System

Abstract

An object of the system according to the embodiment is to immediately analyze the content of the child's speech or conversation so that the parent can easily grasp the content.SOLUTION: A system includes an analysis part, a summarization part, a notification part, a question reception part, and an answer part. The analysis unit immediately analyzes the child's speech or conversation. The summarizing unit summarizes the content analyzed by the analyzing unit. The notification unit notifies the parent's smartphone of the content summarized by the summarization unit. The question reception unit provides an interface when the parent asks a question about a point of concern. The answer unit provides a detailed answer to the question received by the question reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 makes it difficult for parents to immediately grasp the content of their children's comments and conversations, and there is room for improvement.

[0005] The system according to the embodiment aims to instantly analyze the content of a child's statements and conversations, allowing parents to easily understand the content. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a summarization unit, a notification unit, a question reception unit, and an answering unit. The analysis unit instantly analyzes a child's statements or conversations. The summarization unit summarizes the content analyzed by the analysis unit. The notification unit notifies the parent's smartphone of the content summarized by the summarization unit. The question reception unit provides an interface for parents to ask questions about points of concern. The answering unit provides detailed answers to questions received by the question reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can instantly analyze the content of a child's statements and conversations, allowing parents to easily understand the content. [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) In an embodiment of the present invention, a system is designed to have a child wear a device, and AI summarizes the child's statements and conversations for the day and notifies the parent's smartphone. This system analyzes and summarizes the child's statements and conversations in real time and notifies the parent's smartphone. For example, it can analyze what the child said at school and what conversations they had with their friends. The AI ​​then summarizes the analyzed content and notifies the parent's smartphone. For privacy reasons, no recording is performed; the summary is simply a daily report. Furthermore, if the parent asks a question about something they are concerned about, the AI ​​can provide a more detailed answer. For example, if a parent asks, "What did you learn in class today?" the AI ​​can provide a detailed response. This allows the system to grasp the content of the child's statements and conversations and gain a more detailed understanding of the child's growth and learning status. Furthermore, for privacy reasons, no recording is performed; the summary is simply a daily report, thereby protecting the child's privacy.

[0029] An information notification system according to an embodiment includes an analysis unit, a summarization unit, a notification unit, a question reception unit, and an answering unit. The analysis unit instantly analyzes a child's utterances or conversations. For example, the analysis unit converts the utterances or conversations into text in real time using speech recognition technology and analyzes the text. The analysis unit can also estimate the child's emotions and adjust the analysis method for the utterances or conversations based on the estimated child's emotions. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the child's past utterance history when analyzing the utterances or conversations. The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit extracts and summarizes important information from the child's utterances or conversations. The summarization unit can also estimate the child's emotions and adjust the presentation method of the summary based on the estimated child's emotions. Furthermore, the summarization unit can adjust the level of detail of the summary based on the importance of the utterances or conversations when generating the summary. The notification unit notifies the parent of the content summarized by the summarization unit via a method such as push notification, SMS, or email. The notification unit can also estimate the child's emotions and adjust the timing of notifications based on the estimated child's emotions. Furthermore, the notification unit can select the optimal notification method by referring to the parent's past notification history when notifying the child. The question reception unit provides an interface for parents to ask questions about concerns. For example, the question reception unit receives questions by text input through a smartphone app. The question reception unit can also estimate the parent's emotions and adjust the method of receiving the question based on the estimated parent's emotions. Furthermore, the question reception unit can also select the optimal method of receiving the question by referring to the parent's past question history when receiving a question. The answering unit provides a detailed answer to the question received by the question reception unit. For example, the answering unit provides a detailed answer by referring to a history of past statements and conversations. The answering unit can also estimate the parent's emotions and adjust the method of expressing the answer based on the estimated parent's emotions. Furthermore, the answering unit can improve the accuracy of the answer when generating an answer by referring to the parent's past answers. As a result, the information notification system according to the embodiment analyzes a child's statements and conversations in real time, summarizes them, and notifies the parent's smartphone, allowing the parent to understand the situation of the child.

[0030] The analysis unit can instantly convert utterances or conversations into text using speech recognition technology and analyze the text. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and HMM-based speech recognition. The analysis unit can convert children's utterances and conversations into text in real time using, for example, deep learning-based speech recognition technology. The analysis unit can also convert utterances and conversations into text using HMM-based speech recognition technology. Furthermore, the analysis unit can also analyze the content of utterances and conversations using speech recognition technology. As a result, speech recognition technology can accurately convert utterances and conversations into text, improving analysis accuracy. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data converted into text using speech recognition technology into a generation AI, which then performs analysis.

[0031] The summarization unit can extract and summarize necessary information from a child's utterances or conversations. The necessary information includes, but is not limited to, frequently occurring keywords and important phrases. For example, the summarization unit can extract frequently occurring keywords and summarize based on them. The summarization unit can also extract important phrases and summarize based on them. Furthermore, the summarization unit can extract information related to a specific topic from a child's utterances or conversations and summarize based on them. By extracting and summarizing important information, parents can efficiently grasp the information they need. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to extract important information from a child's utterances or conversations and summarize them.

[0032] The notification unit can notify the parent's smartphone of the summarized content. Notification methods include, but are not limited to, push notification, SMS, email, etc. For example, the notification unit can notify the parent's smartphone of the summarized content using push notification. The notification unit can also notify the parent's smartphone of the summarized content using SMS. Furthermore, the notification unit can also notify the parent's smartphone of the summarized content using email. In this way, by notifying the parent's smartphone of the summarized content, the parent can understand the child's situation in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the parent's smartphone of the summarized content using a generation AI.

[0033] The question reception unit can easily receive questions through a smartphone app. Methods for receiving questions include, but are not limited to, text input and voice input, for example. The question reception unit can receive questions by text input through a smartphone app. The question reception unit can also receive questions by voice input through a smartphone app. Furthermore, the question reception unit can estimate the parent's emotions and adjust the method for receiving questions based on the estimated parent's emotions. This allows parents to easily input questions through the smartphone app. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can estimate the parent's emotions using a generation AI and adjust the method for receiving questions based on the estimated parent's emotions.

[0034] The answering unit can provide a specific answer by referring to a history of past statements or conversations. Specific answers include, but are not limited to, past data references and expert opinions. The answering unit can, for example, refer to a history of past statements or conversations and provide a detailed answer based thereon. The answering unit can also refer to expert opinions and provide a detailed answer based thereon. Furthermore, the answering unit can estimate the parent's emotions and adjust the way the answer is expressed based on the estimated parent's emotions. This allows a detailed answer to be provided by referring to the history of past statements and conversations. Some or all of the above-described processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can use a generation AI to refer to a history of past statements and conversations and provide a detailed answer.

[0035] When analyzing a statement or conversation, the analysis unit can improve the accuracy of the analysis by referring to the child's past statement history. Past statement history includes, but is not limited to, text data and audio data. The analysis unit can improve the accuracy of the analysis by, for example, referring to specific phrases or words used by the child in the past. The analysis unit can also analyze the child's past statement patterns to more accurately analyze the content of the child's current statement. Furthermore, the analysis unit can prioritize analysis of statements related to specific topics based on the child's past conversation history. This improves the accuracy of the analysis by referring to the past statement history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using a generation AI to refer to the child's past statement history.

[0036] When analyzing utterances or conversations, the analysis unit can change the analysis algorithm depending on the child's age or grade. For example, the analysis unit adjusts the analysis algorithm depending on the child's age, taking into account the vocabulary used and the complexity of grammar. The analysis unit can also adjust the analysis algorithm depending on the child's grade, taking into account the learning content and topics of interest. Furthermore, the analysis unit can adjust the analysis algorithm depending on the child's developmental stage, taking into account the child's level of comprehension of utterances and expressiveness. This improves analysis accuracy by applying an analysis algorithm depending on the child's age and grade. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis algorithm depending on the child's age and grade using a generation AI.

[0037] When analyzing speech or conversation, the analysis unit can improve analysis accuracy based on background or environmental sounds of the child's speech. For example, when background sounds are loud, the analysis unit can use noise canceling technology to clarify the speech content. Furthermore, when environmental sounds have a specific pattern, the analysis unit can improve accuracy by reflecting that pattern in the analysis. Furthermore, when background sounds are quiet, the analysis unit can improve analysis accuracy by focusing on the speech content. This improves analysis accuracy by taking background and environmental sounds into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can improve analysis accuracy by taking background and environmental sounds into account using a generation AI.

[0038] When analyzing utterances or conversations, the analysis unit can prioritize analysis of highly relevant information based on the child's geographical location information. For example, when the child is at school, the analysis unit prioritizes analysis of utterances related to learning content. Furthermore, when the child is at a park, the analysis unit can prioritize analysis of playtime and conversations with friends. Furthermore, when the child is at home, the analysis unit can prioritize analysis of conversations within the home. In this way, highly relevant information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use a generation AI to prioritize analysis of highly relevant information based on the child's geographical location information.

[0039] The analysis unit can analyze the child's social media activity and analyze related information when analyzing comments or conversations. For example, the analysis unit analyzes related comments based on content shared by the child on social media. The analysis unit can also analyze conversation content taking into account the child's social media friendships. Furthermore, the analysis unit can prioritize analysis of topics of interest based on the child's social media activity history. This allows for efficient analysis of related information by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the child's social media activity and analyze related information using generation AI.

[0040] When analyzing utterances or conversations, the analysis unit can change the analysis method based on the child's past feedback. The analysis unit can adjust the analysis method based on, for example, feedback provided by the child in the past. The analysis unit can also prioritize analysis of utterances on specific topics based on the child's past feedback. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the child's feedback. In this way, by reflecting past feedback, the analysis method can be customized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can use generation AI to change the analysis method based on the child's past feedback.

[0041] When generating a summary, the summarization unit can change the level of detail of the summary based on the importance of the utterance or conversation. For example, the summarization unit summarizes important utterances or conversations in detail and summarizes other utterances briefly. The summarization unit can also adjust the length of the summary based on the importance of the utterance or conversation. Furthermore, the summarization unit can prioritize summarizing utterances with high importance and omit utterances with low importance. In this way, by adjusting the level of detail of the summary based on the importance of the utterance or conversation, parents can efficiently grasp the information they need. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or without AI. For example, the summarization unit can adjust the level of detail of the summary based on the importance of the utterance or conversation using a generation AI.

[0042] When generating a summary, the summarization unit can use different summarization algorithms depending on the category of the utterance or conversation. For example, the summarization unit can apply a summarization algorithm focused on academic content to utterances made at school. The summarization unit can also apply a summarization algorithm focused on social content to conversations with friends. Furthermore, the summarization unit can apply a summarization algorithm focused on family life to conversations made at home. In this way, applying a summarization algorithm according to the category improves the accuracy of the summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to apply different summarization algorithms depending on the category of the utterance or conversation.

[0043] When generating a summary, the summarization unit can improve the accuracy of the summary based on the child's past summarization results. For example, the summarization unit can analyze the child's past summarization results to improve the accuracy of the summary. The summarization unit can also prioritize summarization of specific topics based on the past summarization results. Furthermore, the summarization unit can improve the summarization algorithm based on the child's past summarization results. This improves the accuracy of the summary by referring to the past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to improve the accuracy of the summary based on the child's past summarization results.

[0044] When generating summaries, the summarization unit can set the priority of summaries based on the time of occurrence of utterances or conversations. For example, the summarization unit prioritizes summarization of recent utterances or conversations. The summarization unit can also prioritize summarization of utterances or conversations that occurred during a specific time period. Furthermore, the summarization unit can adjust the order of summaries based on the time of occurrence of utterances or conversations. In this way, by determining the priority of summaries based on the time of occurrence, important information can be prioritized in summarization. Some or all of the above-described processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to set the priority of summaries based on the time of occurrence of utterances or conversations.

[0045] When generating summaries, the summarization unit can change the order of summaries based on the relevance of utterances or conversations. For example, the summarization unit prioritizes summarization of highly relevant utterances or conversations. The summarization unit can also adjust the order of summaries based on the relevance of utterances or conversations. Furthermore, the summarization unit can also briefly summarize less relevant utterances or conversations. In this way, adjusting the order of summaries based on relevance allows parents to efficiently grasp important information. Some or all of the above-described processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can adjust the order of summaries based on the relevance of utterances or conversations using a generation AI.

[0046] When generating a summary, the summarization unit can change the use of technical terms in the summary depending on the parent's level of expertise. For example, if the parent has technical expertise, the summarization unit can provide a summary using technical terms. Alternatively, if the parent does not have technical expertise, the summarization unit can provide a summary in simple language. Furthermore, the summarization unit can adjust the content of the summary depending on the parent's level of expertise. This allows the summary to be easily understood by adjusting the technical terms in the summary depending on the parent's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to change the use of technical terms in the summary depending on the parent's level of expertise.

[0047] When notifying, the notification unit can select an appropriate notification method by referring to the parent's past notification history. For example, the notification unit can prioritize the selection of a notification method that the parent has used favorably in the past. The notification unit can also select the optimal notification timing based on the parent's past notification history. Furthermore, the notification unit can customize the notification content based on the parent's past notification history. In this way, by referring to the past notification history, the optimal notification method for the parent can be selected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to select an appropriate notification method by referring to the parent's past notification history.

[0048] The notification unit can change the notification content based on the parent's current situation or areas of interest at the time of notification. For example, the notification unit can provide brief notification content when the parent is at work. The notification unit can also provide detailed notification content when the parent is on vacation. Furthermore, the notification unit can customize the notification content based on the parent's areas of interest. This allows the parent to efficiently receive necessary information by customizing the notification content according to the parent's situation and areas of interest. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to change the notification content based on the parent's current situation and areas of interest.

[0049] The notification unit can change the notification method based on parental feedback at the time of notification. The notification unit can adjust the notification method based on, for example, feedback provided by the parent. The notification unit can also select the optimal notification timing based on the parental feedback. Furthermore, the notification unit can customize the notification content by reflecting the parental feedback. In this way, by reflecting the parental feedback, the notification method can be improved, enabling optimal notification for the parent. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can change the notification method based on the parental feedback using a generation AI.

[0050] The notification unit can select an appropriate notification method based on the parent's geographical location information when notifying the parent. For example, the notification unit can provide detailed notification content when the parent is at home. The notification unit can also provide concise notification content when the parent is out. Furthermore, the notification unit can select an optimal notification method based on the parent's geographical location information. This makes it possible to select an optimal notification method for the parent by taking the geographical location information into consideration. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to select an appropriate notification method based on the parent's geographical location information.

[0051] The notification unit can analyze the parent's social media activity and change the notification content when sending a notification. For example, the notification unit can provide relevant notification content based on content shared by the parent on social media. The notification unit can also notify the parent of topics of interest based on the parent's social media activity history. Furthermore, the notification unit can customize the notification content taking into account the parent's social media friendships. In this way, by analyzing social media activity, it is possible to provide notification content that is highly relevant to the parent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to analyze the parent's social media activity and change the notification content.

[0052] The notification unit can change the notification method based on the parent's past feedback when notifying the child. The notification unit can adjust the notification method based on, for example, feedback provided by the parent in the past. The notification unit can also select the optimal notification timing based on the parent's past feedback. Furthermore, the notification unit can customize the notification content by reflecting the parent's past feedback. In this way, by reflecting past feedback, it is possible to provide the parent with the optimal notification method. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI, or can be performed without using AI. For example, the notification unit can use a generation AI to change the notification method based on the parent's past feedback.

[0053] When receiving a question, the question receiving unit can select an appropriate question receiving method by referring to the parent's past question history. For example, the question receiving unit can automatically display questions that the parent has frequently asked in the past as candidates. The question receiving unit can also preferentially suggest question methods (voice, text, etc.) that the parent has used in the past. Furthermore, the question receiving unit can predict and suggest question content that will be used at a specific time period based on the parent's past question history. In this way, by referring to the past question history, it is possible to provide the parent with the optimal question receiving method. Some or all of the above-mentioned processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can use a generation AI to select an appropriate question receiving method by referring to the parent's past question history.

[0054] The question receiving unit can change the content of the question based on the parent's current situation or area of ​​interest when receiving a question. For example, if the parent is at work, the question receiving unit can provide a concise question. Furthermore, if the parent is on vacation, the question receiving unit can also provide a detailed question. Furthermore, the question receiving unit can customize the content of the question based on the parent's area of ​​interest. This allows the parent to efficiently obtain the information they need by customizing the content of the question according to the parent's situation or area of ​​interest. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can change the content of the question based on the parent's current situation or area of ​​interest using a generation AI.

[0055] The question reception unit can change the question reception method based on parental feedback when receiving a question. The question reception unit adjusts the question reception method based on, for example, feedback provided by the parent. The question reception unit can also select the optimal timing for receiving a question based on the parental feedback. Furthermore, the question reception unit can customize the content of the question by reflecting the parental feedback. In this way, by reflecting the parental feedback, the question reception method can be improved, enabling the parent to receive a question that is optimal for the parent. Some or all of the above-described processing in the question reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the question reception unit can change the question reception method based on the parental feedback using a generation AI.

[0056] The question reception unit can select an appropriate question reception method based on the parent's geographical location information when receiving a question. For example, if the parent is at home, the question reception unit can provide detailed question content. Furthermore, if the parent is out, the question reception unit can also provide a concise question content. Furthermore, the question reception unit can select an optimal question reception method based on the parent's geographical location information. In this way, by taking the geographical location information into consideration, it is possible to provide an optimal question reception method for the parent. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can select an appropriate question reception method based on the parent's geographical location information using a generation AI.

[0057] The question receiving unit can analyze the parent's social media activity and change the content of the question when receiving a question. The question receiving unit can provide relevant question content based on, for example, content shared by the parent on social media. The question receiving unit can also ask questions about topics of interest to the parent based on the parent's social media activity history. Furthermore, the question receiving unit can customize the question content taking into account the parent's social media friendships. In this way, by analyzing social media activity, it is possible to provide questions that are highly relevant to the parent. Some or all of the above-described processing in the question receiving unit can be performed, for example, using AI or without AI. For example, the question receiving unit can use a generation AI to analyze the parent's social media activity and change the content of the question.

[0058] The question receiving unit can change the question receiving method based on the parent's past feedback when receiving a question. The question receiving unit can adjust the question receiving method based on, for example, feedback provided by the parent in the past. The question receiving unit can also select the optimal timing for receiving a question based on the parent's past feedback. Furthermore, the question receiving unit can customize the content of the question by reflecting the parent's past feedback. In this way, by reflecting the past feedback, it is possible to provide the parent with an optimal question receiving method. Some or all of the above-described processing in the question receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the question receiving unit can use a generation AI to change the question receiving method based on the parent's past feedback.

[0059] When generating an answer, the answering unit can improve the accuracy of the answer by referring to past utterances or conversation history. The answering unit, for example, provides related information based on past utterances and conversation history. The answering unit can also provide a detailed answer on a specific topic from past utterances and conversation history. Furthermore, the answering unit can analyze past utterances and conversation history to provide the most appropriate answer. In this way, the accuracy of the answer is improved by referring to past utterances and conversation history. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can use a generation AI to improve the accuracy of the answer by referring to past utterances and conversation history.

[0060] When generating an answer, the answering unit can use different answering algorithms depending on the content of the parent's question. For example, the answering unit can apply an answering algorithm that focuses on academic content to a question about school. The answering unit can also apply an answering algorithm that focuses on social content to a question about conversations with friends. The answering unit can also apply an answering algorithm that focuses on home life to a question about conversations at home. In this way, by applying an answering algorithm depending on the content of the question, it is possible to provide the parent with an optimal answer. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generating AI to use different answering algorithms depending on the content of the parent's question.

[0061] When generating an answer, the answer unit can improve the accuracy of the answer based on the parent's past answer results. For example, the answer unit can analyze the parent's past answer results to improve the accuracy of the answer. The answer unit can also provide a detailed answer on a specific topic from the past answer results. Furthermore, the answer unit can improve the answer algorithm based on the parent's past answer results. This improves the accuracy of the answer by referring to the past answer results. Some or all of the above-mentioned processing in the answer unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer unit can use a generation AI to improve the accuracy of the answer based on the parent's past answer results.

[0062] When generating answers, the answering unit can prioritize answers based on the time of question generation. For example, the answering unit prioritizes answers to recent questions. The answering unit can also prioritize answers to questions that occurred during a specific time period. Furthermore, the answering unit can adjust the order of answers based on the time of question generation. In this way, by determining the priority of answers based on the time of question generation, important questions can be answered preferentially. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generation AI to prioritize answers based on the time of question generation.

[0063] The answering unit can change the order of answers based on the relevance of the questions when generating answers. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also adjust the order of answers based on the relevance of the questions. Furthermore, the answering unit can also briefly answer questions with low relevance. By adjusting the order of answers based on relevance, parents can efficiently grasp important information. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can change the order of answers based on the relevance of the questions using a generation AI.

[0064] The answering unit can change the use of technical terminology in the answer depending on the parent's level of expertise. For example, if the parent has technical knowledge, the answering unit can provide an answer using technical terminology. Also, if the parent does not have technical knowledge, the answering unit can provide an answer in simple language. Furthermore, the answering unit can adjust the content of the answer depending on the parent's level of expertise. In this way, by adjusting the technical terminology in the answer depending on the parent's level of expertise, an answer that is easy for the parent to understand can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generation AI to change the use of technical terminology in the answer depending on the parent's level of expertise.

[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 generating summaries of a child's utterances and conversations, the summarization unit can customize the content of the summary based on the parent's interests. For example, if a parent is interested in their child's learning status, the summarization unit can prioritize summarizing utterances related to learning. Also, if a parent is interested in their child's friendships, the summarization unit can prioritize summarizing conversations with friends. Furthermore, if a parent is interested in their child's health, the summarization unit can prioritize summarizing utterances related to health. In this way, by customizing the content of the summary according to the parent's interests, parents can efficiently grasp the information they need.

[0067] When notifying the parent's smartphone of the summarized content, the notification unit can adjust the notification method taking into account the parent's current activity status. For example, if the parent is driving, the notification unit can prioritize voice notification. Also, if the parent is in a meeting, the notification unit can prioritize vibration notification. Furthermore, if the parent is relaxed, the notification unit can provide detailed text notification. This allows the parent to receive information at the appropriate time by adjusting the notification method according to the parent's activity status.

[0068] When a parent inputs a question, the question receiving unit can provide an auto-complete function based on the parent's past question history. For example, it can automatically display questions that the parent has frequently asked in the past as candidates. It can also prioritize and suggest question methods (voice, text, etc.) that the parent has used in the past. It can also predict and suggest questions that will be used at specific times based on the parent's past question history. This makes it possible to provide the parent with the optimal question receiving method by referring to the past question history.

[0069] The analysis unit can take the child's learning style into consideration when analyzing the content of a child's utterances and conversations. For example, if the child is a visual learner, the analysis unit can emphasize visual elements in its analysis. If the child is an auditory learner, the analysis unit can emphasize tone and rhythm of the voice. Furthermore, if the child is an experiential learner, the analysis unit can emphasize utterances based on actual experiences. This improves the accuracy of the analysis by adjusting the analysis method according to the child's learning style.

[0070] When generating summaries of a child's utterances or conversations, the summarization unit can adjust the level of detail of the summary based on the frequency and length of the child's utterances. For example, for a child who speaks frequently, the summarization unit can extract important points and provide a concise summary. For a child who speaks less, the summarization unit can provide a detailed summary. Furthermore, for a child who speaks longer, the summarization unit can extract and summarize the main points of the utterances. This allows parents to efficiently grasp the information they need by adjusting the level of detail of the summary based on the frequency and length of the utterances.

[0071] When notifying the parent's smartphone of the summarized content, the notification unit can select the optimal notification method based on the parent's past notification history. For example, it can prioritize the notification method that the parent has used in the past. It can also select the optimal notification timing based on the parent's past notification history. Furthermore, it can customize the notification content based on the parent's past notification history. This allows the optimal notification method for the parent to be selected by referring to the past notification history.

[0072] When a parent inputs a question, the question receiving unit can customize the question content based on the parent's current situation and areas of interest. For example, if the parent is at work, a concise question content can be provided. Alternatively, if the parent is on vacation, a detailed question content can be provided. Furthermore, the question content can be customized based on the parent's areas of interest. This allows the parent to efficiently obtain the information they need by customizing the question content according to the parent's situation and areas of interest.

[0073] When generating an answer to a parent's question, the answering unit can improve the accuracy of the answer based on the parent's past answer results. For example, the answering unit can analyze the parent's past answer results to improve the accuracy of the answer. It can also provide detailed answers on specific topics based on the past answer results. It can also improve the answering algorithm based on the parent's past answer results. This improves the accuracy of the answer by referring to the past answer results.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The analysis unit immediately analyzes the child's statements or conversations. For example, the analysis unit converts the statements and conversations into text in real time using voice recognition technology and analyzes the text. The analysis unit can also estimate the child's emotions and adjust the analysis method for the statements and conversations based on the estimated child's emotions. Furthermore, when analyzing the statements and conversations, the analysis unit can also improve the accuracy of the analysis by referring to the child's past statement history. Step 2: The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit extracts and summarizes important information from the child's utterances and conversations. The summarization unit can also estimate the child's emotions and adjust the way the summary is presented based on the estimated child's emotions. Furthermore, when generating the summary, the summarization unit can adjust the level of detail of the summary based on the importance of the utterances and conversations. Step 3: The notification unit notifies the parent's smartphone of the content summarized by the summarization unit. For example, the notification unit may send notifications by push notification, SMS, email, or other methods. The notification unit may also estimate the child's emotions and adjust the timing of notifications based on the estimated child's emotions. Furthermore, the notification unit may refer to the parent's past notification history when sending notifications to select the optimal notification method. Step 4: The question reception unit provides an interface for parents to ask questions about concerns. For example, the question reception unit may receive questions by text input via a smartphone app. The question reception unit may also estimate the parent's emotions and adjust the method of receiving questions based on the estimated parent's emotions. Furthermore, when receiving a question, the question reception unit may refer to the parent's past question history to select the optimal method of receiving a question. Step 5: The answering unit provides a detailed answer to the question received by the question receiving unit. For example, the answering unit provides a detailed answer by referring to past statements and conversation history. The answering unit can also estimate the parent's emotions and adjust the way the answer is expressed based on the estimated parent's emotions. Furthermore, when generating an answer, the answering unit can also improve the accuracy of the answer by referring to the parent's past answers.

[0076] (Example 2) In an embodiment of the present invention, a system is designed to have a child wear a device, and AI summarizes the child's statements and conversations for the day and notifies the parent's smartphone. This system analyzes and summarizes the child's statements and conversations in real time and notifies the parent's smartphone. For example, it can analyze what the child said at school and what conversations they had with their friends. The AI ​​then summarizes the analyzed content and notifies the parent's smartphone. For privacy reasons, no recording is performed; the summary is simply a daily report. Furthermore, if the parent asks a question about something they are concerned about, the AI ​​can provide a more detailed answer. For example, if a parent asks, "What did you learn in class today?" the AI ​​can provide a detailed response. This allows the system to grasp the content of the child's statements and conversations and gain a more detailed understanding of the child's growth and learning status. Furthermore, for privacy reasons, no recording is performed; the summary is simply a daily report, thereby protecting the child's privacy.

[0077] An information notification system according to an embodiment includes an analysis unit, a summarization unit, a notification unit, a question reception unit, and an answering unit. The analysis unit instantly analyzes a child's utterances or conversations. For example, the analysis unit converts the utterances or conversations into text in real time using speech recognition technology and analyzes the text. The analysis unit can also estimate the child's emotions and adjust the analysis method for the utterances or conversations based on the estimated child's emotions. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to the child's past utterance history when analyzing the utterances or conversations. The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit extracts and summarizes important information from the child's utterances or conversations. The summarization unit can also estimate the child's emotions and adjust the presentation method of the summary based on the estimated child's emotions. Furthermore, the summarization unit can adjust the level of detail of the summary based on the importance of the utterances or conversations when generating the summary. The notification unit notifies the parent of the content summarized by the summarization unit via a method such as push notification, SMS, or email. The notification unit can also estimate the child's emotions and adjust the timing of notifications based on the estimated child's emotions. Furthermore, the notification unit can select the optimal notification method by referring to the parent's past notification history when notifying the child. The question reception unit provides an interface for parents to ask questions about concerns. For example, the question reception unit receives questions by text input through a smartphone app. The question reception unit can also estimate the parent's emotions and adjust the method of receiving the question based on the estimated parent's emotions. Furthermore, the question reception unit can also select the optimal method of receiving the question by referring to the parent's past question history when receiving a question. The answering unit provides a detailed answer to the question received by the question reception unit. For example, the answering unit provides a detailed answer by referring to a history of past statements and conversations. The answering unit can also estimate the parent's emotions and adjust the method of expressing the answer based on the estimated parent's emotions. Furthermore, the answering unit can improve the accuracy of the answer when generating an answer by referring to the parent's past answers. As a result, the information notification system according to the embodiment analyzes a child's statements and conversations in real time, summarizes them, and notifies the parent's smartphone, allowing the parent to understand the situation of the child.

[0078] The analysis unit can instantly convert utterances or conversations into text using speech recognition technology and analyze the text. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and HMM-based speech recognition. The analysis unit can convert children's utterances and conversations into text in real time using, for example, deep learning-based speech recognition technology. The analysis unit can also convert utterances and conversations into text using HMM-based speech recognition technology. Furthermore, the analysis unit can also analyze the content of utterances and conversations using speech recognition technology. As a result, speech recognition technology can accurately convert utterances and conversations into text, improving analysis accuracy. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data converted into text using speech recognition technology into a generation AI, which then performs analysis.

[0079] The summarization unit can extract and summarize necessary information from a child's utterances or conversations. The necessary information includes, but is not limited to, frequently occurring keywords and important phrases. For example, the summarization unit can extract frequently occurring keywords and summarize based on them. The summarization unit can also extract important phrases and summarize based on them. Furthermore, the summarization unit can extract information related to a specific topic from a child's utterances or conversations and summarize based on them. By extracting and summarizing important information, parents can efficiently grasp the information they need. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to extract important information from a child's utterances or conversations and summarize them.

[0080] The notification unit can notify the parent's smartphone of the summarized content. Notification methods include, but are not limited to, push notification, SMS, email, etc. For example, the notification unit can notify the parent's smartphone of the summarized content using push notification. The notification unit can also notify the parent's smartphone of the summarized content using SMS. Furthermore, the notification unit can also notify the parent's smartphone of the summarized content using email. In this way, by notifying the parent's smartphone of the summarized content, the parent can understand the child's situation in real time. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can notify the parent's smartphone of the summarized content using a generation AI.

[0081] The question reception unit can easily receive questions through a smartphone app. Methods for receiving questions include, but are not limited to, text input and voice input, for example. The question reception unit can receive questions by text input through a smartphone app. The question reception unit can also receive questions by voice input through a smartphone app. Furthermore, the question reception unit can estimate the parent's emotions and adjust the method for receiving questions based on the estimated parent's emotions. This allows parents to easily input questions through the smartphone app. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can estimate the parent's emotions using a generation AI and adjust the method for receiving questions based on the estimated parent's emotions.

[0082] The answering unit can provide a specific answer by referring to a history of past statements or conversations. Specific answers include, but are not limited to, past data references and expert opinions. The answering unit can, for example, refer to a history of past statements or conversations and provide a detailed answer based thereon. The answering unit can also refer to expert opinions and provide a detailed answer based thereon. Furthermore, the answering unit can estimate the parent's emotions and adjust the way the answer is expressed based on the estimated parent's emotions. This allows a detailed answer to be provided by referring to the history of past statements and conversations. Some or all of the above-described processing in the answering unit may be performed, for example, using AI, or may be performed without using AI. For example, the answering unit can use a generation AI to refer to a history of past statements and conversations and provide a detailed answer.

[0083] The analysis unit can estimate the child's emotions and change the analysis method of the utterances or conversations based on the estimated child's emotions. For example, if the child is excited, the analysis unit can adjust the analysis method by taking into account the speed and tone of the utterances. Furthermore, if the child is calm, the analysis unit can also adjust the analysis method by focusing on the content of the utterances. Furthermore, if the child is tired, the analysis unit can also adjust the analysis method by taking into account the frequency of the utterances and the conciseness of the content. This improves the accuracy of analysis by adjusting the analysis method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the child's emotions using a generation AI and adjust the analysis method of the utterances or conversations based on the estimated child's emotions.

[0084] When analyzing a statement or conversation, the analysis unit can improve the accuracy of the analysis by referring to the child's past statement history. Past statement history includes, but is not limited to, text data and audio data. The analysis unit can improve the accuracy of the analysis by, for example, referring to specific phrases or words used by the child in the past. The analysis unit can also analyze the child's past statement patterns to more accurately analyze the content of the child's current statement. Furthermore, the analysis unit can prioritize analysis of statements related to specific topics based on the child's past conversation history. This improves the accuracy of the analysis by referring to the past statement history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by using a generation AI to refer to the child's past statement history.

[0085] When analyzing utterances or conversations, the analysis unit can change the analysis algorithm depending on the child's age or grade. For example, the analysis unit adjusts the analysis algorithm depending on the child's age, taking into account the vocabulary used and the complexity of grammar. The analysis unit can also adjust the analysis algorithm depending on the child's grade, taking into account the learning content and topics of interest. Furthermore, the analysis unit can adjust the analysis algorithm depending on the child's developmental stage, taking into account the child's level of comprehension of utterances and expressiveness. This improves analysis accuracy by applying an analysis algorithm depending on the child's age and grade. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis algorithm depending on the child's age and grade using a generation AI.

[0086] When analyzing speech or conversation, the analysis unit can improve analysis accuracy based on background or environmental sounds of the child's speech. For example, when background sounds are loud, the analysis unit can use noise canceling technology to clarify the speech content. Furthermore, when environmental sounds have a specific pattern, the analysis unit can improve accuracy by reflecting that pattern in the analysis. Furthermore, when background sounds are quiet, the analysis unit can improve analysis accuracy by focusing on the speech content. This improves analysis accuracy by taking background and environmental sounds into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can improve analysis accuracy by taking background and environmental sounds into account using a generation AI.

[0087] The analysis unit can estimate the child's emotions and prioritize the analysis results based on the estimated child's emotions. For example, if the child is excited, the analysis unit can prioritize analyzing important utterances. Furthermore, if the child is calm, the analysis unit can analyze the entire content of the utterance evenly. Furthermore, if the child is tired, the analysis unit can prioritize analyzing concise utterances. Thus, by prioritizing the analysis results according to the child's emotions, important information can be analyzed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can estimate the child's emotions using a generation AI and prioritize the analysis results based on the estimated child's emotions.

[0088] When analyzing utterances or conversations, the analysis unit can prioritize analysis of highly relevant information based on the child's geographical location information. For example, when the child is at school, the analysis unit prioritizes analysis of utterances related to learning content. Furthermore, when the child is at a park, the analysis unit can prioritize analysis of playtime and conversations with friends. Furthermore, when the child is at home, the analysis unit can prioritize analysis of conversations within the home. In this way, highly relevant information can be prioritized by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use a generation AI to prioritize analysis of highly relevant information based on the child's geographical location information.

[0089] The analysis unit can analyze the child's social media activity and analyze related information when analyzing comments or conversations. For example, the analysis unit analyzes related comments based on content shared by the child on social media. The analysis unit can also analyze conversation content taking into account the child's social media friendships. Furthermore, the analysis unit can prioritize analysis of topics of interest based on the child's social media activity history. This allows for efficient analysis of related information by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the child's social media activity and analyze related information using generation AI.

[0090] When analyzing utterances or conversations, the analysis unit can change the analysis method based on the child's past feedback. The analysis unit can adjust the analysis method based on, for example, feedback provided by the child in the past. The analysis unit can also prioritize analysis of utterances on specific topics based on the child's past feedback. Furthermore, the analysis unit can improve the analysis algorithm by reflecting the child's feedback. In this way, by reflecting past feedback, the analysis method can be customized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can use generation AI to change the analysis method based on the child's past feedback.

[0091] The summarization unit can estimate the child's emotions and change the way the summary is presented based on the estimated child's emotions. For example, if the child is excited, the summarization unit can simplify the summary content and emphasize important points. The summarization unit can also provide a detailed summary if the child is calm. Furthermore, if the child is tired, the summarization unit can simplify the summary content and provide it in an easy-to-understand format. This allows the summary to be adjusted according to the child's emotions, making it easier for parents to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can estimate the child's emotions using a generation AI and adjust the way the summary is presented based on the estimated child's emotions.

[0092] When generating a summary, the summarization unit can change the level of detail of the summary based on the importance of the utterance or conversation. For example, the summarization unit summarizes important utterances or conversations in detail and summarizes other utterances briefly. The summarization unit can also adjust the length of the summary based on the importance of the utterance or conversation. Furthermore, the summarization unit can prioritize summarizing utterances with high importance and omit utterances with low importance. In this way, by adjusting the level of detail of the summary based on the importance of the utterance or conversation, parents can efficiently grasp the information they need. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or without AI. For example, the summarization unit can adjust the level of detail of the summary based on the importance of the utterance or conversation using a generation AI.

[0093] When generating a summary, the summarization unit can use different summarization algorithms depending on the category of the utterance or conversation. For example, the summarization unit can apply a summarization algorithm focused on academic content to utterances made at school. The summarization unit can also apply a summarization algorithm focused on social content to conversations with friends. Furthermore, the summarization unit can apply a summarization algorithm focused on family life to conversations made at home. In this way, applying a summarization algorithm according to the category improves the accuracy of the summary. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to apply different summarization algorithms depending on the category of the utterance or conversation.

[0094] When generating a summary, the summarization unit can improve the accuracy of the summary based on the child's past summarization results. For example, the summarization unit can analyze the child's past summarization results to improve the accuracy of the summary. The summarization unit can also prioritize summarization of specific topics based on the past summarization results. Furthermore, the summarization unit can improve the summarization algorithm based on the child's past summarization results. This improves the accuracy of the summary by referring to the past summarization results. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to improve the accuracy of the summary based on the child's past summarization results.

[0095] The summarization unit can estimate the child's emotions and change the length of the summary based on the estimated child's emotions. For example, if the child is excited, the summarization unit can shorten the summary and emphasize important points. The summarization unit can also provide a detailed summary if the child is calm. Furthermore, if the child is tired, the summarization unit can simplify the summary and provide it in an easy-to-understand format. This allows the summary to be adjusted in length according to the child's emotions, making it easier for parents to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using AI, or without AI. For example, the summarization unit can estimate the child's emotions using a generation AI and adjust the length of the summary based on the estimated child's emotions.

[0096] When generating summaries, the summarization unit can set the priority of summaries based on the time of occurrence of utterances or conversations. For example, the summarization unit prioritizes summarization of recent utterances or conversations. The summarization unit can also prioritize summarization of utterances or conversations that occurred during a specific time period. Furthermore, the summarization unit can adjust the order of summaries based on the time of occurrence of utterances or conversations. In this way, by determining the priority of summaries based on the time of occurrence, important information can be prioritized in summarization. Some or all of the above-described processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to set the priority of summaries based on the time of occurrence of utterances or conversations.

[0097] When generating summaries, the summarization unit can change the order of summaries based on the relevance of utterances or conversations. For example, the summarization unit prioritizes summarization of highly relevant utterances or conversations. The summarization unit can also adjust the order of summaries based on the relevance of utterances or conversations. Furthermore, the summarization unit can also briefly summarize less relevant utterances or conversations. In this way, adjusting the order of summaries based on relevance allows parents to efficiently grasp important information. Some or all of the above-described processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can adjust the order of summaries based on the relevance of utterances or conversations using a generation AI.

[0098] When generating a summary, the summarization unit can change the use of technical terms in the summary depending on the parent's level of expertise. For example, if the parent has technical expertise, the summarization unit can provide a summary using technical terms. Alternatively, if the parent does not have technical expertise, the summarization unit can provide a summary in simple language. Furthermore, the summarization unit can adjust the content of the summary depending on the parent's level of expertise. This allows the summary to be easily understood by adjusting the technical terms in the summary depending on the parent's level of expertise. Some or all of the above-described processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can use a generation AI to change the use of technical terms in the summary depending on the parent's level of expertise.

[0099] The notification unit can estimate the child's emotion and change the timing of the notification based on the estimated emotion. For example, if the child is excited, the notification unit can immediately send a notification. Also, if the child is calm, the notification unit can send a notification at an appropriate time. Furthermore, if the child is tired, the notification unit can delay the notification. This allows the parent to receive information at an appropriate time by adjusting the timing of the notification according to the child's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can estimate the child's emotion using the generation AI and change the timing of the notification based on the estimated emotion.

[0100] When notifying, the notification unit can select an appropriate notification method by referring to the parent's past notification history. For example, the notification unit can prioritize the selection of a notification method that the parent has used favorably in the past. The notification unit can also select the optimal notification timing based on the parent's past notification history. Furthermore, the notification unit can customize the notification content based on the parent's past notification history. In this way, by referring to the past notification history, the optimal notification method for the parent can be selected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to select an appropriate notification method by referring to the parent's past notification history.

[0101] The notification unit can change the notification content based on the parent's current situation or areas of interest at the time of notification. For example, the notification unit can provide brief notification content when the parent is at work. The notification unit can also provide detailed notification content when the parent is on vacation. Furthermore, the notification unit can customize the notification content based on the parent's areas of interest. This allows the parent to efficiently receive necessary information by customizing the notification content according to the parent's situation and areas of interest. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to change the notification content based on the parent's current situation and areas of interest.

[0102] The notification unit can change the notification method based on parental feedback at the time of notification. The notification unit can adjust the notification method based on, for example, feedback provided by the parent. The notification unit can also select the optimal notification timing based on the parental feedback. Furthermore, the notification unit can customize the notification content by reflecting the parental feedback. In this way, by reflecting the parental feedback, the notification method can be improved, enabling optimal notification for the parent. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or without using AI. For example, the notification unit can change the notification method based on the parental feedback using a generation AI.

[0103] The notification unit can estimate the child's emotions and set a priority order for notification content based on the estimated child's emotions. For example, if the child is excited, the notification unit can prioritize important statements. Furthermore, if the child is calm, the notification unit can also uniformly notify the entire content of the child's statements. Furthermore, if the child is tired, the notification unit can prioritize concise statements. Thus, by determining the priority order of notification content according to the child's emotions, important information can be prioritized. The emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can estimate the child's emotions using a generation AI and set a priority order for notification content based on the estimated child's emotions.

[0104] The notification unit can select an appropriate notification method based on the parent's geographical location information when notifying the parent. For example, the notification unit can provide detailed notification content when the parent is at home. The notification unit can also provide concise notification content when the parent is out. Furthermore, the notification unit can select an optimal notification method based on the parent's geographical location information. This makes it possible to select an optimal notification method for the parent by taking the geographical location information into consideration. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to select an appropriate notification method based on the parent's geographical location information.

[0105] The notification unit can analyze the parent's social media activity and change the notification content when sending a notification. For example, the notification unit can provide relevant notification content based on content shared by the parent on social media. The notification unit can also notify the parent of topics of interest based on the parent's social media activity history. Furthermore, the notification unit can customize the notification content taking into account the parent's social media friendships. In this way, by analyzing social media activity, it is possible to provide notification content that is highly relevant to the parent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can use a generation AI to analyze the parent's social media activity and change the notification content.

[0106] The notification unit can change the notification method based on the parent's past feedback when notifying the child. The notification unit can adjust the notification method based on, for example, feedback provided by the parent in the past. The notification unit can also select the optimal notification timing based on the parent's past feedback. Furthermore, the notification unit can customize the notification content by reflecting the parent's past feedback. In this way, by reflecting past feedback, it is possible to provide the parent with the optimal notification method. Some or all of the above-mentioned processing in the notification unit can be performed, for example, using AI, or can be performed without using AI. For example, the notification unit can use a generation AI to change the notification method based on the parent's past feedback.

[0107] The question reception unit can estimate the parent's emotions and change the question reception method based on the estimated parent's emotions. For example, if the parent is nervous, the question reception unit can provide a simple interface and minimize the question procedure. Furthermore, if the parent is relaxed, the question reception unit can provide detailed question options and suggest a customizable question method. Furthermore, if the parent is in a hurry, the question reception unit can prioritize voice input and quickly accept questions. This allows the parent to easily ask questions by adjusting the question reception method according to the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the question reception unit may be performed using AI, or may be performed without AI. For example, the question reception unit can estimate the parent's emotions using a generation AI and change the question reception method based on the estimated parent's emotions.

[0108] When receiving a question, the question receiving unit can select an appropriate question receiving method by referring to the parent's past question history. For example, the question receiving unit can automatically display questions that the parent has frequently asked in the past as candidates. The question receiving unit can also preferentially suggest question methods (voice, text, etc.) that the parent has used in the past. Furthermore, the question receiving unit can predict and suggest question content that will be used at a specific time period based on the parent's past question history. In this way, by referring to the past question history, it is possible to provide the parent with the optimal question receiving method. Some or all of the above-mentioned processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can use a generation AI to select an appropriate question receiving method by referring to the parent's past question history.

[0109] The question receiving unit can change the content of the question based on the parent's current situation or area of ​​interest when receiving a question. For example, if the parent is at work, the question receiving unit can provide a concise question. Furthermore, if the parent is on vacation, the question receiving unit can also provide a detailed question. Furthermore, the question receiving unit can customize the content of the question based on the parent's area of ​​interest. This allows the parent to efficiently obtain the information they need by customizing the content of the question according to the parent's situation or area of ​​interest. Some or all of the above-described processing in the question receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the question receiving unit can change the content of the question based on the parent's current situation or area of ​​interest using a generation AI.

[0110] The question reception unit can change the question reception method based on parental feedback when receiving a question. The question reception unit adjusts the question reception method based on, for example, feedback provided by the parent. The question reception unit can also select the optimal timing for receiving a question based on the parental feedback. Furthermore, the question reception unit can customize the content of the question by reflecting the parental feedback. In this way, by reflecting the parental feedback, the question reception method can be improved, enabling the parent to receive a question that is optimal for the parent. Some or all of the above-described processing in the question reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the question reception unit can change the question reception method based on the parental feedback using a generation AI.

[0111] The question receiving unit can estimate the parent's emotions and prioritize the questions based on the estimated parent's emotions. For example, if the parent is nervous, the question receiving unit can prioritize important questions. Furthermore, if the parent is relaxed, the question receiving unit can equally prioritize all questions. Furthermore, if the parent is in a hurry, the question receiving unit can prioritize concise questions. In this way, by determining the priority of questions according to the parent's emotions, important questions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the question receiving unit may be performed using an AI, for example, or without an AI. For example, the question receiving unit can estimate the parent's emotions using a generation AI and prioritize the questions based on the estimated parent's emotions.

[0112] The question reception unit can select an appropriate question reception method based on the parent's geographical location information when receiving a question. For example, if the parent is at home, the question reception unit can provide detailed question content. Furthermore, if the parent is out, the question reception unit can also provide a concise question content. Furthermore, the question reception unit can select an optimal question reception method based on the parent's geographical location information. In this way, by taking the geographical location information into consideration, it is possible to provide an optimal question reception method for the parent. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can select an appropriate question reception method based on the parent's geographical location information using a generation AI.

[0113] The question receiving unit can analyze the parent's social media activity and change the content of the question when receiving a question. The question receiving unit can provide relevant question content based on, for example, content shared by the parent on social media. The question receiving unit can also ask questions about topics of interest to the parent based on the parent's social media activity history. Furthermore, the question receiving unit can customize the question content taking into account the parent's social media friendships. In this way, by analyzing social media activity, it is possible to provide questions that are highly relevant to the parent. Some or all of the above-described processing in the question receiving unit can be performed, for example, using AI or without AI. For example, the question receiving unit can use a generation AI to analyze the parent's social media activity and change the content of the question.

[0114] The question receiving unit can change the question receiving method based on the parent's past feedback when receiving a question. The question receiving unit can adjust the question receiving method based on, for example, feedback provided by the parent in the past. The question receiving unit can also select the optimal timing for receiving a question based on the parent's past feedback. Furthermore, the question receiving unit can customize the content of the question by reflecting the parent's past feedback. In this way, by reflecting the past feedback, it is possible to provide the parent with an optimal question receiving method. Some or all of the above-described processing in the question receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the question receiving unit can use a generation AI to change the question receiving method based on the parent's past feedback.

[0115] The answering unit can estimate the parent's emotions and change the way the answer is expressed based on the estimated parent's emotions. For example, if the parent is nervous, the answering unit can provide a simple, highly visible answer. Furthermore, if the parent is relaxed, the answering unit can also provide an answer that includes detailed information. Furthermore, if the parent is in a hurry, the answering unit can provide a concise answer that focuses on the main points. This allows the answering unit to adjust the way the answer is expressed based on the parent's emotions, making it easy for the parent to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answering unit can be performed using, for example, AI, or without AI. For example, the answering unit can estimate the parent's emotions using a generation AI and change the way the answer is expressed based on the estimated parent's emotions.

[0116] When generating an answer, the answering unit can improve the accuracy of the answer by referring to past utterances or conversation history. The answering unit, for example, provides related information based on past utterances and conversation history. The answering unit can also provide a detailed answer on a specific topic from past utterances and conversation history. Furthermore, the answering unit can analyze past utterances and conversation history to provide the most appropriate answer. In this way, the accuracy of the answer is improved by referring to past utterances and conversation history. Some or all of the above-mentioned processing in the answering unit may be performed, for example, using AI or may be performed without using AI. For example, the answering unit can use a generation AI to improve the accuracy of the answer by referring to past utterances and conversation history.

[0117] When generating an answer, the answering unit can use different answering algorithms depending on the content of the parent's question. For example, the answering unit can apply an answering algorithm that focuses on academic content to a question about school. The answering unit can also apply an answering algorithm that focuses on social content to a question about conversations with friends. The answering unit can also apply an answering algorithm that focuses on home life to a question about conversations at home. In this way, by applying an answering algorithm depending on the content of the question, it is possible to provide the parent with an optimal answer. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generating AI to use different answering algorithms depending on the content of the parent's question.

[0118] When generating an answer, the answer unit can improve the accuracy of the answer based on the parent's past answer results. For example, the answer unit can analyze the parent's past answer results to improve the accuracy of the answer. The answer unit can also provide a detailed answer on a specific topic from the past answer results. Furthermore, the answer unit can improve the answer algorithm based on the parent's past answer results. This improves the accuracy of the answer by referring to the past answer results. Some or all of the above-mentioned processing in the answer unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer unit can use a generation AI to improve the accuracy of the answer based on the parent's past answer results.

[0119] The answering unit can estimate the parent's emotions and change the length of the answer based on the estimated parent's emotions. For example, if the parent is nervous, the answering unit can provide a short, to-the-point answer. Alternatively, if the parent is relaxed, the answering unit can provide a longer answer with detailed information. Furthermore, if the parent is in a hurry, the answering unit can provide a concise, quick answer. This allows the length of the answer to be adjusted according to the parent's emotions, making it possible to provide an answer that is easy for the parent to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answering unit can be performed using, for example, AI, or without AI. For example, the answering unit can estimate the parent's emotions using a generation AI and change the length of the answer based on the estimated parent's emotions.

[0120] When generating answers, the answering unit can prioritize answers based on the time of question generation. For example, the answering unit prioritizes answers to recent questions. The answering unit can also prioritize answers to questions that occurred during a specific time period. Furthermore, the answering unit can adjust the order of answers based on the time of question generation. In this way, by determining the priority of answers based on the time of question generation, important questions can be answered preferentially. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generation AI to prioritize answers based on the time of question generation.

[0121] The answering unit can change the order of answers based on the relevance of the questions when generating answers. For example, the answering unit prioritizes answers to highly relevant questions. The answering unit can also adjust the order of answers based on the relevance of the questions. Furthermore, the answering unit can also briefly answer questions with low relevance. By adjusting the order of answers based on relevance, parents can efficiently grasp important information. Some or all of the above-described processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can change the order of answers based on the relevance of the questions using a generation AI.

[0122] The answering unit can change the use of technical terminology in the answer depending on the parent's level of expertise. For example, if the parent has technical knowledge, the answering unit can provide an answer using technical terminology. Also, if the parent does not have technical knowledge, the answering unit can provide an answer in simple language. Furthermore, the answering unit can adjust the content of the answer depending on the parent's level of expertise. In this way, by adjusting the technical terminology in the answer depending on the parent's level of expertise, an answer that is easy for the parent to understand can be provided. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can use a generation AI to change the use of technical terminology in the answer depending on the parent's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, notification unit, question reception unit, and answer unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the summarization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing device 12. For example, the question reception unit is realized by the reception device 38 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, notification unit, question reception unit, and answer unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the summarization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing device 12. For example, the question reception unit is realized by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, notification unit, question reception unit, and answering unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the summarization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the communication I / F 44 of the headset type terminal 314 or the communication I / F 26 of the data processing device 12. For example, the question reception unit is realized by the microphone 238 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, summarization unit, notification unit, question reception unit, and answering unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the summarization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the notification unit is realized by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing device 12. For example, the question reception unit is realized by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the answering unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The analysis unit can take into account the child's health condition when analyzing the content of a child's statements and conversations. For example, if a child has a cold, the tone and frequency of their statements may change, so the analysis unit adjusts the analysis method taking this into account. Also, if a child is tired, the content of their statements is expected to be brief, so the analysis unit can improve the accuracy of the analysis by taking this into account. Furthermore, if a child is stressed, the content and tone of their statements may change, so the analysis unit can adjust the analysis method by taking this into account. In this way, the analysis accuracy is improved by adjusting the analysis method according to the child's health condition.

[0125] When generating summaries of a child's utterances and conversations, the summarization unit can customize the content of the summary based on the parent's interests. For example, if a parent is interested in their child's learning status, the summarization unit can prioritize summarizing utterances related to learning. Also, if a parent is interested in their child's friendships, the summarization unit can prioritize summarizing conversations with friends. Furthermore, if a parent is interested in their child's health, the summarization unit can prioritize summarizing utterances related to health. In this way, by customizing the content of the summary according to the parent's interests, parents can efficiently grasp the information they need.

[0126] When notifying the parent's smartphone of the summarized content, the notification unit can adjust the notification method taking into account the parent's current activity status. For example, if the parent is driving, the notification unit can prioritize voice notification. Also, if the parent is in a meeting, the notification unit can prioritize vibration notification. Furthermore, if the parent is relaxed, the notification unit can provide detailed text notification. This allows the parent to receive information at the appropriate time by adjusting the notification method according to the parent's activity status.

[0127] When a parent inputs a question, the question receiving unit can provide an auto-complete function based on the parent's past question history. For example, it can automatically display questions that the parent has frequently asked in the past as candidates. It can also prioritize and suggest question methods (voice, text, etc.) that the parent has used in the past. It can also predict and suggest questions that will be used at specific times based on the parent's past question history. This makes it possible to provide the parent with the optimal question receiving method by referring to the past question history.

[0128] When generating an answer to a parent's question, the answering unit can adjust the way the answer is expressed, taking into account the parent's current emotional state. For example, if the parent is nervous, a simple, highly visible answer can be provided. If the parent is relaxed, an answer containing detailed information can be provided. Furthermore, if the parent is in a hurry, a concise answer that hits the main points can be provided. In this way, by adjusting the way the answer is expressed depending on the parent's emotions, it is possible to provide an answer that is easy for the parent to understand.

[0129] The analysis unit can take the child's learning style into consideration when analyzing the content of a child's utterances and conversations. For example, if the child is a visual learner, the analysis unit can emphasize visual elements in its analysis. If the child is an auditory learner, the analysis unit can emphasize tone and rhythm of the voice. Furthermore, if the child is an experiential learner, the analysis unit can emphasize utterances based on actual experiences. This improves the accuracy of the analysis by adjusting the analysis method according to the child's learning style.

[0130] When generating summaries of a child's utterances or conversations, the summarization unit can adjust the level of detail of the summary based on the frequency and length of the child's utterances. For example, for a child who speaks frequently, the summarization unit can extract important points and provide a concise summary. For a child who speaks less, the summarization unit can provide a detailed summary. Furthermore, for a child who speaks longer, the summarization unit can extract and summarize the main points of the utterances. This allows parents to efficiently grasp the information they need by adjusting the level of detail of the summary based on the frequency and length of the utterances.

[0131] When notifying the parent's smartphone of the summarized content, the notification unit can select the optimal notification method based on the parent's past notification history. For example, it can prioritize the notification method that the parent has used in the past. It can also select the optimal notification timing based on the parent's past notification history. Furthermore, it can customize the notification content based on the parent's past notification history. This allows the optimal notification method for the parent to be selected by referring to the past notification history.

[0132] When a parent inputs a question, the question receiving unit can customize the question content based on the parent's current situation and areas of interest. For example, if the parent is at work, a concise question content can be provided. Alternatively, if the parent is on vacation, a detailed question content can be provided. Furthermore, the question content can be customized based on the parent's areas of interest. This allows the parent to efficiently obtain the information they need by customizing the question content according to the parent's situation and areas of interest.

[0133] When generating an answer to a parent's question, the answering unit can improve the accuracy of the answer based on the parent's past answer results. For example, the answering unit can analyze the parent's past answer results to improve the accuracy of the answer. It can also provide detailed answers on specific topics based on the past answer results. It can also improve the answering algorithm based on the parent's past answer results. This improves the accuracy of the answer by referring to the past answer results.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The analysis unit immediately analyzes the child's statements or conversations. For example, the analysis unit converts the statements and conversations into text in real time using voice recognition technology and analyzes the text. The analysis unit can also estimate the child's emotions and adjust the analysis method for the statements and conversations based on the estimated child's emotions. Furthermore, when analyzing the statements and conversations, the analysis unit can also improve the accuracy of the analysis by referring to the child's past statement history. Step 2: The summarization unit summarizes the content analyzed by the analysis unit. For example, the summarization unit extracts and summarizes important information from the child's utterances and conversations. The summarization unit can also estimate the child's emotions and adjust the way the summary is presented based on the estimated child's emotions. Furthermore, when generating the summary, the summarization unit can adjust the level of detail of the summary based on the importance of the utterances and conversations. Step 3: The notification unit notifies the parent's smartphone of the content summarized by the summarization unit. For example, the notification unit may send notifications by push notification, SMS, email, or other methods. The notification unit may also estimate the child's emotions and adjust the timing of notifications based on the estimated child's emotions. Furthermore, the notification unit may refer to the parent's past notification history when sending notifications to select the optimal notification method. Step 4: The question reception unit provides an interface for parents to ask questions about concerns. For example, the question reception unit may receive questions by text input via a smartphone app. The question reception unit may also estimate the parent's emotions and adjust the method of receiving questions based on the estimated parent's emotions. Furthermore, when receiving a question, the question reception unit may refer to the parent's past question history to select the optimal method of receiving a question. Step 5: The answering unit provides a detailed answer to the question received by the question receiving unit. For example, the answering unit provides a detailed answer by referring to past statements and conversation history. The answering unit can also estimate the parent's emotions and adjust the way the answer is expressed based on the estimated parent's emotions. Furthermore, when generating an answer, the answering unit can also improve the accuracy of the answer by referring to the parent's past answers.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 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. an analysis unit that instantly analyzes a child's utterances or conversations; a summarizing unit that summarizes the content analyzed by the analyzing unit; a notification unit that notifies the parent's smartphone of the content summarized by the summarization unit; a question reception unit that provides an interface for parents to ask questions about concerns; an answering unit that provides detailed answers to the questions received by the question receiving unit; Equipped with A system characterized by:

2. The analysis unit Using voice recognition technology, speech or conversation is instantly converted into text and the text is analyzed.

2. The system of claim 1.

3. The summary section Extract and summarize necessary information from a child's statements or conversations 2. The system of claim 1.

4. The notification unit The summary is sent to the parent's smartphone.

2. The system of claim 1.

5. The question receiving unit Easily accept questions via a smartphone app 2. The system of claim 1.

6. The answering section Refer to previous statements or conversation history to provide specific answers 2. The system of claim 1.

7. The analysis unit Inferring a child's emotions and changing how utterances or conversations are analyzed based on the estimated emotions of the child 2. The system of claim 1.

8. The analysis unit When analyzing speech or conversation, the accuracy of the analysis is improved by referring to the child's past speech history.

2. The system of claim 1.

Images