system

Abstract

The system according to this embodiment aims to centrally manage children's growth records, health status checks, educational advice, behavioral analysis, and schedule management. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a checking unit, an advice unit, a behavioral analysis unit, and a management unit. The reception unit inputs growth records. The analysis unit analyzes the data input by the reception unit. The checking unit checks the health status based on the data analyzed by the analysis unit. The advice unit provides educational advice based on the results obtained by the checking unit. The behavioral analysis unit analyzes the behavior based on the advice provided by the advice unit. The management unit manages the schedule based on the results obtained by the behavioral analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is impossible to perform unified management of children's growth records, health checks, educational advice, behavior analysis, and schedule management, and it is difficult for parents to effectively support them.

[0005] The system according to the embodiment aims to perform unified management of children's growth records, health checks, educational advice, behavior analysis, and schedule management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a checking unit, an advice unit, a behavioral analysis unit, and a management unit. The reception unit inputs growth records. The analysis unit analyzes the data input by the reception unit. The checking unit checks the health status based on the data analyzed by the analysis unit. The advice unit provides educational advice based on the results obtained by the checking unit. The behavioral analysis unit analyzes the behavior based on the advice provided by the advice unit. The management unit manages the schedule based on the results obtained by the behavioral analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage a child's growth record, health status checks, educational advice, behavioral analysis, and schedule management. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The childcare support agent according to an embodiment of the present invention is a system designed to enable parents to effectively support their children's growth. This childcare support agent is a system that centrally provides daily growth records, health checks, growth advice, behavioral analysis, and schedule management. Parents input daily growth records of their children, and the AI ​​analyzes this data to check the child's health status. Furthermore, the AI ​​provides educational advice tailored to the child's development and analyzes the child's behavior. Finally, it supports parents in efficiently managing their time through a schedule management function. This mechanism allows parents to overcome information deficiencies, skill deficiencies, and time deficiencies in childcare, and increase the quality of time they spend with their children. For example, the childcare support agent is equipped with precise data analysis using AI technology, a user-friendly interface, and advanced security features to protect privacy. This allows parents to use the childcare support agent with peace of mind. Furthermore, it uses AI-powered big data analysis and machine learning to analyze children's behavioral patterns and provide individualized childcare advice. In this way, parents can effectively support their children's growth and reduce the stress of childcare.

[0029] The childcare support agent according to this embodiment comprises a reception unit, an analysis unit, a checking unit, an advice unit, a behavioral analysis unit, and a management unit. The reception unit allows parents to input their child's daily growth records. The reception unit allows parents to input growth records using, for example, a smartphone or personal computer. The reception unit also supports voice input and handwriting input. For example, if a parent inputs growth records by voice, the reception unit can convert the voice data into text data using voice recognition technology. Furthermore, the reception unit can scan handwritten growth records and convert them into digital data. The analysis unit analyzes the data input by the reception unit. The analysis unit analyzes the growth record data using, for example, AI technology to evaluate the child's health status. Based on the results of the data analysis, the analysis unit can extract important information regarding the child's growth. For example, the analysis unit analyzes changes in the child's height and weight to evaluate the progress of growth. The checking unit checks the health status based on the data analyzed by the analysis unit. The checking unit evaluates health data such as the child's body temperature and heart rate to check for any abnormalities. The Checking Unit can propose necessary measures based on the results of health checks. For example, if the Checking Unit finds that a child has a high temperature, it will advise parents to see a doctor. The Advice Unit provides educational advice based on the results obtained by the Checking Unit. For example, the Advice Unit provides advice on the child's learning plan and behavioral improvement. The Advice Unit can select appropriate expressions to make the educational advice easy for parents to understand. For example, if a parent is feeling stressed, the Advice Unit will provide concise and easy-to-understand advice. The Behavioral Analysis Unit analyzes behavior based on the advice provided by the Advice Unit. For example, the Behavioral Analysis Unit analyzes the child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, the Behavioral Analysis Unit can make specific suggestions to improve the child's behavior. For example, if a child lacks concentration, the Behavioral Analysis Unit will suggest ways to improve concentration. The Management Unit manages schedules based on the results obtained by the Behavioral Analysis Unit.The management unit, for example, manages children's learning schedules and daily routines, supporting parents in efficiently managing their time. The management unit notifies parents of the results of schedule management and can make necessary adjustments. For example, if the management unit needs to change a child's learning schedule, it notifies parents and adjusts the schedule. In this way, the childcare support agent according to the embodiment can enable parents to effectively support their child's development and reduce the stress of childcare.

[0030] The reception system allows parents to input their child's daily growth records. Parents can input growth records using, for example, a smartphone or personal computer. The system also supports voice and handwriting input. For instance, when parents input growth records by voice, the system uses voice recognition technology to convert the voice data into text. Furthermore, the system can scan handwritten growth records and convert them into digital data. Specifically, the voice recognition technology includes a function that analyzes the parent's voice using natural language processing technology and accurately converts it into text. This allows parents to easily input growth records without using their hands. For handwriting input, the system uses optical character recognition (OCR) technology to convert handwritten characters into digital data. This allows growth records written on paper to be easily digitized and imported into the system. Additionally, the system has a function to automatically classify the input data and organize it into appropriate categories. For example, height and weight data are classified into the growth record category, while diet and sleep data are classified into the lifestyle category. This allows parents to easily manage the input data and quickly search for necessary information. The reception area also features security functions to safely store data entered by parents and protect their privacy. For example, data is encrypted, and a firewall is in place to prevent unauthorized access from external sources. This allows parents to confidently enter and manage their child's growth records.

[0031] The analysis unit analyzes the data entered by the reception unit. For example, the analysis unit uses AI technology to analyze growth record data and evaluate the child's health status. Based on the results of the data analysis, the analysis unit can extract important information about the child's growth. For example, the analysis unit analyzes changes in the child's height and weight to evaluate the progress of growth. Specifically, machine learning algorithms are used in the AI ​​technology, which can predict future growth patterns based on past data. This allows parents to check whether their child's growth is normal and take necessary measures. The analysis unit also has the function to detect anomalies and monitor changes in trends in real time. For example, if a child's weight increases rapidly, the analysis unit will detect the anomaly and notify the parents. Furthermore, the analysis unit can integrate multiple data sources to perform a comprehensive health assessment. For example, by including data on diet and exercise in the analysis, in addition to height and weight, a more accurate health assessment becomes possible. The analysis unit also has a data visualization function, allowing parents to intuitively understand their child's growth status through graphs and charts. This allows parents to grasp the progress of their child's growth at a glance and take appropriate action.

[0032] The checking unit checks the child's health status based on data analyzed by the analysis unit. For example, the checking unit evaluates health data such as the child's body temperature and heart rate to check for any abnormalities. Based on the results of the health check, the checking unit can suggest necessary measures. For example, if the child's body temperature is high, the checking unit will advise the parents to see a doctor. Specifically, the checking unit uses an algorithm that detects abnormalities by comparing the data provided by the analysis unit with standard values ​​for health status. This allows parents to constantly monitor their child's health status and detect abnormalities early. The checking unit also has a function to automatically issue an alert when an abnormality is detected. For example, if the child's body temperature exceeds a certain standard, a notification is sent to the parent's smartphone. Furthermore, the checking unit also has a function to suggest specific measures based on the results of the health check. For example, if the child's body temperature is high, it will advise appropriate hydration and rest. It will also recommend seeing a doctor if necessary. This allows parents to properly manage their child's health status and respond quickly.

[0033] The Advice Unit provides educational advice based on the results obtained by the Check Unit. For example, the Advice Unit provides advice on children's learning plans and behavioral improvement. The Advice Unit can select appropriate expressions to clearly communicate the educational advice to parents. For example, if parents are feeling stressed, the Advice Unit provides concise and easy-to-understand advice. Specifically, the Advice Unit analyzes the child's learning situation and behavioral patterns to provide individually optimized advice. For example, if a child has difficulty with a particular subject, it suggests effective learning methods for that subject. Regarding behavioral improvement, it provides concrete action plans and supports parents in implementing them. Furthermore, the Advice Unit has a function to continuously improve its advice based on parental feedback. For example, by inputting opinions and impressions on the advice provided, the Advice Unit can review its content and provide more effective advice. This allows the Advice Unit to provide flexible support tailored to the needs of both parents and children, effectively supporting the child's growth.

[0034] The Behavioral Analysis Department analyzes behavior based on advice provided by the Advice Department. For example, the Behavioral Analysis Department analyzes a child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, the Behavioral Analysis Department can make specific suggestions for improving the child's behavior. For example, if a child lacks concentration, the Behavioral Analysis Department will suggest ways to improve concentration. Specifically, the Behavioral Analysis Department collects data on the child's daily behavior and analyzes behavioral patterns using machine learning algorithms. This allows for an accurate understanding of the child's behavioral tendencies and problems. The Behavioral Analysis Department also has the function of tracking changes in behavior by comparing them with past data and evaluating the progress of improvement. For example, if a child tries a new learning method, the department evaluates its effectiveness and modifies the advice as needed. Furthermore, the Behavioral Analysis Department provides parents with specific suggestions for behavioral improvement. For example, it may recommend that children take breaks at regular intervals or create a suitable learning environment to improve their concentration. In this way, the Behavioral Analysis Department can effectively improve the child's behavior and enhance their learning and quality of life.

[0035] The Management Department manages schedules based on the results obtained by the Behavioral Analysis Department. For example, the Management Department manages children's learning schedules and daily routines, supporting parents in efficiently managing their time. The Management Department notifies parents of the results of schedule management and can make necessary adjustments. For example, if the Management Department needs to change a child's learning schedule, it notifies parents and adjusts the schedule. Specifically, the Management Department uses an algorithm that automatically generates an optimal schedule based on data on the child's learning and daily life. This allows parents to manage their child's schedule without any effort. The Management Department also has a function to monitor schedule progress in real time and make adjustments as needed. For example, if a child finishes learning earlier than planned, it suggests starting the next learning content earlier. Furthermore, the Management Department has a function to notify parents of the results of schedule management in an easy-to-understand manner. For example, it allows parents to check their child's learning progress and daily routine in real time through a smartphone app. This allows parents to always be aware of their child's schedule and take appropriate action. The Management Department also has a function to continuously improve the schedule management algorithm based on parental feedback. This allows the management department to provide flexible scheduling tailored to the needs of parents and children, effectively supporting the child's development.

[0036] The reception desk can analyze past growth records and suggest the optimal input method. For example, it can suggest a preferred input method (voice, text, etc.) based on past input history. It can also analyze past input frequency and suggest the optimal input interval. Furthermore, it can suggest a format that is easy for parents to use, based on past input content. In this way, by analyzing past growth records, it can suggest the optimal input method for parents. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past growth record data into AI and have the AI ​​suggest the optimal input method.

[0037] The reception unit can filter the input of growth records based on the parents' current lifestyle and areas of interest. For example, if a parent is busy with work, the reception unit can prioritize displaying concise input fields. It can also prioritize displaying health-related input fields if the parent is interested in health. Furthermore, if the parent is interested in education, it can prioritize displaying education-related input fields. This facilitates the input of growth records tailored to the parents' lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input parent lifestyle data and areas of interest data into the AI ​​and have the AI ​​perform the filtering.

[0038] The reception unit can prioritize inputting highly relevant records when entering growth records, taking into account the parents' geographical location information. For example, if the parents are in a specific region, the reception unit will prioritize inputting growth records related to that region. Furthermore, if the parents are traveling, the reception unit can prioritize inputting growth records related to their travel destination. Additionally, if the parents are at home, the reception unit can prioritize inputting growth records recorded at home. This allows for the input of highly relevant growth records based on the parents' geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the parents' geographical location data into an AI and have the AI ​​prioritize inputting highly relevant records.

[0039] The reception desk can analyze parents' social media activity and input relevant records when entering growth records. For example, the reception desk can input relevant growth records based on what parents have shared on social media. It can also input relevant growth records based on topics that parents have shown interest in on social media. Furthermore, the reception desk can input relevant growth records based on information about accounts that parents follow on social media. This allows for the input of relevant growth records based on parents' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input parents' social media data into AI and have the AI ​​input relevant records.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the growth records during data analysis. For example, the analysis unit can perform detailed data analysis on important growth records. It can also perform simplified data analysis on general growth records. Furthermore, the analysis unit can perform data analysis with a level of detail appropriate to the category for growth records belonging to a specific category. This allows for detailed analysis of important records by adjusting the level of detail of the analysis according to the importance of the growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth record importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the growth record during data analysis. For example, the analysis unit can apply a health data analysis algorithm to health-related growth records. It can also apply an education data analysis algorithm to education-related growth records. Furthermore, it can apply a behavior data analysis algorithm to behavior-related growth records. By applying an analysis algorithm appropriate to the category of the growth record, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the growth record into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the submission date of growth records during data analysis. For example, the analysis unit may prioritize the analysis of recently submitted growth records. It can also prioritize the analysis of growth records submitted during a specific period. Furthermore, the analysis unit may prioritize the analysis of growth records of high importance based on their submission date. This allows for the prioritization of the analysis of the most recent records by determining the priority of analysis based on the submission date of growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth record submission date data into AI and have the AI ​​determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of growth records during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant growth records. It can also postpone the analysis of less relevant growth records. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the growth records. This allows for the prioritization of highly relevant records by adjusting the order of analysis based on the relevance of the growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of growth records into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0044] The checking unit can improve the accuracy of health checks by referring to past health data. For example, the checking unit can evaluate the current health status based on past health data. The checking unit can also adjust the health check items by referring to past health data. Furthermore, the checking unit can analyze past health data to improve the accuracy of health checks. As a result, the accuracy of health checks is improved by referring to past health data. Some or all of the above processes in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input past health data into AI and have the AI ​​perform the task of improving the accuracy of health checks.

[0045] The checking unit can apply different checking methods to each category of growth records during health checks. For example, the checking unit can apply a health checking method to health-related growth records. It can also apply an education-related checking method to education-related growth records. Furthermore, it can apply a behavior-related checking method to behavior-related growth records. By applying different checking methods to each category of growth records, the accuracy of the checks is improved. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the category data of the growth records into the AI ​​and have the AI ​​perform the application of the checking methods.

[0046] The checking unit can determine the priority of checks based on the submission date of growth records during health checks. For example, the checking unit can prioritize checking recently submitted growth records. It can also prioritize checking growth records submitted within a specific period. Furthermore, the checking unit can prioritize checking growth records of high importance depending on the submission date. This allows for prioritizing the checking of the most recent records by determining the priority of checks based on the submission date of the growth records. Some or all of the above processing in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input growth record submission date data into AI and have the AI ​​perform the determination of check priorities.

[0047] The checking unit can perform health checks by referring to relevant market data from growth records. For example, the checking unit can adjust the health check items based on the relevant market data. The checking unit can also improve the accuracy of the health check by referring to the relevant market data. Furthermore, the checking unit can analyze the relevant market data and evaluate the results of the health check. This improves the accuracy of the health check by referring to the relevant market data from growth records. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input relevant market data into AI and have the AI ​​perform the adjustments to the health check.

[0048] The advice unit can adjust the level of detail of educational advice based on the importance of the growth record. For example, it can provide detailed advice for important growth records. It can also provide concise advice for general growth records. Furthermore, it can provide advice with a level of detail appropriate to the category for growth records belonging to a specific category. This allows for detailed advice to be provided for important records by adjusting the level of detail according to the importance of the growth record. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input growth record importance data into AI and have the AI ​​adjust the level of detail of the advice.

[0049] The advice unit can apply different advice algorithms depending on the category of the growth record when providing educational advice. For example, the advice unit can apply a health advice algorithm to health-related growth records. It can also apply an education advice algorithm to education-related growth records. Furthermore, it can apply a behavior advice algorithm to behavior-related growth records. This improves the accuracy of the advice by applying an advice algorithm appropriate to the category of the growth record. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input growth record category data into AI and have the AI ​​perform the application of the advice algorithm.

[0050] The advice department can prioritize educational advice based on the timing of growth record submissions. For example, it may provide advice based on recently submitted growth records. It may also provide advice based on growth records submitted during a specific period. Furthermore, it may provide advice based on the importance of growth records depending on the submission timing. This allows for the provision of advice based on the most recent records by prioritizing advice based on the submission timing of growth records. Some or all of the above processes in the advice department may be performed using AI, for example, or not. For example, the advice department can input growth record submission timing data into an AI and have the AI ​​determine the priority of advice.

[0051] The advice unit can adjust the order of advice based on the relevance of growth records when providing educational advice. For example, the advice unit provides advice based on highly relevant growth records. The advice unit can also postpone less relevant growth records. Furthermore, the advice unit can dynamically adjust the order of advice according to the relevance of the growth records. This allows the advice unit to provide advice based on highly relevant records by adjusting the order of advice based on the relevance of the growth records. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the relevance data of the growth records into the AI ​​and have the AI ​​perform the adjustment of the advice order.

[0052] The behavioral analysis unit can improve the accuracy of its analysis by considering the interrelationships of growth records during behavioral analysis. For example, the behavioral analysis unit can analyze the interrelationships of growth records to improve the accuracy of behavioral analysis. The behavioral analysis unit can also adjust the items of behavioral analysis by considering the interrelationships of growth records. Furthermore, the behavioral analysis unit can evaluate the results of behavioral analysis based on the interrelationships of growth records. In this way, the accuracy of behavioral analysis is improved by considering the interrelationships of growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the interrelationship data of growth records into AI and have AI perform the improvement of behavioral analysis accuracy.

[0053] The behavioral analysis unit can perform behavioral analysis while considering the attribute information of the person submitting the growth record. For example, the behavioral analysis unit can perform behavioral analysis while considering the submitter's age and gender. It can also perform behavioral analysis while considering the submitter's occupation and living environment. Furthermore, the behavioral analysis unit can perform behavioral analysis while considering the submitter's health status and lifestyle. This improves the accuracy of the behavioral analysis by considering the submitter's attribute information. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the behavioral analysis.

[0054] The behavioral analysis unit can perform behavioral analysis while considering the geographical distribution of growth records. For example, the behavioral analysis unit can analyze the geographical distribution of growth records and perform behavioral analysis. The behavioral analysis unit can also adjust the items of the behavioral analysis based on the geographical distribution. Furthermore, the behavioral analysis unit can evaluate the results of the behavioral analysis while considering the geographical distribution. This improves the accuracy of the behavioral analysis by considering the geographical distribution of growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input geographical distribution data of growth records into AI and have the AI ​​perform the behavioral analysis.

[0055] The behavioral analysis unit can improve the accuracy of its analysis by referring to relevant literature on growth records during the behavioral analysis. For example, the behavioral analysis unit can adjust the items of the behavioral analysis based on the relevant literature. The behavioral analysis unit can also improve the accuracy of the behavioral analysis by referring to relevant literature. Furthermore, the behavioral analysis unit can analyze the relevant literature and evaluate the results of the behavioral analysis. As a result, the accuracy of the behavioral analysis is improved by referring to relevant literature on growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input relevant literature data into AI and have the AI ​​perform the behavioral analysis.

[0056] The management department can optimize its management algorithm by referring to past schedule data when managing schedules. For example, the management department can optimize the current schedule based on past schedule data. The management department can also adjust the items of schedule management by referring to past schedule data. Furthermore, the management department can analyze past schedule data to improve the accuracy of schedule management. Thus, the accuracy of schedule management is improved by referring to past schedule data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past schedule data into AI and have the AI ​​perform the optimization of the management algorithm.

[0057] The management department can weight management data based on the submission timing of growth records when managing schedules. For example, the management department can manage schedules based on recently submitted growth records. It can also manage schedules based on growth records submitted during a specific period. Furthermore, the management department can manage schedules based on growth records of high importance, depending on the submission timing. This allows for schedule management based on the latest records by weighting management data based on the submission timing of growth records. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input growth record submission timing data into AI and have the AI ​​perform the weighting of management data.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] Childcare support agents can provide region-specific childcare information, taking into account the parents' geographical location. For example, if parents live in a particular area, they can provide information on local childcare events and services. If parents are traveling, they can also provide information on childcare services and emergency contacts available at their destination. Furthermore, if parents are planning to move, they can provide childcare information for their new area in advance. This ensures that parents can access appropriate childcare information no matter where they are located.

[0060] Childcare support agents can analyze parents' social media activity and provide relevant childcare information. For example, if a parent posts about childcare on social media, they can provide relevant advice and information based on that content. They can also aggregate and provide information from childcare-related accounts that parents follow. Furthermore, they can provide relevant childcare information based on topics that parents have shown interest in on social media. This allows parents to effectively utilize the information they obtain through social media.

[0061] Childcare support agents can provide customized parenting advice based on parents' lifestyles and areas of interest. For example, if parents are busy with work, they can provide advice that can be implemented in a short amount of time. If parents are interested in health, they can prioritize health-related parenting advice. Furthermore, if parents are interested in education, they can provide education-related advice. This allows parents to effectively support their children's development by providing parenting advice tailored to their lifestyles and areas of interest.

[0062] Childcare support agents can analyze past growth records and provide optimal parenting advice. For example, they can analyze a child's growth patterns from past records and provide predictions for future growth. They can also provide parenting advice needed at specific stages based on past growth records. Furthermore, they can provide advice that is easy for parents to implement, using past growth records as a reference. In this way, by utilizing past growth records, parents can effectively support their child's development.

[0063] The childcare support agent can apply different analysis algorithms depending on the category of the growth record. For example, a health data analysis algorithm can be applied to health-related growth records. Similarly, an education data analysis algorithm can be applied to education-related growth records. Furthermore, a behavior data analysis algorithm can be applied to behavior-related growth records. By applying an analysis algorithm appropriate to the category of the growth record, the accuracy of the analysis is improved, enabling parents to effectively support their child's development.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk allows parents to input their child's daily growth records. Parents can input growth records using smartphones or personal computers, and the system supports voice input and handwriting input. For example, voice recognition technology can be used to convert voice data into text data, or handwritten growth records can be scanned and converted into digital data. Step 2: The analysis unit analyzes the data entered by the reception unit. Using AI technology, it analyzes the growth record data and evaluates the child's health status. For example, it analyzes changes in the child's height and weight to evaluate the progress of their growth. Step 3: The checking unit checks the health status based on the data analyzed by the analysis unit. For example, it evaluates health data such as a child's body temperature and heart rate to check for any abnormalities. Based on the results of the health status check, necessary countermeasures can be proposed. Step 4: The advice section provides educational advice based on the results obtained by the checking section. For example, it provides advice on the child's learning plan and behavioral improvement, and selects appropriate language to communicate it clearly to the parents. Step 5: The Behavioral Analysis Unit analyzes the behavior based on the advice provided by the Advice Unit. For example, it analyzes the child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, it can make specific suggestions for improving the child's behavior. Step 6: The management department manages schedules based on the results obtained by the behavioral analysis department. For example, it manages children's learning schedules and daily routines, supporting parents in managing their time efficiently. The results of the schedule management are notified to parents, and necessary adjustments can be made.

[0066] (Example of form 2) The childcare support agent according to an embodiment of the present invention is a system designed to enable parents to effectively support their children's growth. This childcare support agent is a system that centrally provides daily growth records, health checks, growth advice, behavioral analysis, and schedule management. Parents input daily growth records of their children, and the AI ​​analyzes this data to check the child's health status. Furthermore, the AI ​​provides educational advice tailored to the child's development and analyzes the child's behavior. Finally, it supports parents in efficiently managing their time through a schedule management function. This mechanism allows parents to overcome information deficiencies, skill deficiencies, and time deficiencies in childcare, and increase the quality of time they spend with their children. For example, the childcare support agent is equipped with precise data analysis using AI technology, a user-friendly interface, and advanced security features to protect privacy. This allows parents to use the childcare support agent with peace of mind. Furthermore, it uses AI-powered big data analysis and machine learning to analyze children's behavioral patterns and provide individualized childcare advice. In this way, parents can effectively support their children's growth and reduce the stress of childcare.

[0067] The childcare support agent according to this embodiment comprises a reception unit, an analysis unit, a checking unit, an advice unit, a behavioral analysis unit, and a management unit. The reception unit allows parents to input their child's daily growth records. The reception unit allows parents to input growth records using, for example, a smartphone or personal computer. The reception unit also supports voice input and handwriting input. For example, if a parent inputs growth records by voice, the reception unit can convert the voice data into text data using voice recognition technology. Furthermore, the reception unit can scan handwritten growth records and convert them into digital data. The analysis unit analyzes the data input by the reception unit. The analysis unit analyzes the growth record data using, for example, AI technology to evaluate the child's health status. Based on the results of the data analysis, the analysis unit can extract important information regarding the child's growth. For example, the analysis unit analyzes changes in the child's height and weight to evaluate the progress of growth. The checking unit checks the health status based on the data analyzed by the analysis unit. The checking unit evaluates health data such as the child's body temperature and heart rate to check for any abnormalities. The Checking Unit can propose necessary measures based on the results of health checks. For example, if the Checking Unit finds that a child has a high temperature, it will advise parents to see a doctor. The Advice Unit provides educational advice based on the results obtained by the Checking Unit. For example, the Advice Unit provides advice on the child's learning plan and behavioral improvement. The Advice Unit can select appropriate expressions to make the educational advice easy for parents to understand. For example, if a parent is feeling stressed, the Advice Unit will provide concise and easy-to-understand advice. The Behavioral Analysis Unit analyzes behavior based on the advice provided by the Advice Unit. For example, the Behavioral Analysis Unit analyzes the child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, the Behavioral Analysis Unit can make specific suggestions to improve the child's behavior. For example, if a child lacks concentration, the Behavioral Analysis Unit will suggest ways to improve concentration. The Management Unit manages schedules based on the results obtained by the Behavioral Analysis Unit.The management unit, for example, manages children's learning schedules and daily routines, supporting parents in efficiently managing their time. The management unit notifies parents of the results of schedule management and can make necessary adjustments. For example, if the management unit needs to change a child's learning schedule, it notifies parents and adjusts the schedule. In this way, the childcare support agent according to the embodiment can enable parents to effectively support their child's development and reduce the stress of childcare.

[0068] The reception system allows parents to input their child's daily growth records. Parents can input growth records using, for example, a smartphone or personal computer. The system also supports voice and handwriting input. For instance, when parents input growth records by voice, the system uses voice recognition technology to convert the voice data into text. Furthermore, the system can scan handwritten growth records and convert them into digital data. Specifically, the voice recognition technology includes a function that analyzes the parent's voice using natural language processing technology and accurately converts it into text. This allows parents to easily input growth records without using their hands. For handwriting input, the system uses optical character recognition (OCR) technology to convert handwritten characters into digital data. This allows growth records written on paper to be easily digitized and imported into the system. Additionally, the system has a function to automatically classify the input data and organize it into appropriate categories. For example, height and weight data are classified into the growth record category, while diet and sleep data are classified into the lifestyle category. This allows parents to easily manage the input data and quickly search for necessary information. The reception area also features security functions to safely store data entered by parents and protect their privacy. For example, data is encrypted, and a firewall is in place to prevent unauthorized access from external sources. This allows parents to confidently enter and manage their child's growth records.

[0069] The analysis unit analyzes the data entered by the reception unit. For example, the analysis unit uses AI technology to analyze growth record data and evaluate the child's health status. Based on the results of the data analysis, the analysis unit can extract important information about the child's growth. For example, the analysis unit analyzes changes in the child's height and weight to evaluate the progress of growth. Specifically, machine learning algorithms are used in the AI ​​technology, which can predict future growth patterns based on past data. This allows parents to check whether their child's growth is normal and take necessary measures. The analysis unit also has the function to detect anomalies and monitor changes in trends in real time. For example, if a child's weight increases rapidly, the analysis unit will detect the anomaly and notify the parents. Furthermore, the analysis unit can integrate multiple data sources to perform a comprehensive health assessment. For example, by including data on diet and exercise in the analysis, in addition to height and weight, a more accurate health assessment becomes possible. The analysis unit also has a data visualization function, allowing parents to intuitively understand their child's growth status through graphs and charts. This allows parents to grasp the progress of their child's growth at a glance and take appropriate action.

[0070] The checking unit checks the child's health status based on data analyzed by the analysis unit. For example, the checking unit evaluates health data such as the child's body temperature and heart rate to check for any abnormalities. Based on the results of the health check, the checking unit can suggest necessary measures. For example, if the child's body temperature is high, the checking unit will advise the parents to see a doctor. Specifically, the checking unit uses an algorithm that detects abnormalities by comparing the data provided by the analysis unit with standard values ​​for health status. This allows parents to constantly monitor their child's health status and detect abnormalities early. The checking unit also has a function to automatically issue an alert when an abnormality is detected. For example, if the child's body temperature exceeds a certain standard, a notification is sent to the parent's smartphone. Furthermore, the checking unit also has a function to suggest specific measures based on the results of the health check. For example, if the child's body temperature is high, it will advise appropriate hydration and rest. It will also recommend seeing a doctor if necessary. This allows parents to properly manage their child's health status and respond quickly.

[0071] The Advice Unit provides educational advice based on the results obtained by the Check Unit. For example, the Advice Unit provides advice on children's learning plans and behavioral improvement. The Advice Unit can select appropriate expressions to clearly communicate the educational advice to parents. For example, if parents are feeling stressed, the Advice Unit provides concise and easy-to-understand advice. Specifically, the Advice Unit analyzes the child's learning situation and behavioral patterns to provide individually optimized advice. For example, if a child has difficulty with a particular subject, it suggests effective learning methods for that subject. Regarding behavioral improvement, it provides concrete action plans and supports parents in implementing them. Furthermore, the Advice Unit has a function to continuously improve its advice based on parental feedback. For example, by inputting opinions and impressions on the advice provided, the Advice Unit can review its content and provide more effective advice. This allows the Advice Unit to provide flexible support tailored to the needs of both parents and children, effectively supporting the child's growth.

[0072] The Behavioral Analysis Department analyzes behavior based on advice provided by the Advice Department. For example, the Behavioral Analysis Department analyzes a child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, the Behavioral Analysis Department can make specific suggestions for improving the child's behavior. For example, if a child lacks concentration, the Behavioral Analysis Department will suggest ways to improve concentration. Specifically, the Behavioral Analysis Department collects data on the child's daily behavior and analyzes behavioral patterns using machine learning algorithms. This allows for an accurate understanding of the child's behavioral tendencies and problems. The Behavioral Analysis Department also has the function of tracking changes in behavior by comparing them with past data and evaluating the progress of improvement. For example, if a child tries a new learning method, the department evaluates its effectiveness and modifies the advice as needed. Furthermore, the Behavioral Analysis Department provides parents with specific suggestions for behavioral improvement. For example, it may recommend that children take breaks at regular intervals or create a suitable learning environment to improve their concentration. In this way, the Behavioral Analysis Department can effectively improve the child's behavior and enhance their learning and quality of life.

[0073] The Management Department manages schedules based on the results obtained by the Behavioral Analysis Department. For example, the Management Department manages children's learning schedules and daily routines, supporting parents in efficiently managing their time. The Management Department notifies parents of the results of schedule management and can make necessary adjustments. For example, if the Management Department needs to change a child's learning schedule, it notifies parents and adjusts the schedule. Specifically, the Management Department uses an algorithm that automatically generates an optimal schedule based on data on the child's learning and daily life. This allows parents to manage their child's schedule without any effort. The Management Department also has a function to monitor schedule progress in real time and make adjustments as needed. For example, if a child finishes learning earlier than planned, it suggests starting the next learning content earlier. Furthermore, the Management Department has a function to notify parents of the results of schedule management in an easy-to-understand manner. For example, it allows parents to check their child's learning progress and daily routine in real time through a smartphone app. This allows parents to always be aware of their child's schedule and take appropriate action. The Management Department also has a function to continuously improve the schedule management algorithm based on parental feedback. This allows the management department to provide flexible scheduling tailored to the needs of parents and children, effectively supporting the child's development.

[0074] The reception desk can estimate the parent's emotions and adjust the timing of inputting growth records based on those emotions. For example, if the parent is feeling stressed, the reception desk can delay the input timing to allow time for relaxation. It can also adjust the timing to allow for quicker input if the parent is busy. Furthermore, if the parent is relaxed, the reception desk can suggest a time to prompt more detailed input. This reduces the burden on parents by adjusting the timing of growth record input according to their emotions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input parent emotion data into AI and have the AI ​​perform emotion-based input timing adjustments.

[0075] The reception desk can analyze past growth records and suggest the optimal input method. For example, it can suggest a preferred input method (voice, text, etc.) based on past input history. It can also analyze past input frequency and suggest the optimal input interval. Furthermore, it can suggest a format that is easy for parents to use, based on past input content. In this way, by analyzing past growth records, it can suggest the optimal input method for parents. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past growth record data into AI and have the AI ​​suggest the optimal input method.

[0076] The reception unit can filter the input of growth records based on the parents' current lifestyle and areas of interest. For example, if a parent is busy with work, the reception unit can prioritize displaying concise input fields. It can also prioritize displaying health-related input fields if the parent is interested in health. Furthermore, if the parent is interested in education, it can prioritize displaying education-related input fields. This facilitates the input of growth records tailored to the parents' lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input parent lifestyle data and areas of interest data into the AI ​​and have the AI ​​perform the filtering.

[0077] The reception unit can estimate the parent's emotions and determine the priority of growth records to be entered based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit may prompt them to prioritize entering important growth records. It can also prompt the parent to enter detailed growth records if they are relaxed. Furthermore, if the parent is busy, it may prompt them to prioritize entering concise growth records. This ensures that important records are prioritized by determining the input priority of growth records according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input parent emotion data into a generative AI and have the generative AI determine the input priority based on emotions.

[0078] The reception unit can prioritize inputting highly relevant records when entering growth records, taking into account the parents' geographical location information. For example, if the parents are in a specific region, the reception unit will prioritize inputting growth records related to that region. Furthermore, if the parents are traveling, the reception unit can prioritize inputting growth records related to their travel destination. Additionally, if the parents are at home, the reception unit can prioritize inputting growth records recorded at home. This allows for the input of highly relevant growth records based on the parents' geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the parents' geographical location data into an AI and have the AI ​​prioritize inputting highly relevant records.

[0079] The reception desk can analyze parents' social media activity and input relevant records when entering growth records. For example, the reception desk can input relevant growth records based on what parents have shared on social media. It can also input relevant growth records based on topics that parents have shown interest in on social media. Furthermore, the reception desk can input relevant growth records based on information about accounts that parents follow on social media. This allows for the input of relevant growth records based on parents' social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input parents' social media data into AI and have the AI ​​input relevant records.

[0080] The analysis unit can estimate the parent's emotions and adjust the data analysis method based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit can provide a concise data analysis result. If the parent is relaxed, the analysis unit can also provide a detailed data analysis result. Furthermore, if the parent is busy, the analysis unit can prioritize providing important data analysis results. In this way, by adjusting the data analysis method according to the parent's emotions, it is possible to provide analysis results that are appropriate for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the parent's emotion data into the generative AI and have the generative AI perform adjustments to the data analysis method based on emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the growth records during data analysis. For example, the analysis unit can perform detailed data analysis on important growth records. It can also perform simplified data analysis on general growth records. Furthermore, the analysis unit can perform data analysis with a level of detail appropriate to the category for growth records belonging to a specific category. This allows for detailed analysis of important records by adjusting the level of detail of the analysis according to the importance of the growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth record importance data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the growth record during data analysis. For example, the analysis unit can apply a health data analysis algorithm to health-related growth records. It can also apply an education data analysis algorithm to education-related growth records. Furthermore, it can apply a behavior data analysis algorithm to behavior-related growth records. By applying an analysis algorithm appropriate to the category of the growth record, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category data of the growth record into the AI ​​and have the AI ​​execute the application of the analysis algorithm.

[0083] The analysis unit can estimate the parent's emotions and determine the priority of analysis based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit may prioritize important data analysis. It may also prioritize detailed data analysis if the parent is relaxed. Furthermore, if the parent is busy, the analysis unit may prioritize concise data analysis. This allows for prioritizing important analysis by determining the priority of analysis according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input parent emotion data into a generative AI and have the generative AI determine the analysis priority based on emotions.

[0084] The analysis unit can determine the priority of analysis based on the submission date of growth records during data analysis. For example, the analysis unit may prioritize the analysis of recently submitted growth records. It can also prioritize the analysis of growth records submitted during a specific period. Furthermore, the analysis unit may prioritize the analysis of growth records of high importance based on their submission date. This allows for the prioritization of the analysis of the most recent records by determining the priority of analysis based on the submission date of growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input growth record submission date data into AI and have the AI ​​determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of growth records during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant growth records. It can also postpone the analysis of less relevant growth records. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the growth records. This allows for the prioritization of highly relevant records by adjusting the order of analysis based on the relevance of the growth records. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of growth records into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0086] The checking unit can estimate the parent's emotions and adjust the health check method based on the estimated emotions. For example, if the parent is stressed, the checking unit can perform a brief health check. If the parent is relaxed, the checking unit can also perform a more detailed health check. Furthermore, if the parent is busy, the checking unit can prioritize important health checks. This allows the system to provide a health check that is appropriate for the parent by adjusting the health check method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not using AI. For example, the checking unit can input parent emotion data into the generative AI and have the generative AI perform the adjustment of the health check method based on emotions.

[0087] The checking unit can improve the accuracy of health checks by referring to past health data. For example, the checking unit can evaluate the current health status based on past health data. The checking unit can also adjust the health check items by referring to past health data. Furthermore, the checking unit can analyze past health data to improve the accuracy of health checks. As a result, the accuracy of health checks is improved by referring to past health data. Some or all of the above processes in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input past health data into AI and have the AI ​​perform the task of improving the accuracy of health checks.

[0088] The checking unit can apply different checking methods to each category of growth records during health checks. For example, the checking unit can apply a health checking method to health-related growth records. It can also apply an education-related checking method to education-related growth records. Furthermore, it can apply a behavior-related checking method to behavior-related growth records. By applying different checking methods to each category of growth records, the accuracy of the checks is improved. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the category data of the growth records into the AI ​​and have the AI ​​perform the application of the checking methods.

[0089] The checking unit can estimate the parent's emotions and determine the priority of health checks based on the estimated emotions. For example, if the parent is stressed, the checking unit will prioritize important health checks. It can also prioritize detailed health checks if the parent is relaxed. Furthermore, if the parent is busy, it can prioritize brief health checks. This allows important checks to be prioritized by determining the priority of health checks according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, or not. For example, the checking unit can input parent emotion data into a generative AI and have the generative AI determine the priority of health checks based on emotions.

[0090] The checking unit can determine the priority of checks based on the submission date of growth records during health checks. For example, the checking unit can prioritize checking recently submitted growth records. It can also prioritize checking growth records submitted within a specific period. Furthermore, the checking unit can prioritize checking growth records of high importance depending on the submission date. This allows for prioritizing the checking of the most recent records by determining the priority of checks based on the submission date of the growth records. Some or all of the above processing in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input growth record submission date data into AI and have the AI ​​perform the determination of check priorities.

[0091] The checking unit can perform health checks by referring to relevant market data from growth records. For example, the checking unit can adjust the health check items based on the relevant market data. The checking unit can also improve the accuracy of the health check by referring to the relevant market data. Furthermore, the checking unit can analyze the relevant market data and evaluate the results of the health check. This improves the accuracy of the health check by referring to the relevant market data from growth records. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input relevant market data into AI and have the AI ​​perform the adjustments to the health check.

[0092] The advice unit can estimate the parent's emotions and adjust the way educational advice is expressed based on those estimated emotions. For example, if the parent is stressed, the advice unit will provide concise and easy-to-understand advice. If the parent is relaxed, the advice unit can also provide detailed advice. Furthermore, if the parent is busy, the advice unit can prioritize providing important advice. In this way, by adjusting the way educational advice is expressed according to the parent's emotions, it is possible to provide advice that is appropriate for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input parent emotion data into the generative AI and have the generative AI adjust the way educational advice is expressed based on those emotions.

[0093] The advice unit can adjust the level of detail of educational advice based on the importance of the growth record. For example, it can provide detailed advice for important growth records. It can also provide concise advice for general growth records. Furthermore, it can provide advice with a level of detail appropriate to the category for growth records belonging to a specific category. This allows for detailed advice to be provided for important records by adjusting the level of detail according to the importance of the growth record. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input growth record importance data into AI and have the AI ​​adjust the level of detail of the advice.

[0094] The advice unit can apply different advice algorithms depending on the category of the growth record when providing educational advice. For example, the advice unit can apply a health advice algorithm to health-related growth records. It can also apply an education advice algorithm to education-related growth records. Furthermore, it can apply a behavior advice algorithm to behavior-related growth records. This improves the accuracy of the advice by applying an advice algorithm appropriate to the category of the growth record. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input growth record category data into AI and have the AI ​​perform the application of the advice algorithm.

[0095] The advice unit can estimate the parent's emotions and adjust the length of educational advice based on the estimated emotions. For example, if the parent is stressed, the advice unit can provide short, concise advice. If the parent is relaxed, it can provide detailed advice. Furthermore, if the parent is busy, it can provide brief, important advice. This allows the advice unit to provide advice that is appropriate for the parent by adjusting the length of educational advice according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input parent emotion data into the generative AI and have the generative AI adjust the length of the educational advice based on the emotions.

[0096] The advice department can prioritize educational advice based on the timing of growth record submissions. For example, it may provide advice based on recently submitted growth records. It may also provide advice based on growth records submitted during a specific period. Furthermore, it may provide advice based on the importance of growth records depending on the submission timing. This allows for the provision of advice based on the most recent records by prioritizing advice based on the submission timing of growth records. Some or all of the above processes in the advice department may be performed using AI, for example, or not. For example, the advice department can input growth record submission timing data into an AI and have the AI ​​determine the priority of advice.

[0097] The advice unit can adjust the order of advice based on the relevance of growth records when providing educational advice. For example, the advice unit provides advice based on highly relevant growth records. The advice unit can also postpone less relevant growth records. Furthermore, the advice unit can dynamically adjust the order of advice according to the relevance of the growth records. This allows the advice unit to provide advice based on highly relevant records by adjusting the order of advice based on the relevance of the growth records. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the relevance data of the growth records into the AI ​​and have the AI ​​perform the adjustment of the advice order.

[0098] The behavioral analysis unit can estimate the parent's emotions and adjust the behavioral analysis criteria based on the estimated parent's emotions. For example, if the parent is stressed, the behavioral analysis unit can provide a concise behavioral analysis result. It can also provide a detailed behavioral analysis result if the parent is relaxed. Furthermore, if the parent is busy, the behavioral analysis unit can prioritize providing important behavioral analysis results. This allows for the provision of behavioral analysis results tailored to the parent by adjusting the criteria according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the behavioral analysis unit may be performed using AI, or not. For example, the behavioral analysis unit can input parent emotion data into a generative AI and have the generative AI adjust the behavioral analysis criteria based on the emotions.

[0099] The behavioral analysis unit can improve the accuracy of its analysis by considering the interrelationships of growth records during behavioral analysis. For example, the behavioral analysis unit can analyze the interrelationships of growth records to improve the accuracy of behavioral analysis. The behavioral analysis unit can also adjust the items of behavioral analysis by considering the interrelationships of growth records. Furthermore, the behavioral analysis unit can evaluate the results of behavioral analysis based on the interrelationships of growth records. In this way, the accuracy of behavioral analysis is improved by considering the interrelationships of growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the interrelationship data of growth records into AI and have AI perform the improvement of behavioral analysis accuracy.

[0100] The behavioral analysis unit can perform behavioral analysis while considering the attribute information of the person submitting the growth record. For example, the behavioral analysis unit can perform behavioral analysis while considering the submitter's age and gender. It can also perform behavioral analysis while considering the submitter's occupation and living environment. Furthermore, the behavioral analysis unit can perform behavioral analysis while considering the submitter's health status and lifestyle. This improves the accuracy of the behavioral analysis by considering the submitter's attribute information. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input the submitter's attribute information data into AI and have the AI ​​perform the behavioral analysis.

[0101] The behavioral analysis unit can estimate the parent's emotions and adjust the order in which the behavioral analysis results are displayed based on the estimated parent's emotions. For example, if the parent is stressed, the behavioral analysis unit can prioritize displaying important behavioral analysis results. It can also display detailed behavioral analysis results if the parent is relaxed. Furthermore, if the parent is busy, it can prioritize displaying concise behavioral analysis results. This allows for the provision of results tailored to the parent by adjusting the order in which the behavioral analysis results are displayed according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the behavioral analysis unit may be performed using AI, or not. For example, the behavioral analysis unit can input parent emotion data into a generative AI and have the generative AI adjust the display order of the emotion-based behavioral analysis results.

[0102] The behavioral analysis unit can perform behavioral analysis while considering the geographical distribution of growth records. For example, the behavioral analysis unit can analyze the geographical distribution of growth records and perform behavioral analysis. The behavioral analysis unit can also adjust the items of the behavioral analysis based on the geographical distribution. Furthermore, the behavioral analysis unit can evaluate the results of the behavioral analysis while considering the geographical distribution. This improves the accuracy of the behavioral analysis by considering the geographical distribution of growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without AI. For example, the behavioral analysis unit can input geographical distribution data of growth records into AI and have the AI ​​perform the behavioral analysis.

[0103] The behavioral analysis unit can improve the accuracy of its analysis by referring to relevant literature on growth records during the behavioral analysis. For example, the behavioral analysis unit can adjust the items of the behavioral analysis based on the relevant literature. The behavioral analysis unit can also improve the accuracy of the behavioral analysis by referring to relevant literature. Furthermore, the behavioral analysis unit can analyze the relevant literature and evaluate the results of the behavioral analysis. As a result, the accuracy of the behavioral analysis is improved by referring to relevant literature on growth records. Some or all of the above processing in the behavioral analysis unit may be performed using AI, for example, or without using AI. For example, the behavioral analysis unit can input relevant literature data into AI and have the AI ​​perform the behavioral analysis.

[0104] The management unit can estimate the parent's emotions and adjust the schedule management method based on the estimated emotions. For example, if the parent is stressed, the management unit will perform a simplified schedule management. If the parent is relaxed, the management unit can also perform a detailed schedule management. Furthermore, if the parent is busy, the management unit can prioritize important schedule management. In this way, by adjusting the schedule management method according to the parent's emotions, it is possible to provide schedule management that is suitable for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input parent emotion data into a generative AI and have the generative AI perform adjustments to the schedule management method based on emotions.

[0105] The management department can optimize its management algorithm by referring to past schedule data when managing schedules. For example, the management department can optimize the current schedule based on past schedule data. The management department can also adjust the items of schedule management by referring to past schedule data. Furthermore, the management department can analyze past schedule data to improve the accuracy of schedule management. Thus, the accuracy of schedule management is improved by referring to past schedule data. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past schedule data into AI and have the AI ​​perform the optimization of the management algorithm.

[0106] The management unit can estimate the parent's emotions and determine schedule management priorities based on the estimated emotions. For example, if the parent is stressed, the management unit will prioritize important schedules. It can also manage detailed schedules if the parent is relaxed. Furthermore, if the parent is busy, the management unit can prioritize concise schedules. This allows for prioritizing important schedules by determining schedule management priorities according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not. For example, the management unit can input parent emotion data into a generative AI and have the generative AI determine schedule management priorities based on emotions.

[0107] The management department can weight management data based on the submission timing of growth records when managing schedules. For example, the management department can manage schedules based on recently submitted growth records. It can also manage schedules based on growth records submitted during a specific period. Furthermore, the management department can manage schedules based on growth records of high importance, depending on the submission timing. This allows for schedule management based on the latest records by weighting management data based on the submission timing of growth records. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input growth record submission timing data into AI and have the AI ​​perform the weighting of management data.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] Childcare support agents can estimate parents' emotions and adjust the content of childcare advice based on those estimates. For example, if parents are feeling stressed, the advice team can provide concise and easy-to-follow advice. If parents are relaxed, the advice team can provide more detailed advice. Furthermore, if parents are busy, the advice team can prioritize providing important advice. By providing appropriate advice tailored to parents' emotions, this reduces the stress of childcare and enables parents to effectively support their children's development.

[0110] Childcare support agents can estimate parents' emotions and adjust how they input growth records based on those estimates. For example, if a parent is stressed, the reception desk can offer voice input or simple options. If the parent is relaxed, it can encourage more detailed text input. Furthermore, if the parent is busy, the reception desk can suggest methods that allow for quick input. By providing input methods tailored to the parent's emotions, the process of inputting growth records becomes smoother.

[0111] Childcare support agents can estimate a parent's emotions and adjust scheduling based on those estimates. For example, if a parent is stressed, the agency can suggest a simplified schedule. Conversely, if the parent is relaxed, they can provide a more detailed schedule. Furthermore, if the parent is busy, the agency can prioritize important appointments. This allows parents to manage their time more efficiently by providing scheduling tailored to their emotions.

[0112] Childcare support agents can estimate parents' emotions and adjust the health check method based on those estimates. For example, if a parent is stressed, the check can be brief. If the parent is relaxed, a more detailed check can be performed. Furthermore, if the parent is busy, the check can prioritize important health checks. By providing health checks tailored to the parent's emotions, this allows parents to manage their child's health with peace of mind.

[0113] The childcare support agent can estimate the parent's emotions and adjust the order in which behavioral analysis results are displayed based on those estimated emotions. For example, if the parent is stressed, the behavioral analysis unit can prioritize displaying important results. If the parent is relaxed, it can also display detailed results. Furthermore, if the parent is busy, the behavioral analysis unit can prioritize displaying concise results. By providing behavioral analysis results tailored to the parent's emotions, this allows parents to effectively understand and improve their child's behavior.

[0114] Childcare support agents can provide region-specific childcare information, taking into account the parents' geographical location. For example, if parents live in a particular area, they can provide information on local childcare events and services. If parents are traveling, they can also provide information on childcare services and emergency contacts available at their destination. Furthermore, if parents are planning to move, they can provide childcare information for their new area in advance. This ensures that parents can access appropriate childcare information no matter where they are located.

[0115] Childcare support agents can analyze parents' social media activity and provide relevant childcare information. For example, if a parent posts about childcare on social media, they can provide relevant advice and information based on that content. They can also aggregate and provide information from childcare-related accounts that parents follow. Furthermore, they can provide relevant childcare information based on topics that parents have shown interest in on social media. This allows parents to effectively utilize the information they obtain through social media.

[0116] Childcare support agents can provide customized parenting advice based on parents' lifestyles and areas of interest. For example, if parents are busy with work, they can provide advice that can be implemented in a short amount of time. If parents are interested in health, they can prioritize health-related parenting advice. Furthermore, if parents are interested in education, they can provide education-related advice. This allows parents to effectively support their children's development by providing parenting advice tailored to their lifestyles and areas of interest.

[0117] Childcare support agents can analyze past growth records and provide optimal parenting advice. For example, they can analyze a child's growth patterns from past records and provide predictions for future growth. They can also provide parenting advice needed at specific stages based on past growth records. Furthermore, they can provide advice that is easy for parents to implement, using past growth records as a reference. In this way, by utilizing past growth records, parents can effectively support their child's development.

[0118] The childcare support agent can apply different analysis algorithms depending on the category of the growth record. For example, a health data analysis algorithm can be applied to health-related growth records. Similarly, an education data analysis algorithm can be applied to education-related growth records. Furthermore, a behavior data analysis algorithm can be applied to behavior-related growth records. By applying an analysis algorithm appropriate to the category of the growth record, the accuracy of the analysis is improved, enabling parents to effectively support their child's development.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk allows parents to input their child's daily growth records. Parents can input growth records using smartphones or personal computers, and the system supports voice input and handwriting input. For example, voice recognition technology can be used to convert voice data into text data, or handwritten growth records can be scanned and converted into digital data. Step 2: The analysis unit analyzes the data entered by the reception unit. Using AI technology, it analyzes the growth record data and evaluates the child's health status. For example, it analyzes changes in the child's height and weight to evaluate the progress of their growth. Step 3: The checking unit checks the health status based on the data analyzed by the analysis unit. For example, it evaluates health data such as a child's body temperature and heart rate to check for any abnormalities. Based on the results of the health status check, necessary countermeasures can be proposed. Step 4: The advice section provides educational advice based on the results obtained by the checking section. For example, it provides advice on the child's learning plan and behavioral improvement, and selects appropriate language to communicate it clearly to the parents. Step 5: The Behavioral Analysis Unit analyzes the behavior based on the advice provided by the Advice Unit. For example, it analyzes the child's behavioral patterns and identifies areas for improvement. Based on the results of the behavioral analysis, it can make specific suggestions for improving the child's behavior. Step 6: The management department manages schedules based on the results obtained by the behavioral analysis department. For example, it manages children's learning schedules and daily routines, supporting parents in managing their time efficiently. The results of the schedule management are notified to parents, and necessary adjustments can be made.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, checking unit, advice unit, behavioral analysis unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing parents to input growth records using a smartphone or personal computer. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the growth record data using AI technology. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and checks the health status. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides educational advice. The behavioral analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the child's behavioral patterns. The management unit is implemented by, for example, the control unit 46A of the smart device 14, and manages the schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, checking unit, advice unit, behavioral analysis unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing parents to input growth records using the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the growth record data using AI technology. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and checks the health status. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides educational advice. The behavioral analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the child's behavioral patterns. The management unit is implemented, for example, by the control unit 46A of the smart glasses 214, and manages the schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, checking unit, advice unit, behavioral analysis unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing parents to input growth records using the headset terminal 314. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the growth record data using AI technology. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks the health status. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides educational advice. The behavioral analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the child's behavioral patterns. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314, which manages the schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, checking unit, advice unit, behavioral analysis unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing parents to input growth records using the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the growth record data using AI technology. The checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which checks the health status. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides educational advice. The behavioral analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the child's behavioral patterns. The management unit is implemented by, for example, the control unit 46A of the robot 414, which manages the schedule. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The reception area for entering growth records, An analysis unit analyzes the data input by the reception unit, A checking unit checks the health status based on the data analyzed by the aforementioned analysis unit, An advice unit provides educational advice based on the results obtained by the aforementioned checking unit, A behavioral analysis unit analyzes behavior based on the advice provided by the aforementioned advice unit, The system includes a management unit that performs schedule management based on the results obtained by the behavioral analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the parents' emotions and adjusts the timing of inputting growth records based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is We analyze past growth records and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering growth records, filtering is performed based on the parents' current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates parental emotions and determines the priority of growth records to input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering growth records, the system prioritizes inputting records that are highly relevant, taking into account the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering growth records, analyze the parents' social media activity and enter relevant records. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the parents' emotions and adjust the data analysis method based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During data analysis, adjust the level of detail based on the importance of the growth record. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During data analysis, different analysis algorithms are applied depending on the category of the growth record. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the parents' emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During data analysis, the priority of analyses is determined based on when growth records were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During data analysis, the order of analysis is adjusted based on the relevance of growth records. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned checking unit is The system estimates the parents' emotions and adjusts the health check method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned checking unit is During health checks, past health data is referenced to improve the accuracy of the checks. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned checking unit is During health checks, different checking methods are applied to each category of growth record. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned checking unit is The system estimates the parents' emotions and prioritizes health checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned checking unit is During health checks, the priority of checks will be determined based on when the growth record was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned checking unit is During health checks, the relevant market data for growth records is referenced. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, The system estimates the parents' emotions and adjusts the way educational advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing educational advice, adjust the level of detail based on the importance of the growth record. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing educational advice, different advice algorithms are applied depending on the category of the growth record. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the parents' emotions and adjusts the length of educational advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing educational advice, we prioritize the advice based on when the growth record is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing educational advice, we adjust the order of advice based on the relevance of the growth record. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned behavioral analysis unit, We estimate the parents' emotions and adjust the criteria for behavioral analysis based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned behavioral analysis unit, When performing behavioral analysis, consider the interrelationships of growth records to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned behavioral analysis unit, During behavioral analysis, the attribute information of the person who submitted the growth record will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned behavioral analysis unit, The system estimates the parent's emotions and adjusts the order in which the behavioral analysis results are displayed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned behavioral analysis unit, When performing behavioral analysis, the geographical distribution of growth records should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned behavioral analysis unit, When performing behavioral analysis, we improve the accuracy of the analysis by referring to relevant literature on growth records. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, It estimates the parents' emotions and adjusts the schedule management method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing schedules, the management algorithm is optimized by referring to past schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, It estimates the parents' emotions and determines schedule management priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, When managing schedules, weight management data based on when growth records are submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for entering growth records, An analysis unit analyzes the data input by the reception unit, A checking unit checks the health status based on the data analyzed by the aforementioned analysis unit, An advice unit provides educational advice based on the results obtained by the aforementioned checking unit, A behavioral analysis unit analyzes behavior based on the advice provided by the aforementioned advice unit, The system includes a management unit that performs schedule management based on the results obtained by the behavioral analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the parents' emotions and adjusts the timing of inputting growth records based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is We analyze past growth records and suggest the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When entering growth records, filtering is performed based on the parents' current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates parental emotions and determines the priority of growth records to input based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering growth records, the system prioritizes inputting records that are highly relevant, taking into account the parents' geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering growth records, analyze the parents' social media activity and enter relevant records. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the parents' emotions and adjust the data analysis method based on the estimated parents' emotions. The system according to feature 1.

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