system

Abstract

The system according to this embodiment aims to facilitate communication between parents and children even amidst busy daily lives, and to enhance children's self-esteem. [Solution] The system according to the embodiment comprises a reception unit, an advice unit, and a dialogue unit. The reception unit is accessed when the parent puts on audio glasses and activates the generation AI application. The advice unit provides advice in real time based on the information received by the reception unit. The dialogue unit allows the parent to interact with the child based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for parents to smoothly communicate with their children during busy days, and there is a lack of support for enhancing the self-affirmation of children.

[0005] The system according to the embodiment aims to smooth the communication between parents and children during busy days and enhance the self-affirmation of children.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an advice unit, and a dialogue unit. The reception unit is accessed when the parent puts on audio glasses and activates a generation AI application. The advice unit provides advice in real time based on the information received by the reception unit. The dialogue unit allows the parent to interact with the child based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can facilitate communication with children even during busy days for parents, and can enhance children's self-esteem. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The generative AI application according to an embodiment of the present invention is a system for facilitating communication between parents and children and enhancing children's self-esteem. This system provides real-time advice when a parent wears audio glasses and activates the generative AI application, supporting the parent in listening to and responding gently to their child. For example, when a parent begins a conversation with their child, the generative AI provides real-time advice, supporting the parent in listening to and responding gently. This advice is conveyed to the parent through the audio glasses, allowing the parent to continue the conversation with their child naturally. Furthermore, the generative AI application includes a function that allows parents to consult about parenting and daily concerns, reducing feelings of isolation. It also sends notifications during specific events (such as school events or grade announcements), prompting parents to converse at the appropriate time and ensuring they don't miss opportunities to communicate with their children. This improves children's self-esteem, reduces the burden of childcare, and increases mental well-being and smiles in families with children. For example, a parent wears audio glasses and activates the generative AI application. In this case, from a privacy perspective, audio is collected only when the application is activated. Next, when a parent begins a conversation with their child, the generating AI provides advice in real time. For example, if a child is talking about something that happened at school, the generating AI might suggest a gentle response like, "That must have been tough." Parents can then use this advice to listen to their child and respond appropriately. Furthermore, the generating AI app includes a feature that allows parents to consult about parenting and everyday worries. For example, if a parent consults about a problem such as, "My child had a fight with a friend at school," the generating AI might offer advice like, "First, listen carefully to what your child has to say." This gives parents a sense of security and reduces feelings of isolation. The app also sends notifications for specific events (such as school events or grade announcements), prompting parents to engage in conversation at the appropriate time. For example, a notification might be sent on the day grades are announced, allowing parents to start a conversation with their child by saying, "Today is the day grades are announced, how did it go?" This ensures that parents don't miss opportunities to communicate with their children and can provide appropriate feedback.In this way, using a generative AI app can facilitate communication between parents and children and improve children's self-esteem. Furthermore, parents can consult about parenting and daily concerns, reducing the burden of childcare and increasing mental well-being and smiles in families with children. Thus, generative AI apps can facilitate communication between parents and children and enhance children's self-esteem.

[0029] The generative AI application according to this embodiment comprises a reception unit, an advice unit, and a dialogue unit. The reception unit receives audio from the parent when they put on audio glasses and launch the generative AI application. The reception unit collects audio from the parent when they put on audio glasses and launch the generative AI application and inputs it into the generative AI. The advice unit provides advice in real time based on the information received by the reception unit. The advice unit provides appropriate advice from the parent when they are talking to their child, for example, using the generative AI. The generative AI generates appropriate advice from the parent when they are talking to their child, using a text generation AI (e.g., LLM). The dialogue unit allows the parent to talk to their child based on the advice provided by the advice unit. The dialogue unit provides appropriate responses from the parent when they are talking to their child, for example, by referring to the advice from the generative AI. As a result, the generative AI application according to this embodiment allows the parent to practice active listening by talking to their child while receiving advice in real time through the audio glasses. Some or all of the above-described processing in the advice unit is performed using the generative AI. For example, the advice unit provides advice in real time using the generative AI when the parent is talking to their child. The generating AI generates appropriate advice when a parent interacts with their child and provides it to the advice unit. Some or all of the processing described above in the dialogue unit is performed using the generating AI. For example, the dialogue unit responds appropriately when a parent interacts with their child, taking into account the advice from the generating AI. The generating AI generates appropriate advice when a parent interacts with their child and provides it to the dialogue unit. As a result, the generating AI application according to this embodiment allows parents to practice active listening by interacting with their children while receiving advice in real time through audio glasses.

[0030] The reception desk works by having the parent put on audio glasses and launch the generation AI app. Specifically, when the parent puts on the audio glasses, the microphone built into the glasses begins to collect ambient sounds. When the generation AI app is launched, the reception desk collects this audio data in real time and inputs it into the generation AI. The audio glasses can collect not only the parent's voice but also the child's voice and ambient sounds, allowing for a comprehensive understanding of the conversation. Furthermore, the audio glasses are equipped with noise cancellation, which removes ambient noise and allows for the collection of clear audio data. The collected audio data is sent to a server within the generation AI app, where it is converted into text data using speech recognition technology. This text data is used as input data for the generation AI, and the content of the parent-child conversation is analyzed in real time. In addition to collecting audio data, the reception desk can also collect additional information to understand the parent's intentions and emotions. For example, if the parent utters a specific keyword, it provides the generation AI with information related to that keyword to help generate more appropriate advice. In this way, the reception desk can play a crucial role in facilitating smooth conversations between parents and children.

[0031] The advice unit provides real-time advice based on information received by the reception unit. Specifically, it uses generative AI to provide appropriate advice when parents interact with their children. The generative AI uses text generation AI (e.g., LLM) to generate appropriate advice when parents interact with their children. The generative AI analyzes the content of the parent-child conversation and determines in real time how the parent should respond and what questions they should ask. For example, if a child is talking about something that happened at school, the generative AI analyzes the content and advises the parent to ask questions such as, "How did that feel?" or "What are you going to do next?" Also, if a child is facing a difficult situation, the generative AI will offer words of encouragement and suggest concrete solutions to the parent. The advice unit notifies parents of the advice provided by the generative AI through the speakers of the audio glasses. This allows parents to receive advice naturally during the conversation. Furthermore, the advice unit monitors the responses of both the parent and the child and can modify the advice as needed. For example, if a child reacts negatively to the advice, the generative AI analyzes the reaction and re-evaluates what approach should be taken next. This allows the advisory department to provide support to facilitate smooth dialogue between parents and children, and to help parents deepen their communication with their children.

[0032] The dialogue unit facilitates parent-child interactions based on advice provided by the advice unit. Specifically, parents use the advice from the generative AI to provide appropriate responses during their conversations with their children. The dialogue unit provides an interface for parents to receive the generative AI's advice and incorporate it into their actual conversations. For example, while a parent is talking to their child, the generative AI's advice is played aloud through the speakers of the audio glasses. As the parent listens to the advice, they can naturally provide appropriate responses to their child. The dialogue unit can adjust the content and timing of the advice to make it easier for parents to accept. For example, it provides less advice when the parent is focused on what the child is saying, and offers more specific advice when the parent is struggling to respond. The dialogue unit also monitors how parents accept and implement the generative AI's advice and provides this feedback to the generative AI. This allows the generative AI to learn from the parent's responses and improve the accuracy of future advice. Furthermore, the dialogue unit provides additional features to improve the quality of parent-child interactions. For example, it provides a dashboard that visualizes the progress of the conversation, allowing parents to understand what topics they are discussing with their children. Furthermore, it provides a recording function for conversations, allowing parents to review the content of those conversations later. This enables the conversation function to support parents in effectively utilizing the advice generated by the AI ​​and enriching their conversations with their children.

[0033] The generative AI application according to this embodiment includes a consultation section where parents can consult about childcare and daily concerns. The consultation section allows parents to consult about childcare and daily concerns. For example, when a parent consults about childcare or daily concerns, the consultation section uses the generative AI to provide appropriate advice. The generative AI generates appropriate advice when a parent consults about childcare or daily concerns and provides it to the consultation section. This reduces feelings of loneliness because parents can consult about childcare and daily concerns. Some or all of the above-described processes in the consultation section are performed using the generative AI. For example, when a parent consults about childcare or daily concerns, the consultation section uses the generative AI to provide appropriate advice. The generative AI generates appropriate advice when a parent consults about childcare or daily concerns and provides it to the consultation section. This reduces feelings of loneliness because parents can consult about childcare and daily concerns.

[0034] The generative AI application according to this embodiment includes a notification unit that sends notifications at specific events. The notification unit sends notifications at specific events. For example, the notification unit sends notifications to parents at specific events such as school events or grade announcements. This allows parents to prompt conversation at the appropriate time. Some or all of the above processing in the notification unit is performed using the generative AI. For example, the notification unit sends notifications via the generative AI at specific events. The generative AI sends notifications to parents at specific events. This allows parents to prompt conversation at the appropriate time.

[0035] The advice unit provides advice in real time using a generative AI. For example, the advice unit uses the generative AI to provide appropriate advice when parents interact with their children. The generative AI generates appropriate advice when parents interact with their children and provides it to the advice unit. This allows parents to respond appropriately by providing advice in real time via the generative AI. Some or all of the above processing in the advice unit is performed using the generative AI. For example, the advice unit provides advice in real time using the generative AI when parents interact with their children. The generative AI generates appropriate advice when parents interact with their children and provides it to the advice unit. This allows parents to respond appropriately by providing advice in real time via the generative AI.

[0036] The dialogue unit takes advice from the generative AI into consideration when parents interact with their children. For example, when parents interact with their children, the dialogue unit takes advice from the generative AI into consideration and responds appropriately. The generative AI generates appropriate advice and provides it to the dialogue unit when parents interact with their children. This allows parents to engage in appropriate dialogue by taking advice from the generative AI. Some or all of the above processing in the dialogue unit is performed using the generative AI. For example, when parents interact with their children, the dialogue unit takes advice from the generative AI into consideration and responds appropriately. The generative AI generates appropriate advice and provides it to the dialogue unit when parents interact with their children. This allows parents to engage in appropriate dialogue by taking advice from the generative AI.

[0037] The notification unit sends notifications during specific events such as school events and grade announcements. For example, the notification unit sends notifications to parents during specific events such as school events and grade announcements. This allows parents to prompt conversation at the appropriate time. Some or all of the above processing in the notification unit is performed using a generative AI. For example, the notification unit sends notifications via the generative AI during specific events. The generative AI sends notifications to parents during specific events. This allows parents to prompt conversation at the appropriate time.

[0038] The reception desk analyzes the parent's past app usage history and selects the optimal reception method. For example, the reception desk may prompt the parent to launch the app during times when the parent frequently used it in the past. The reception desk prioritizes displaying features that the parent has preferred to use in the past. The reception desk automatically adjusts settings to provide optimal advice based on the parent's past usage history. This allows the reception desk to provide the optimal reception method by analyzing the parent's past app usage history. Some or all of the above processes in the reception desk are performed using a generative AI. For example, the reception desk inputs the parent's past app usage history into the generative AI and selects the optimal reception method. The generative AI analyzes the parent's past app usage history and selects the optimal reception method. This allows the reception desk to provide the optimal reception method by analyzing the parent's past app usage history.

[0039] The reception desk filters the app upon launch based on the parent's current lifestyle and areas of interest. For example, if the parent is at work, the reception desk prioritizes displaying features that can be used in a short amount of time. If the parent is relaxed, the reception desk displays features that provide detailed advice. The reception desk prioritizes displaying relevant advice and information based on the parent's areas of interest. This allows for the provision of highly relevant information by filtering based on the parent's current lifestyle and areas of interest. Some or all of the above processing in the reception desk is performed using a generative AI. For example, when the app is launched, the reception desk inputs the parent's current lifestyle and areas of interest into the generative AI and performs filtering. The generative AI filters based on the parent's current lifestyle and areas of interest. This allows for the provision of highly relevant information by filtering based on the parent's current lifestyle and areas of interest.

[0040] The reception desk prioritizes receiving highly relevant information when the app is launched, taking into account the parent's geographical location. For example, if the parent is at home, the reception desk prioritizes providing information that can be used within the home. If the parent is out, the reception desk prioritizes providing information that can be used while out. If the parent is in a specific location, the reception desk prioritizes providing information related to that location. This allows the reception desk to provide highly relevant information by taking into account the parent's geographical location. Some or all of the above processing in the reception desk is performed using a generative AI. For example, when the app is launched, the reception desk inputs the parent's geographical location into the generative AI and prioritizes receiving highly relevant information. The generative AI prioritizes receiving highly relevant information by taking into account the parent's geographical location. This allows the reception desk to provide highly relevant information by taking into account the parent's geographical location.

[0041] The reception desk analyzes the parent's social media activity and receives relevant information when the app is launched. For example, the reception desk provides relevant advice based on information the parent has shared on social media. The reception desk provides relevant information based on accounts the parent follows on social media. The reception desk provides relevant information based on topics the parent has shown interest in on social media. This allows the reception desk to provide highly relevant information by analyzing the parent's social media activity. Some or all of the above processing in the reception desk is performed using generative AI. For example, when the app is launched, the reception desk inputs the parent's social media activity into the generative AI and receives relevant information. The generative AI analyzes the parent's social media activity and receives relevant information. This allows the reception desk to provide highly relevant information by analyzing the parent's social media activity.

[0042] The advice unit adjusts the level of detail in the advice based on the importance of the child's story when providing advice. For example, if the child's story is important, the advice unit provides detailed advice. If the child's story is routine, the advice unit provides concise advice. If the child's story is urgent, the advice unit provides prompt advice. This allows the advice unit to provide appropriate advice by adjusting the level of detail based on the importance of the child's story. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the importance of the child's story into the generative AI and adjusts the level of detail in the advice. The generative AI adjusts the level of detail in the advice based on the importance of the child's story. This allows the advice unit to provide appropriate advice by adjusting the level of detail based on the importance of the child's story.

[0043] The advice unit applies different advice algorithms depending on the category of the child's story when providing advice. For example, if the child's story concerns school events, the advice unit provides advice on school life. If the child's story concerns friendships, the advice unit provides advice on friendships. If the child's story concerns domestic events, the advice unit provides advice on how to handle domestic situations. By applying different advice algorithms depending on the category of the child's story, appropriate advice can be provided. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the category of the child's story into the generative AI and applies a different advice algorithm. The generative AI then applies a different advice algorithm depending on the category of the child's story. By applying different advice algorithms depending on the category of the child's story, appropriate advice can be provided.

[0044] The advice department prioritizes advice based on when the child's story is submitted. For example, if the child's story concerns a recent event, the advice department will prioritize providing advice. If the child's story concerns a past event, the advice department will provide advice at an appropriate time. If the child's story concerns a future plan, the advice department will provide advice in advance. This allows the advice department to provide advice at an appropriate time by prioritizing advice based on when the child's story is submitted. Some or all of the above processes in the advice department are performed using a generative AI. For example, when providing advice, the advice department inputs the child's story submission date into the generative AI and determines the advice priority. The generative AI then determines the advice priority based on when the child's story is submitted. This allows the advice department to provide advice at an appropriate time by prioritizing advice based on when the child's story is submitted.

[0045] The advice unit adjusts the order of advice based on the relevance of the child's story when providing advice. For example, the advice unit prioritizes advice when the child's story is related to the current situation. When the child's story is related to past events, the advice unit provides advice at the appropriate time. When the child's story is related to future plans, the advice unit provides advice in advance. This allows the advice to be provided in the appropriate order by adjusting the order of advice based on the relevance of the child's story. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the relevance of the child's story into the generative AI and adjusts the order of advice. The generative AI adjusts the order of advice based on the relevance of the child's story. This allows the advice to be provided in the appropriate order by adjusting the order of advice based on the relevance of the child's story.

[0046] The dialogue unit selects the optimal dialogue method by referring to the child's past conversation history during a dialogue. For example, the dialogue unit provides relevant dialogue methods based on the child's past conversation history. The dialogue unit selects the optimal dialogue method from the child's past conversation history. The dialogue unit analyzes the child's past conversation history and provides the most effective dialogue method. In this way, the optimal dialogue method can be provided by referring to the child's past conversation history. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, during a dialogue, the dialogue unit inputs the child's past conversation history into the generative AI and selects the optimal dialogue method. The generative AI selects the optimal dialogue method by referring to the child's past conversation history. In this way, the optimal dialogue method can be provided by referring to the child's past conversation history.

[0047] The dialogue unit customizes the means of dialogue based on the child's current living situation during a dialogue. For example, the dialogue unit provides the optimal means of dialogue based on the child's current living situation. The dialogue unit customizes the means of dialogue considering the child's current living situation. The dialogue unit adjusts the means of dialogue according to the child's current living situation. This allows for the provision of appropriate dialogue by customizing the means of dialogue based on the child's current living situation. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, the dialogue unit inputs the child's current living situation into the generative AI during a dialogue and customizes the means of dialogue. The generative AI customizes the means of dialogue based on the child's current living situation. This allows for the provision of appropriate dialogue by customizing the means of dialogue based on the child's current living situation.

[0048] The dialogue unit selects the optimal dialogue method during a dialogue, taking into account the child's geographical location. For example, the dialogue unit provides the optimal dialogue method based on the child's geographical location. The dialogue unit selects a dialogue method considering the child's geographical location. The dialogue unit adjusts the dialogue method according to the child's geographical location. This allows the optimal dialogue method to be provided by taking the child's geographical location into account. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, during a dialogue, the dialogue unit inputs the child's geographical location into the generative AI and selects the optimal dialogue method. The generative AI selects the optimal dialogue method considering the child's geographical location. This allows the optimal dialogue method to be provided by taking the child's geographical location into account.

[0049] The dialogue unit analyzes the child's social media activity during a dialogue and proposes a dialogue method. For example, the dialogue unit provides the optimal dialogue method based on the child's social media activity. The dialogue unit analyzes the child's social media activity and proposes a dialogue method. The dialogue unit adjusts the dialogue method according to the child's social media activity. In this way, by analyzing the child's social media activity, the optimal dialogue method can be provided. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, during a dialogue, the dialogue unit inputs the child's social media activity into the generative AI and proposes a dialogue method. The generative AI analyzes the child's social media activity and proposes a dialogue method. In this way, by analyzing the child's social media activity, the optimal dialogue method can be provided.

[0050] The consultation department provides optimal advice by referring to the parent's past consultation history during the consultation. For example, the consultation department provides relevant advice based on the parent's past consultation history. The consultation department provides optimal advice from the parent's past consultation history. The consultation department analyzes the parent's past consultation history and provides the most effective advice. This allows the consultation department to provide optimal advice by referring to the parent's past consultation history. Some or all of the above processes in the consultation department are performed using a generative AI. For example, the consultation department inputs the parent's past consultation history into the generative AI during the consultation and provides optimal advice. The generative AI provides optimal advice by referring to the parent's past consultation history. This allows the consultation department to provide optimal advice by referring to the parent's past consultation history.

[0051] The consultation department provides optimal advice based on the parents' living situation and areas of interest during consultations. For example, the consultation department provides optimal advice based on the parents' current living situation. The consultation department provides relevant advice based on the parents' areas of interest. The consultation department adjusts the content of the advice considering the parents' living situation and areas of interest. In this way, by providing optimal advice based on the parents' living situation and areas of interest, the consultation department can provide the best possible advice for the parents. Some or all of the above processes in the consultation department are performed using a generative AI. For example, during a consultation, the consultation department inputs the parents' living situation and areas of interest into the generative AI and provides optimal advice. The generative AI provides optimal advice based on the parents' living situation and areas of interest. In this way, by providing optimal advice based on the parents' living situation and areas of interest, the consultation department can provide the best possible advice for the parents.

[0052] The notification unit, when issuing a notification, selects the optimal notification method by referring to the parent's past notification history. The notification unit, for example, provides relevant notification methods based on the parent's past notification history. The notification unit selects the optimal notification method from the parent's past notification history. The notification unit analyzes the parent's past notification history and provides the most effective notification method. This allows the optimal notification method to be provided by referring to the parent's past notification history. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when issuing a notification, the notification unit inputs the parent's past notification history into the generation AI and selects the optimal notification method. The generation AI selects the optimal notification method by referring to the parent's past notification history. This allows the optimal notification method to be provided by referring to the parent's past notification history.

[0053] The notification unit selects the optimal notification method when sending a notification, taking into account the parent's geographical location information. The notification unit provides the optimal notification method based on the parent's geographical location information, for example. The notification unit selects the notification method, taking into account the parent's geographical location information. The notification unit adjusts the notification method according to the parent's geographical location information. This allows the system to provide the optimal notification method by taking the parent's geographical location information into account. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when sending a notification, the notification unit inputs the parent's geographical location information into the generation AI and selects the optimal notification method. The generation AI selects the optimal notification method, taking into account the parent's geographical location information. This allows the system to provide the optimal notification method by taking the parent's geographical location information into account.

[0054] The notification unit provides optimal notification content based on the parent's living situation and areas of interest when a notification is sent. For example, the notification unit provides optimal notification content based on the parent's current living situation. The notification unit provides relevant notification content based on the parent's areas of interest. The notification unit adjusts the notification content considering the parent's living situation and areas of interest. This allows the system to provide the most suitable notification for the parent by providing the most suitable notification content based on the parent's living situation and areas of interest. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when a notification is sent, the notification unit inputs the parent's living situation and areas of interest into the generation AI and provides the optimal notification content. The generation AI provides the most suitable notification content based on the parent's living situation and areas of interest. This allows the system to provide the most suitable notification for the parent by providing the most suitable notification content based on the parent's living situation and areas of interest.

[0055] The notification unit analyzes the parent's social media activity and provides relevant notification content when sending a notification. For example, the notification unit provides the most appropriate notification content based on the parent's social media activity. The notification unit analyzes the parent's social media activity and provides relevant notification content. The notification unit adjusts the notification content according to the parent's social media activity. This allows the system to provide highly relevant notification content by analyzing the parent's social media activity. Some or all of the above processing in the notification unit is performed using a generative AI. For example, when sending a notification, the notification unit inputs the parent's social media activity into the generative AI and provides relevant notification content. The generative AI analyzes the parent's social media activity and provides relevant notification content. This allows the system to provide highly relevant notification content by analyzing the parent's social media activity.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The generated AI app can analyze a parent's past app usage history and provide optimal advice. For example, it can prompt the parent to launch the app during times when the parent frequently used it in the past, and prioritize displaying features the parent preferred. It can also automatically adjust settings to provide optimal advice based on the parent's past usage history. This allows for more effective advice based on the parent's past app usage history, leading to smoother communication between parents and children.

[0058] The AI-generated app can provide advice based on the parent's current lifestyle and areas of interest. For example, if the parent is at work, it can provide quick advice, and if the parent is relaxed, it can provide more detailed advice. It can also prioritize relevant advice and information based on the parent's areas of interest. This allows for the provision of optimal advice based on the parent's current lifestyle and areas of interest, leading to smoother communication between parents and children.

[0059] The AI-generated app can provide advice while considering the parent's geographical location. For example, if the parent is at home, it can provide advice that can be used within the home; if the parent is out, it can provide advice that can be used while out. It can also provide advice relevant to a specific location if the parent is in that location. By considering the parent's geographical location, it can provide optimal advice, making communication between parents and children smoother.

[0060] The AI-generated app can analyze parents' social media activity and provide relevant advice. For example, it can provide relevant advice based on information parents have shared on social media, and relevant information based on accounts parents follow. It can also provide relevant advice based on topics parents have shown interest in. By analyzing parents' social media activity, it can provide optimal advice and make communication between parents and children smoother.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk instructs the parent to put on audio glasses and launch the generation AI app. This collects audio and inputs it into the generation AI. Step 2: The advice department provides real-time advice based on the information received by the reception department. Using a generation AI, it generates and provides appropriate advice when parents interact with their children. Step 3: The dialogue unit allows the parent to interact with the child based on the advice provided by the advice unit. The parent then responds appropriately, taking into account the advice from the generated AI.

[0063] (Example of form 2) The generative AI application according to an embodiment of the present invention is a system for facilitating communication between parents and children and enhancing children's self-esteem. This system provides real-time advice when a parent wears audio glasses and activates the generative AI application, supporting the parent in listening to and responding gently to their child. For example, when a parent begins a conversation with their child, the generative AI provides real-time advice, supporting the parent in listening to and responding gently. This advice is conveyed to the parent through the audio glasses, allowing the parent to continue the conversation with their child naturally. Furthermore, the generative AI application includes a function that allows parents to consult about parenting and daily concerns, reducing feelings of isolation. It also sends notifications during specific events (such as school events or grade announcements), prompting parents to converse at the appropriate time and ensuring they don't miss opportunities to communicate with their children. This improves children's self-esteem, reduces the burden of childcare, and increases mental well-being and smiles in families with children. For example, a parent wears audio glasses and activates the generative AI application. In this case, from a privacy perspective, audio is collected only when the application is activated. Next, when a parent begins a conversation with their child, the generating AI provides advice in real time. For example, if a child is talking about something that happened at school, the generating AI might suggest a gentle response like, "That must have been tough." Parents can then use this advice to listen to their child and respond appropriately. Furthermore, the generating AI app includes a feature that allows parents to consult about parenting and everyday worries. For example, if a parent consults about a problem such as, "My child had a fight with a friend at school," the generating AI might offer advice like, "First, listen carefully to what your child has to say." This gives parents a sense of security and reduces feelings of isolation. The app also sends notifications for specific events (such as school events or grade announcements), prompting parents to engage in conversation at the appropriate time. For example, a notification might be sent on the day grades are announced, allowing parents to start a conversation with their child by saying, "Today is the day grades are announced, how did it go?" This ensures that parents don't miss opportunities to communicate with their children and can provide appropriate feedback.In this way, using a generative AI app can facilitate communication between parents and children and improve children's self-esteem. Furthermore, parents can consult about parenting and daily concerns, reducing the burden of childcare and increasing mental well-being and smiles in families with children. Thus, generative AI apps can facilitate communication between parents and children and enhance children's self-esteem.

[0064] The generative AI application according to this embodiment comprises a reception unit, an advice unit, and a dialogue unit. The reception unit receives audio from the parent when they put on audio glasses and launch the generative AI application. The reception unit collects audio from the parent when they put on audio glasses and launch the generative AI application and inputs it into the generative AI. The advice unit provides advice in real time based on the information received by the reception unit. The advice unit provides appropriate advice from the parent when they are talking to their child, for example, using the generative AI. The generative AI generates appropriate advice from the parent when they are talking to their child, using a text generation AI (e.g., LLM). The dialogue unit allows the parent to talk to their child based on the advice provided by the advice unit. The dialogue unit provides appropriate responses from the parent when they are talking to their child, for example, by referring to the advice from the generative AI. As a result, the generative AI application according to this embodiment allows the parent to practice active listening by talking to their child while receiving advice in real time through the audio glasses. Some or all of the above-described processing in the advice unit is performed using the generative AI. For example, the advice unit provides advice in real time using the generative AI when the parent is talking to their child. The generating AI generates appropriate advice when a parent interacts with their child and provides it to the advice unit. Some or all of the processing described above in the dialogue unit is performed using the generating AI. For example, the dialogue unit responds appropriately when a parent interacts with their child, taking into account the advice from the generating AI. The generating AI generates appropriate advice when a parent interacts with their child and provides it to the dialogue unit. As a result, the generating AI application according to this embodiment allows parents to practice active listening by interacting with their children while receiving advice in real time through audio glasses.

[0065] The reception desk works by having the parent put on audio glasses and launch the generation AI app. Specifically, when the parent puts on the audio glasses, the microphone built into the glasses begins to collect ambient sounds. When the generation AI app is launched, the reception desk collects this audio data in real time and inputs it into the generation AI. The audio glasses can collect not only the parent's voice but also the child's voice and ambient sounds, allowing for a comprehensive understanding of the conversation. Furthermore, the audio glasses are equipped with noise cancellation, which removes ambient noise and allows for the collection of clear audio data. The collected audio data is sent to a server within the generation AI app, where it is converted into text data using speech recognition technology. This text data is used as input data for the generation AI, and the content of the parent-child conversation is analyzed in real time. In addition to collecting audio data, the reception desk can also collect additional information to understand the parent's intentions and emotions. For example, if the parent utters a specific keyword, it provides the generation AI with information related to that keyword to help generate more appropriate advice. In this way, the reception desk can play a crucial role in facilitating smooth conversations between parents and children.

[0066] The advice unit provides real-time advice based on information received by the reception unit. Specifically, it uses generative AI to provide appropriate advice when parents interact with their children. The generative AI uses text generation AI (e.g., LLM) to generate appropriate advice when parents interact with their children. The generative AI analyzes the content of the parent-child conversation and determines in real time how the parent should respond and what questions they should ask. For example, if a child is talking about something that happened at school, the generative AI analyzes the content and advises the parent to ask questions such as, "How did that feel?" or "What are you going to do next?" Also, if a child is facing a difficult situation, the generative AI will offer words of encouragement and suggest concrete solutions to the parent. The advice unit notifies parents of the advice provided by the generative AI through the speakers of the audio glasses. This allows parents to receive advice naturally during the conversation. Furthermore, the advice unit monitors the responses of both the parent and the child and can modify the advice as needed. For example, if a child reacts negatively to the advice, the generative AI analyzes the reaction and re-evaluates what approach should be taken next. This allows the advisory department to provide support to facilitate smooth dialogue between parents and children, and to help parents deepen their communication with their children.

[0067] The dialogue unit facilitates parent-child interactions based on advice provided by the advice unit. Specifically, parents use the advice from the generative AI to provide appropriate responses during their conversations with their children. The dialogue unit provides an interface for parents to receive the generative AI's advice and incorporate it into their actual conversations. For example, while a parent is talking to their child, the generative AI's advice is played aloud through the speakers of the audio glasses. As the parent listens to the advice, they can naturally provide appropriate responses to their child. The dialogue unit can adjust the content and timing of the advice to make it easier for parents to accept. For example, it provides less advice when the parent is focused on what the child is saying, and offers more specific advice when the parent is struggling to respond. The dialogue unit also monitors how parents accept and implement the generative AI's advice and provides this feedback to the generative AI. This allows the generative AI to learn from the parent's responses and improve the accuracy of future advice. Furthermore, the dialogue unit provides additional features to improve the quality of parent-child interactions. For example, it provides a dashboard that visualizes the progress of the conversation, allowing parents to understand what topics they are discussing with their children. Furthermore, it provides a recording function for conversations, allowing parents to review the content of those conversations later. This enables the conversation function to support parents in effectively utilizing the advice generated by the AI ​​and enriching their conversations with their children.

[0068] The generative AI application according to this embodiment includes a consultation section where parents can consult about childcare and daily concerns. The consultation section allows parents to consult about childcare and daily concerns. For example, when a parent consults about childcare or daily concerns, the consultation section uses the generative AI to provide appropriate advice. The generative AI generates appropriate advice when a parent consults about childcare or daily concerns and provides it to the consultation section. This reduces feelings of loneliness because parents can consult about childcare and daily concerns. Some or all of the above-described processes in the consultation section are performed using the generative AI. For example, when a parent consults about childcare or daily concerns, the consultation section uses the generative AI to provide appropriate advice. The generative AI generates appropriate advice when a parent consults about childcare or daily concerns and provides it to the consultation section. This reduces feelings of loneliness because parents can consult about childcare and daily concerns.

[0069] The generative AI application according to this embodiment includes a notification unit that sends notifications at specific events. The notification unit sends notifications at specific events. For example, the notification unit sends notifications to parents at specific events such as school events or grade announcements. This allows parents to prompt conversation at the appropriate time. Some or all of the above processing in the notification unit is performed using the generative AI. For example, the notification unit sends notifications via the generative AI at specific events. The generative AI sends notifications to parents at specific events. This allows parents to prompt conversation at the appropriate time.

[0070] The advice unit provides advice in real time using a generative AI. For example, the advice unit uses the generative AI to provide appropriate advice when parents interact with their children. The generative AI generates appropriate advice when parents interact with their children and provides it to the advice unit. This allows parents to respond appropriately by providing advice in real time via the generative AI. Some or all of the above processing in the advice unit is performed using the generative AI. For example, the advice unit provides advice in real time using the generative AI when parents interact with their children. The generative AI generates appropriate advice when parents interact with their children and provides it to the advice unit. This allows parents to respond appropriately by providing advice in real time via the generative AI.

[0071] The dialogue unit takes advice from the generative AI into consideration when parents interact with their children. For example, when parents interact with their children, the dialogue unit takes advice from the generative AI into consideration and responds appropriately. The generative AI generates appropriate advice and provides it to the dialogue unit when parents interact with their children. This allows parents to engage in appropriate dialogue by taking advice from the generative AI. Some or all of the above processing in the dialogue unit is performed using the generative AI. For example, when parents interact with their children, the dialogue unit takes advice from the generative AI into consideration and responds appropriately. The generative AI generates appropriate advice and provides it to the dialogue unit when parents interact with their children. This allows parents to engage in appropriate dialogue by taking advice from the generative AI.

[0072] The notification unit sends notifications during specific events such as school events and grade announcements. For example, the notification unit sends notifications to parents during specific events such as school events and grade announcements. This allows parents to prompt conversation at the appropriate time. Some or all of the above processing in the notification unit is performed using a generative AI. For example, the notification unit sends notifications via the generative AI during specific events. The generative AI sends notifications to parents during specific events. This allows parents to prompt conversation at the appropriate time.

[0073] The reception desk estimates the parent's emotions and adjusts the app launch timing based on the estimated emotions. For example, if the parent is stressed, the reception desk prompts them to launch the app during a time when they can relax. If the parent is busy, the reception desk adjusts the app launch timing so that it can be used in a short amount of time. If the parent is relaxed, the reception desk prompts them to launch the app to provide detailed advice. This allows parents to use the app in a relaxed state by adjusting the app launch timing based on their emotions. Emotion estimation is achieved using emotion estimation functionality, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using generative AI. For example, the reception desk estimates the parent's emotions and adjusts the app launch timing based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the app launch timing based on the estimated emotions. This allows parents to use the app in a relaxed state by adjusting the app launch timing based on their emotions.

[0074] The reception desk analyzes the parent's past app usage history and selects the optimal reception method. For example, the reception desk may prompt the parent to launch the app during times when the parent frequently used it in the past. The reception desk prioritizes displaying features that the parent has preferred to use in the past. The reception desk automatically adjusts settings to provide optimal advice based on the parent's past usage history. This allows the reception desk to provide the optimal reception method by analyzing the parent's past app usage history. Some or all of the above processes in the reception desk are performed using a generative AI. For example, the reception desk inputs the parent's past app usage history into the generative AI and selects the optimal reception method. The generative AI analyzes the parent's past app usage history and selects the optimal reception method. This allows the reception desk to provide the optimal reception method by analyzing the parent's past app usage history.

[0075] The reception desk filters the app upon launch based on the parent's current lifestyle and areas of interest. For example, if the parent is at work, the reception desk prioritizes displaying features that can be used in a short amount of time. If the parent is relaxed, the reception desk displays features that provide detailed advice. The reception desk prioritizes displaying relevant advice and information based on the parent's areas of interest. This allows for the provision of highly relevant information by filtering based on the parent's current lifestyle and areas of interest. Some or all of the above processing in the reception desk is performed using a generative AI. For example, when the app is launched, the reception desk inputs the parent's current lifestyle and areas of interest into the generative AI and performs filtering. The generative AI filters based on the parent's current lifestyle and areas of interest. This allows for the provision of highly relevant information by filtering based on the parent's current lifestyle and areas of interest.

[0076] The reception desk estimates the parent's emotions and prioritizes the information to be received based on the estimated emotions. For example, if the parent is stressed, the reception desk prioritizes providing information that helps them relax. If the parent is busy, the reception desk prioritizes providing information that can be used in a short time. If the parent is relaxed, the reception desk prioritizes providing detailed information. In this way, by prioritizing information based on the parent's emotions, the system can provide the most suitable information for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using generative AI. For example, the reception desk estimates the parent's emotions and prioritizes the information based on the estimated emotions. The generative AI estimates the parent's emotions and prioritizes the information based on the estimated emotions. In this way, by prioritizing information based on the parent's emotions, the system can provide the most suitable information for the parent.

[0077] The reception desk prioritizes receiving highly relevant information when the app is launched, taking into account the parent's geographical location. For example, if the parent is at home, the reception desk prioritizes providing information that can be used within the home. If the parent is out, the reception desk prioritizes providing information that can be used while out. If the parent is in a specific location, the reception desk prioritizes providing information related to that location. This allows the reception desk to provide highly relevant information by taking into account the parent's geographical location. Some or all of the above processing in the reception desk is performed using a generative AI. For example, when the app is launched, the reception desk inputs the parent's geographical location into the generative AI and prioritizes receiving highly relevant information. The generative AI prioritizes receiving highly relevant information by taking into account the parent's geographical location. This allows the reception desk to provide highly relevant information by taking into account the parent's geographical location.

[0078] The reception desk analyzes the parent's social media activity and receives relevant information when the app is launched. For example, the reception desk provides relevant advice based on information the parent has shared on social media. The reception desk provides relevant information based on accounts the parent follows on social media. The reception desk provides relevant information based on topics the parent has shown interest in on social media. This allows the reception desk to provide highly relevant information by analyzing the parent's social media activity. Some or all of the above processing in the reception desk is performed using generative AI. For example, when the app is launched, the reception desk inputs the parent's social media activity into the generative AI and receives relevant information. The generative AI analyzes the parent's social media activity and receives relevant information. This allows the reception desk to provide highly relevant information by analyzing the parent's social media activity.

[0079] The advice unit estimates the parent's emotions and adjusts the way it expresses the advice based on the estimated emotions. For example, if the parent is stressed, the advice unit provides advice in gentle words. If the parent is relaxed, the advice unit provides detailed advice. If the parent is in a hurry, the advice unit provides concise and quick advice. This allows the system to provide the best possible advice for the parent by adjusting the way it expresses the advice based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit is performed using generative AI. For example, the advice unit estimates the parent's emotions and adjusts the way it expresses the advice based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the way it expresses the advice based on the estimated emotions. This allows the system to provide the best possible advice for the parent by adjusting the way it expresses the advice based on their emotions.

[0080] The advice unit adjusts the level of detail in the advice based on the importance of the child's story when providing advice. For example, if the child's story is important, the advice unit provides detailed advice. If the child's story is routine, the advice unit provides concise advice. If the child's story is urgent, the advice unit provides prompt advice. This allows the advice unit to provide appropriate advice by adjusting the level of detail based on the importance of the child's story. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the importance of the child's story into the generative AI and adjusts the level of detail in the advice. The generative AI adjusts the level of detail in the advice based on the importance of the child's story. This allows the advice unit to provide appropriate advice by adjusting the level of detail based on the importance of the child's story.

[0081] The advice unit applies different advice algorithms depending on the category of the child's story when providing advice. For example, if the child's story concerns school events, the advice unit provides advice on school life. If the child's story concerns friendships, the advice unit provides advice on friendships. If the child's story concerns domestic events, the advice unit provides advice on how to handle domestic situations. By applying different advice algorithms depending on the category of the child's story, appropriate advice can be provided. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the category of the child's story into the generative AI and applies a different advice algorithm. The generative AI then applies a different advice algorithm depending on the category of the child's story. By applying different advice algorithms depending on the category of the child's story, appropriate advice can be provided.

[0082] The advice unit estimates the parent's emotions and adjusts the length of the advice based on the estimated emotions. For example, if the parent is stressed, the advice unit provides short, to-the-point advice. If the parent is relaxed, the advice unit provides detailed advice. If the parent is in a hurry, the advice unit provides quick and concise advice. This allows the system to provide the best possible advice for the parent by adjusting the length of the advice based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit is performed using generative AI. For example, the advice unit estimates the parent's emotions and adjusts the length of the advice based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the length of the advice based on the estimated emotions. This allows the system to provide the best possible advice for the parent by adjusting the length of the advice based on their emotions.

[0083] The advice department prioritizes advice based on when the child's story is submitted. For example, if the child's story concerns a recent event, the advice department will prioritize providing advice. If the child's story concerns a past event, the advice department will provide advice at an appropriate time. If the child's story concerns a future plan, the advice department will provide advice in advance. This allows the advice department to provide advice at an appropriate time by prioritizing advice based on when the child's story is submitted. Some or all of the above processes in the advice department are performed using a generative AI. For example, when providing advice, the advice department inputs the child's story submission date into the generative AI and determines the advice priority. The generative AI then determines the advice priority based on when the child's story is submitted. This allows the advice department to provide advice at an appropriate time by prioritizing advice based on when the child's story is submitted.

[0084] The advice unit adjusts the order of advice based on the relevance of the child's story when providing advice. For example, the advice unit prioritizes advice when the child's story is related to the current situation. When the child's story is related to past events, the advice unit provides advice at the appropriate time. When the child's story is related to future plans, the advice unit provides advice in advance. This allows the advice to be provided in the appropriate order by adjusting the order of advice based on the relevance of the child's story. Some or all of the above processing in the advice unit is performed using a generative AI. For example, when providing advice, the advice unit inputs the relevance of the child's story into the generative AI and adjusts the order of advice. The generative AI adjusts the order of advice based on the relevance of the child's story. This allows the advice to be provided in the appropriate order by adjusting the order of advice based on the relevance of the child's story.

[0085] The dialogue unit estimates the parent's emotions and adjusts the dialogue process based on the estimated emotions. For example, if the parent is stressed, the dialogue unit provides a relaxing dialogue method. If the parent is relaxed, the dialogue unit provides a detailed dialogue method. If the parent is in a hurry, the dialogue unit provides a concise and quick dialogue method. This allows the parent to relax and proceed with the dialogue by adjusting the dialogue process based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, the dialogue unit estimates the parent's emotions and adjusts the dialogue process based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the dialogue process based on the estimated emotions. This allows the parent to relax and proceed with the dialogue by adjusting the dialogue process based on their emotions.

[0086] The dialogue unit selects the optimal dialogue method by referring to the child's past conversation history during a dialogue. For example, the dialogue unit provides relevant dialogue methods based on the child's past conversation history. The dialogue unit selects the optimal dialogue method from the child's past conversation history. The dialogue unit analyzes the child's past conversation history and provides the most effective dialogue method. In this way, the optimal dialogue method can be provided by referring to the child's past conversation history. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, during a dialogue, the dialogue unit inputs the child's past conversation history into the generative AI and selects the optimal dialogue method. The generative AI selects the optimal dialogue method by referring to the child's past conversation history. In this way, the optimal dialogue method can be provided by referring to the child's past conversation history.

[0087] The dialogue unit customizes the means of dialogue based on the child's current living situation during a dialogue. For example, the dialogue unit provides the optimal means of dialogue based on the child's current living situation. The dialogue unit customizes the means of dialogue considering the child's current living situation. The dialogue unit adjusts the means of dialogue according to the child's current living situation. This allows for the provision of appropriate dialogue by customizing the means of dialogue based on the child's current living situation. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, the dialogue unit inputs the child's current living situation into the generative AI during a dialogue and customizes the means of dialogue. The generative AI customizes the means of dialogue based on the child's current living situation. This allows for the provision of appropriate dialogue by customizing the means of dialogue based on the child's current living situation.

[0088] The dialogue unit estimates the parent's emotions and determines the priority of the dialogue based on the estimated emotions. For example, if the parent is stressed, the dialogue unit prioritizes providing relaxing dialogue. If the parent is relaxed, the dialogue unit prioritizes providing detailed dialogue. If the parent is in a hurry, the dialogue unit prioritizes providing concise and quick dialogue. In this way, by determining the priority of dialogue based on the parent's emotions, the optimal dialogue for the parent can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, the dialogue unit estimates the parent's emotions and determines the priority of the dialogue based on the estimated emotions. The generative AI estimates the parent's emotions and determines the priority of the dialogue based on the estimated emotions. In this way, by determining the priority of dialogue based on the parent's emotions, the optimal dialogue for the parent can be provided.

[0089] The dialogue unit selects the optimal dialogue method during a dialogue, taking into account the child's geographical location. For example, the dialogue unit provides the optimal dialogue method based on the child's geographical location. The dialogue unit selects a dialogue method considering the child's geographical location. The dialogue unit adjusts the dialogue method according to the child's geographical location. This allows the optimal dialogue method to be provided by taking the child's geographical location into account. Some or all of the above processing in the dialogue unit is performed using a generative AI. For example, during a dialogue, the dialogue unit inputs the child's geographical location into the generative AI and selects the optimal dialogue method. The generative AI selects the optimal dialogue method considering the child's geographical location. This allows the optimal dialogue method to be provided by taking the child's geographical location into account.

[0090] The dialogue unit analyzes the child's social media activity during a dialogue and proposes a dialogue method. For example, the dialogue unit provides the optimal dialogue method based on the child's social media activity. The dialogue unit analyzes the child's social media activity and proposes a dialogue method. The dialogue unit adjusts the dialogue method according to the child's social media activity. In this way, by analyzing the child's social media activity, the optimal dialogue method can be provided. Some or all of the above processing in the dialogue unit is performed using generative AI. For example, during a dialogue, the dialogue unit inputs the child's social media activity into the generative AI and proposes a dialogue method. The generative AI analyzes the child's social media activity and proposes a dialogue method. In this way, by analyzing the child's social media activity, the optimal dialogue method can be provided.

[0091] The counseling service estimates the parent's emotions and prioritizes the content of the consultation based on the estimated emotions. For example, if the parent is stressed, the counseling service prioritizes providing relaxing consultation content. If the parent is relaxed, the counseling service prioritizes providing detailed consultation content. If the parent is in a hurry, the counseling service prioritizes providing concise and quick consultation content. In this way, by prioritizing the content of the consultation based on the parent's emotions, the service can provide the most suitable consultation for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the counseling service is performed using generative AI. For example, the counseling service estimates the parent's emotions and prioritizes the content of the consultation based on the estimated emotions. The generative AI estimates the parent's emotions and prioritizes the content of the consultation based on the estimated emotions. In this way, by prioritizing the content of the consultation based on the parent's emotions, the service can provide the most suitable consultation for the parent.

[0092] The consultation department provides optimal advice by referring to the parent's past consultation history during the consultation. For example, the consultation department provides relevant advice based on the parent's past consultation history. The consultation department provides optimal advice from the parent's past consultation history. The consultation department analyzes the parent's past consultation history and provides the most effective advice. This allows the consultation department to provide optimal advice by referring to the parent's past consultation history. Some or all of the above processes in the consultation department are performed using a generative AI. For example, the consultation department inputs the parent's past consultation history into the generative AI during the consultation and provides optimal advice. The generative AI provides optimal advice by referring to the parent's past consultation history. This allows the consultation department to provide optimal advice by referring to the parent's past consultation history.

[0093] The counseling department estimates the parent's emotions and adjusts the counseling process based on the estimated emotions. For example, if the parent is stressed, the counseling department provides a relaxing counseling method. If the parent is relaxed, the counseling department provides a detailed counseling method. If the parent is in a hurry, the counseling department provides a concise and quick counseling method. This allows the parent to relax during the counseling process by adjusting the counseling process based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the counseling department is performed using generative AI. For example, the counseling department estimates the parent's emotions and adjusts the counseling process based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the counseling process based on the estimated emotions. This allows the parent to relax during the counseling process by adjusting the counseling process based on their emotions.

[0094] The consultation department provides optimal advice based on the parents' living situation and areas of interest during consultations. For example, the consultation department provides optimal advice based on the parents' current living situation. The consultation department provides relevant advice based on the parents' areas of interest. The consultation department adjusts the content of the advice considering the parents' living situation and areas of interest. In this way, by providing optimal advice based on the parents' living situation and areas of interest, the consultation department can provide the best possible advice for the parents. Some or all of the above processes in the consultation department are performed using a generative AI. For example, during a consultation, the consultation department inputs the parents' living situation and areas of interest into the generative AI and provides optimal advice. The generative AI provides optimal advice based on the parents' living situation and areas of interest. In this way, by providing optimal advice based on the parents' living situation and areas of interest, the consultation department can provide the best possible advice for the parents.

[0095] The notification unit estimates the parent's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the parent is stressed, the notification unit will send a notification during a time when the parent can relax. If the parent is busy, the notification unit will send a notification that can be checked quickly. If the parent is relaxed, the notification unit will send a detailed notification. In this way, by adjusting the timing of notifications based on the parent's emotions, notifications can be provided at the optimal time for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit is performed using generative AI. For example, the notification unit estimates the parent's emotions and adjusts the timing of notifications based on the estimated emotions. The generative AI estimates the parent's emotions and adjusts the timing of notifications based on the estimated emotions. In this way, by adjusting the timing of notifications based on the parent's emotions, notifications can be provided at the optimal time for the parent.

[0096] The notification unit, when issuing a notification, selects the optimal notification method by referring to the parent's past notification history. The notification unit, for example, provides relevant notification methods based on the parent's past notification history. The notification unit selects the optimal notification method from the parent's past notification history. The notification unit analyzes the parent's past notification history and provides the most effective notification method. This allows the optimal notification method to be provided by referring to the parent's past notification history. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when issuing a notification, the notification unit inputs the parent's past notification history into the generation AI and selects the optimal notification method. The generation AI selects the optimal notification method by referring to the parent's past notification history. This allows the optimal notification method to be provided by referring to the parent's past notification history.

[0097] The notification unit estimates the parent's emotions and determines the priority of notifications based on the estimated emotions. For example, if the parent is stressed, the notification unit prioritizes providing relaxing notifications. If the parent is relaxed, the notification unit prioritizes providing detailed notifications. If the parent is in a hurry, the notification unit prioritizes providing concise and quick notifications. This allows the notification unit to provide the most suitable notifications for the parent by prioritizing notifications based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit is performed using generative AI. For example, the notification unit estimates the parent's emotions and determines the priority of notifications based on the estimated emotions. The generative AI estimates the parent's emotions and determines the priority of notifications based on the estimated emotions. This allows the notification unit to provide the most suitable notifications for the parent by prioritizing notifications based on their emotions.

[0098] The notification unit selects the optimal notification method when sending a notification, taking into account the parent's geographical location information. The notification unit provides the optimal notification method based on the parent's geographical location information, for example. The notification unit selects the notification method, taking into account the parent's geographical location information. The notification unit adjusts the notification method according to the parent's geographical location information. This allows the system to provide the optimal notification method by taking the parent's geographical location information into account. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when sending a notification, the notification unit inputs the parent's geographical location information into the generation AI and selects the optimal notification method. The generation AI selects the optimal notification method, taking into account the parent's geographical location information. This allows the system to provide the optimal notification method by taking the parent's geographical location information into account.

[0099] The notification unit provides optimal notification content based on the parent's living situation and areas of interest when a notification is sent. For example, the notification unit provides optimal notification content based on the parent's current living situation. The notification unit provides relevant notification content based on the parent's areas of interest. The notification unit adjusts the notification content considering the parent's living situation and areas of interest. This allows the system to provide the most suitable notification for the parent by providing the most suitable notification content based on the parent's living situation and areas of interest. Some or all of the above processing in the notification unit is performed using a generation AI. For example, when a notification is sent, the notification unit inputs the parent's living situation and areas of interest into the generation AI and provides the optimal notification content. The generation AI provides the most suitable notification content based on the parent's living situation and areas of interest. This allows the system to provide the most suitable notification for the parent by providing the most suitable notification content based on the parent's living situation and areas of interest.

[0100] The notification unit analyzes the parent's social media activity and provides relevant notification content when sending a notification. For example, the notification unit provides the most appropriate notification content based on the parent's social media activity. The notification unit analyzes the parent's social media activity and provides relevant notification content. The notification unit adjusts the notification content according to the parent's social media activity. This allows the system to provide highly relevant notification content by analyzing the parent's social media activity. Some or all of the above processing in the notification unit is performed using a generative AI. For example, when sending a notification, the notification unit inputs the parent's social media activity into the generative AI and provides relevant notification content. The generative AI analyzes the parent's social media activity and provides relevant notification content. This allows the system to provide highly relevant notification content by analyzing the parent's social media activity.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The AI-generated app can estimate a parent's emotions and adjust the advice based on those emotions. For example, if a parent is stressed, the advice will choose gentle, relaxing words; if the parent is relaxed, it can provide more detailed advice. It can also provide concise and quick advice if the parent is in a hurry. This allows for the provision of optimal advice tailored to the parent's emotions, leading to smoother communication between parents and children.

[0103] The generated AI app can analyze a parent's past app usage history and provide optimal advice. For example, it can prompt the parent to launch the app during times when the parent frequently used it in the past, and prioritize displaying features the parent preferred. It can also automatically adjust settings to provide optimal advice based on the parent's past usage history. This allows for more effective advice based on the parent's past app usage history, leading to smoother communication between parents and children.

[0104] The AI-generated app can provide advice based on the parent's current lifestyle and areas of interest. For example, if the parent is at work, it can provide quick advice, and if the parent is relaxed, it can provide more detailed advice. It can also prioritize relevant advice and information based on the parent's areas of interest. This allows for the provision of optimal advice based on the parent's current lifestyle and areas of interest, leading to smoother communication between parents and children.

[0105] The AI-generated app can provide advice while considering the parent's geographical location. For example, if the parent is at home, it can provide advice that can be used within the home; if the parent is out, it can provide advice that can be used while out. It can also provide advice relevant to a specific location if the parent is in that location. By considering the parent's geographical location, it can provide optimal advice, making communication between parents and children smoother.

[0106] The AI-generated app can analyze parents' social media activity and provide relevant advice. For example, it can provide relevant advice based on information parents have shared on social media, and relevant information based on accounts parents follow. It can also provide relevant advice based on topics parents have shown interest in. By analyzing parents' social media activity, it can provide optimal advice and make communication between parents and children smoother.

[0107] The AI-generated app can estimate a parent's emotions and adjust the app's launch timing based on those emotions. For example, if a parent is stressed, it can prompt them to launch the app during a time when they can relax; if they are busy, it can adjust the app's launch time to allow for short-term use. It can also prompt a parent to launch the app to provide detailed advice when they are relaxed. In this way, by adjusting the app's launch timing based on the parent's emotions, it can help parents use the app in a relaxed state.

[0108] The AI-generated advice app can estimate a parent's emotions and adjust the way it expresses advice based on those emotions. For example, if a parent is stressed, it can offer advice in gentle words; if a parent is relaxed, it can offer detailed advice. It can also provide concise and quick advice if a parent is in a hurry. By adjusting the way advice is expressed based on the parent's emotions, it can provide the most appropriate advice for the parent.

[0109] The AI-generating app can estimate a parent's emotions and adjust the length of the advice based on that estimation. For example, if a parent is stressed, it can provide short, to-the-point advice; if a parent is relaxed, it can provide detailed advice. It can also provide quick and concise advice if a parent is in a hurry. By adjusting the length of the advice based on the parent's emotions, it can provide the most appropriate advice for the parent.

[0110] The AI-generating app can estimate the parent's emotions and adjust the conversation flow based on those estimates. For example, if the parent is stressed, it can offer a relaxing conversation approach; if the parent is relaxed, it can offer a more detailed approach. It can also offer a concise and quick approach if the parent is in a hurry. This allows the conversation to proceed more smoothly and comfortably by adjusting the flow based on the parent's emotions.

[0111] The AI-generated app can estimate a parent's emotions and adjust the timing of notifications based on those emotions. For example, if a parent is stressed, it can send notifications during a time when they can relax; if a parent is busy, it can send notifications that can be checked quickly. It can also send detailed notifications if a parent is relaxed. In this way, by adjusting the timing of notifications based on the parent's emotions, it can provide notifications at the optimal time for the parent.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk instructs the parent to put on audio glasses and launch the generation AI app. This collects audio and inputs it into the generation AI. Step 2: The advice department provides real-time advice based on the information received by the reception department. Using a generation AI, it generates and provides appropriate advice when parents interact with their children. Step 3: The dialogue unit allows the parent to interact with the child based on the advice provided by the advice unit. The parent then responds appropriately, taking into account the advice from the generated AI.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0117] Each of the multiple elements described above, including the reception unit, advice unit, dialogue unit, consultation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which collects voice when the parent wears audio glasses and activates the generation AI application. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides advice in real time. The dialogue unit is implemented by the control unit 46A of the smart device 14, which allows the parent to converse with the child while referring to the advice of the generation AI. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice when the parent consults about childcare or daily worries. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which sends a notification to the parent when a specific event occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, advice unit, dialogue unit, consultation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which collects voice when the parent wears the audio glasses and activates the generating AI application. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides advice in real time. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, which allows the parent to converse with the child while referring to the advice of the generating AI. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice when the parent consults about childcare or daily worries. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which sends a notification to the parent at the time of a specific event. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, advice unit, dialogue unit, consultation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which collects voice when the parent wears audio glasses and activates the generation AI application. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides advice in real time. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, which allows the parent to converse with the child while referring to the advice of the generation AI. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice when the parent consults about childcare or daily worries. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which sends a notification to the parent when a specific event occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, advice unit, dialogue unit, consultation unit, and notification unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which collects voice when the parent wears audio glasses and activates the generation AI application. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides advice in real time. The dialogue unit is implemented by the control unit 46A of the robot 414, which allows the parent to converse with the child while referring to the advice of the generation AI. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice when the parent consults about childcare or daily worries. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which sends a notification to the parent when a specific event occurs. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The reception area where the parent puts on audio glasses and launches the AI ​​generating app, An advice unit provides real-time advice based on the information received by the aforementioned reception unit, The system includes a dialogue unit in which the parent interacts with the child based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) The facility includes a consultation service where parents can seek advice on childcare and everyday concerns. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a notification unit that sends notifications when specific events occur. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, AI generates and provides real-time advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Parents can use the advice of a generative AI to guide their conversations with their children. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Notifications will be sent during specific events such as school events and grade announcements. The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the parent's emotions and adjusts the app's launch timing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the parents' past app usage history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When the app is launched, it filters based on the parent's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the parent's emotions and determines the priority of information to receive based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When the app is launched, it prioritizes receiving highly relevant information by taking into account the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When the app is launched, it analyzes the parent's social media activity and accepts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned advice section, It estimates the parent's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of what the child says. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the child's story. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, It estimates the parent's emotions and adjusts the length of the advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, prioritize the advice based on when the child's story was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, adjust the order of the advice based on the relevance of what the child is saying. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, Estimate the parent's emotions and adjust the way the conversation proceeds based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, During the conversation, refer to the child's past history of conversations to select the most appropriate method of communication. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During the conversation, customize the means of communication based on the child's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, It estimates the parent's emotions and determines the priority of dialogue based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, During the conversation, the optimal method of communication is selected, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During the dialogue, we analyze the child's social media activity and propose methods for dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, We estimate the parents' emotions and prioritize the topics of consultation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned consultation department, During consultations, we refer to the parents' past consultation history to provide the most appropriate advice. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned consultation department, We estimate the parents' emotions and adjust the consultation process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned consultation department, During consultations, we provide the most appropriate advice based on the parents' living situation and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the parent's emotions and adjusts the timing of notifications based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending a notification, the system will refer to the parent's past notification history to select the most suitable notification method. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the parent's emotions and determines the priority of notifications based on the estimated parent's emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the parent's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, the system provides the most relevant information based on the parents' living situation and areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, the system analyzes the parents' social media activity to provide relevant notification content. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where the parent puts on audio glasses and launches the AI ​​generating app, An advice unit provides real-time advice based on the information received by the aforementioned reception unit, The system includes a dialogue unit in which the parent interacts with the child based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The facility includes a consultation service where parents can seek advice on childcare and everyday concerns. The system according to feature 1.

3. It includes a notification unit that sends notifications when specific events occur. The system according to feature 1.

4. The aforementioned advice section, AI generates and provides real-time advice. The system according to feature 1.

5. The aforementioned dialogue unit, Parents can use the advice of generative AI when interacting with their children. The system according to feature 1.

6. The aforementioned notification unit, Notifications will be sent during specific events such as school events and grade announcements. The system according to claim 3.

7. The aforementioned reception unit is It estimates the parent's emotions and adjusts the app's launch timing based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the parents' past app usage history to select the most suitable registration method. The system according to feature 1.

9. The aforementioned reception unit is When the app is launched, it filters based on the parent's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the parent's emotions and determines the priority of information to receive based on the estimated parent's emotions. The system according to feature 1.

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