system

Abstract

The system according to this embodiment aims to generate dialogues tailored to the child's needs and preferences, and to predict the next care and support that will be needed. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a prediction unit, and a sharing unit. The collection unit receives multimodal input. The analysis unit analyzes the input received by the collection unit. The generation unit generates dialogue based on the results analyzed by the analysis unit. The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. The sharing unit shares the content of the care or support predicted by the prediction unit with the guardian.

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, the method including: 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, generating conversations that match the needs and preferences of children and predicting the next required care and support have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to generate conversations that match the needs and preferences of children and predict the next required care and support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a prediction unit, and a sharing unit. The collection unit receives multimodal input. The analysis unit analyzes the input received by the collection unit. The generation unit generates dialogue based on the results analyzed by the analysis unit. The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. The sharing unit shares the content of the care or support predicted by the prediction unit with the guardian. [Effects of the Invention]

[0007] The system according to this embodiment can generate dialogues tailored to a child's needs and preferences, and predict the next care and support they will need. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 Future Nurturing AI Navigator according to an embodiment of the present invention is a system that creates a virtual childcare worker (AI avatar) that generates dialogues tailored to a child's needs and preferences using a generative AI that understands multimodal inputs (speech, visuals, and emotions). This system uses a generative AI that receives multimodal inputs such as speech, visuals, and emotions, analyzes these inputs, and generates dialogues tailored to the child's needs and preferences. Furthermore, the generative AI analyzes the child's behavior and emotional state and predicts the care and support that may be needed next. These predictions are shared with the parents, allowing them to gain concrete and predictive insights into their child's daily growth and development. For example, the Future Nurturing AI Navigator detects the words, facial expressions, and even emotional changes of the child. This information is input into the generative AI. Next, the generative AI analyzes the input information and generates dialogues tailored to the child's needs and preferences. The generative AI understands the child's words, facial expressions, and emotional changes and generates dialogues accordingly. For example, if the child says, "I'm hungry," the generative AI will generate a dialogue such as, "What would you like to eat?" Furthermore, the generative AI analyzes children's behavior and emotional states to predict the next care and support they may need. For example, if a child appears tired, the generative AI might suggest, "Shall we take a short break?" This prediction is shared with the parents. The generative AI notifies parents of the care and support it predicts based on the child's behavior and emotional state. This allows parents to gain specific and predictive insights into their child's daily growth and development. For example, if the generative AI notifies parents that "the child appears tired and needs a short break," they can take appropriate action. This system reduces the burden on childcare workers and alleviates the shortage of childcare staff. The virtual childcare provider, powered by the generative AI, provides appropriate care to individual children in all situations, reducing the burden on childcare workers. Parents also gain specific insights into their child's growth and development, as well as predictions about future care and support needs. This helps to alleviate the shortage of childcare workers.This allows the future-oriented AI navigator to generate conversations tailored to the child's needs and preferences, predict the next necessary care and support, and share this information with parents.

[0029] The future-nurturing AI navigator according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a prediction unit, and a sharing unit. The collection unit accepts multimodal input. The collection unit can accept, for example, audio, visual, and emotional input. The collection unit can detect, for example, the words and facial expressions of a child, as well as changes in their emotions. The collection unit can convert the child's words into text data using speech recognition technology. The collection unit can analyze the child's facial expressions and detect changes in their emotions using image recognition technology. The collection unit can analyze the child's emotional state using emotion analysis technology. The analysis unit analyzes the input received by the collection unit. The analysis unit can analyze, for example, the child's needs and preferences. The analysis unit can analyze the meaning of the child's words using natural language processing technology. The analysis unit can estimate emotions from the child's facial expressions using image analysis technology. The analysis unit can analyze the child's emotional state using emotion analysis technology. The generation unit generates dialogue based on the results analyzed by the analysis unit. The generation unit can, for example, understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. The generation unit can generate dialogue suitable for the child using natural language generation technology. The generation unit can generate avatar facial expressions that correspond to the child's facial expressions using image generation technology. The generation unit can generate dialogue that corresponds to the child's emotions using emotion generation technology. The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. The prediction unit can, for example, analyze a child's behavior patterns and emotional changes to predict the next care or support needed. The prediction unit can predict a child's next behavior using behavior prediction technology. The prediction unit can predict a child's next emotional state using emotion prediction technology. The prediction unit can predict the next care or support needed using care prediction technology. The sharing unit shares the content of care and support predicted by the prediction unit with the guardian. The sharing unit can, for example, notify the guardian of the content of care and support predicted based on the child's behavior and emotional state. The sharing unit can notify the guardian of the content of care and support using notification technology.The shared unit can display the details of care and support to parents using display technology. The shared unit can also communicate the details of care and support to parents verbally using voice technology. As a result, the future-nurturing AI navigator according to the embodiment can generate dialogue tailored to the child's needs and preferences, predict the next necessary care and support, and share it with the parents.

[0030] The data collection unit accepts multimodal inputs. For example, it can accept audio, visual, and emotional inputs. Specifically, the data collection unit utilizes multiple sensors and devices to detect the words a child speaks, their facial expressions, and even changes in their emotions. Speech recognition technology can be used to convert a child's words into text data. For example, audio data collected through a microphone is analyzed by a speech recognition engine and converted into text data. This text data is then used for processing in the subsequent analysis unit. Image recognition technology can be used to analyze a child's facial expressions and detect changes in their emotions. For example, video data collected through a camera is analyzed by an image recognition algorithm to identify the child's facial expressions and movements. This allows for real-time detection of emotions such as joy, sadness, and surprise. Emotion analysis technology can be used to analyze a child's emotional state. For example, based on audio and facial data, a child's emotional state can be analyzed in detail, allowing for early detection of signs of stress and anxiety. This enables the data collection unit to efficiently collect diverse input data from children and provide it to the analysis unit. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the input received by the data collection unit. For example, the analysis unit can analyze a child's needs and preferences. Specifically, it can use natural language processing technology to analyze the meaning of a child's words. For example, it can analyze collected text data to understand the intent and emotions behind a child's statements. This allows for an understanding of the child's interests and concerns, and the suggestion of appropriate dialogues and activities. It can use image analysis technology to estimate emotions from a child's facial expressions. For example, it can analyze collected video data to detect changes in a child's facial expressions. This allows for a real-time understanding of the child's emotional state and appropriate responses. It can also use emotion analysis technology to analyze a child's emotional state. For example, it can analyze voice data and facial expression data to understand changes in a child's emotions in detail. This allows for early detection of signs of stress and anxiety in a child, and the provision of appropriate care. Furthermore, the analysis unit can utilize past data and statistical information to predict long-term changes in needs and preferences. For example, it can analyze changes in a child's interests and concerns based on past dialogue data and suggest future activities and learning plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to predict long-term needs and detect anomalies, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates dialogue based on the results analyzed by the analysis unit. For example, the generation unit can understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. Specifically, it can use natural language generation technology to generate dialogue suitable for children. For example, it can generate appropriate responses and questions based on a child's statements and emotional state, facilitating smooth dialogue with the child. It can use image generation technology to generate avatar expressions that correspond to a child's facial expressions. For example, by changing the avatar's facial expressions according to a child's emotional state, it can create a more natural and approachable dialogue. It can use emotion generation technology to generate dialogue that corresponds to a child's emotions. For example, if a child is feeling anxious, it can generate dialogue that provides reassurance, allowing the child to relax. Furthermore, the generation unit can generate more personalized dialogue by considering past dialogue data and the child's preferences. For example, it can generate dialogue based on a child's favorite characters or themes to capture the child's interest. In addition, the generation unit can adjust the content and tone of the dialogue to provide appropriate dialogue according to the child's age and developmental stage. As a result, the generation unit can generate dialogue that matches the child's needs and preferences, facilitating smooth communication with the child.

[0033] The prediction unit predicts the next care and support needed based on the dialogue generated by the generation unit. For example, the prediction unit can analyze a child's behavioral patterns and emotional changes to predict the next care and support needed. Specifically, it can use behavioral prediction technology to predict a child's next behavior. For example, based on past behavioral data, it can predict what kind of activity a child will want next and make appropriate suggestions. It can use emotional prediction technology to predict a child's next emotional state. For example, based on the child's current emotional state and past emotional data, it can predict what kind of emotion a child will feel next and prepare appropriate responses. It can also use care prediction technology to predict the next care and support needed. For example, if a child is feeling stressed, it can suggest relaxing activities and provide a safe environment for the child. Furthermore, the prediction unit can also develop long-term care plans. For example, it can suggest future learning plans and activities considering the child's developmental stage and learning progress. In addition, the prediction unit can use anomaly detection algorithms to detect unusual behavior and emotional changes early and take appropriate responses. As a result, the prediction unit can predict a child's next behavior and emotional state with high accuracy and provide appropriate care and support.

[0034] The sharing unit shares the care and support predicted by the prediction unit with parents. For example, the sharing unit can notify parents of the care and support predicted based on the child's behavior and emotional state. Specifically, it can notify parents of the care and support using notification technology. For example, it can send notifications about the child's current condition and necessary care through a smartphone app. It can also display the care and support information to parents using display technology. For example, it can visually display changes in the child's behavior and emotions through a dedicated dashboard, making it easy for parents to understand. It can also communicate the care and support information to parents verbally using voice technology. For example, it can provide information about the child's condition and necessary care verbally through a voice assistant. Furthermore, the sharing unit can collect feedback from parents and continuously improve the accuracy and effectiveness of the system. For example, it can review the prediction algorithm and notification content based on feedback provided by parents to provide more appropriate care and support. In addition, the sharing unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email, SMS, and voice calls. This allows the shared department to quickly and reliably share information about care and support with parents, thereby supporting the child's growth and development.

[0035] The data collection unit can accept audio, visual, and emotional inputs. For example, the data collection unit can convert a child's speech into text data using speech recognition technology. For example, the data collection unit can analyze a child's facial expressions using image recognition technology and detect changes in emotion. For example, the data collection unit can analyze a child's emotional state using emotion analysis technology. In this way, the data collection unit can collect multimodal data by accepting audio, visual, and emotional inputs. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may be performed without generative AI. For example, when the data collection unit converts a child's speech into text data using speech recognition technology, it may use generative AI to analyze the audio data and generate the text data.

[0036] The analysis unit can analyze a child's needs and preferences. For example, the analysis unit uses natural language processing technology to analyze the meaning of a child's words. For example, the analysis unit uses image analysis technology to estimate a child's emotions from their facial expressions. For example, the analysis unit uses emotion analysis technology to analyze a child's emotional state. As a result, the analysis unit can generate individually tailored dialogues by analyzing a child's needs and preferences. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, when the analysis unit analyzes the meaning of a child's words using natural language processing technology, it may use generative AI to analyze text data and extract meaning.

[0037] The generation unit can understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. For example, the generation unit can use natural language generation technology to generate dialogue suitable for a child. For example, the generation unit can use image generation technology to generate avatar expressions that correspond to the child's facial expressions. For example, the generation unit can use emotion generation technology to generate dialogue that corresponds to the child's emotions. In this way, the generation unit can provide dialogue suitable for a child by understanding a child's words, facial expressions, and emotional changes, and generating dialogue accordingly. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when the generation unit generates dialogue suitable for a child using natural language generation technology, it may use a generation AI to generate text data.

[0038] The prediction unit can analyze a child's behavioral patterns and emotional changes to predict the next care or support needed. For example, the prediction unit uses behavioral prediction technology to predict the child's next behavior. For example, the prediction unit uses emotion prediction technology to predict the child's next emotional state. For example, the prediction unit uses care prediction technology to predict the next care or support needed. In this way, the prediction unit can provide appropriate care and support by analyzing the child's behavioral patterns and emotional changes and predicting the next care or support needed. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, when the prediction unit uses behavioral prediction technology to predict a child's next behavior, it can use generative AI to analyze behavioral data and predict the next behavior.

[0039] The sharing unit can notify parents of the care and support they have predicted based on the child's behavior and emotional state. The sharing unit notifies parents of the care and support using, for example, notification technology. The sharing unit displays the care and support to parents using, for example, display technology. The sharing unit verbally communicates the care and support to parents using, for example, voice technology. In this way, by notifying parents of the care and support they have predicted based on the child's behavior and emotional state, the sharing unit enables parents to take appropriate action. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when the sharing unit notifies parents of the care and support using notification technology, it may use generative AI to generate the notification content.

[0040] The data collection unit can estimate a child's emotions and dynamically adjust audio, visual, and emotional inputs based on the estimated emotions. For example, if a child is excited, the data collection unit may reduce the sensitivity of audio input and prioritize visual input. For example, if a child is calm, the data collection unit may increase the sensitivity of audio input to facilitate detailed dialogue. For example, if a child is tired, the data collection unit may increase the sensitivity of emotional input and provide visual inputs that encourage rest. This allows the data collection unit to collect more appropriate data by estimating the child's emotions and dynamically adjusting the inputs based on the estimated emotions. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, when estimating a child's emotions, the data collection unit may use generative AI to analyze emotional data and adjust the inputs.

[0041] The data collection unit can analyze a child's past behavioral history and select the optimal input method. For example, the data collection unit may prioritize providing input methods that the child has previously preferred (e.g., voice, visual). For example, the data collection unit may suggest an input method suitable for a specific time period based on the child's past behavioral patterns. For example, the data collection unit may select the optimal input method for a specific situation based on the child's past behavioral history. This enables effective data collection by analyzing the child's past behavioral history and selecting the optimal input method. Some or all of the above processing in the data collection unit may be performed using generative AI, or not. For example, when analyzing a child's past behavioral history, the data collection unit may use generative AI to analyze behavioral data and select the optimal input method.

[0042] The data collection unit can filter input data based on the child's current activities and environment. For example, if the child is playing outdoors, the data collection unit can filter out ambient noise to improve the accuracy of voice input. If the child is quietly indoors, the data collection unit can prioritize acquiring visual input. If the child is engaged in a specific activity, the data collection unit can prioritize acquiring data related to that activity. This allows the data collection unit to collect data with high accuracy by filtering based on the child's current activities and environment. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to analyze and filter environmental data when acquiring input data.

[0043] The data collection unit can estimate a child's emotions and prioritize input data based on the estimated emotions. For example, if the child is excited, the data collection unit will prioritize acquiring visual input data. For example, if the child is calm, the data collection unit will prioritize acquiring audio input data. For example, if the child is tired, the data collection unit will prioritize acquiring emotional input data. In this way, the data collection unit can prioritize the collection of important data by estimating the child's emotions and prioritizing input data based on the estimated emotions. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, when estimating a child's emotions, the data collection unit may use generative AI to analyze emotional data and determine the priority of input data.

[0044] The data collection unit can prioritize the acquisition of highly relevant data by considering the child's geographical location when acquiring input data. For example, if the child is in a park, the data collection unit will prioritize acquiring data related to the park. For example, if the child is at school, the data collection unit will prioritize acquiring data related to the school. For example, if the child is at home, the data collection unit will prioritize acquiring data related to home. This allows the data collection unit to collect appropriate data by prioritizing the acquisition of highly relevant data by considering the child's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, when acquiring input data, the data collection unit can analyze geographical location information using a generative AI and prioritize the acquisition of highly relevant data.

[0045] The data collection unit can analyze a child's social media activity and obtain relevant data when acquiring input data. For example, the data collection unit can obtain relevant data based on the content a child shows interest in on social media. For example, the data collection unit can analyze the content of a child's social media posts and obtain relevant data. For example, the data collection unit can obtain relevant data by considering a child's social media friendships. This allows the data collection unit to collect a wider variety of data by analyzing a child's social media activity and obtaining relevant data. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can use generative AI to analyze social media data and obtain relevant data when acquiring input data.

[0046] The analysis unit can estimate a child's emotions and adjust the analysis method for needs and preferences based on the estimated emotions. For example, if the child is excited, the analysis unit will prioritize emotional data to analyze needs. For example, if the child is calm, the analysis unit will prioritize vocal data to analyze preferences. For example, if the child is tired, the analysis unit will prioritize visual data to analyze needs. This allows the analysis unit to estimate the child's emotions and adjust the analysis method based on the estimated emotions, enabling a more accurate analysis of needs and preferences. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, when estimating a child's emotions, the analysis unit can use generative AI to analyze emotional data and adjust the analysis method for needs and preferences.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the child's past behavioral patterns during the analysis. For example, the analysis unit analyzes the child's current needs based on the child's past behavioral patterns. For example, the analysis unit predicts and analyzes the child's preferences from the child's past behavioral patterns. For example, the analysis unit improves the accuracy of the analysis results by referring to the child's past behavioral patterns. As a result, the analysis unit can obtain more accurate analysis results by improving the accuracy of its analysis by referring to the child's past behavioral patterns. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can improve the accuracy of its analysis by using generative AI to analyze past behavioral patterns during the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the child's age and developmental stage during analysis. For example, the analysis unit applies a simple analysis algorithm to infants. For example, the analysis unit applies a detailed analysis algorithm to elementary school students. For example, the analysis unit applies a complex analysis algorithm to junior high school students. This allows the analysis unit to perform appropriate analysis by applying different analysis algorithms according to the child's age and developmental stage. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can use generative AI to apply an analysis algorithm appropriate to the age and developmental stage during analysis.

[0049] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is excited, the analysis unit provides a visually stimulating display method. For example, if the child is calm, the analysis unit provides a display method that includes detailed information. For example, if the child is tired, the analysis unit provides a simple and easily visible display method. In this way, the analysis unit can provide an appropriate display by estimating the child's emotions and adjusting the display method based on the estimated emotions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, when estimating the child's emotions, the analysis unit can use a generative AI to analyze the emotion data and adjust the display method.

[0050] The analysis unit can determine the priority of analysis based on the child's activity history during the analysis. For example, the analysis unit may prioritize analyzing the child's current needs based on the child's past activity history. For example, the analysis unit may prioritize analyzing the child's preferences based on the child's past activity history. For example, the analysis unit may determine the priority of analysis by referring to the child's past activity history. In this way, the analysis unit can prioritize the analysis of important needs and preferences by determining the priority of analysis based on the child's activity history. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit may use generative AI to analyze the activity history and determine the priority of analysis during the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to the child's relevant data during the analysis. For example, the analysis unit analyzes the child's current needs based on the child's relevant data. For example, the analysis unit predicts and analyzes preferences from the child's relevant data. For example, the analysis unit improves the accuracy of the analysis results by referring to the child's relevant data. As a result, the analysis unit can obtain more accurate analysis results by improving the accuracy of its analysis by referring to the child's relevant data. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can improve the accuracy of its analysis by analyzing the relevant data using generative AI during the analysis.

[0052] The generation unit can estimate a child's emotions and adjust the way the dialogue is expressed based on those estimated emotions. For example, if the child is excited, the generation unit will generate a visually stimulating dialogue. For example, if the child is calm, the generation unit will generate a dialogue that includes detailed information. For example, if the child is tired, the generation unit will generate a simple and easy-to-understand dialogue. In this way, the generation unit can provide an appropriate dialogue by estimating the child's emotions and adjusting the way the dialogue is expressed based on those estimated emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when estimating a child's emotions, the generation unit may use a generation AI to analyze emotion data and adjust the way the dialogue is expressed.

[0053] The generation unit can adjust the level of detail in the dialogue based on the child's needs when generating the dialogue. For example, if the child asks a specific question, the generation unit will generate a detailed dialogue. For example, if the child asks a simple question, the generation unit will generate a simple dialogue. The generation unit adjusts the level of detail in the dialogue according to the child's needs. In this way, the generation unit can provide an appropriate dialogue by adjusting the level of detail in the dialogue based on the child's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can analyze needs data using a generation AI when generating the dialogue and adjust the level of detail in the dialogue.

[0054] The generation unit can apply different dialogue algorithms depending on the child's age and developmental stage when generating dialogue. For example, the generation unit applies a simple dialogue algorithm to a toddler. For example, the generation unit applies a detailed dialogue algorithm to an elementary school student. For example, the generation unit applies a complex dialogue algorithm to a middle school student. In this way, the generation unit can provide appropriate dialogue by applying different dialogue algorithms depending on the child's age and developmental stage. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can use a generation AI to apply a dialogue algorithm appropriate to the child's age and developmental stage when generating dialogue.

[0055] The generation unit can estimate the child's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the child is excited, the generation unit will generate a short, to-the-point dialogue. For example, if the child is calm, the generation unit will generate a longer dialogue that includes detailed explanations. For example, if the child is tired, the generation unit will generate a simple, short dialogue. In this way, the generation unit can provide an appropriate dialogue by estimating the child's emotions and adjusting the length of the dialogue based on the estimated emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when estimating the child's emotions, the generation unit may use a generation AI to analyze emotion data and adjust the length of the dialogue.

[0056] The generation unit can determine dialogue priorities based on the child's activity history when generating dialogues. For example, the generation unit may prioritize generating dialogues that meet the child's current needs based on the child's past activity history. For example, the generation unit may prioritize generating dialogues that meet the child's preferences based on the child's past activity history. For example, the generation unit may determine dialogue priorities by referring to the child's past activity history. In this way, the generation unit can provide appropriate dialogues by determining dialogue priorities based on the child's activity history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit may use a generation AI to analyze the activity history and determine dialogue priorities when generating dialogues.

[0057] The generation unit can improve the accuracy of the dialogue by referring to the child's relevant data when generating the dialogue. For example, the generation unit generates a dialogue that meets the child's current needs based on the child's relevant data. For example, the generation unit generates a dialogue that meets the child's preferences from the child's relevant data. For example, the generation unit improves the accuracy of the dialogue by referring to the child's relevant data. In this way, the generation unit can provide an appropriate dialogue by improving the accuracy of the dialogue by referring to the child's relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can improve the accuracy of the dialogue by analyzing the relevant data using a generation AI when generating the dialogue.

[0058] The prediction unit can estimate a child's emotions and adjust its care and support prediction method based on the estimated emotions. For example, if the child is agitated, the prediction unit will prioritize emotional data to predict care. For example, if the child is calm, the prediction unit will prioritize auditory data to predict support. For example, if the child is tired, the prediction unit will prioritize visual data to predict care. In this way, the prediction unit can provide appropriate care and support by estimating the child's emotions and adjusting its prediction method based on the estimated emotions. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, when estimating a child's emotions, the prediction unit may use generative AI to analyze emotional data and adjust its care and support prediction method.

[0059] The prediction unit can improve the accuracy of its predictions by referring to the child's past behavioral patterns during the prediction process. For example, the prediction unit predicts current care based on the child's past behavioral patterns. For example, the prediction unit predicts support tailored to the child's preferences based on the child's past behavioral patterns. For example, the prediction unit improves the accuracy of its prediction results by referring to the child's past behavioral patterns. In this way, the prediction unit can provide appropriate care and support by improving the accuracy of its predictions by referring to the child's past behavioral patterns. Some or all of the above processing in the prediction unit may be performed using generative AI, or not. For example, the prediction unit can improve the accuracy of its predictions by analyzing past behavioral patterns using generative AI during the prediction process.

[0060] The prediction unit can apply different prediction algorithms depending on the child's age and developmental stage during prediction. For example, the prediction unit might apply a simple prediction algorithm to a toddler, a detailed prediction algorithm to an elementary school student, and a complex prediction algorithm to a middle school student. This allows the prediction unit to provide appropriate care and support by applying different prediction algorithms depending on the child's age and developmental stage. Some or all of the above processing in the prediction unit may be performed using generative AI, or not. For example, the prediction unit can use generative AI to apply age- and developmental-stage-appropriate prediction algorithms during prediction.

[0061] The prediction unit can estimate a child's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the child is excited, the prediction unit provides a visually stimulating display method. For example, if the child is calm, the prediction unit provides a display method that includes detailed information. For example, if the child is tired, the prediction unit provides a simple and easy-to-read display method. In this way, the prediction unit can provide an appropriate display by estimating the child's emotions and adjusting the display method based on the estimated emotions. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, when estimating a child's emotions, the prediction unit can use generative AI to analyze emotion data and adjust the display method.

[0062] The prediction unit can determine prediction priorities based on the child's activity history during prediction. For example, the prediction unit may prioritize current care based on the child's past activity history. For example, the prediction unit may prioritize support tailored to the child's preferences based on the child's past activity history. For example, the prediction unit may determine prediction priorities by referring to the child's past activity history. In this way, the prediction unit can prioritize important care and support by determining prediction priorities based on the child's activity history. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit may use generative AI to analyze the activity history and determine prediction priorities during prediction.

[0063] The prediction unit can improve the accuracy of its predictions by referring to the child's relevant data during the prediction process. For example, the prediction unit predicts current care based on the child's relevant data. For example, the prediction unit predicts support tailored to the child's preferences from the child's relevant data. For example, the prediction unit improves the accuracy of its prediction results by referring to the child's relevant data. In this way, the prediction unit can provide appropriate care and support by improving the accuracy of its predictions by referring to the child's relevant data. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit can improve the accuracy of its predictions by analyzing the relevant data using generative AI during the prediction process.

[0064] The sharing unit can estimate a child's emotions and adjust the way the shared content is presented based on the estimated emotions. For example, if the child is excited, the sharing unit provides a visually stimulating presentation. For example, if the child is calm, the sharing unit provides a presentation that includes detailed information. For example, if the child is tired, the sharing unit provides a simple and easily understandable presentation. In this way, the sharing unit can enable appropriate sharing by estimating the child's emotions and adjusting the presentation based on the estimated emotions. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when estimating a child's emotions, the sharing unit can use generative AI to analyze emotion data and adjust the presentation.

[0065] The sharing unit can improve the accuracy of the shared content by referring to the child's past behavioral patterns during sharing. For example, the sharing unit shares current care based on the child's past behavioral patterns. For example, the sharing unit shares support tailored to the child's preferences based on the child's past behavioral patterns. For example, the sharing unit improves the accuracy of the shared content by referring to the child's past behavioral patterns. As a result, the sharing unit can improve the accuracy of the shared content by referring to the child's past behavioral patterns, enabling appropriate sharing. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without using generative AI. For example, the sharing unit can analyze past behavioral patterns using generative AI during sharing to improve the accuracy of the shared content.

[0066] The sharing function can apply different sharing algorithms depending on the child's age and developmental stage during sharing. For example, the sharing function might apply a simple sharing algorithm to a toddler, a detailed sharing algorithm to an elementary school student, and a complex sharing algorithm to a middle school student. This allows the sharing function to enable appropriate sharing by applying different sharing algorithms according to the child's age and developmental stage. Some or all of the above-described processes in the sharing function may be performed using generative AI, or they may be performed without generative AI. For example, the sharing function can use generative AI to apply age- and developmental-stage-appropriate sharing algorithms during sharing.

[0067] The sharing unit can estimate a child's emotions and prioritize the content to share based on those emotions. For example, if a child is excited, the sharing unit will prioritize sharing visually stimulating content. If a child is calm, the sharing unit will prioritize sharing detailed information. If a child is tired, the sharing unit will prioritize sharing simple and easily visible content. In this way, the sharing unit can prioritize sharing important information by estimating the child's emotions and prioritizing the content to share based on those emotions. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when estimating a child's emotions, the sharing unit can use generative AI to analyze emotion data and determine the priority of the content to share.

[0068] The sharing function can adjust how shared content is displayed based on the child's activity history when sharing. For example, the sharing function may prioritize displaying current care based on the child's past activity history. For example, the sharing function may prioritize displaying support tailored to the child's preferences based on the child's past activity history. For example, the sharing function may adjust the display method by referring to the child's past activity history. This allows the sharing function to enable appropriate sharing by adjusting the display method based on the child's activity history. Some or all of the above processing in the sharing function may be performed using a generative AI, or not. For example, the sharing function may analyze the activity history using a generative AI and adjust the display method when sharing.

[0069] The sharing unit can improve the accuracy of the shared content by referring to the child's relevant data during sharing. For example, the sharing unit can share current care based on the child's relevant data. For example, the sharing unit can share support tailored to the child's preferences based on the child's relevant data. For example, the sharing unit can improve the accuracy of the shared content by referring to the child's relevant data. This enables appropriate sharing by improving the accuracy of the shared content by referring to the child's relevant data. Some or all of the above processing in the sharing unit may be performed using generative AI, or not. For example, the sharing unit can analyze the relevant data using generative AI during sharing to improve the accuracy of the shared content.

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

[0071] The Future Development AI Navigator can also track a child's learning progress and provide individualized learning plans. For example, the data collection unit records the child's learning activities, and the analysis unit analyzes that data to evaluate learning progress. The generation unit generates a learning plan suitable for the child based on the analysis results. The prediction unit analyzes the child's learning patterns and predicts the next learning content needed. The sharing unit shares this information with parents so that parents can support their child's learning. In this way, the Future Development AI Navigator can improve the effectiveness of a child's learning by tracking their learning progress and providing individualized learning plans.

[0072] The Future Nurturing AI Navigator can also monitor a child's health and support their health management. For example, the data collection unit collects biometric data such as the child's body temperature and heart rate. The analysis unit analyzes this data to assess the child's health. The generation unit generates a health management plan tailored to the child based on the analysis results. The prediction unit analyzes the child's health patterns and predicts the next necessary health management steps. The sharing unit shares this information with parents, enabling them to support their child's health. In this way, the Future Nurturing AI Navigator can maintain a child's health by monitoring their health and supporting their health management.

[0073] The Future Development AI Navigator can also support the social development of children. For example, the data collection unit records the child's friendships and social activities. The analysis unit analyzes this data to evaluate social development. The generation unit generates social activity plans suitable for the child based on the analysis results. The prediction unit analyzes the child's social patterns and predicts the next social activities needed. The sharing unit shares this information with parents, enabling them to support the child's social development. In this way, the Future Development AI Navigator can improve children's social skills by supporting their social development.

[0074] The Future Development AI Navigator can also suggest activities to foster children's creativity. For example, the data collection unit records children's creative activities (such as drawing or making music). The analysis unit analyzes this data to evaluate the development of creativity. The generation unit generates creative activity plans suitable for the child based on the analysis results. The prediction unit analyzes the child's creative patterns and predicts the next creative activity needed. The sharing unit shares this information with parents so that parents can support their child's creativity. In this way, the Future Development AI Navigator can improve children's creativity by suggesting activities to foster their creativity.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The data collection unit accepts multimodal input. For example, it can accept audio, visual, and emotional input, detecting the words a child speaks, their facial expressions, and changes in their emotions. It uses speech recognition technology to convert the child's words into text data, image recognition technology to analyze the child's facial expressions, and emotion analysis technology to analyze the child's emotional state. Step 2: The analysis unit analyzes the input received by the collection unit. For example, it analyzes the child's needs and preferences, analyzes the meaning of the child's words using natural language processing technology, estimates emotions from the child's facial expressions using image analysis technology, and analyzes the child's emotional state using emotion analysis technology. Step 3: The generation unit generates dialogue based on the results analyzed by the analysis unit. For example, it understands the child's words, facial expressions, and emotional changes, and generates dialogue accordingly. It uses natural language generation technology to generate dialogue suitable for the child, image generation technology to generate avatar facial expressions that correspond to the child's facial expressions, and emotion generation technology to generate dialogue that corresponds to the child's emotions. Step 4: The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. For example, it analyzes the child's behavioral patterns and emotional changes to predict the next care or support needed. It uses behavioral prediction technology to predict the child's next behavior, emotion prediction technology to predict the child's next emotional state, and care prediction technology to predict the next care or support needed. Step 5: The sharing unit shares the care and support predicted by the prediction unit with the parents. For example, it notifies parents of the care and support predicted based on the child's behavior and emotional state. Notification technology is used to notify parents of the care and support, display technology is used to display the care and support to parents, and voice technology is used to verbally communicate the care and support to parents.

[0077] (Example of form 2) The Future Nurturing AI Navigator according to an embodiment of the present invention is a system that creates a virtual childcare worker (AI avatar) that generates dialogues tailored to a child's needs and preferences using a generative AI that understands multimodal inputs (speech, visuals, and emotions). This system uses a generative AI that receives multimodal inputs such as speech, visuals, and emotions, analyzes these inputs, and generates dialogues tailored to the child's needs and preferences. Furthermore, the generative AI analyzes the child's behavior and emotional state and predicts the care and support that may be needed next. These predictions are shared with the parents, allowing them to gain concrete and predictive insights into their child's daily growth and development. For example, the Future Nurturing AI Navigator detects the words, facial expressions, and even emotional changes of the child. This information is input into the generative AI. Next, the generative AI analyzes the input information and generates dialogues tailored to the child's needs and preferences. The generative AI understands the child's words, facial expressions, and emotional changes and generates dialogues accordingly. For example, if the child says, "I'm hungry," the generative AI will generate a dialogue such as, "What would you like to eat?" Furthermore, the generative AI analyzes children's behavior and emotional states to predict the next care and support they may need. For example, if a child appears tired, the generative AI might suggest, "Shall we take a short break?" This prediction is shared with the parents. The generative AI notifies parents of the care and support it predicts based on the child's behavior and emotional state. This allows parents to gain specific and predictive insights into their child's daily growth and development. For example, if the generative AI notifies parents that "the child appears tired and needs a short break," they can take appropriate action. This system reduces the burden on childcare workers and alleviates the shortage of childcare staff. The virtual childcare provider, powered by the generative AI, provides appropriate care to individual children in all situations, reducing the burden on childcare workers. Parents also gain specific insights into their child's growth and development, as well as predictions about future care and support needs. This helps to alleviate the shortage of childcare workers.This allows the future-oriented AI navigator to generate conversations tailored to the child's needs and preferences, predict the next necessary care and support, and share this information with parents.

[0078] The future-nurturing AI navigator according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a prediction unit, and a sharing unit. The collection unit accepts multimodal input. The collection unit can accept, for example, audio, visual, and emotional input. The collection unit can detect, for example, the words and facial expressions of a child, as well as changes in their emotions. The collection unit can convert the child's words into text data using speech recognition technology. The collection unit can analyze the child's facial expressions and detect changes in their emotions using image recognition technology. The collection unit can analyze the child's emotional state using emotion analysis technology. The analysis unit analyzes the input received by the collection unit. The analysis unit can analyze, for example, the child's needs and preferences. The analysis unit can analyze the meaning of the child's words using natural language processing technology. The analysis unit can estimate emotions from the child's facial expressions using image analysis technology. The analysis unit can analyze the child's emotional state using emotion analysis technology. The generation unit generates dialogue based on the results analyzed by the analysis unit. The generation unit can, for example, understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. The generation unit can generate dialogue suitable for the child using natural language generation technology. The generation unit can generate avatar facial expressions that correspond to the child's facial expressions using image generation technology. The generation unit can generate dialogue that corresponds to the child's emotions using emotion generation technology. The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. The prediction unit can, for example, analyze a child's behavior patterns and emotional changes to predict the next care or support needed. The prediction unit can predict a child's next behavior using behavior prediction technology. The prediction unit can predict a child's next emotional state using emotion prediction technology. The prediction unit can predict the next care or support needed using care prediction technology. The sharing unit shares the content of care and support predicted by the prediction unit with the guardian. The sharing unit can, for example, notify the guardian of the content of care and support predicted based on the child's behavior and emotional state. The sharing unit can notify the guardian of the content of care and support using notification technology.The shared unit can display the details of care and support to parents using display technology. The shared unit can also communicate the details of care and support to parents verbally using voice technology. As a result, the future-nurturing AI navigator according to the embodiment can generate dialogue tailored to the child's needs and preferences, predict the next necessary care and support, and share it with the parents.

[0079] The data collection unit accepts multimodal inputs. For example, it can accept audio, visual, and emotional inputs. Specifically, the data collection unit utilizes multiple sensors and devices to detect the words a child speaks, their facial expressions, and even changes in their emotions. Speech recognition technology can be used to convert a child's words into text data. For example, audio data collected through a microphone is analyzed by a speech recognition engine and converted into text data. This text data is then used for processing in the subsequent analysis unit. Image recognition technology can be used to analyze a child's facial expressions and detect changes in their emotions. For example, video data collected through a camera is analyzed by an image recognition algorithm to identify the child's facial expressions and movements. This allows for real-time detection of emotions such as joy, sadness, and surprise. Emotion analysis technology can be used to analyze a child's emotional state. For example, based on audio and facial data, a child's emotional state can be analyzed in detail, allowing for early detection of signs of stress and anxiety. This enables the data collection unit to efficiently collect diverse input data from children and provide it to the analysis unit. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0080] The analysis unit analyzes the input received by the data collection unit. For example, the analysis unit can analyze a child's needs and preferences. Specifically, it can use natural language processing technology to analyze the meaning of a child's words. For example, it can analyze collected text data to understand the intent and emotions behind a child's statements. This allows for an understanding of the child's interests and concerns, and the suggestion of appropriate dialogues and activities. It can use image analysis technology to estimate emotions from a child's facial expressions. For example, it can analyze collected video data to detect changes in a child's facial expressions. This allows for a real-time understanding of the child's emotional state and appropriate responses. It can also use emotion analysis technology to analyze a child's emotional state. For example, it can analyze voice data and facial expression data to understand changes in a child's emotions in detail. This allows for early detection of signs of stress and anxiety in a child, and the provision of appropriate care. Furthermore, the analysis unit can utilize past data and statistical information to predict long-term changes in needs and preferences. For example, it can analyze changes in a child's interests and concerns based on past dialogue data and suggest future activities and learning plans. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to predict long-term needs and detect anomalies, thereby improving the reliability and safety of the entire system.

[0081] The generation unit generates dialogue based on the results analyzed by the analysis unit. For example, the generation unit can understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. Specifically, it can use natural language generation technology to generate dialogue suitable for children. For example, it can generate appropriate responses and questions based on a child's statements and emotional state, facilitating smooth dialogue with the child. It can use image generation technology to generate avatar expressions that correspond to a child's facial expressions. For example, by changing the avatar's facial expressions according to a child's emotional state, it can create a more natural and approachable dialogue. It can use emotion generation technology to generate dialogue that corresponds to a child's emotions. For example, if a child is feeling anxious, it can generate dialogue that provides reassurance, allowing the child to relax. Furthermore, the generation unit can generate more personalized dialogue by considering past dialogue data and the child's preferences. For example, it can generate dialogue based on a child's favorite characters or themes to capture the child's interest. In addition, the generation unit can adjust the content and tone of the dialogue to provide appropriate dialogue according to the child's age and developmental stage. As a result, the generation unit can generate dialogue that matches the child's needs and preferences, facilitating smooth communication with the child.

[0082] The prediction unit predicts the next care and support needed based on the dialogue generated by the generation unit. For example, the prediction unit can analyze a child's behavioral patterns and emotional changes to predict the next care and support needed. Specifically, it can use behavioral prediction technology to predict a child's next behavior. For example, based on past behavioral data, it can predict what kind of activity a child will want next and make appropriate suggestions. It can use emotional prediction technology to predict a child's next emotional state. For example, based on the child's current emotional state and past emotional data, it can predict what kind of emotion a child will feel next and prepare appropriate responses. It can also use care prediction technology to predict the next care and support needed. For example, if a child is feeling stressed, it can suggest relaxing activities and provide a safe environment for the child. Furthermore, the prediction unit can also develop long-term care plans. For example, it can suggest future learning plans and activities considering the child's developmental stage and learning progress. In addition, the prediction unit can use anomaly detection algorithms to detect unusual behavior and emotional changes early and take appropriate responses. As a result, the prediction unit can predict a child's next behavior and emotional state with high accuracy and provide appropriate care and support.

[0083] The sharing unit shares the care and support predicted by the prediction unit with parents. For example, the sharing unit can notify parents of the care and support predicted based on the child's behavior and emotional state. Specifically, it can notify parents of the care and support using notification technology. For example, it can send notifications about the child's current condition and necessary care through a smartphone app. It can also display the care and support information to parents using display technology. For example, it can visually display changes in the child's behavior and emotions through a dedicated dashboard, making it easy for parents to understand. It can also communicate the care and support information to parents verbally using voice technology. For example, it can provide information about the child's condition and necessary care verbally through a voice assistant. Furthermore, the sharing unit can collect feedback from parents and continuously improve the accuracy and effectiveness of the system. For example, it can review the prediction algorithm and notification content based on feedback provided by parents to provide more appropriate care and support. In addition, the sharing unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also email, SMS, and voice calls. This allows the shared department to quickly and reliably share information about care and support with parents, thereby supporting the child's growth and development.

[0084] The data collection unit can accept audio, visual, and emotional inputs. For example, the data collection unit can convert a child's speech into text data using speech recognition technology. For example, the data collection unit can analyze a child's facial expressions using image recognition technology and detect changes in emotion. For example, the data collection unit can analyze a child's emotional state using emotion analysis technology. In this way, the data collection unit can collect multimodal data by accepting audio, visual, and emotional inputs. Some or all of the above-described processes in the data collection unit may be performed using generative AI, or they may be performed without generative AI. For example, when the data collection unit converts a child's speech into text data using speech recognition technology, it may use generative AI to analyze the audio data and generate the text data.

[0085] The analysis unit can analyze a child's needs and preferences. For example, the analysis unit uses natural language processing technology to analyze the meaning of a child's words. For example, the analysis unit uses image analysis technology to estimate a child's emotions from their facial expressions. For example, the analysis unit uses emotion analysis technology to analyze a child's emotional state. As a result, the analysis unit can generate individually tailored dialogues by analyzing a child's needs and preferences. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, when the analysis unit analyzes the meaning of a child's words using natural language processing technology, it may use generative AI to analyze text data and extract meaning.

[0086] The generation unit can understand a child's words, facial expressions, and emotional changes, and generate dialogue accordingly. For example, the generation unit can use natural language generation technology to generate dialogue suitable for a child. For example, the generation unit can use image generation technology to generate avatar expressions that correspond to the child's facial expressions. For example, the generation unit can use emotion generation technology to generate dialogue that corresponds to the child's emotions. In this way, the generation unit can provide dialogue suitable for a child by understanding a child's words, facial expressions, and emotional changes, and generating dialogue accordingly. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when the generation unit generates dialogue suitable for a child using natural language generation technology, it may use a generation AI to generate text data.

[0087] The prediction unit can analyze a child's behavioral patterns and emotional changes to predict the next care or support needed. For example, the prediction unit uses behavioral prediction technology to predict the child's next behavior. For example, the prediction unit uses emotion prediction technology to predict the child's next emotional state. For example, the prediction unit uses care prediction technology to predict the next care or support needed. In this way, the prediction unit can provide appropriate care and support by analyzing the child's behavioral patterns and emotional changes and predicting the next care or support needed. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, when the prediction unit uses behavioral prediction technology to predict a child's next behavior, it can use generative AI to analyze behavioral data and predict the next behavior.

[0088] The sharing unit can notify parents of the care and support they have predicted based on the child's behavior and emotional state. The sharing unit notifies parents of the care and support using, for example, notification technology. The sharing unit displays the care and support to parents using, for example, display technology. The sharing unit verbally communicates the care and support to parents using, for example, voice technology. In this way, by notifying parents of the care and support they have predicted based on the child's behavior and emotional state, the sharing unit enables parents to take appropriate action. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when the sharing unit notifies parents of the care and support using notification technology, it may use generative AI to generate the notification content.

[0089] The data collection unit can estimate a child's emotions and dynamically adjust audio, visual, and emotional inputs based on the estimated emotions. For example, if a child is excited, the data collection unit may reduce the sensitivity of audio input and prioritize visual input. For example, if a child is calm, the data collection unit may increase the sensitivity of audio input to facilitate detailed dialogue. For example, if a child is tired, the data collection unit may increase the sensitivity of emotional input and provide visual inputs that encourage rest. This allows the data collection unit to collect more appropriate data by estimating the child's emotions and dynamically adjusting the inputs based on the estimated emotions. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, when estimating a child's emotions, the data collection unit may use generative AI to analyze emotional data and adjust the inputs.

[0090] The data collection unit can analyze a child's past behavioral history and select the optimal input method. For example, the data collection unit may prioritize providing input methods that the child has previously preferred (e.g., voice, visual). For example, the data collection unit may suggest an input method suitable for a specific time period based on the child's past behavioral patterns. For example, the data collection unit may select the optimal input method for a specific situation based on the child's past behavioral history. This enables effective data collection by analyzing the child's past behavioral history and selecting the optimal input method. Some or all of the above processing in the data collection unit may be performed using generative AI, or not. For example, when analyzing a child's past behavioral history, the data collection unit may use generative AI to analyze behavioral data and select the optimal input method.

[0091] The data collection unit can filter input data based on the child's current activities and environment. For example, if the child is playing outdoors, the data collection unit can filter out ambient noise to improve the accuracy of voice input. If the child is quietly indoors, the data collection unit can prioritize acquiring visual input. If the child is engaged in a specific activity, the data collection unit can prioritize acquiring data related to that activity. This allows the data collection unit to collect data with high accuracy by filtering based on the child's current activities and environment. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without generative AI. For example, the data collection unit can use generative AI to analyze and filter environmental data when acquiring input data.

[0092] The data collection unit can estimate a child's emotions and prioritize input data based on the estimated emotions. For example, if the child is excited, the data collection unit will prioritize acquiring visual input data. For example, if the child is calm, the data collection unit will prioritize acquiring audio input data. For example, if the child is tired, the data collection unit will prioritize acquiring emotional input data. In this way, the data collection unit can prioritize the collection of important data by estimating the child's emotions and prioritizing input data based on the estimated emotions. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, when estimating a child's emotions, the data collection unit may use generative AI to analyze emotional data and determine the priority of input data.

[0093] The data collection unit can prioritize the acquisition of highly relevant data by considering the child's geographical location when acquiring input data. For example, if the child is in a park, the data collection unit will prioritize acquiring data related to the park. For example, if the child is at school, the data collection unit will prioritize acquiring data related to the school. For example, if the child is at home, the data collection unit will prioritize acquiring data related to home. This allows the data collection unit to collect appropriate data by prioritizing the acquisition of highly relevant data by considering the child's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, when acquiring input data, the data collection unit can analyze geographical location information using a generative AI and prioritize the acquisition of highly relevant data.

[0094] The data collection unit can analyze a child's social media activity and obtain relevant data when acquiring input data. For example, the data collection unit can obtain relevant data based on the content a child shows interest in on social media. For example, the data collection unit can analyze the content of a child's social media posts and obtain relevant data. For example, the data collection unit can obtain relevant data by considering a child's social media friendships. This allows the data collection unit to collect a wider variety of data by analyzing a child's social media activity and obtaining relevant data. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can use generative AI to analyze social media data and obtain relevant data when acquiring input data.

[0095] The analysis unit can estimate a child's emotions and adjust the analysis method for needs and preferences based on the estimated emotions. For example, if the child is excited, the analysis unit will prioritize emotional data to analyze needs. For example, if the child is calm, the analysis unit will prioritize vocal data to analyze preferences. For example, if the child is tired, the analysis unit will prioritize visual data to analyze needs. This allows the analysis unit to estimate the child's emotions and adjust the analysis method based on the estimated emotions, enabling a more accurate analysis of needs and preferences. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, when estimating a child's emotions, the analysis unit can use generative AI to analyze emotional data and adjust the analysis method for needs and preferences.

[0096] The analysis unit can improve the accuracy of its analysis by referring to the child's past behavioral patterns during the analysis. For example, the analysis unit analyzes the child's current needs based on the child's past behavioral patterns. For example, the analysis unit predicts and analyzes the child's preferences from the child's past behavioral patterns. For example, the analysis unit improves the accuracy of the analysis results by referring to the child's past behavioral patterns. As a result, the analysis unit can obtain more accurate analysis results by improving the accuracy of its analysis by referring to the child's past behavioral patterns. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can improve the accuracy of its analysis by using generative AI to analyze past behavioral patterns during the analysis.

[0097] The analysis unit can apply different analysis algorithms depending on the child's age and developmental stage during analysis. For example, the analysis unit applies a simple analysis algorithm to infants. For example, the analysis unit applies a detailed analysis algorithm to elementary school students. For example, the analysis unit applies a complex analysis algorithm to junior high school students. This allows the analysis unit to perform appropriate analysis by applying different analysis algorithms according to the child's age and developmental stage. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can use generative AI to apply an analysis algorithm appropriate to the age and developmental stage during analysis.

[0098] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is excited, the analysis unit provides a visually stimulating display method. For example, if the child is calm, the analysis unit provides a display method that includes detailed information. For example, if the child is tired, the analysis unit provides a simple and easily visible display method. In this way, the analysis unit can provide an appropriate display by estimating the child's emotions and adjusting the display method based on the estimated emotions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, when estimating the child's emotions, the analysis unit can use a generative AI to analyze the emotion data and adjust the display method.

[0099] The analysis unit can determine the priority of analysis based on the child's activity history during the analysis. For example, the analysis unit may prioritize analyzing the child's current needs based on the child's past activity history. For example, the analysis unit may prioritize analyzing the child's preferences based on the child's past activity history. For example, the analysis unit may determine the priority of analysis by referring to the child's past activity history. In this way, the analysis unit can prioritize the analysis of important needs and preferences by determining the priority of analysis based on the child's activity history. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit may use generative AI to analyze the activity history and determine the priority of analysis during the analysis.

[0100] The analysis unit can improve the accuracy of its analysis by referring to the child's relevant data during the analysis. For example, the analysis unit analyzes the child's current needs based on the child's relevant data. For example, the analysis unit predicts and analyzes preferences from the child's relevant data. For example, the analysis unit improves the accuracy of the analysis results by referring to the child's relevant data. As a result, the analysis unit can obtain more accurate analysis results by improving the accuracy of its analysis by referring to the child's relevant data. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can improve the accuracy of its analysis by analyzing the relevant data using generative AI during the analysis.

[0101] The generation unit can estimate a child's emotions and adjust the way the dialogue is expressed based on those estimated emotions. For example, if the child is excited, the generation unit will generate a visually stimulating dialogue. For example, if the child is calm, the generation unit will generate a dialogue that includes detailed information. For example, if the child is tired, the generation unit will generate a simple and easy-to-understand dialogue. In this way, the generation unit can provide an appropriate dialogue by estimating the child's emotions and adjusting the way the dialogue is expressed based on those estimated emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when estimating a child's emotions, the generation unit may use a generation AI to analyze emotion data and adjust the way the dialogue is expressed.

[0102] The generation unit can adjust the level of detail in the dialogue based on the child's needs when generating the dialogue. For example, if the child asks a specific question, the generation unit will generate a detailed dialogue. For example, if the child asks a simple question, the generation unit will generate a simple dialogue. The generation unit adjusts the level of detail in the dialogue according to the child's needs. In this way, the generation unit can provide an appropriate dialogue by adjusting the level of detail in the dialogue based on the child's needs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can analyze needs data using a generation AI when generating the dialogue and adjust the level of detail in the dialogue.

[0103] The generation unit can apply different dialogue algorithms depending on the child's age and developmental stage when generating dialogue. For example, the generation unit applies a simple dialogue algorithm to a toddler. For example, the generation unit applies a detailed dialogue algorithm to an elementary school student. For example, the generation unit applies a complex dialogue algorithm to a middle school student. In this way, the generation unit can provide appropriate dialogue by applying different dialogue algorithms depending on the child's age and developmental stage. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can use a generation AI to apply a dialogue algorithm appropriate to the child's age and developmental stage when generating dialogue.

[0104] The generation unit can estimate the child's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the child is excited, the generation unit will generate a short, to-the-point dialogue. For example, if the child is calm, the generation unit will generate a longer dialogue that includes detailed explanations. For example, if the child is tired, the generation unit will generate a simple, short dialogue. In this way, the generation unit can provide an appropriate dialogue by estimating the child's emotions and adjusting the length of the dialogue based on the estimated emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, when estimating the child's emotions, the generation unit may use a generation AI to analyze emotion data and adjust the length of the dialogue.

[0105] The generation unit can determine dialogue priorities based on the child's activity history when generating dialogues. For example, the generation unit may prioritize generating dialogues that meet the child's current needs based on the child's past activity history. For example, the generation unit may prioritize generating dialogues that meet the child's preferences based on the child's past activity history. For example, the generation unit may determine dialogue priorities by referring to the child's past activity history. In this way, the generation unit can provide appropriate dialogues by determining dialogue priorities based on the child's activity history. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit may use a generation AI to analyze the activity history and determine dialogue priorities when generating dialogues.

[0106] The generation unit can improve the accuracy of the dialogue by referring to the child's relevant data when generating the dialogue. For example, the generation unit generates a dialogue that meets the child's current needs based on the child's relevant data. For example, the generation unit generates a dialogue that meets the child's preferences from the child's relevant data. For example, the generation unit improves the accuracy of the dialogue by referring to the child's relevant data. In this way, the generation unit can provide an appropriate dialogue by improving the accuracy of the dialogue by referring to the child's relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can improve the accuracy of the dialogue by analyzing the relevant data using a generation AI when generating the dialogue.

[0107] The prediction unit can estimate a child's emotions and adjust its care and support prediction method based on the estimated emotions. For example, if the child is agitated, the prediction unit will prioritize emotional data to predict care. For example, if the child is calm, the prediction unit will prioritize auditory data to predict support. For example, if the child is tired, the prediction unit will prioritize visual data to predict care. In this way, the prediction unit can provide appropriate care and support by estimating the child's emotions and adjusting its prediction method based on the estimated emotions. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, when estimating a child's emotions, the prediction unit may use generative AI to analyze emotional data and adjust its care and support prediction method.

[0108] The prediction unit can improve the accuracy of its predictions by referring to the child's past behavioral patterns during the prediction process. For example, the prediction unit predicts current care based on the child's past behavioral patterns. For example, the prediction unit predicts support tailored to the child's preferences based on the child's past behavioral patterns. For example, the prediction unit improves the accuracy of its prediction results by referring to the child's past behavioral patterns. In this way, the prediction unit can provide appropriate care and support by improving the accuracy of its predictions by referring to the child's past behavioral patterns. Some or all of the above processing in the prediction unit may be performed using generative AI, or not. For example, the prediction unit can improve the accuracy of its predictions by analyzing past behavioral patterns using generative AI during the prediction process.

[0109] The prediction unit can apply different prediction algorithms depending on the child's age and developmental stage during prediction. For example, the prediction unit might apply a simple prediction algorithm to a toddler, a detailed prediction algorithm to an elementary school student, and a complex prediction algorithm to a middle school student. This allows the prediction unit to provide appropriate care and support by applying different prediction algorithms depending on the child's age and developmental stage. Some or all of the above processing in the prediction unit may be performed using generative AI, or not. For example, the prediction unit can use generative AI to apply age- and developmental-stage-appropriate prediction algorithms during prediction.

[0110] The prediction unit can estimate a child's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the child is excited, the prediction unit provides a visually stimulating display method. For example, if the child is calm, the prediction unit provides a display method that includes detailed information. For example, if the child is tired, the prediction unit provides a simple and easy-to-read display method. In this way, the prediction unit can provide an appropriate display by estimating the child's emotions and adjusting the display method based on the estimated emotions. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, when estimating a child's emotions, the prediction unit can use generative AI to analyze emotion data and adjust the display method.

[0111] The prediction unit can determine prediction priorities based on the child's activity history during prediction. For example, the prediction unit may prioritize current care based on the child's past activity history. For example, the prediction unit may prioritize support tailored to the child's preferences based on the child's past activity history. For example, the prediction unit may determine prediction priorities by referring to the child's past activity history. In this way, the prediction unit can prioritize important care and support by determining prediction priorities based on the child's activity history. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit may use generative AI to analyze the activity history and determine prediction priorities during prediction.

[0112] The prediction unit can improve the accuracy of its predictions by referring to the child's relevant data during the prediction process. For example, the prediction unit predicts current care based on the child's relevant data. For example, the prediction unit predicts support tailored to the child's preferences from the child's relevant data. For example, the prediction unit improves the accuracy of its prediction results by referring to the child's relevant data. In this way, the prediction unit can provide appropriate care and support by improving the accuracy of its predictions by referring to the child's relevant data. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit can improve the accuracy of its predictions by analyzing the relevant data using generative AI during the prediction process.

[0113] The sharing unit can estimate a child's emotions and adjust the way the shared content is presented based on the estimated emotions. For example, if the child is excited, the sharing unit provides a visually stimulating presentation. For example, if the child is calm, the sharing unit provides a presentation that includes detailed information. For example, if the child is tired, the sharing unit provides a simple and easily understandable presentation. In this way, the sharing unit can enable appropriate sharing by estimating the child's emotions and adjusting the presentation based on the estimated emotions. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when estimating a child's emotions, the sharing unit can use generative AI to analyze emotion data and adjust the presentation.

[0114] The sharing unit can improve the accuracy of the shared content by referring to the child's past behavioral patterns during sharing. For example, the sharing unit shares current care based on the child's past behavioral patterns. For example, the sharing unit shares support tailored to the child's preferences based on the child's past behavioral patterns. For example, the sharing unit improves the accuracy of the shared content by referring to the child's past behavioral patterns. As a result, the sharing unit can improve the accuracy of the shared content by referring to the child's past behavioral patterns, enabling appropriate sharing. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without using generative AI. For example, the sharing unit can analyze past behavioral patterns using generative AI during sharing to improve the accuracy of the shared content.

[0115] The sharing function can apply different sharing algorithms depending on the child's age and developmental stage during sharing. For example, the sharing function might apply a simple sharing algorithm to a toddler, a detailed sharing algorithm to an elementary school student, and a complex sharing algorithm to a middle school student. This allows the sharing function to enable appropriate sharing by applying different sharing algorithms according to the child's age and developmental stage. Some or all of the above-described processes in the sharing function may be performed using generative AI, or they may be performed without generative AI. For example, the sharing function can use generative AI to apply age- and developmental-stage-appropriate sharing algorithms during sharing.

[0116] The sharing unit can estimate a child's emotions and prioritize the content to share based on those emotions. For example, if a child is excited, the sharing unit will prioritize sharing visually stimulating content. If a child is calm, the sharing unit will prioritize sharing detailed information. If a child is tired, the sharing unit will prioritize sharing simple and easily visible content. In this way, the sharing unit can prioritize sharing important information by estimating the child's emotions and prioritizing the content to share based on those emotions. Some or all of the above processing in the sharing unit may be performed using generative AI, or it may be performed without generative AI. For example, when estimating a child's emotions, the sharing unit can use generative AI to analyze emotion data and determine the priority of the content to share.

[0117] The sharing function can adjust how shared content is displayed based on the child's activity history when sharing. For example, the sharing function may prioritize displaying current care based on the child's past activity history. For example, the sharing function may prioritize displaying support tailored to the child's preferences based on the child's past activity history. For example, the sharing function may adjust the display method by referring to the child's past activity history. This allows the sharing function to enable appropriate sharing by adjusting the display method based on the child's activity history. Some or all of the above processing in the sharing function may be performed using a generative AI, or not. For example, the sharing function may analyze the activity history using a generative AI and adjust the display method when sharing.

[0118] The sharing unit can improve the accuracy of the shared content by referring to the child's relevant data during sharing. For example, the sharing unit can share current care based on the child's relevant data. For example, the sharing unit can share support tailored to the child's preferences based on the child's relevant data. For example, the sharing unit can improve the accuracy of the shared content by referring to the child's relevant data. This enables appropriate sharing by improving the accuracy of the shared content by referring to the child's relevant data. Some or all of the above processing in the sharing unit may be performed using generative AI, or not. For example, the sharing unit can analyze the relevant data using generative AI during sharing to improve the accuracy of the shared content.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The Future Development AI Navigator can also track a child's learning progress and provide individualized learning plans. For example, the data collection unit records the child's learning activities, and the analysis unit analyzes that data to evaluate learning progress. The generation unit generates a learning plan suitable for the child based on the analysis results. The prediction unit analyzes the child's learning patterns and predicts the next learning content needed. The sharing unit shares this information with parents so that parents can support their child's learning. In this way, the Future Development AI Navigator can improve the effectiveness of a child's learning by tracking their learning progress and providing individualized learning plans.

[0121] The Future Nurturing AI Navigator can also monitor a child's health and support their health management. For example, the data collection unit collects biometric data such as the child's body temperature and heart rate. The analysis unit analyzes this data to assess the child's health. The generation unit generates a health management plan tailored to the child based on the analysis results. The prediction unit analyzes the child's health patterns and predicts the next necessary health management steps. The sharing unit shares this information with parents, enabling them to support their child's health. In this way, the Future Nurturing AI Navigator can maintain a child's health by monitoring their health and supporting their health management.

[0122] The Future Development AI Navigator can also support the social development of children. For example, the data collection unit records the child's friendships and social activities. The analysis unit analyzes this data to evaluate social development. The generation unit generates social activity plans suitable for the child based on the analysis results. The prediction unit analyzes the child's social patterns and predicts the next social activities needed. The sharing unit shares this information with parents, enabling them to support the child's social development. In this way, the Future Development AI Navigator can improve children's social skills by supporting their social development.

[0123] The Future Development AI Navigator can also suggest activities to foster children's creativity. For example, the data collection unit records children's creative activities (such as drawing or making music). The analysis unit analyzes this data to evaluate the development of creativity. The generation unit generates creative activity plans suitable for the child based on the analysis results. The prediction unit analyzes the child's creative patterns and predicts the next creative activity needed. The sharing unit shares this information with parents so that parents can support their child's creativity. In this way, the Future Development AI Navigator can improve children's creativity by suggesting activities to foster their creativity.

[0124] The Future Development AI Navigator can also estimate a child's emotions and suggest relaxation methods based on those emotions. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to assess the stress level. The generation unit generates relaxation methods (such as deep breathing and meditation) suitable for the child based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next relaxation method needed. The sharing unit shares this information with parents, enabling them to support the child's stress management. In this way, the Future Development AI Navigator can reduce a child's stress by estimating their emotions and suggesting relaxation methods based on those emotions.

[0125] The Future Development AI Navigator can also estimate a child's emotions and suggest appropriate play based on those emotions. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to evaluate the child's current emotional state. The generation unit generates play activities (puzzles, exercise, etc.) suitable for the child based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next activity needed. The sharing unit shares this information with parents so that parents can support their child's play. In this way, the Future Development AI Navigator can stabilize a child's emotions by estimating their emotions and suggesting appropriate play based on those emotions.

[0126] The Future Development AI Navigator can also estimate a child's emotions and suggest an appropriate learning environment based on those emotions. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to evaluate the child's current emotional state. The generation unit generates a suitable learning environment for the child (such as a quiet place or music) based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next learning environment needed. The sharing unit shares this information with parents, enabling them to support the parents in creating a suitable learning environment for their child. In this way, the Future Development AI Navigator can enhance a child's learning effectiveness by estimating their emotions and suggesting an appropriate learning environment based on those emotions.

[0127] The Future Nurturing AI Navigator can also estimate a child's emotions and suggest appropriate meals based on those emotions. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to evaluate the child's current emotional state. The generation unit generates meals suitable for the child (snacks, nutritionally balanced meals, etc.) based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next meal needed. The sharing unit shares this information with parents so that parents can support their child's diet. In this way, the Future Nurturing AI Navigator can maintain the child's health by estimating the child's emotions and suggesting appropriate meals based on those emotions.

[0128] The Future Development AI Navigator can also estimate a child's emotions and suggest appropriate rest times based on those emotions. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to evaluate the child's current emotional state. The generation unit generates appropriate rest times for the child (short rest, long rest, etc.) based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next necessary rest time. The sharing unit shares this information with parents so that parents can support their child's rest. In this way, the Future Development AI Navigator can reduce a child's fatigue by estimating their emotions and suggesting appropriate rest times based on those emotions.

[0129] The Future Development AI Navigator can also estimate a child's emotions and suggest appropriate communication methods based on those estimates. For example, the data collection unit records the child's emotional state. The analysis unit analyzes this data to evaluate the child's current emotional state. The generation unit generates appropriate communication methods (speaking style, expression, etc.) for the child based on the analysis results. The prediction unit analyzes the child's emotional patterns and predicts the next necessary communication method. The sharing unit shares this information with parents, enabling them to support their child's communication. In this way, the Future Development AI Navigator can improve a child's communication skills by estimating their emotions and suggesting appropriate communication methods based on those estimates.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The data collection unit accepts multimodal input. For example, it can accept audio, visual, and emotional input, detecting the words a child speaks, their facial expressions, and changes in their emotions. It uses speech recognition technology to convert the child's words into text data, image recognition technology to analyze the child's facial expressions, and emotion analysis technology to analyze the child's emotional state. Step 2: The analysis unit analyzes the input received by the collection unit. For example, it analyzes the child's needs and preferences, analyzes the meaning of the child's words using natural language processing technology, estimates emotions from the child's facial expressions using image analysis technology, and analyzes the child's emotional state using emotion analysis technology. Step 3: The generation unit generates dialogue based on the results analyzed by the analysis unit. For example, it understands the child's words, facial expressions, and emotional changes, and generates dialogue accordingly. It uses natural language generation technology to generate dialogue suitable for the child, image generation technology to generate avatar facial expressions that correspond to the child's facial expressions, and emotion generation technology to generate dialogue that corresponds to the child's emotions. Step 4: The prediction unit predicts the next care or support needed based on the dialogue generated by the generation unit. For example, it analyzes the child's behavioral patterns and emotional changes to predict the next care or support needed. It uses behavioral prediction technology to predict the child's next behavior, emotion prediction technology to predict the child's next emotional state, and care prediction technology to predict the next care or support needed. Step 5: The sharing unit shares the care and support predicted by the prediction unit with the parents. For example, it notifies parents of the care and support predicted based on the child's behavior and emotional state. Notification technology is used to notify parents of the care and support, display technology is used to display the care and support to parents, and voice technology is used to verbally communicate the care and support to parents.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, prediction unit, and sharing unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to detect changes in the child's words, facial expressions, and emotions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates dialogue based on the analysis results. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the next care or support needed. The sharing unit notifies the guardian using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, prediction unit, and sharing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect changes in the child's words, facial expressions, and emotions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates dialogue based on the analysis results. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the next care or support needed. The sharing unit uses the speaker 240 of the smart glasses 214 to notify the guardian. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, prediction unit, and sharing unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect changes in the child's words, facial expressions, and emotions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates dialogue based on the analysis results. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the next necessary care or support. The sharing unit notifies the guardian using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, prediction unit, and sharing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to detect changes in the child's words, facial expressions, and emotions. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates dialogue based on the analysis results. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the next necessary care or support. The sharing unit uses the speaker 240 of the robot 414 to notify the guardian. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] (Note 1) A data collection unit that accepts multimodal inputs, An analysis unit analyzes the input received by the aforementioned collection unit, A generation unit that generates a dialogue based on the results of the analysis performed by the analysis unit, A prediction unit predicts the next necessary care or support based on the dialogue generated by the generation unit, The system includes a sharing unit that shares the content of care and support predicted by the prediction unit with the guardian. A system characterized by the following features. (Note 2) The aforementioned collection unit is It accepts audio, visual, and emotional input. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze children's needs and preferences The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Understanding children's words, facial expressions, and emotional changes, and generating appropriate dialogue accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, By analyzing children's behavioral patterns and emotional changes, we can predict the next care and support they will need. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, Parents will be notified of the care and support they will receive, based on their child's behavior and emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates a child's emotions and dynamically adjusts audio, visual, and emotional inputs based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the child's past behavioral history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When acquiring input data, filtering is performed based on the child's current activities and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the child's emotions and prioritizes input data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When acquiring input data, the system prioritizes acquiring highly relevant data by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring input data, the system analyzes children's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the child's emotions and adjust the analysis method for their needs and preferences based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referring to the child's past behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the child's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant data about children to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Estimate the child's emotions and adjust the way you express yourself in the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating dialogue, adjust the level of detail in the dialogue based on the child's needs. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating dialogue, different dialogue algorithms are applied depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the child's emotions and adjusts the length of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating dialogue, the priority of the dialogue is determined based on the child's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating dialogues, we refer to relevant data about the child to improve the accuracy of the dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, We estimate a child's emotions and adjust how we predict care and support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, referencing the child's past behavioral patterns improves the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, different prediction algorithms are applied depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, It estimates the child's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the system prioritizes predictions based on the child's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, we refer to relevant data about children to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shared portion is, The system estimates the child's emotions and adjusts the way shared content is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, When sharing information, referencing the child's past behavioral patterns improves the accuracy of the shared content. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, When sharing, different sharing algorithms are applied depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, The system estimates the child's emotions and prioritizes the shared content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, When sharing, adjust how the shared content is displayed based on the child's activity history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing, we refer to the child's relevant data to improve the accuracy of the shared content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 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. A data collection unit that accepts multimodal inputs, An analysis unit analyzes the input received by the aforementioned collection unit, A generation unit that generates a dialogue based on the results of the analysis performed by the analysis unit, A prediction unit predicts the next necessary care or support based on the dialogue generated by the generation unit, The system includes a sharing unit that shares the content of care and support predicted by the prediction unit with the guardian. A system characterized by the following features.

2. The aforementioned collection unit is It accepts audio, visual, and emotional input. The system according to feature 1.

3. The aforementioned analysis unit, Analyze children's needs and preferences The system according to feature 1.

4. The generating unit is Understanding children's words, facial expressions, and emotional changes, and generating appropriate dialogue accordingly. The system according to feature 1.

5. The prediction unit, By analyzing children's behavioral patterns and emotional changes, we can predict the next care and support they will need. The system according to feature 1.

6. The aforementioned shared portion is, Parents will be notified of the care and support they will receive, based on their child's behavior and emotional state. The system according to feature 1.

7. The aforementioned collection unit is It estimates a child's emotions and dynamically adjusts audio, visual, and emotional inputs based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the child's past behavioral history and select the optimal input method. The system according to feature 1.

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