System

Abstract

An object of a system according to an embodiment is to propose an optimal childcare method based on an individual character or behavior of a child.SOLUTION: A system according to an embodiment includes a data-recording unit, a video analysis unit, a generation / AI unit, and a suggestion unit. The data recorder collects data manually recorded by the parent. The video analysis unit analyzes a video or an image captured by the parent. The generation AI unit analyzes the character, behavior, and nature of the child based on the information collected and analyzed by the information recording unit and the video analysis unit. The suggestion unit suggests a child care method based on the result analyzed by the generation and AI unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to find a parenting method based on a child's individual personality and behavior, which can be exhausting for parents.

[0005] The system according to the embodiment aims to propose an optimal child-rearing method based on the individual personality and behavior of each child. [Means for solving the problem]

[0006] The system according to the embodiment includes a data recording unit, a video analysis unit, a generation AI unit, and a suggestion unit. The data recording unit collects data manually recorded by the parent. The video analysis unit analyzes videos or images taken by the parent. The generation AI unit analyzes the child's personality, behavior, and characteristics based on the data collected and analyzed by the data recording unit and the video analysis unit. The suggestion unit suggests child-rearing methods based on the results of the analysis by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable child-rearing method based on the individual personality and behavior of each child. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The personalized child-rearing system according to an embodiment of the present invention uses a generative AI to analyze a child's personality, behavior, and characteristics based on data, videos, and images manually recorded by parents, and suggests appropriate child-rearing methods. This makes it easier for parents to find a child-rearing method that suits their child, thereby reducing the burden of child-rearing.

[0029] The personalized parenting system according to the embodiment includes a data recording unit, a video analysis unit, a generation AI unit, and a suggestion unit. The data recording unit collects data manually recorded by the parent. For example, it collects data such as sleep, breastfeeding, and weight manually recorded by the parent. The data recording unit can also collect data recorded by the parent using an app. For example, it can automatically collect data recorded by the parent using a smartphone app. The video analysis unit analyzes videos or images taken by the parent. For example, it analyzes videos taken by the parent with a smartphone to analyze the child's behavior and facial expressions. The video analysis unit can also analyze images taken with a digital camera. For example, it analyzes images taken by the parent with a digital camera to analyze the child's movements. The generation AI unit analyzes the child's personality, behavior, and characteristics based on the data collected and analyzed by the data recording unit and the video analysis unit. For example, the generation AI analyzes the child's personality and behavioral patterns based on data, videos, and images manually recorded by the parent. The generation AI can also use psychological evaluation criteria to analyze the child's characteristics. For example, the generation AI analyzes a child's personality and behavioral patterns based on behavioral observation data. The suggestion unit proposes child-rearing methods based on the analysis results of the generation AI unit. For example, the suggestion unit proposes appropriate child-rearing methods to parents based on the analysis results of the generation AI unit. The suggestion unit can also make suggestions in chat format to suggest child-rearing methods in a way that is easy for parents to understand. For example, based on the analysis results of the generation AI, the suggestion unit may make suggestions such as, "This child has this personality, so this method to prevent night crying would be good." This makes it easier for parents to find a child-rearing method that suits their child and reduces the burden of child-rearing. For example, parents can receive specific suggestions tailored to their child's individual needs, such as measures to prevent night crying, feeding methods, and play methods. This allows parents to approach child-rearing with peace of mind and is expected to contribute to the healthy growth of their children.

[0030] The generation AI unit can analyze a child's daily behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior. For example, the generation AI unit can analyze a child's daily behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior. For example, the generation AI can monitor a child's sleep patterns, meal times, play times, etc. in real time and suggest child-rearing methods that respond to changes in behavior. The generation AI can also use streaming data analysis technology to analyze a child's behavioral patterns. For example, the generation AI can analyze a child's behavioral data in real time and suggest child-rearing methods that respond to changes in behavior. This makes it possible to analyze a child's behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior.

[0031] The generation AI unit can analyze data on siblings and propose child-rearing methods for the entire family. The generation AI unit can, for example, analyze data on siblings and propose child-rearing methods for the entire family. For example, the generation AI can analyze the personalities and behavioral patterns of siblings and propose child-rearing methods that are suitable for all of them. The generation AI can also use data such as age, gender, and behavioral patterns to analyze data on siblings. For example, the generation AI can propose child-rearing methods for the entire family based on behavioral data on siblings. This makes it possible to analyze data on siblings and propose child-rearing methods for the entire family.

[0032] The generative AI unit can learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture. The generative AI unit can, for example, learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture. For example, the generative AI can compare child-rearing methods in Japan with those in the United States and propose the optimal method. The generative AI can also use data by country, region, or religion to learn child-rearing methods from different cultural spheres. For example, the generative AI can propose child-rearing methods that are appropriate for that culture based on child-rearing data from different cultural spheres. This allows the system to learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture.

[0033] The data recording unit can automatically collect data using a smart device. The data recording unit automatically collects data using, for example, a smart device. For example, a smart watch can be used to automatically record a child's sleep data. The data recording unit can also automatically record a child's weight data using a smart scale. For example, a smart baby bottle can be used to automatically record breastfeeding data. This allows data recording by parents to be automated, and data to be automatically collected using a smart device.

[0034] The generative AI unit can build a predictive model based on the recorded data and suggest future child-rearing methods. The generative AI unit, for example, builds a predictive model based on the recorded data and suggests future child-rearing methods. For example, the generative AI predicts changes in sleep patterns as a child grows and suggests appropriate child-rearing methods. The generative AI can also use machine learning algorithms and training datasets to build the predictive model. For example, the generative AI predicts future behavioral patterns based on past data and suggests appropriate child-rearing methods. This makes it possible to build a predictive model based on the recorded data and suggest future child-rearing methods.

[0035] Recorded data can be shared on the cloud and comparative analysis can be conducted with other parents. Recorded data can be shared on the cloud and comparative analysis can be conducted with other parents. For example, parents with children of the same age can share data and compare parenting methods. Data can also be shared using cloud storage services and data sharing platforms. For example, data can be shared on the cloud and comparative analysis can be conducted using statistical analysis and data mining techniques. This allows recorded data to be shared on the cloud and comparative analysis can be conducted with other parents.

[0036] The video analysis unit can analyze video and image data to detect subtle changes in a child's behavior. The video analysis unit, for example, analyzes video and image data to detect subtle changes in a child's behavior. For example, the video analysis unit detects changes in a child's facial expressions, limb movements, and eye movements to detect changes in behavior. The video analysis unit can also use image recognition technology and motion analysis technology to detect changes in a child's behavior. For example, the video analysis unit detects subtle changes in a child's behavior based on data on the child's movements. In this way, the video and image data can be analyzed to detect subtle changes in a child's behavior.

[0037] The generation AI unit can use video and image data to recreate a child's growth process in a 3D model and suggest child-rearing methods. The generation AI unit can, for example, use video and image data to recreate a child's growth process in a 3D model and suggest child-rearing methods. For example, the generation AI can recreate a child's physical growth and changes in movement in a 3D model and suggest appropriate child-rearing methods. The generation AI can also use 3D scanning technology or modeling software to create the 3D model. For example, the generation AI can create a 3D model based on the child's growth data and suggest appropriate child-rearing methods. This allows the generation AI to recreate a child's growth process in a 3D model and suggest appropriate child-rearing methods using video and image data.

[0038] Parents can share video and image data with other parents and form communities to solve common parenting challenges. Parents can share video and image data with other parents and form communities to solve common parenting challenges. For example, parents with children of the same age can share videos and share parenting methods. Parenting challenges can also be shared and solutions found through online forums, social media groups, and local communities. For example, parenting challenges such as dealing with nighttime crying, feeding problems, and behavioral issues can be shared and solutions found through communities. This allows parents to share video and image data with other parents and form communities to solve common parenting challenges.

[0039] Video and image data can be shared with educational institutions to receive feedback from experts. Video and image data can be shared with educational institutions to receive feedback from experts. For example, videos about children's behavior and development can be provided to educational institutions to receive advice from experts. It is also possible to share video and image data with educational institutions such as kindergartens, nursery schools, and elementary schools to receive feedback on behavior, development, and learning. For example, by sharing video and image data with educational institutions, feedback on children's behavior and development can be received and appropriate child-rearing methods can be suggested. This allows video and image data to be shared with educational institutions to receive feedback from experts.

[0040] The generation AI unit can respond to parents' questions in real time when making suggestions in chat format. For example, when a parent asks, "My child cries at night, what should I do?", the generation AI immediately suggests an appropriate child-rearing method. The generation AI can also use streaming data analysis technology and real-time data processing technology to respond in real time. For example, the generation AI can respond to parents' questions in real time and suggest appropriate child-rearing methods. This allows the generation AI to respond to parents' questions in real time when making suggestions in chat format.

[0041] The generation AI unit can learn the parent's past question history and make more personalized suggestions when making suggestions in chat format. The generation AI unit can, for example, learn the parent's past question history and make more personalized suggestions when making suggestions in chat format. For example, the generation AI can suggest more appropriate child-rearing methods based on the questions the parent has asked in the past. The generation AI can also save the question history and analyze it using natural language processing technology. For example, the generation AI can make suggestions based on the parent's question history and make suggestions that meet individual needs. This allows the generation AI to learn the parent's past question history and make more personalized suggestions when making suggestions in chat format.

[0042] Suggestions in chat format can be made multilingual to accommodate parents from different language speaking regions. Suggestions in chat format can be made multilingual to accommodate parents from different language speaking regions. For example, parenting methods can be suggested in multiple languages, such as Japanese, English, and Chinese. The generative AI can also achieve multilingual support by using translation technology and a multilingual database. For example, the generative AI can translate parents' questions and respond in the appropriate language. This makes suggestions in chat format available in multiple languages, enabling them to accommodate parents from different language speaking regions.

[0043] Suggestions in chat format can be linked with a voice assistant to make suggestions via voice. Suggestions in chat format can be linked with a voice assistant to make suggestions via voice. For example, when a parent asks a question via voice, the generation AI will suggest child-rearing methods via voice. The generation AI can also make suggestions via voice using voice synthesis technology and voice recognition technology. For example, the generation AI can analyze the parent's voice and suggest appropriate child-rearing methods via voice. This allows suggestions in chat format to be linked with a voice assistant to make suggestions via voice.

[0044] The generation AI unit can automatically generate new child-rearing methods based on the learning data. The generation AI unit, for example, automatically generates new child-rearing methods based on the learning data. For example, the generation AI proposes a new method to prevent night crying based on past data. The generation AI can also automatically generate new child-rearing methods using a generation algorithm or machine learning model. For example, the generation AI automatically generates new child-rearing methods based on behavioral data and emotional data. This makes it possible to automatically generate new child-rearing methods based on the learning data.

[0045] Parents can share their learning data with other parents and work together to improve their parenting methods. Parents can share their learning data with other parents and work together to improve their parenting methods. For example, parents with children of the same age can share data and improve their parenting methods. Parents can also share their parenting methods and find ways to improve them through online forums and collaborative research. For example, parents can share data with each other and brainstorm ideas to improve their parenting methods. This allows parents to share their learning data with other parents and work together to improve their parenting methods.

[0046] The learning data can be linked with experts to receive professional advice. The learning data can be linked with experts to receive professional advice. For example, a child's weight data can be provided to an expert to receive advice on appropriate eating habits. It is also possible to link with experts to receive medical or educational advice. For example, by linking with an expert, advice can be received on how to manage a child's health and how to educate the child. This allows the learning data to be linked with experts to receive professional advice.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The personalized parenting system can further include a voice recognition unit. The voice recognition unit analyzes the voices of parents and children to help suggest parenting methods. For example, it can analyze the tone and language used when parents speak to their children and suggest appropriate communication methods based on the children's reactions. It can also analyze the children's crying and laughter to evaluate their emotional state. This allows the voice recognition unit to utilize the voice data of parents and children to suggest more effective parenting methods.

[0049] The personalized childcare system can further include an environmental sensor unit. The environmental sensor unit collects environmental data about the child's surroundings and uses it to suggest childcare methods. For example, it can monitor room temperature, humidity, and lighting brightness to suggest an environment where the child can be comfortable. It can also measure noise levels and provide advice on providing a quiet environment. In this way, the environmental sensor unit can utilize environmental data about the child's surroundings to provide a more comfortable childcare environment.

[0050] The personalized childcare system can further include a health monitoring unit. The health monitoring unit monitors the child's health condition in real time and uses this information to help suggest childcare methods. For example, it can monitor the child's body temperature, heart rate, and respiratory rate and issue an alert if any abnormalities are detected. The health monitoring unit can also suggest appropriate dietary and exercise methods based on the child's growth data. This allows the health monitoring unit to constantly monitor the child's health condition and suggest appropriate childcare methods.

[0051] The personalized childcare system can further include a learning support unit. The learning support unit analyzes a child's learning situation and suggests appropriate learning methods. For example, it monitors a child's learning progress, identifies weak areas, and suggests supplementary lessons. The learning support unit can also suggest appropriate teaching materials and learning programs based on the child's interests. This allows the learning support unit to grasp a child's learning situation and suggest effective learning methods.

[0052] The personalized childcare system can further include a social assessment unit. The social assessment unit analyzes a child's social behavior and suggests appropriate childcare methods. For example, it evaluates a child's interactions with friends and communication skills and provides advice on how to foster social skills. The social assessment unit can also suggest ways to develop appropriate social skills based on data on the child's group activities and play. This allows the social assessment unit to support a child's social growth and suggest appropriate childcare methods.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data recording unit collects data manually recorded by the parent. For example, it collects data such as sleep, feeding, and weight that are manually recorded by the parent. It can also collect data recorded by the parent using an app. For example, it can automatically collect data recorded by the parent using a smartphone app. Step 2: The video analysis unit analyzes the video or images taken by the parent. For example, it can analyze videos taken by the parent with a smartphone to analyze the child's behavior and facial expressions. It can also analyze images taken with a digital camera to analyze the child's movements. Step 3: The generating AI analyzes the child's personality, behavior, and characteristics based on the data collected and analyzed by the data recording and video analysis units. For example, it can analyze the child's personality and behavior patterns based on data manually recorded by parents, videos, and images. It can also analyze the child's characteristics using psychological evaluation criteria. Step 4: The suggestion unit proposes child-rearing methods based on the results of the analysis by the generation AI unit. For example, the suggestion unit can propose appropriate child-rearing methods to parents based on the results of the analysis by the generation AI. It can also make suggestions in chat format to make the proposals easier for parents to understand.

[0055] (Example 2) The personalized child-rearing system according to an embodiment of the present invention uses a generative AI to analyze a child's personality, behavior, and characteristics based on data, videos, and images manually recorded by parents, and suggests appropriate child-rearing methods. This makes it easier for parents to find a child-rearing method that suits their child, thereby reducing the burden of child-rearing.

[0056] The personalized parenting system according to the embodiment includes a data recording unit, a video analysis unit, a generation AI unit, and a suggestion unit. The data recording unit collects data manually recorded by the parent. For example, it collects data such as sleep, breastfeeding, and weight manually recorded by the parent. The data recording unit can also collect data recorded by the parent using an app. For example, it can automatically collect data recorded by the parent using a smartphone app. The video analysis unit analyzes videos or images taken by the parent. For example, it analyzes videos taken by the parent with a smartphone to analyze the child's behavior and facial expressions. The video analysis unit can also analyze images taken with a digital camera. For example, it analyzes images taken by the parent with a digital camera to analyze the child's movements. The generation AI unit analyzes the child's personality, behavior, and characteristics based on the data collected and analyzed by the data recording unit and the video analysis unit. For example, the generation AI analyzes the child's personality and behavioral patterns based on data, videos, and images manually recorded by the parent. The generation AI can also use psychological evaluation criteria to analyze the child's characteristics. For example, the generation AI analyzes a child's personality and behavioral patterns based on behavioral observation data. The suggestion unit proposes child-rearing methods based on the analysis results of the generation AI unit. For example, the suggestion unit proposes appropriate child-rearing methods to parents based on the analysis results of the generation AI unit. The suggestion unit can also make suggestions in chat format to suggest child-rearing methods in a way that is easy for parents to understand. For example, based on the analysis results of the generation AI, the suggestion unit may make suggestions such as, "This child has this personality, so this method to prevent night crying would be good." This makes it easier for parents to find a child-rearing method that suits their child and reduces the burden of child-rearing. For example, parents can receive specific suggestions tailored to their child's individual needs, such as measures to prevent night crying, feeding methods, and play methods. This allows parents to approach child-rearing with peace of mind and is expected to contribute to the healthy growth of their children.

[0057] The generation AI unit can analyze a child's daily behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior. For example, the generation AI unit can analyze a child's daily behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior. For example, the generation AI can monitor a child's sleep patterns, meal times, play times, etc. in real time and suggest child-rearing methods that respond to changes in behavior. The generation AI can also use streaming data analysis technology to analyze a child's behavioral patterns. For example, the generation AI can analyze a child's behavioral data in real time and suggest child-rearing methods that respond to changes in behavior. This makes it possible to analyze a child's behavioral patterns in real time and suggest child-rearing methods that respond to changes in behavior.

[0058] The generation AI unit can analyze the parent's emotional state and suggest a parenting method according to the parent's stress level. The generation AI unit can, for example, analyze the parent's emotional state and suggest a parenting method according to the parent's stress level. For example, the generation AI can analyze the parent's facial expressions and voice to evaluate the parent's emotional state. The generation AI can also use psychological tests and biofeedback technology to evaluate the parent's stress level. For example, the generation AI can evaluate the parent's stress level based on the parent's self-reported data and suggest an appropriate parenting method. This makes it possible to analyze the parent's emotional state and suggest a parenting method according to the parent's stress level.

[0059] The generation AI unit can use the emotion estimation function to analyze a child's emotional state and suggest a parenting method based on the emotion. The generation AI unit can, for example, use the emotion estimation function to analyze a child's emotional state and suggest a parenting method based on the emotion. For example, the generation AI can analyze a child's facial expressions and voice to evaluate the emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate a child's emotional state. For example, the generation AI can evaluate a child's emotional state based on the child's behavioral data and suggest an appropriate parenting method. This makes it possible to analyze a child's emotional state and suggest a parenting method based on the emotion.

[0060] The generation AI unit can analyze data on siblings and propose child-rearing methods for the entire family. The generation AI unit can, for example, analyze data on siblings and propose child-rearing methods for the entire family. For example, the generation AI can analyze the personalities and behavioral patterns of siblings and propose child-rearing methods that are suitable for all of them. The generation AI can also use data such as age, gender, and behavioral patterns to analyze data on siblings. For example, the generation AI can propose child-rearing methods for the entire family based on behavioral data on siblings. This makes it possible to analyze data on siblings and propose child-rearing methods for the entire family.

[0061] The generative AI unit can learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture. The generative AI unit can, for example, learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture. For example, the generative AI can compare child-rearing methods in Japan with those in the United States and propose the optimal method. The generative AI can also use data by country, region, or religion to learn child-rearing methods from different cultural spheres. For example, the generative AI can propose child-rearing methods that are appropriate for that culture based on child-rearing data from different cultural spheres. This allows the system to learn child-rearing methods from different cultural spheres and propose child-rearing methods that are appropriate for that culture.

[0062] The generation AI unit uses the emotion estimation function to collect emotional reactions when parents try out different child-rearing methods, and can continuously suggest the most appropriate method. The generation AI unit, for example, uses the emotion estimation function to collect emotional reactions when parents try out different child-rearing methods, and can continuously suggest the most appropriate method. For example, the generation AI analyzes the parent's facial expressions and voice to evaluate the emotional reactions. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional reactions. For example, the generation AI evaluates the emotional reactions based on the parent's behavioral data and suggests an appropriate child-rearing method. This allows the generation AI to collect emotional reactions when parents try out different child-rearing methods, and can continuously suggest the most appropriate method.

[0063] The data recording unit can automatically collect data using a smart device. The data recording unit automatically collects data using, for example, a smart device. For example, a smart watch can be used to automatically record a child's sleep data. The data recording unit can also automatically record a child's weight data using a smart scale. For example, a smart baby bottle can be used to automatically record breastfeeding data. This allows data recording by parents to be automated, and data to be automatically collected using a smart device.

[0064] The generative AI unit can build a predictive model based on the recorded data and suggest future child-rearing methods. The generative AI unit, for example, builds a predictive model based on the recorded data and suggests future child-rearing methods. For example, the generative AI predicts changes in sleep patterns as a child grows and suggests appropriate child-rearing methods. The generative AI can also use machine learning algorithms and training datasets to build the predictive model. For example, the generative AI predicts future behavioral patterns based on past data and suggests appropriate child-rearing methods. This makes it possible to build a predictive model based on the recorded data and suggest future child-rearing methods.

[0065] The generation AI unit can use the emotion estimation function to analyze the emotional state of the parent when making a record and improve the accuracy of the record. The generation AI unit can, for example, use the emotion estimation function to analyze the emotional state of the parent when making a record and improve the accuracy of the record. For example, the generation AI can analyze the parent's facial expressions and voice to evaluate the emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional state. For example, the generation AI can evaluate the emotional state based on the parent's behavioral data and improve the accuracy of the record. This allows the generation AI to analyze the parent's emotional state when making a record and improve the accuracy of the record.

[0066] Recorded data can be shared on the cloud and comparative analysis can be conducted with other parents. Recorded data can be shared on the cloud and comparative analysis can be conducted with other parents. For example, parents with children of the same age can share data and compare parenting methods. Data can also be shared using cloud storage services and data sharing platforms. For example, data can be shared on the cloud and comparative analysis can be conducted using statistical analysis and data mining techniques. This allows recorded data to be shared on the cloud and comparative analysis can be conducted with other parents.

[0067] The generation AI unit can use the emotion estimation function to suggest child-rearing methods based on the recorded data and provide advice that takes into account the parent's emotional state. The generation AI unit can, for example, use the emotion estimation function to suggest child-rearing methods based on the recorded data and provide advice that takes into account the parent's emotional state. For example, the generation AI can analyze the parent's facial expressions and voice to evaluate the parent's emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional state. For example, the generation AI can evaluate the parent's emotional state based on the parent's behavioral data and suggest an appropriate child-rearing method. This allows the generation AI to suggest child-rearing methods based on the recorded data and provide advice that takes into account the parent's emotional state.

[0068] The video analysis unit can analyze video and image data to detect subtle changes in a child's behavior. The video analysis unit, for example, analyzes video and image data to detect subtle changes in a child's behavior. For example, the video analysis unit detects changes in a child's facial expressions, limb movements, and eye movements to detect changes in behavior. The video analysis unit can also use image recognition technology and motion analysis technology to detect changes in a child's behavior. For example, the video analysis unit detects subtle changes in a child's behavior based on data on the child's movements. In this way, the video and image data can be analyzed to detect subtle changes in a child's behavior.

[0069] The generation AI unit can use video and image data to recreate a child's growth process in a 3D model and suggest child-rearing methods. The generation AI unit can, for example, use video and image data to recreate a child's growth process in a 3D model and suggest child-rearing methods. For example, the generation AI can recreate a child's physical growth and changes in movement in a 3D model and suggest appropriate child-rearing methods. The generation AI can also use 3D scanning technology or modeling software to create the 3D model. For example, the generation AI can create a 3D model based on the child's growth data and suggest appropriate child-rearing methods. This allows the generation AI to recreate a child's growth process in a 3D model and suggest appropriate child-rearing methods using video and image data.

[0070] The generation AI unit can use the emotion estimation function to analyze a child's emotional state from video and image data and suggest a parenting method based on the emotion. The generation AI unit can, for example, use the emotion estimation function to analyze a child's emotional state from video and image data and suggest a parenting method based on the emotion. For example, the generation AI can analyze a child's facial expressions and voice to evaluate the child's emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate a child's emotional state. For example, the generation AI can evaluate a child's emotional state based on the child's behavioral data and suggest an appropriate parenting method. This makes it possible to analyze a child's emotional state from video and image data and suggest a parenting method based on the emotion.

[0071] Parents can share video and image data with other parents and form communities to solve common parenting challenges. Parents can share video and image data with other parents and form communities to solve common parenting challenges. For example, parents with children of the same age can share videos and share parenting methods. Parenting challenges can also be shared and solutions found through online forums, social media groups, and local communities. For example, parenting challenges such as dealing with nighttime crying, feeding problems, and behavioral issues can be shared and solutions found through communities. This allows parents to share video and image data with other parents and form communities to solve common parenting challenges.

[0072] Video and image data can be shared with educational institutions to receive feedback from experts. Video and image data can be shared with educational institutions to receive feedback from experts. For example, videos about children's behavior and development can be provided to educational institutions to receive advice from experts. It is also possible to share video and image data with educational institutions such as kindergartens, nursery schools, and elementary schools to receive feedback on behavior, development, and learning. For example, by sharing video and image data with educational institutions, feedback on children's behavior and development can be received and appropriate child-rearing methods can be suggested. This allows video and image data to be shared with educational institutions to receive feedback from experts.

[0073] The generation AI unit can use the emotion estimation function to analyze parents' emotional reactions from video and image data and suggest parenting methods that will reduce parental stress. The generation AI unit can, for example, use the emotion estimation function to analyze parents' emotional reactions from video and image data and suggest parenting methods that will reduce parental stress. For example, the generation AI can analyze parents' facial expressions and voice and evaluate their emotional reactions. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate parents' emotional reactions. For example, the generation AI can evaluate parents' emotional reactions based on their behavioral data and suggest appropriate parenting methods. This makes it possible to analyze parents' emotional reactions from video and image data and suggest parenting methods that will reduce parental stress.

[0074] The generation AI unit can respond to parents' questions in real time when making suggestions in chat format. For example, when a parent asks, "My child cries at night, what should I do?", the generation AI immediately suggests an appropriate child-rearing method. The generation AI can also use streaming data analysis technology and real-time data processing technology to respond in real time. For example, the generation AI can respond to parents' questions in real time and suggest appropriate child-rearing methods. This allows the generation AI to respond to parents' questions in real time when making suggestions in chat format.

[0075] The generation AI unit can learn the parent's past question history and make more personalized suggestions when making suggestions in chat format. The generation AI unit can, for example, learn the parent's past question history and make more personalized suggestions when making suggestions in chat format. For example, the generation AI can suggest more appropriate child-rearing methods based on the questions the parent has asked in the past. The generation AI can also save the question history and analyze it using natural language processing technology. For example, the generation AI can make suggestions based on the parent's question history and make suggestions that meet individual needs. This allows the generation AI to learn the parent's past question history and make more personalized suggestions when making suggestions in chat format.

[0076] The generation AI unit can analyze the parent's emotional state using the emotion estimation function and make suggestions based on the emotion. The generation AI unit can, for example, analyze the parent's emotional state using the emotion estimation function and make suggestions based on the emotion. For example, the generation AI can analyze the parent's facial expressions and voice to evaluate the parent's emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional state. For example, the generation AI can evaluate the parent's emotional state based on the parent's behavioral data and suggest appropriate child-rearing methods. This allows the generation AI to analyze the parent's emotional state and make suggestions based on the emotion.

[0077] Suggestions in chat format can be made multilingual to accommodate parents from different language speaking regions. Suggestions in chat format can be made multilingual to accommodate parents from different language speaking regions. For example, parenting methods can be suggested in multiple languages, such as Japanese, English, and Chinese. The generative AI can also achieve multilingual support by using translation technology and a multilingual database. For example, the generative AI can translate parents' questions and respond in the appropriate language. This makes suggestions in chat format available in multiple languages, enabling them to accommodate parents from different language speaking regions.

[0078] Suggestions in chat format can be linked with a voice assistant to make suggestions via voice. Suggestions in chat format can be linked with a voice assistant to make suggestions via voice. For example, when a parent asks a question via voice, the generation AI will suggest child-rearing methods via voice. The generation AI can also make suggestions via voice using voice synthesis technology and voice recognition technology. For example, the generation AI can analyze the parent's voice and suggest appropriate child-rearing methods via voice. This allows suggestions in chat format to be linked with a voice assistant to make suggestions via voice.

[0079] The generation AI unit can use the emotion estimation function to collect the parent's emotional response to suggestions made in chat format and improve the content of the suggestions. The generation AI unit can, for example, use the emotion estimation function to collect the parent's emotional response to suggestions made in chat format and improve the content of the suggestions. For example, the generation AI can analyze the parent's facial expressions and voice and evaluate the emotional response. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional response. For example, the generation AI can evaluate the emotional response based on the parent's behavioral data and suggest appropriate child-rearing methods. In this way, the generation AI can collect the parent's emotional response to suggestions made in chat format and improve the content of the suggestions.

[0080] The generation AI unit can automatically generate new child-rearing methods based on the learning data. The generation AI unit, for example, automatically generates new child-rearing methods based on the learning data. For example, the generation AI proposes a new method to prevent night crying based on past data. The generation AI can also automatically generate new child-rearing methods using a generation algorithm or machine learning model. For example, the generation AI automatically generates new child-rearing methods based on behavioral data and emotional data. This makes it possible to automatically generate new child-rearing methods based on the learning data.

[0081] The generation AI unit can use the emotion estimation function to analyze the parent's emotional reactions as the learning data is updated and optimize the content of the suggestions. The generation AI unit can, for example, use the emotion estimation function to analyze the parent's emotional reactions as the learning data is updated and optimize the content of the suggestions. For example, the generation AI can analyze the parent's facial expressions and voice and evaluate the emotional reactions. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional reactions. For example, the generation AI can evaluate the emotional reactions based on the parent's behavioral data and suggest appropriate child-rearing methods. This makes it possible to analyze the parent's emotional reactions as the learning data is updated and optimize the content of the suggestions.

[0082] Parents can share their learning data with other parents and work together to improve their parenting methods. Parents can share their learning data with other parents and work together to improve their parenting methods. For example, parents with children of the same age can share data and improve their parenting methods. Parents can also share their parenting methods and find ways to improve them through online forums and collaborative research. For example, parents can share data with each other and brainstorm ideas to improve their parenting methods. This allows parents to share their learning data with other parents and work together to improve their parenting methods.

[0083] The learning data can be linked with experts to receive professional advice. The learning data can be linked with experts to receive professional advice. For example, a child's weight data can be provided to an expert to receive advice on appropriate eating habits. It is also possible to link with experts to receive medical or educational advice. For example, by linking with an expert, advice can be received on how to manage a child's health and how to educate the child. This allows the learning data to be linked with experts to receive professional advice.

[0084] The generation AI unit can use the emotion estimation function to analyze a child's emotional state as the learning data is updated and propose a parenting method based on the emotion. The generation AI unit can, for example, use the emotion estimation function to analyze a child's emotional state as the learning data is updated and propose a parenting method based on the emotion. For example, the generation AI can analyze a child's facial expressions and voice to evaluate the emotional state. The generation AI can also use facial expression recognition technology and voice analysis technology to evaluate a child's emotional state. For example, the generation AI can evaluate a child's emotional state based on the child's behavioral data and propose an appropriate parenting method. This makes it possible to analyze a child's emotional state as the learning data is updated and propose a parenting method based on the emotion.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The personalized parenting system can further include a voice recognition unit. The voice recognition unit analyzes the voices of parents and children to help suggest parenting methods. For example, it can analyze the tone and language used when parents speak to their children and suggest appropriate communication methods based on the children's reactions. It can also analyze the children's crying and laughter to evaluate their emotional state. This allows the voice recognition unit to utilize the voice data of parents and children to suggest more effective parenting methods.

[0087] The personalized childcare system can further include an environmental sensor unit. The environmental sensor unit collects environmental data about the child's surroundings and uses it to suggest childcare methods. For example, it can monitor room temperature, humidity, and lighting brightness to suggest an environment where the child can be comfortable. It can also measure noise levels and provide advice on providing a quiet environment. In this way, the environmental sensor unit can utilize environmental data about the child's surroundings to provide a more comfortable childcare environment.

[0088] The personalized childcare system can further include a health monitoring unit. The health monitoring unit monitors the child's health condition in real time and uses this information to help suggest childcare methods. For example, it can monitor the child's body temperature, heart rate, and respiratory rate and issue an alert if any abnormalities are detected. The health monitoring unit can also suggest appropriate dietary and exercise methods based on the child's growth data. This allows the health monitoring unit to constantly monitor the child's health condition and suggest appropriate childcare methods.

[0089] The personalized childcare system can further include a learning support unit. The learning support unit analyzes a child's learning situation and suggests appropriate learning methods. For example, it monitors a child's learning progress, identifies weak areas, and suggests supplementary lessons. The learning support unit can also suggest appropriate teaching materials and learning programs based on the child's interests. This allows the learning support unit to grasp a child's learning situation and suggest effective learning methods.

[0090] The personalized childcare system can further include a social assessment unit. The social assessment unit analyzes a child's social behavior and suggests appropriate childcare methods. For example, it evaluates a child's interactions with friends and communication skills and provides advice on how to foster social skills. The social assessment unit can also suggest ways to develop appropriate social skills based on data on the child's group activities and play. This allows the social assessment unit to support a child's social growth and suggest appropriate childcare methods.

[0091] The personalized parenting system can also use emotion estimation to analyze the parent's emotional state and suggest relaxation methods based on the parent's stress level. For example, it can analyze the parent's facial expressions and voice and suggest relaxation music or deep breathing techniques if stress levels are high. It can also use psychological tests and biofeedback technology to evaluate the parent's emotional state. This allows it to analyze the parent's emotional state and suggest specific methods for reducing stress.

[0092] The personalized parenting system can also use an emotion estimation function to analyze a child's emotional state and suggest play activities based on that emotion. For example, it can analyze a child's facial expressions and voice, and if the child is enjoying the activity, it can suggest continuing that activity, or if the child is feeling anxious or stressed, it can suggest a different activity. The emotion estimation function can also be used to monitor a child's emotional state in real time and suggest appropriate play activities. This makes it possible to analyze a child's emotional state and suggest play activities based on that emotion.

[0093] The personalized parenting system can also use an emotion estimation function to collect emotional responses when parents try different parenting methods and continuously suggest optimal methods. For example, the system can analyze the parent's facial expressions and voice to evaluate their emotional responses. It can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional responses. This allows the system to collect emotional responses when parents try different parenting methods and continuously suggest optimal methods.

[0094] The personalized parenting system can further analyze the parent's emotional state using an emotion estimation function to improve the accuracy of the recording. For example, the emotional state can be evaluated by analyzing the parent's facial expressions and voice. In addition, facial expression recognition technology and voice analysis technology can be used to evaluate the parent's emotional state. This allows the parent's emotional state to be analyzed when recording, improving the accuracy of the recording.

[0095] The personalized parenting system can further use an emotion estimation function to analyze the parent's emotional state and make suggestions according to the emotion. For example, the system can analyze the parent's facial expressions and voice to evaluate the parent's emotional state. It can also use facial expression recognition technology and voice analysis technology to evaluate the parent's emotional state. This allows the system to analyze the parent's emotional state and make suggestions according to the emotion.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The data recording unit collects data manually recorded by the parent. For example, it collects data such as sleep, feeding, and weight that are manually recorded by the parent. It can also collect data recorded by the parent using an app. For example, it can automatically collect data recorded by the parent using a smartphone app. Step 2: The video analysis unit analyzes the video or images taken by the parent. For example, it can analyze videos taken by the parent with a smartphone to analyze the child's behavior and facial expressions. It can also analyze images taken with a digital camera to analyze the child's movements. Step 3: The generating AI analyzes the child's personality, behavior, and characteristics based on the data collected and analyzed by the data recording and video analysis units. For example, it can analyze the child's personality and behavior patterns based on data manually recorded by parents, videos, and images. It can also analyze the child's characteristics using psychological evaluation criteria. Step 4: The suggestion unit proposes child-rearing methods based on the results of the analysis by the generation AI unit. For example, the suggestion unit can propose appropriate child-rearing methods to parents based on the results of the analysis by the generation AI. It can also make suggestions in chat format to make the proposals easier for parents to understand.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data recording unit that collects data manually recorded by a parent; a video analysis unit that analyzes videos or images taken by the parent; a generation AI unit that analyzes the personality, behavior, and characteristics of the child based on the data collected and analyzed by the data recording unit and the video analysis unit; a suggestion unit that suggests a child-rearing method based on the results of the analysis by the generation AI unit; A system characterized by:

2. The generation AI unit Analyze the child's emotional state and propose a parenting method based on the child's emotions.

2. The system of claim 1.

3. The generation AI unit Learn about child-rearing methods in different cultural spheres and propose culturally appropriate child-rearing methods.

2. The system of claim 1.

4. The data recording unit Automatically collect data using smart devices 2. The system of claim 1.

5. The video analysis unit Analyze video and image data to detect subtle changes in the child's behavior 2. The system of claim 1.

6. The generation AI unit In the chat-style proposal, respond to the parent's questions in real time.

2. The system of claim 1.

7. The generation AI unit Increase the frequency of updating learning data and improve proposals in real time 2. The system of claim 1.

8. The generation AI unit Analyzing the emotional state of the parent and proposing a child-rearing method according to the parent's stress level 2. The system of claim 1.

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