update detailed information in an orderly manner. Also, if the user is in a hurry, the database unit can update information that emphasizes the main points first. In this way, by determining the priority of the database according to the user's emotions, more important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The database unit can select the optimal update method when updating the database, taking into account the user's geographical location information. For example, when updating the database, the database unit selects the optimal update method by taking into account the user's geographical location information. For example, when the user is in a specific area, the database unit can prioritize updating information related to that area. Furthermore, when the user is traveling, the database unit can also prioritize updating information related to the travel destination. Furthermore, when the user is at home, the database unit can prioritize updating information related to child-rearing at home. In this way, a more appropriate update method can be provided by taking into account the user's geographical location information.

[0119] The database unit can analyze the user's social media activity and customize the update content when updating the database. For example, the database unit analyzes the user's social media activity and customizes the update content when updating the database. For example, the database unit updates related information based on posts about child-rearing shared by the user on social media. The database unit can also analyze the user's social media activity and update information related to topics of interest. The database unit can also update related information based on the activity of the user's friends on social media. In this way, more relevant information can be provided by analyzing the user's social media activity.

[0120] The database unit can customize the update method by reflecting the user's past feedback when updating the database. For example, the database unit customizes the update method by reflecting the user's past feedback when updating the database. For example, the database unit suggests an optimal update method based on feedback provided by the user in the past. The database unit can also preferentially suggest a specific update format based on the user's past feedback. The database unit can also analyze the user's past feedback and customize the update method. In this way, a more appropriate update method can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and sharing 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 question from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question and generates an answer. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated answer to the user. The sharing unit is realized by the specific processing unit 290 of the data processing device 12 and shares the generated answer with a partner. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and sharing 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 a question from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and generates an answer. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the generated answer with a partner. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and sharing 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 a question from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question and generates an answer. The provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the generated answer to the user. The sharing unit is realized by the specific processing unit 290 of the data processing device 12 and shares the generated answer with a partner, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and sharing 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 a question from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question and generates an answer. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated answer to the user. The sharing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and shares the generated answer with a partner.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] When accepting a user's question, the acceptance unit refers to the user's past question history, and if a similar question already exists, it can provide that answer preferentially. For example, if a user asks, "What causes babies to cry at night?", if a similar question has been asked in the past, the acceptance unit can immediately provide the answer. The acceptance unit can also analyze the user's past question history and automatically display frequently asked questions as candidates. Furthermore, the acceptance unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. This makes it possible to accept questions more efficiently by utilizing the user's past question history.

[0123] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, a concise and easy-to-understand answer can be generated. If the user is relaxed, an answer including detailed explanations can be generated. Furthermore, if the user is in a hurry, a short answer that gets straight to the point can be generated. In this way, by adjusting the way the answer is expressed according to the user's emotions, it is possible to provide a more appropriate answer.

[0124] When sharing the generated answer with a partner, the sharing unit can adjust the sharing method based on the partner's current situation (at work, raising children, etc.). For example, if the partner is at work, a simple notification can be sent, allowing the partner to check detailed information later. Alternatively, if the partner is raising children, a notification containing detailed information can be sent. Furthermore, the sharing unit can adjust the timing of sharing according to the partner's current situation. This makes it possible to provide the optimal sharing method according to the partner's situation.

[0125] When providing the generated answer to the user, the providing unit can customize the display content based on the user's current task (e.g., at work, raising children, etc.). For example, when the user is at work, concise information can be displayed preferentially, and detailed information can be checked later. Also, when the user is raising children, a display including detailed information can be provided. Furthermore, the providing unit can adjust the display method according to the user's current task. This makes it possible to provide the optimal display content according to the user's current task.

[0126] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly accept questions to allow the user to receive an answer immediately. Alternatively, if the user is relaxed, the reception unit can slowly accept questions to allow the user to enter detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question input interface to quickly accept questions. In this way, by adjusting the timing of question reception according to the user's emotions, questions can be accepted at a more appropriate time.

[0127] When generating an answer to a question, the generation unit can customize the answer by taking into account the user's current geographical location information. For example, if the user is in a specific area, information related to that area can be provided preferentially. Also, if the user is traveling, information related to the travel destination can be provided. Furthermore, if the user is at home, information related to child-rearing at home can be provided. In this way, by taking into account the user's geographical location information, more relevant answers can be provided.

[0128] The providing unit can estimate the user's emotions and adjust the display method of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the answer according to the user's emotions, a more appropriate display method can be provided.

[0129] When sharing the generated answer with a partner, the sharing unit can select the optimal sharing method by referring to the partner's past response history. For example, the optimal sharing method can be provided based on the partner's preferred sharing method in the past. It can also select a sharing method with high visibility from the partner's past response history. Furthermore, it can analyze the partner's past response history and customize the sharing method. In this way, it is possible to provide a more appropriate sharing method by referring to the partner's past response history.

[0130] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user's emotions. For example, if the user is feeling stressed, a short and to-the-point answer can be generated. Alternatively, if the user is relaxed, a longer answer including detailed explanations can be generated. Furthermore, if the user is in a hurry, a concise and quick answer can be generated. In this way, by adjusting the length of the answer according to the user's emotions, more appropriate answers can be provided.

[0131] When providing the generated answer to the user, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, by taking into account the user's device information, a more appropriate display method can be provided.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit receives a question from a user. For example, the user inputs a question such as, "What causes my baby to cry at night?" Step 2: The generation unit analyzes the received question and generates an answer. For example, the generation unit generates an answer to the question based on previously learned knowledge about child-rearing. The generation unit generates an answer to the question using, for example, a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The providing unit provides the generated answer to the user. For example, the providing unit displays the generated answer on the user's smartphone. Step 4: The sharing unit shares the provided answer with the partner. For example, the sharing unit notifies the partner's smartphone of the generated answer.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] 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.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] 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.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] 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.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0197] 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.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 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 that receives questions from users; a generation unit that analyzes the question received by the reception unit and generates an answer; a providing unit that provides the answer generated by the generating unit to a user; a sharing unit that shares the answer provided by the providing unit with partners; A system characterized by:

2. The generation unit Generate answers to questions based on learned parenting knowledge 2. The system of claim 1.

3. The common part is Send the generated answer to your partner's mobile phone 2. The system of claim 1.

4. The providing unit Display the generated answer on the user's mobile phone 2. The system of claim 1.

5. The reception unit Input user questions into the AI 2. The system of claim 1.

6. The generation unit Provides a database to reference when generating answers to questions 2. The system of claim 1.

7. The reception unit Analyze user emotions and adjust the timing of accepting questions based on the analyzed user emotions.

2. The system of claim 1.

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

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