System

Abstract

An object of the system according to the embodiment is to collect and analyze voices and sounds of children in a classroom and report the voices and sounds to a guardian.SOLUTION: A system includes a voice collection unit, an analysis unit, and a report unit. The voice collection unit collects voices or voices of children in the classroom. The analysis unit analyzes the voice data collected by the voice collection unit. The reporting unit reports a result analyzed by the analysis unit to the protector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to effectively collect and analyze the voices and sounds of children in the classroom and report the results appropriately to parents.

[0005] The system according to the embodiment aims to collect and analyze the voices and sounds of children in a classroom and report them to their parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio collection unit, an analysis unit, and a reporting unit. The audio collection unit collects the voices or sounds of children in the classroom. The analysis unit analyzes the audio data collected by the audio collection unit. The reporting unit reports the results of the analysis by the analysis unit to parents. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze the voices and sounds of children in the classroom and report them to their parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The child growth reporting system according to an embodiment of the present invention collects the voices and sounds of children in a classroom, analyzes them using a generation AI, and reports them to parents. This allows the child growth reporting system to provide parents with information about their child's growth process and peace of mind.

[0029] A child growth reporting system according to an embodiment includes an audio collection unit, an analysis unit, and a reporting unit. The audio collection unit collects the voices and sounds of children in a classroom. For example, it uses microphones installed in the classroom to collect speech during class, conversations with friends, and voices while playing. The audio collection unit can also collect audio data using sensors. For example, the audio collection unit links multiple microphones in the classroom to collect audio data with high accuracy. The analysis unit analyzes the audio data collected by the audio collection unit. For example, a generation AI analyzes the audio data and extracts the content of children's speech, changes in emotions, and communication patterns. The generation AI converts the audio data into text and analyzes it using a text generation AI (e.g., GPT-3). The generation AI can also estimate changes in emotions from the audio data using emotion analysis technology. For example, the generation AI analyzes the tone and pitch of the audio data to estimate changes in emotions. The reporting unit reports the results of the analysis by the analysis unit to parents. For example, the reporting unit creates a report summarizing the content of the child's statements, changes in emotions, and communication patterns based on the results of the analysis by the generation AI. The reporting unit can also periodically send reports to parents. For example, the reporting unit sends reports to parents via email or app notification. In this way, the child growth reporting system according to the embodiment can provide parents with information about their child's growth process and peace of mind.

[0030] When collecting voice data in the classroom, the voice collection unit can simultaneously collect information on the children's movements and locations and analyze the association between the voice and their actions. The voice collection unit, for example, links microphones and cameras installed in the classroom to collect information on the children's movements and locations in real time. For example, it records the positions and movements of children when they speak and integrates the information with the voice data. The voice collection unit can also use wearable devices to collect information on the children's movements and locations. For example, it can use a wristband-type device worn by the children to obtain location information and associate it with the voice data. The voice collection unit can also use sensors in the classroom to collect information on the children's movements and locations and integrate it with the voice data. For example, it can use infrared sensors or ultrasonic sensors to detect the children's movements and associate it with the voice data. This allows for a detailed understanding of the children's behavioral patterns by analyzing the association between the voice and their actions.

[0031] The audio collection unit can detect specific audio patterns in real time and simultaneously record video of that moment. For example, the audio collection unit can link microphones and cameras installed in a classroom to build a system that detects specific audio patterns in real time. For example, when laughter or crying is detected, video of that moment is recorded. The audio collection unit can also develop a system that uses audio recognition technology to detect specific audio patterns in real time and automatically record video of that moment. For example, it can detect children's laughter or crying and save video of that moment. The audio collection unit can also develop an algorithm that analyzes audio data in a classroom in real time and detects specific audio patterns. For example, a system can be built that records video of the moment when laughter or crying is detected. By detecting specific audio patterns and recording video of that moment, changes in children's emotions and behavior can be understood in detail.

[0032] The voice collection unit can apply the voice data collection system in the classroom to other environments such as at home or in a park to understand children's behavior in various environments. For example, the voice collection unit introduces the voice data collection system in the classroom into homes to understand children's behavior at home. For example, it collects and analyzes voices during conversations and play at home. The voice collection unit can also install a voice data collection system in a park or playground to understand children's behavior outside. For example, it collects and analyzes voices during play and conversations with friends. The voice collection unit can also apply the voice data collection system in the classroom to other environments to understand children's behavior in various environments. For example, it collects and analyzes voice data from homes, parks, extracurricular activities, etc. By understanding behavior in various environments, it is possible to comprehensively understand children's growth and behavioral patterns.

[0033] The voice collection unit can monitor the children's health conditions when collecting voice data, and use the information for health management. For example, the voice collection unit adds a health condition monitoring function to the voice data collection system and records the frequency of the children's coughs and sneezes. For example, it detects the sounds of coughs and sneezes and analyzes their frequency. The voice collection unit can also monitor the children's health conditions using the voice data collection system. For example, it detects the sounds of coughs and sneezes in real time and evaluates the health conditions. The voice collection unit can also integrate a health condition monitoring function into the voice data collection system to grasp the children's health conditions. For example, it records the frequency of coughs and sneezes and uses the information for health management. In this way, monitoring the health conditions can be useful for health management of the children.

[0034] The analysis unit can use the generation AI to analyze children's learning progress and level of understanding from the voice data and propose individual learning plans. The analysis unit can, for example, use the generation AI to build a system that analyzes children's learning progress from voice data. For example, it can analyze the content of comments and questions made during class and evaluate the level of understanding. The analysis unit can also develop a system that analyzes children's learning progress from voice data and proposes individual learning plans based on the results. For example, it can propose learning materials and assignments according to the level of understanding. The analysis unit can also use the generation AI to build a system that analyzes children's learning progress and level of understanding from voice data and proposes individual learning plans. For example, it can analyze the content of comments made during class and create a learning plan according to the level of understanding. In this way, by analyzing learning progress and level of understanding and proposing individual learning plans, it is possible to improve children's learning effectiveness.

[0035] The analysis unit can analyze the frequency and content of children's speech in analyzing the voice data to evaluate the development of their social skills and communication skills. For example, the analysis unit can record the frequency of children's speech in analyzing the voice data and build a system to evaluate their social skills and communication skills. For example, the number of speeches and content are analyzed and evaluation is performed. The analysis unit can also analyze the content of children's speech and develop a system to evaluate the development of their social skills and communication skills. For example, the content of speech in conversations with friends and group activities is analyzed and evaluation is performed. The analysis unit can also analyze the frequency and content of children's speech in analyzing the voice data and build a system to evaluate the development of their social skills and communication skills. For example, the content and tone of speech are analyzed and evaluation is performed. In this way, by analyzing the frequency and content of speech and evaluating the development of their social skills and communication skills, it is possible to comprehensively grasp the growth of children.

[0036] The analysis unit can integrate the results of the voice data analysis with other data to perform a comprehensive growth assessment. The analysis unit, for example, integrates the results of the voice data analysis with academic performance data to build a system that performs a comprehensive growth assessment. For example, the content of comments is associated with performance data to perform an assessment. The analysis unit can also integrate the results of the voice data analysis with health data to develop a system that performs a comprehensive growth assessment. For example, the content of comments is associated with health status to perform an assessment. The analysis unit can also integrate the results of the voice data analysis with other data to build a system that performs a comprehensive growth assessment. For example, academic performance and health data are integrated to perform a comprehensive growth assessment. In this way, by integrating the results of the voice data analysis with other data, it is possible to comprehensively assess children's growth.

[0037] The analysis unit can identify children's interests and concerns based on the analysis results, and suggest learning materials and activities based on them. The analysis unit, for example, builds a system that identifies children's interests and concerns based on the analysis results of the voice data. For example, it analyzes the content and tone of speech to identify interests and concerns. The analysis unit can also develop a system that identifies children's interests and concerns and suggests learning materials and activities based on them. For example, it can suggest learning materials and activities related to themes of interest. The analysis unit can also build a system that identifies children's interests and concerns based on the analysis results of the voice data and suggests learning materials and activities based on them. For example, it can analyze the content of speech to suggest learning materials and activities that match the interests and concerns. In this way, it is possible to identify interests and concerns and suggest learning materials and activities based on them, thereby increasing children's motivation to learn.

[0038] The reporting unit can use the generating AI to record the children's growth process in detail and report long-term growth trends. The reporting unit, for example, uses the generating AI to build a system that records the children's growth process in detail. For example, it records changes in speech content and behavior over the long term and analyzes growth trends. The reporting unit can also develop a system that records the children's growth process in detail and reports long-term growth trends based on the results. For example, it analyzes changes in speech content and behavior and reports growth trends. The reporting unit can also use the generating AI to build a system that records the children's growth process in detail and reports long-term growth trends. For example, it records changes in speech content and behavior and analyzes growth trends. In this way, by recording the growth process in detail and reporting long-term growth trends, it is possible to continuously grasp the children's growth.

[0039] The reporting unit can identify children's strengths and weaknesses in the reports on their growth process and propose individual growth plans. The reporting unit, for example, builds a system that identifies children's strengths and weaknesses in the reports on their growth process. For example, it analyzes changes in what is said and what is done in their behavior to identify strengths and weaknesses. The reporting unit can also develop a system that identifies children's strengths and weaknesses and proposes individual growth plans based on the results. For example, it can propose a plan to develop strengths and overcome weaknesses. The reporting unit can also build a system that identifies children's strengths and weaknesses in the reports on their growth process and proposes individual growth plans. For example, it can analyze changes in what is said and what is done in their behavior to create individual growth plans. In this way, it is possible to effectively support children's growth by identifying strengths and weaknesses and proposing individual growth plans.

[0040] The reporting unit can visualize and provide the growth process report, allowing parents to intuitively understand. The reporting unit, for example, builds a system that visualizes the growth process report. For example, it visually displays the growth trends of children using graphs and charts. The reporting unit can also develop a system that provides visualized growth process reports to parents. For example, it can display the content of comments and changes in behavior in graphs and charts, allowing for intuitive understanding. The reporting unit can also build a system that visualizes the growth process report, allowing parents to intuitively understand. For example, it can visually display the growth trends and provide them to parents. In this way, by visualizing the growth process report, parents can intuitively understand.

[0041] The reporting department can share the report content with other parents and promote information exchange within the community. The reporting department, for example, builds a system for sharing the report content of the growth process with other parents. For example, the report content is shared using an online platform and promotes information exchange. The reporting department can also develop a system for sharing the report content of the growth process among parents and promotes information exchange within the community. For example, a dedicated app can be provided for sharing the report content. The reporting department can also build a system for sharing the report content of the growth process with other parents and promotes information exchange within the community. For example, an online forum can be provided for sharing the report content. In this way, the report content can be shared with other parents and promote information exchange within the community.

[0042] The analysis unit can use generative AI to monitor children's safety in real time and immediately notify parents if an abnormality is detected. The analysis unit can, for example, use generative AI to build a system that monitors children's safety in real time. For example, it can analyze voice data and movement data to detect abnormalities. The analysis unit can also develop a system that monitors children's safety in real time and immediately notifies parents if an abnormality is detected. For example, it can send an alert to parents when abnormal voice or movement is detected. The analysis unit can also use generative AI to build a system that monitors children's safety in real time and immediately notifies parents if an abnormality is detected. For example, it can notify parents when abnormal behavior or voice is detected. This makes it possible to ensure the safety of children by monitoring safety in real time and immediately notifying parents if an abnormality is detected.

[0043] The analysis unit can analyze children's behavioral patterns, predict high-risk behavior, and take measures in advance. The analysis unit, for example, analyzes children's behavioral patterns and builds a system that predicts high-risk behavior. For example, it identifies high-risk behavior based on past behavioral data and takes measures in advance. The analysis unit can also develop a system that analyzes behavioral patterns, predicts high-risk behavior, and takes measures in advance. For example, if high-risk behavior is predicted, it notifies parents. The analysis unit can also build a system that analyzes children's behavioral patterns, predicts high-risk behavior, and takes measures in advance. For example, if high-risk behavior is predicted, it adjusts the environment in the classroom. In this way, the safety of children can be ensured by analyzing behavioral patterns, predicting high-risk behavior, and taking measures in advance.

[0044] The analysis unit can provide peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. The analysis unit, for example, builds a system that provides peace of mind not only in the classroom but also at home and on the go. For example, it collects and analyzes voice data and movement data at home and on the go. The analysis unit can also develop a system that provides peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. For example, it detects abnormalities at home and on the go and notifies parents. The analysis unit can also build a system that provides peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. For example, it analyzes behavioral patterns at home and on the go and predicts high-risk behavior. This allows peace of mind to be provided not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management.

[0045] The analysis unit can provide parents with education and training on child safety, thereby improving safety awareness within the home. The analysis unit, for example, builds a system that provides parents with education and training on child safety. For example, it provides safety videos and materials to improve safety awareness within the home. The analysis unit can also develop a system that provides parents with education and training on child safety, thereby improving safety awareness within the home. For example, it can provide online safety courses. The analysis unit can also build a system that provides parents with education and training on child safety, thereby improving safety awareness within the home. For example, it can hold safety workshops and seminars. In this way, safety education and training can be provided to parents, thereby improving safety awareness within the home.

[0046] The analysis unit can use the generation AI to analyze feedback from parents and provide detailed reports tailored to individual needs. The analysis unit, for example, uses the generation AI to build a system that analyzes feedback from parents. For example, it analyzes the content of the feedback and creates a report tailored to individual needs. The analysis unit can also develop a system that analyzes feedback from parents and, based on the results, provides detailed reports tailored to individual needs. For example, it reports in detail on changes in a child's growth and behavior based on the content of the feedback. The analysis unit can also use the generation AI to build a system that analyzes feedback from parents and provides detailed reports tailored to individual needs. For example, it analyzes the content of the feedback and provides individual advice and support. In this way, support tailored to the needs of parents can be provided by analyzing feedback from parents and providing detailed reports tailored to individual needs.

[0047] The analysis unit can analyze in detail changes in children's behavior and speech based on the feedback and provide specific advice to parents. The analysis unit, for example, builds a system that analyzes in detail changes in children's behavior and speech based on the feedback. For example, it analyzes changes in behavior and speech based on the content of the feedback and provides specific advice. The analysis unit can also develop a system that analyzes in detail changes in children's behavior and speech and provides specific advice to parents based on the results. For example, it analyzes changes in behavior and speech and provides appropriate support. The analysis unit can also build a system that analyzes in detail changes in children's behavior and speech based on the feedback and provides specific advice to parents. For example, it analyzes changes in behavior and speech based on the content of the feedback and provides individual advice. In this way, it is possible to strengthen support for parents by analyzing in detail changes in behavior and speech based on the feedback and providing specific advice.

[0048] The analysis unit can share feedback from parents with other parents and promote information exchange within the community. For example, the analysis unit builds a system for sharing feedback from parents with other parents. For example, the analysis unit can use an online platform to share the feedback content and promote information exchange. The analysis unit can also develop a system for sharing feedback between parents and promoting information exchange within the community. For example, the analysis unit can provide a dedicated app for sharing the feedback content. The analysis unit can also build a system for sharing feedback from parents with other parents and promoting information exchange within the community. For example, the analysis unit can provide an online forum for sharing the feedback content. In this way, sharing feedback from parents can promote information exchange within the community.

[0049] The analysis unit can provide a customized program to support the development of children based on the feedback. The analysis unit, for example, builds a system that provides a customized program to support the development of children based on the feedback. For example, the analysis unit suggests an individual learning plan or activity based on the feedback content. The analysis unit can also develop a system that provides a customized program to support the development of children. For example, the analysis unit creates an individual development plan based on the feedback content. The analysis unit can also build a system that provides a customized program to support the development of children based on the feedback. For example, the analysis unit provides individual advice and support based on the feedback content. As a result, by providing a customized program based on the feedback, it is possible to effectively support the development of children.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The analysis unit can analyze the frequency and content of children's speech from the collected audio data to evaluate the development of their social skills and communication abilities. For example, it can analyze and evaluate the number and content of speech. It can also analyze and evaluate the content of speech in conversations with friends and group activities. It can also analyze the content and tone of speech to build a system that evaluates the development of social skills and communication abilities. This allows for a comprehensive understanding of children's growth by analyzing the frequency and content of speech and evaluating the development of social skills and communication abilities.

[0052] The analysis unit uses generative AI to analyze children's learning progress and level of understanding from voice data and propose individual learning plans. For example, it can analyze comments and questions made during class to evaluate their level of understanding. It can also develop a system that suggests learning materials and assignments according to their level of understanding. It can also analyze comments made during class and create learning plans according to their level of understanding. This makes it possible to improve children's learning effectiveness by analyzing their learning progress and level of understanding and proposing individual learning plans.

[0053] The analysis unit can use the generation AI to analyze feedback from parents and provide detailed reports tailored to individual needs. For example, it can analyze the feedback content and create reports tailored to individual needs. It can also develop a system that provides detailed reports on children's growth and behavioral changes based on the feedback content. It can also build a system that analyzes the feedback content and provides individual advice and support. This makes it possible to analyze feedback from parents and provide detailed reports tailored to individual needs, thereby providing support tailored to parents' needs.

[0054] The analysis unit can perform a detailed analysis of changes in children's behavior and speech based on the feedback and provide specific advice to parents. For example, changes in behavior and speech can be analyzed based on the content of the feedback and specific advice can be provided. It is also possible to develop a system that analyzes changes in behavior and speech and provides appropriate support. Furthermore, it is also possible to build a system that analyzes changes in behavior and speech based on the content of the feedback and provides individualized advice. This makes it possible to strengthen support for parents by analyzing changes in behavior and speech in detail based on feedback and providing specific advice.

[0055] The analysis unit can share feedback from parents with other parents and promote information exchange within the community. For example, an online platform can be used to share feedback and promote information exchange. A system can also be developed that allows parents to share feedback among themselves and promote information exchange within the community. Furthermore, a system can be built that provides a dedicated app or online forum for sharing feedback. This allows feedback from parents to be shared and promote information exchange within the community.

[0056] The analysis unit can provide a customized program to support children's development based on the feedback. For example, it can suggest individual learning plans and activities based on the feedback. It can also develop a system that creates individual development plans. It can also build a system that provides individual advice and support based on the feedback. This makes it possible to effectively support children's development by providing a customized program based on the feedback.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The voice collection unit collects the voices and sounds of the children in the classroom. For example, microphones installed in the classroom are used to collect speech during class, conversations with friends, and voices while playing. The voice collection unit can also collect voice data using sensors. For example, the voice collection unit links multiple microphones in the classroom to collect voice data with high accuracy. Step 2: The analysis unit analyzes the voice data collected by the voice collection unit. For example, the generation AI analyzes the voice data and extracts the content of what the children say, changes in their emotions, and communication patterns. The generation AI uses a text generation AI (e.g., GPT-3) to convert the voice data into text and analyze it. The generation AI can also use emotion analysis technology to infer changes in emotions from the voice data. For example, the generation AI analyzes the tone and pitch of the voice data to infer changes in emotions. Step 3: The reporting unit reports the results of the analysis by the analysis unit to the guardian. For example, the reporting unit creates a report summarizing the content of the child's statements, changes in emotions, and communication patterns based on the results of the analysis by the generation AI. The reporting unit can also send reports to the guardian on a regular basis. For example, the reporting unit sends reports to the guardian via email or app notification.

[0059] (Example 2) The child growth reporting system according to an embodiment of the present invention collects the voices and sounds of children in a classroom, analyzes them using a generation AI, and reports them to parents. This allows the child growth reporting system to provide parents with information about their child's growth process and peace of mind.

[0060] A child growth reporting system according to an embodiment includes an audio collection unit, an analysis unit, and a reporting unit. The audio collection unit collects the voices and sounds of children in a classroom. For example, it uses microphones installed in the classroom to collect speech during class, conversations with friends, and voices while playing. The audio collection unit can also collect audio data using sensors. For example, the audio collection unit links multiple microphones in the classroom to collect audio data with high accuracy. The analysis unit analyzes the audio data collected by the audio collection unit. For example, a generation AI analyzes the audio data and extracts the content of children's speech, changes in emotions, and communication patterns. The generation AI converts the audio data into text and analyzes it using a text generation AI (e.g., GPT-3). The generation AI can also estimate changes in emotions from the audio data using emotion analysis technology. For example, the generation AI analyzes the tone and pitch of the audio data to estimate changes in emotions. The reporting unit reports the results of the analysis by the analysis unit to parents. For example, the reporting unit creates a report summarizing the content of the child's statements, changes in emotions, and communication patterns based on the results of the analysis by the generation AI. The reporting unit can also periodically send reports to parents. For example, the reporting unit sends reports to parents via email or app notification. In this way, the child growth reporting system according to the embodiment can provide parents with information about their child's growth process and peace of mind.

[0061] When collecting voice data in the classroom, the voice collection unit can simultaneously collect information on the children's movements and locations and analyze the association between the voice and their actions. The voice collection unit, for example, links microphones and cameras installed in the classroom to collect information on the children's movements and locations in real time. For example, it records the positions and movements of children when they speak and integrates the information with the voice data. The voice collection unit can also use wearable devices to collect information on the children's movements and locations. For example, it can use a wristband-type device worn by the children to obtain location information and associate it with the voice data. The voice collection unit can also use sensors in the classroom to collect information on the children's movements and locations and integrate it with the voice data. For example, it can use infrared sensors or ultrasonic sensors to detect the children's movements and associate it with the voice data. This allows for a detailed understanding of the children's behavioral patterns by analyzing the association between the voice and their actions.

[0062] The audio collection unit can detect specific audio patterns in real time and simultaneously record video of that moment. For example, the audio collection unit can link microphones and cameras installed in a classroom to build a system that detects specific audio patterns in real time. For example, when laughter or crying is detected, video of that moment is recorded. The audio collection unit can also develop a system that uses audio recognition technology to detect specific audio patterns in real time and automatically record video of that moment. For example, it can detect children's laughter or crying and save video of that moment. The audio collection unit can also develop an algorithm that analyzes audio data in a classroom in real time and detects specific audio patterns. For example, a system can be built that records video of the moment when laughter or crying is detected. By detecting specific audio patterns and recording video of that moment, changes in children's emotions and behavior can be understood in detail.

[0063] The analysis unit can estimate changes in children's emotions in real time from the collected voice data and send alerts to parents in response to these changes. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes changes in children's emotions in real time from the collected voice data. For example, it analyzes the tone and pitch of children's voices to estimate changes in emotions. The analysis unit can also develop a system that estimates changes in emotions from voice data and sends alerts to parents based on the results. For example, it notifies parents when children are feeling stressed. The analysis unit can also build a system that uses an emotion estimation function to analyze changes in children's emotions in real time from the collected voice data and send alerts to parents in response to these changes in emotions. For example, it notifies parents when children are excited. In this way, by sending alerts to parents in response to changes in emotions, it is possible to grasp children's emotional states in real time.

[0064] The voice collection unit can apply the voice data collection system in the classroom to other environments such as at home or in a park to understand children's behavior in various environments. For example, the voice collection unit introduces the voice data collection system in the classroom into homes to understand children's behavior at home. For example, it collects and analyzes voices during conversations and play at home. The voice collection unit can also install a voice data collection system in a park or playground to understand children's behavior outside. For example, it collects and analyzes voices during play and conversations with friends. The voice collection unit can also apply the voice data collection system in the classroom to other environments to understand children's behavior in various environments. For example, it collects and analyzes voice data from homes, parks, extracurricular activities, etc. By understanding behavior in various environments, it is possible to comprehensively understand children's growth and behavioral patterns.

[0065] The voice collection unit can monitor the children's health conditions when collecting voice data, and use the information for health management. For example, the voice collection unit adds a health condition monitoring function to the voice data collection system and records the frequency of the children's coughs and sneezes. For example, it detects the sounds of coughs and sneezes and analyzes their frequency. The voice collection unit can also monitor the children's health conditions using the voice data collection system. For example, it detects the sounds of coughs and sneezes in real time and evaluates the health conditions. The voice collection unit can also integrate a health condition monitoring function into the voice data collection system to grasp the children's health conditions. For example, it records the frequency of coughs and sneezes and uses the information for health management. In this way, monitoring the health conditions can be useful for health management of the children.

[0066] The analysis unit can estimate children's stress levels from the collected voice data and suggest relaxation methods when stress levels increase. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes children's stress levels from the collected voice data in real time. For example, it analyzes the tone and pitch of the voice to estimate the stress level. The analysis unit can also develop a system that estimates stress levels from voice data and suggests relaxation methods based on the results. For example, it can suggest deep breathing or relaxing music when stress levels increase. The analysis unit can also build a system that uses an emotion estimation function to analyze children's stress levels from the collected voice data in real time and suggest relaxation methods when stress levels increase. For example, it can suggest relaxation methods to parents when stress levels increase. In this way, estimating stress levels and suggesting relaxation methods can be useful for managing children's stress.

[0067] The analysis unit can use the generation AI to analyze children's learning progress and level of understanding from the voice data and propose individual learning plans. The analysis unit can, for example, use the generation AI to build a system that analyzes children's learning progress from voice data. For example, it can analyze the content of comments and questions made during class and evaluate the level of understanding. The analysis unit can also develop a system that analyzes children's learning progress from voice data and proposes individual learning plans based on the results. For example, it can propose learning materials and assignments according to the level of understanding. The analysis unit can also use the generation AI to build a system that analyzes children's learning progress and level of understanding from voice data and proposes individual learning plans. For example, it can analyze the content of comments made during class and create a learning plan according to the level of understanding. In this way, by analyzing learning progress and level of understanding and proposing individual learning plans, it is possible to improve children's learning effectiveness.

[0068] The analysis unit can analyze the frequency and content of children's speech in analyzing the voice data to evaluate the development of their social skills and communication skills. For example, the analysis unit can record the frequency of children's speech in analyzing the voice data and build a system to evaluate their social skills and communication skills. For example, the number of speeches and content are analyzed and evaluation is performed. The analysis unit can also analyze the content of children's speech and develop a system to evaluate the development of their social skills and communication skills. For example, the content of speech in conversations with friends and group activities is analyzed and evaluation is performed. The analysis unit can also analyze the frequency and content of children's speech in analyzing the voice data and build a system to evaluate the development of their social skills and communication skills. For example, the content and tone of speech are analyzed and evaluation is performed. In this way, by analyzing the frequency and content of speech and evaluating the development of their social skills and communication skills, it is possible to comprehensively grasp the growth of children.

[0069] The analysis unit can use the emotion estimation function to analyze changes in children's emotions in detail from the voice data and provide support in accordance with the changes in emotions. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes changes in children's emotions in detail from the voice data. For example, it analyzes the tone and pitch of the voice to estimate changes in emotions. The analysis unit can also develop a system that analyzes changes in emotions in detail from the voice data and provides support based on the results. For example, it can provide advice and support in accordance with changes in emotions. The analysis unit can also use the emotion estimation function to build a system that analyzes changes in children's emotions in detail from the voice data and provides support in accordance with changes in emotions. For example, it can provide counseling and support in accordance with changes in emotions. In this way, by analyzing changes in emotions in detail and providing support in accordance with emotions, it is possible to support the psychological growth of children.

[0070] The analysis unit can integrate the results of the voice data analysis with other data to perform a comprehensive growth assessment. The analysis unit, for example, integrates the results of the voice data analysis with academic performance data to build a system that performs a comprehensive growth assessment. For example, the content of comments is associated with performance data to perform an assessment. The analysis unit can also integrate the results of the voice data analysis with health data to develop a system that performs a comprehensive growth assessment. For example, the content of comments is associated with health status to perform an assessment. The analysis unit can also integrate the results of the voice data analysis with other data to build a system that performs a comprehensive growth assessment. For example, academic performance and health data are integrated to perform a comprehensive growth assessment. In this way, by integrating the results of the voice data analysis with other data, it is possible to comprehensively assess children's growth.

[0071] The analysis unit can identify children's interests and concerns based on the analysis results, and suggest learning materials and activities based on them. The analysis unit, for example, builds a system that identifies children's interests and concerns based on the analysis results of the voice data. For example, it analyzes the content and tone of speech to identify interests and concerns. The analysis unit can also develop a system that identifies children's interests and concerns and suggests learning materials and activities based on them. For example, it can suggest learning materials and activities related to themes of interest. The analysis unit can also build a system that identifies children's interests and concerns based on the analysis results of the voice data and suggests learning materials and activities based on them. For example, it can analyze the content of speech to suggest learning materials and activities that match the interests and concerns. In this way, it is possible to identify interests and concerns and suggest learning materials and activities based on them, thereby increasing children's motivation to learn.

[0072] The analysis unit can use the emotion estimation function to analyze changes in children's emotions from the voice data and provide counseling and support based on those emotions. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes changes in children's emotions from the voice data. For example, it analyzes the tone and pitch of the voice to estimate changes in emotions. The analysis unit can also develop a system that analyzes changes in emotions from the voice data and provides counseling and support based on the results. For example, it can provide advice and support in accordance with changes in emotions. The analysis unit can also use the emotion estimation function to build a system that analyzes changes in children's emotions from the voice data and provides counseling and support based on those emotions. For example, it can provide counseling and support in accordance with changes in emotions. In this way, by analyzing changes in emotions and providing counseling and support based on those emotions, it is possible to support the psychological growth of children.

[0073] The reporting unit can use the generating AI to record the children's growth process in detail and report long-term growth trends. The reporting unit, for example, uses the generating AI to build a system that records the children's growth process in detail. For example, it records changes in speech content and behavior over the long term and analyzes growth trends. The reporting unit can also develop a system that records the children's growth process in detail and reports long-term growth trends based on the results. For example, it analyzes changes in speech content and behavior and reports growth trends. The reporting unit can also use the generating AI to build a system that records the children's growth process in detail and reports long-term growth trends. For example, it records changes in speech content and behavior and analyzes growth trends. In this way, by recording the growth process in detail and reporting long-term growth trends, it is possible to continuously grasp the children's growth.

[0074] The reporting unit can identify children's strengths and weaknesses in the reports on their growth process and propose individual growth plans. The reporting unit, for example, builds a system that identifies children's strengths and weaknesses in the reports on their growth process. For example, it analyzes changes in what is said and what is done in their behavior to identify strengths and weaknesses. The reporting unit can also develop a system that identifies children's strengths and weaknesses and proposes individual growth plans based on the results. For example, it can propose a plan to develop strengths and overcome weaknesses. The reporting unit can also build a system that identifies children's strengths and weaknesses in the reports on their growth process and proposes individual growth plans. For example, it can analyze changes in what is said and what is done in their behavior to create individual growth plans. In this way, it is possible to effectively support children's growth by identifying strengths and weaknesses and proposing individual growth plans.

[0075] The reporting unit can use the emotion estimation function to include emotional changes in the growth process report and provide emotional support to the guardian. The reporting unit, for example, uses an emotion estimation algorithm to build a system that includes emotional changes in the growth process report. For example, the reporting unit analyzes the tone and pitch of the voice and reflects the emotional changes in the report. The reporting unit can also develop a system that includes emotional changes in the growth process report and provides emotional support to the guardian based on the results. For example, advice and support are provided in accordance with the emotional changes. The reporting unit can also use the emotion estimation function to build a system that includes emotional changes in the growth process report and provides emotional support to the guardian. For example, counseling and support are provided in accordance with the emotional changes. In this way, emotional support can be provided to the guardian by reporting the growth process including emotional changes.

[0076] The reporting unit can visualize and provide the growth process report, allowing parents to intuitively understand. The reporting unit, for example, builds a system that visualizes the growth process report. For example, it visually displays the growth trends of children using graphs and charts. The reporting unit can also develop a system that provides visualized growth process reports to parents. For example, it can display the content of comments and changes in behavior in graphs and charts, allowing for intuitive understanding. The reporting unit can also build a system that visualizes the growth process report, allowing parents to intuitively understand. For example, it can visually display the growth trends and provide them to parents. In this way, by visualizing the growth process report, parents can intuitively understand.

[0077] The reporting department can share the report content with other parents and promote information exchange within the community. The reporting department, for example, builds a system for sharing the report content of the growth process with other parents. For example, the report content is shared using an online platform and promotes information exchange. The reporting department can also develop a system for sharing the report content of the growth process among parents and promotes information exchange within the community. For example, a dedicated app can be provided for sharing the report content. The reporting department can also build a system for sharing the report content of the growth process with other parents and promotes information exchange within the community. For example, an online forum can be provided for sharing the report content. In this way, the report content can be shared with other parents and promote information exchange within the community.

[0078] The reporting unit can use the emotion estimation function to emphasize changes in children's emotions in the growth process report and provide emotion-based advice. The reporting unit, for example, uses an emotion estimation algorithm to build a system that emphasizes changes in children's emotions in the growth process report. For example, the reporting unit analyzes the tone and pitch of voice and reflects the changes in emotions in the report. The reporting unit can also develop a system that emphasizes changes in emotions in the growth process report and provides emotion-based advice based on the results. For example, advice and support are provided in accordance with the changes in emotions. The reporting unit can also use the emotion estimation function to build a system that emphasizes changes in children's emotions in the growth process report and provides emotion-based advice. For example, counseling and support are provided in accordance with the changes in emotions. In this way, emotion-based advice can be provided by reporting the growth process with emphasis on changes in emotions.

[0079] The analysis unit can use generative AI to monitor children's safety in real time and immediately notify parents if an abnormality is detected. The analysis unit can, for example, use generative AI to build a system that monitors children's safety in real time. For example, it can analyze voice data and movement data to detect abnormalities. The analysis unit can also develop a system that monitors children's safety in real time and immediately notifies parents if an abnormality is detected. For example, it can send an alert to parents when abnormal voice or movement is detected. The analysis unit can also use generative AI to build a system that monitors children's safety in real time and immediately notifies parents if an abnormality is detected. For example, it can notify parents when abnormal behavior or voice is detected. This makes it possible to ensure the safety of children by monitoring safety in real time and immediately notifying parents if an abnormality is detected.

[0080] The analysis unit can analyze children's behavioral patterns, predict high-risk behavior, and take measures in advance. The analysis unit, for example, analyzes children's behavioral patterns and builds a system that predicts high-risk behavior. For example, it identifies high-risk behavior based on past behavioral data and takes measures in advance. The analysis unit can also develop a system that analyzes behavioral patterns, predicts high-risk behavior, and takes measures in advance. For example, if high-risk behavior is predicted, it notifies parents. The analysis unit can also build a system that analyzes children's behavioral patterns, predicts high-risk behavior, and takes measures in advance. For example, if high-risk behavior is predicted, it adjusts the environment in the classroom. In this way, the safety of children can be ensured by analyzing behavioral patterns, predicting high-risk behavior, and taking measures in advance.

[0081] The analysis unit can use the emotion estimation function to monitor changes in children's emotions and notify parents when emotional support is needed. The analysis unit, for example, uses an emotion estimation algorithm to build a system that monitors changes in children's emotions. For example, it analyzes the tone and pitch of voice to detect changes in emotion. The analysis unit can also develop a system that monitors changes in children's emotions and notifies parents when emotional support is needed. For example, it sends an alert to parents when an emotional change is detected. The analysis unit can also use the emotion estimation function to build a system that monitors changes in children's emotions and notifies parents when emotional support is needed. For example, it notifies parents when an emotional change is detected. In this way, psychological support can be provided to children by monitoring changes in emotions and notifying parents when emotional support is needed.

[0082] The analysis unit can provide peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. The analysis unit, for example, builds a system that provides peace of mind not only in the classroom but also at home and on the go. For example, it collects and analyzes voice data and movement data at home and on the go. The analysis unit can also develop a system that provides peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. For example, it detects abnormalities at home and on the go and notifies parents. The analysis unit can also build a system that provides peace of mind not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management. For example, it analyzes behavioral patterns at home and on the go and predicts high-risk behavior. This allows peace of mind to be provided not only in the classroom but also at home and on the go, thereby realizing comprehensive safety management.

[0083] The analysis unit can provide parents with education and training on child safety, thereby improving safety awareness within the home. The analysis unit, for example, builds a system that provides parents with education and training on child safety. For example, it provides safety videos and materials to improve safety awareness within the home. The analysis unit can also develop a system that provides parents with education and training on child safety, thereby improving safety awareness within the home. For example, it can provide online safety courses. The analysis unit can also build a system that provides parents with education and training on child safety, thereby improving safety awareness within the home. For example, it can hold safety workshops and seminars. In this way, safety education and training can be provided to parents, thereby improving safety awareness within the home.

[0084] The analysis unit can use the emotion estimation function to monitor changes in children's emotions and provide expert advice when emotional support is needed. The analysis unit, for example, uses an emotion estimation algorithm to build a system that monitors changes in children's emotions. For example, it analyzes the tone and pitch of a voice to detect changes in emotion. The analysis unit can also develop a system that monitors changes in children's emotions and provides expert advice when emotional support is needed. For example, when an emotional change is detected, expert advice is provided to parents. The analysis unit can also use the emotion estimation function to build a system that monitors changes in children's emotions and provides expert advice when emotional support is needed. For example, when an emotional change is detected, expert counseling is provided. In this way, psychological support can be provided to children by monitoring changes in emotions and providing expert advice when emotional support is needed.

[0085] The analysis unit can use the generation AI to analyze feedback from parents and provide detailed reports tailored to individual needs. The analysis unit, for example, uses the generation AI to build a system that analyzes feedback from parents. For example, it analyzes the content of the feedback and creates a report tailored to individual needs. The analysis unit can also develop a system that analyzes feedback from parents and, based on the results, provides detailed reports tailored to individual needs. For example, it reports in detail on changes in a child's growth and behavior based on the content of the feedback. The analysis unit can also use the generation AI to build a system that analyzes feedback from parents and provides detailed reports tailored to individual needs. For example, it analyzes the content of the feedback and provides individual advice and support. In this way, support tailored to the needs of parents can be provided by analyzing feedback from parents and providing detailed reports tailored to individual needs.

[0086] The analysis unit can analyze in detail changes in children's behavior and speech based on the feedback and provide specific advice to parents. The analysis unit, for example, builds a system that analyzes in detail changes in children's behavior and speech based on the feedback. For example, it analyzes changes in behavior and speech based on the content of the feedback and provides specific advice. The analysis unit can also develop a system that analyzes in detail changes in children's behavior and speech and provides specific advice to parents based on the results. For example, it analyzes changes in behavior and speech and provides appropriate support. The analysis unit can also build a system that analyzes in detail changes in children's behavior and speech based on the feedback and provides specific advice to parents. For example, it analyzes changes in behavior and speech based on the content of the feedback and provides individual advice. In this way, it is possible to strengthen support for parents by analyzing in detail changes in behavior and speech based on the feedback and providing specific advice.

[0087] The analysis unit can use the emotion estimation function to analyze children's emotional reactions to feedback from their parents and provide emotion-based support. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes children's emotional reactions to feedback from their parents. For example, it analyzes the tone and pitch of their voices to detect changes in emotion. The analysis unit can also analyze children's emotional reactions to feedback from their parents and develop a system that provides emotion-based support based on the results of the analysis. For example, it can provide advice and support in accordance with changes in emotion. The analysis unit can also use the emotion estimation function to build a system that analyzes children's emotional reactions to feedback from their parents and provides emotion-based support. For example, it can provide counseling and support in accordance with changes in emotion. In this way, psychological support for children can be strengthened by analyzing emotional reactions to feedback and providing emotion-based support.

[0088] The analysis unit can share feedback from parents with other parents and promote information exchange within the community. For example, the analysis unit builds a system for sharing feedback from parents with other parents. For example, the analysis unit can use an online platform to share the feedback content and promote information exchange. The analysis unit can also develop a system for sharing feedback between parents and promoting information exchange within the community. For example, the analysis unit can provide a dedicated app for sharing the feedback content. The analysis unit can also build a system for sharing feedback from parents with other parents and promoting information exchange within the community. For example, the analysis unit can provide an online forum for sharing the feedback content. In this way, sharing feedback from parents can promote information exchange within the community.

[0089] The analysis unit can provide a customized program to support the development of children based on the feedback. The analysis unit, for example, builds a system that provides a customized program to support the development of children based on the feedback. For example, the analysis unit suggests an individual learning plan or activity based on the feedback content. The analysis unit can also develop a system that provides a customized program to support the development of children. For example, the analysis unit creates an individual development plan based on the feedback content. The analysis unit can also build a system that provides a customized program to support the development of children based on the feedback. For example, the analysis unit provides individual advice and support based on the feedback content. As a result, by providing a customized program based on the feedback, it is possible to effectively support the development of children.

[0090] The analysis unit can use the emotion estimation function to analyze children's emotional reactions to feedback from their parents and provide emotion-based counseling. The analysis unit, for example, uses an emotion estimation algorithm to build a system that analyzes children's emotional reactions to feedback from their parents. For example, it analyzes the tone and pitch of their voices to detect changes in emotion. The analysis unit can also analyze children's emotional reactions to feedback from their parents and develop a system that provides emotion-based counseling based on the results of the analysis. For example, it can provide advice and support in accordance with changes in emotion. The analysis unit can also use the emotion estimation function to build a system that analyzes children's emotional reactions to feedback from their parents and provides emotion-based counseling. For example, it can provide counseling and support in accordance with changes in emotion. In this way, by analyzing emotional reactions to feedback and providing emotion-based counseling, it is possible to strengthen psychological support for children.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can analyze the frequency and content of children's speech from the collected audio data to evaluate the development of their social skills and communication abilities. For example, it can analyze and evaluate the number and content of speech. It can also analyze and evaluate the content of speech in conversations with friends and group activities. It can also analyze the content and tone of speech to build a system that evaluates the development of social skills and communication abilities. This allows for a comprehensive understanding of children's growth by analyzing the frequency and content of speech and evaluating the development of social skills and communication abilities.

[0093] The analysis unit uses generative AI to analyze children's learning progress and level of understanding from voice data and propose individual learning plans. For example, it can analyze comments and questions made during class to evaluate their level of understanding. It can also develop a system that suggests learning materials and assignments according to their level of understanding. It can also analyze comments made during class and create learning plans according to their level of understanding. This makes it possible to improve children's learning effectiveness by analyzing their learning progress and level of understanding and proposing individual learning plans.

[0094] The analysis unit can estimate changes in children's emotions in real time from the collected voice data and send alerts to parents according to the changes. For example, it can analyze the tone and pitch of children's voices to estimate changes in emotions. It can also develop a system that estimates changes in emotions and sends alerts to parents based on the results. It can also build a system that notifies parents when children are feeling stressed. This allows parents to understand their children's emotional state in real time by sending alerts according to changes in emotions.

[0095] The analysis unit can estimate children's stress levels from the collected voice data and suggest relaxation methods when stress levels rise. For example, it can analyze the tone and pitch of the voice to estimate stress levels. It can also develop a system that suggests deep breathing or relaxing music when stress levels rise. It can also build a system that suggests relaxation methods to parents when stress levels rise. This can help children manage their stress by estimating stress levels and suggesting relaxation methods.

[0096] The analysis unit can use the emotion estimation function to analyze changes in children's emotions in detail from voice data and provide support according to those changes. For example, it can analyze the tone and pitch of a voice to estimate changes in emotion. It can also develop a system that provides advice and support according to changes in emotion. It can also build a system that provides counseling and support according to changes in emotion. This allows for a detailed analysis of changes in emotion and the provision of support according to those emotions, thereby supporting children's psychological growth.

[0097] The analysis unit can use the generation AI to analyze feedback from parents and provide detailed reports tailored to individual needs. For example, it can analyze the feedback content and create reports tailored to individual needs. It can also develop a system that provides detailed reports on children's growth and behavioral changes based on the feedback content. It can also build a system that analyzes the feedback content and provides individual advice and support. This makes it possible to analyze feedback from parents and provide detailed reports tailored to individual needs, thereby providing support tailored to parents' needs.

[0098] The analysis unit can perform a detailed analysis of changes in children's behavior and speech based on the feedback and provide specific advice to parents. For example, changes in behavior and speech can be analyzed based on the content of the feedback and specific advice can be provided. It is also possible to develop a system that analyzes changes in behavior and speech and provides appropriate support. Furthermore, it is also possible to build a system that analyzes changes in behavior and speech based on the content of the feedback and provides individualized advice. This makes it possible to strengthen support for parents by analyzing changes in behavior and speech in detail based on feedback and providing specific advice.

[0099] The analysis unit can share feedback from parents with other parents and promote information exchange within the community. For example, an online platform can be used to share feedback and promote information exchange. A system can also be developed that allows parents to share feedback among themselves and promote information exchange within the community. Furthermore, a system can be built that provides a dedicated app or online forum for sharing feedback. This allows feedback from parents to be shared and promote information exchange within the community.

[0100] The analysis unit can provide a customized program to support children's development based on the feedback. For example, it can suggest individual learning plans and activities based on the feedback. It can also develop a system that creates individual development plans. It can also build a system that provides individual advice and support based on the feedback. This makes it possible to effectively support children's development by providing a customized program based on the feedback.

[0101] The analysis unit can use the emotion estimation function to analyze children's emotional responses to feedback from their parents and provide counseling based on their emotions. For example, it can analyze the tone and pitch of a voice to detect changes in emotion. It can also develop a system that provides advice and support in response to changes in emotion. It can also build a system that provides counseling and support in response to changes in emotion. This makes it possible to analyze emotional responses to feedback and provide counseling based on emotions, thereby strengthening psychological support for children.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The voice collection unit collects the voices and sounds of the children in the classroom. For example, microphones installed in the classroom are used to collect speech during class, conversations with friends, and voices while playing. The voice collection unit can also collect voice data using sensors. For example, the voice collection unit links multiple microphones in the classroom to collect voice data with high accuracy. Step 2: The analysis unit analyzes the voice data collected by the voice collection unit. For example, the generation AI analyzes the voice data and extracts the content of what the children say, changes in their emotions, and communication patterns. The generation AI uses a text generation AI (e.g., GPT-3) to convert the voice data into text and analyze it. The generation AI can also use emotion analysis technology to infer changes in emotions from the voice data. For example, the generation AI analyzes the tone and pitch of the voice data to infer changes in emotions. Step 3: The reporting unit reports the results of the analysis by the analysis unit to the guardian. For example, the reporting unit creates a report summarizing the content of the child's statements, changes in emotions, and communication patterns based on the results of the analysis by the generation AI. The reporting unit can also send reports to the guardian on a regular basis. For example, the reporting unit sends reports to the guardian via email or app notification.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0150] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0152] The data processing system 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 voice collecting unit for collecting voices or sounds of children in the classroom; an analysis unit that analyzes the voice data collected by the voice collection unit; a reporting unit that reports the results of the analysis by the analysis unit to a parent or guardian. A system characterized by:

2. The sound collection unit When collecting voice data in the classroom, information on the movements or positions of the children is also collected at the same time, and the relationship between the voices and their actions is analyzed.

2. The system of claim 1.

3. The sound collection unit Detects specific audio patterns in real time and simultaneously records video of that moment.

2. The system of claim 1.

4. The analysis unit The system estimates changes in children's emotions in real time from collected voice data and sends alerts to parents based on these changes.

2. The system of claim 1.

5. The sound collection unit The voice data collection system in the classroom will be applied to other environments such as at home or in a park to understand the children's behavior in various environments.

2. The system of claim 1.

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