system

Abstract

The system according to this embodiment aims to identify a child's strengths and future potential and provide appropriate advice. [Solution] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a reception unit. The data collection unit collects data such as the child's academic performance, interests, and personality traits. The analysis unit analyzes the data collected by the data collection unit to identify the child's strengths and future potential. The data provision unit provides advice based on the results identified by the analysis unit. The reception unit handles registration for parents to use this service.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it cannot be said that sufficiently identifying a child's field of expertise and future potential and providing appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to identify a child's field of expertise and future potential and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a reception unit. The data collection unit collects data such as a child's academic performance, interests, and personality traits. The analysis unit analyzes the data collected by the data collection unit to identify the child's strengths and future potential. The data provision unit provides advice based on the results identified by the analysis unit. The reception unit handles registration for parents to use this service. [Effects of the Invention]

[0007] The system according to this embodiment can identify a child's strengths and future potential, and provide appropriate advice. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The guide system according to an embodiment of the present invention is a system that uses generative AI to teach and guide children about their strengths and future potential. In this guide system, the generative AI accumulates and analyzes data on children and provides advice to parents through a chatbot. This allows parents to make optimal choices regarding their children's extracurricular activities and education without hesitation. The target audience is parents who are struggling with parenting, especially working parents who have little time to spend with their children. By leveraging the strengths of generative AI, it is possible to maximize a child's potential. For example, the data collected by the generative AI in the guide system could include a child's academic performance, interests, personality traits, etc. By analyzing this data, the system identifies the child's strengths and future potential. The chatbot provides advice based on the results of the generative AI's analysis in response to questions from parents. Parents can access this service through a smartphone app or website. In this way, the guide system can support a child's growth by identifying their strengths and future potential and providing advice to parents.

[0029] The guide system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a data reception unit. The data collection unit collects data such as a child's academic performance, interests, and personality traits. For example, the data collection unit collects school report cards and test scores. The data collection unit can also collect data on a child's hobbies, favorite subjects, and activities. Furthermore, the data collection unit can also collect data on a child's personality traits. For example, the data collection unit collects data on a child's sociability, introversion, and curiosity. The analysis unit analyzes the data collected by the data collection unit to identify a child's strengths and future potential. For example, the analysis unit analyzes the collected academic performance data to identify a child's strong subjects. Furthermore, the analysis unit can analyze the collected interest data to identify areas of interest for a child. Furthermore, the analysis unit can analyze the collected personality trait data to identify a child's future potential based on their personality. The data provision unit provides advice based on the results identified by the analysis unit. For example, the data provision unit provides advice to parents regarding their child's strengths based on the analysis results. Furthermore, the service provider can provide parents with advice on their child's future potential based on the analysis results. The service provider can also provide parents with advice on their child's education and extracurricular activities based on the analysis results. The reception department handles registration for parents to use this service. The reception department enables parents to use the service, for example, through a smartphone app or website. The reception department can also handle the registration process when parents use the service. Furthermore, the reception department can handle inquiries from parents when they use the service. In this way, the guide system according to this embodiment can support a child's growth by identifying their strengths and future potential and providing advice to parents.

[0030] The data collection department collects data on children's academic performance, interests, personality traits, and more. Specifically, it collects school report cards and test scores, digitizing and storing this data. Report cards include evaluations for each subject and overall grades, while test scores include results from regular tests and mock exams. The data collection department can also collect data on children's hobbies, favorite subjects, and activities. For example, it can use questionnaires and interviews to understand what activities children are interested in and which subjects they like. Furthermore, the data collection department can collect data on children's personality traits. For example, it can use psychological tests and observation records to collect data on children's sociability, introversion, and curiosity. This data is often collected based on expert evaluations and feedback from parents. The data collection department centrally manages this diverse data and stores it in a database. The database is securely managed, and personal information is thoroughly protected. The data collection department regularly updates the data to maintain the most up-to-date information. The data collection department can also flexibly adjust the data collection methods and frequency. For example, it can collect academic data every semester and update interest data monthly. This ensures that the data collection unit always maintains the latest data that reflects the child's growth and changes, making it available to the analysis and data provision units.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify children's strengths and future potential. Specifically, it analyzes collected academic performance data to identify children's strong subjects. For example, a child who consistently performs well in mathematics is judged to have a high level of understanding of and interest in mathematics. The analysis unit can also analyze collected interest data to identify areas of interest for children. For example, if a child is interested in science experiments or nature observation, they are judged to have a high level of interest in science and biology. Furthermore, the analysis unit can analyze collected personality trait data to identify future potential based on a child's personality. For example, a child who is highly sociable and exhibits leadership qualities is judged to be suited to occupations that require leadership in the future. The analysis unit uses AI to analyze this data and identify patterns and trends. The AI ​​uses machine learning algorithms to learn from past data and make predictions for the future. For example, it predicts changes in a child's future performance and interests based on past performance data and interest data. The AI ​​also uses anomaly detection algorithms to detect unusual patterns and abnormal data, and can issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected data and identify the child's strengths and future potential.

[0032] The service provider offers advice based on the results identified by the analysis team. Specifically, based on the analysis results, they provide parents with advice on their child's strengths. For example, if a child is identified as being good at mathematics, they recommend reinforcement learning in mathematics or related extracurricular activities to the parents. The service provider can also provide parents with advice on their child's future potential based on the analysis results. For example, if a child has personality traits that indicate leadership potential, they recommend activities and programs to foster leadership. Furthermore, the service provider can also provide parents with advice on their child's education and extracurricular activities based on the analysis results. For example, if a child is interested in science, they recommend science classes or experiment classes to the parents. To make this advice easy for parents to understand, the service provider can provide information in the form of reports and presentations. The service provider also supports parents in implementing the advice. For example, they assist with the application process for extracurricular activities or programs selected based on the advice. In addition, the service provider can collect feedback from parents and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide parents with appropriate advice and support their child's growth.

[0033] The reception department handles registration for parents to use this service. Specifically, it enables parents to use the service through a smartphone app or website. The reception department can also handle the registration process when parents use the service. For example, it assists parents in downloading the app and creating an account by entering the necessary information. The reception department can also handle inquiries from parents using the service. For example, it provides prompt and courteous answers to questions about how to use the service and pricing. Furthermore, the reception department also troubleshoots issues when parents use the service. For example, it provides technical support to resolve problems such as app malfunctions or login issues. The reception department can provide 24 / 7 support to ensure parents can use the service smoothly. In addition, the reception department collects feedback from parents and uses it to improve the service. For example, it considers improvement measures to enhance the functionality and usability of the service based on parents' opinions and requests. In this way, the reception department can improve the convenience and satisfaction of parents using the service.

[0034] The data collection unit can analyze a child's past academic performance and changes in interests and select the optimal data collection method. For example, the data collection unit can analyze a child's past performance and focus on collecting data related to their strong subjects. It can also track changes in a child's interests and collect data based on their current interests. Furthermore, the data collection unit can analyze fluctuations in a child's performance and focus on collecting data from periods when their performance improved. This allows for optimal data collection based on current interests and strengths by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter data based on the child's current living situation and environment during data collection. For example, the data collection unit can collect data on academic performance while the child is at school. It can also collect data on the home environment while the child is at home. Furthermore, it can collect data on extracurricular activities while the child is attending them. This allows for the collection of more relevant data by tailoring data collection to the child's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation into a generating AI and have the generating AI perform data filtering.

[0036] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data related to academic performance. Similarly, if the child is at home, the data collection unit can prioritize the collection of data related to the home environment. Furthermore, if the child is attending extracurricular activities, the data collection unit can prioritize the collection of data related to those activities. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into a generating AI and have the generating AI determine the data prioritization.

[0037] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on topics that a child has shown interest in on social media. The data collection unit can also analyze the content of accounts that a child follows on social media and collect relevant data. Furthermore, the data collection unit can analyze content that a child has shared on social media and collect data on their interests. In this way, data on a child's interests can be collected by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's social media data into a generating AI and have the generating AI collect relevant data.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the data, efficient analysis becomes possible. 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm for improving academic performance to data related to academic performance. It can also apply an analysis algorithm to measure the depth of interest to data related to interests. Furthermore, it can apply an analysis algorithm for personality diagnosis to data related to personality traits. This allows for more accurate analysis by applying analysis algorithms appropriate to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0040] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the analysis of current data while also considering past data. Furthermore, the analysis unit can focus its analysis on data collected during a specific period. This allows for analysis that prioritizes the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can evaluate the relevance of the data and perform the analysis in the optimal order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0042] The advice provider can adjust the level of detail in the advice based on the importance of the child's strengths and potential. For example, the provider can provide detailed advice for strengths of high importance. It can also provide concise advice for strengths of low importance. Furthermore, it can provide advice of moderate level of detail for strengths of moderate importance. By adjusting the level of detail in the advice according to the importance of the child's strengths and potential, more effective advice can be provided. Some or all of the above processing in the advice provider may be performed using AI, for example, or without AI. For example, the advice provider can input the child's strengths data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0043] The service provider can apply different advice algorithms depending on the parent's area of ​​interest when providing advice. For example, if the parent is interested in education, the service provider can apply an advice algorithm related to education. It can also apply an advice algorithm related to sports if the parent is interested in sports. Furthermore, if the parent is interested in art, it can apply an advice algorithm related to art. This allows for more appropriate advice to be provided by applying an advice algorithm tailored to the parent's area of ​​interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input parent's area of ​​interest data into a generating AI and have the generating AI apply the advice algorithm.

[0044] The advice delivery unit can prioritize advice based on the child's developmental stage. For example, it might prioritize basic advice for preschoolers. It could also prioritize academic advice for elementary school students. Furthermore, it could prioritize career guidance for middle school students. By prioritizing advice based on the child's developmental stage, it can provide more appropriate advice. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not. For example, the advice delivery unit could input child developmental stage data into a generating AI and have the generating AI determine the priority of advice.

[0045] The advice provider can adjust the order of advice by referring to the parent's past feedback when providing advice. For example, the provider may prioritize advice that the parent has received favorably in the past. It can also postpone advice that the parent has received negatively in the past. Furthermore, the provider can analyze the parent's past feedback and provide advice in the optimal order. This allows for the provision of advice in a more appropriate order by referring to the parent's past feedback. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI. For example, the provider can input the parent's past feedback data into a generating AI and have the generating AI adjust the order of advice.

[0046] The reception desk can select the optimal reception method by referring to the parent's past usage history at the time of reception. For example, the reception desk may prioritize suggesting reception methods that the parent has used in the past. The reception desk can also select the optimal reception method based on the parent's past usage history. Furthermore, the reception desk can analyze the parent's past usage history and suggest the most efficient reception method. In this way, the optimal reception method can be selected by referring to the parent's past usage history. 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 the parent's past usage history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0047] The reception unit can select the optimal reception method at the time of reception, taking into account the parent's device information. For example, if the parent is using a smartphone, the reception unit can provide a reception method that matches the screen size. Furthermore, if the parent is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Additionally, if the parent is using a computer, the reception unit can provide a reception method that allows for the input of detailed information. This allows the reception unit to select the optimal reception method by considering the parent's device 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 parent's device information into a generating AI and have the generating AI select the optimal reception method.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The analysis unit can collect and analyze data on children's academic performance and interests, as well as their health data. For example, by collecting and analyzing data such as children's sleep patterns, diet, and exercise habits, it is possible to understand the child's health status. Furthermore, based on the health data, it can identify factors that influence a child's academic performance and interests, and provide parents with advice on health management. This allows for the provision of comprehensive advice that takes the child's health status into consideration.

[0050] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their friendships. For example, it can collect data on the types of friends children interact with, the quality and frequency of these friendships. This allows for an understanding of children's social skills and interpersonal abilities, and enables the provision of advice to parents regarding friendships. Furthermore, based on the friendship data, it can identify factors influencing children's interests and personality traits, and provide comprehensive advice.

[0051] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their hobbies and special skills. For example, it can collect data on what hobbies and special skills a child has, the frequency of their activities, and their results. This allows for a more accurate identification of a child's strengths and future potential, and enables the provision of advice to parents regarding hobbies and special skills. Furthermore, based on the data on hobbies and special skills, it can identify factors that influence a child's interests and personality traits, and provide comprehensive advice.

[0052] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their home environment. For example, it can collect data on the family's economic situation, parents' educational policies, and the quality of communication within the family. This allows for the identification of factors in the home environment that influence a child's development and enables the provision of advice to parents regarding the home environment. Furthermore, based on the data on the home environment, it can also identify factors that influence a child's academic performance and interests and provide comprehensive advice.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The data collection department collects data on children's academic performance, interests, personality traits, etc. For example, it collects school report cards and test scores, data on children's hobbies and favorite subjects and activities, and data on children's personality traits such as sociability, introversion, and curiosity. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the child's strengths and future potential. For example, it analyzes academic performance data to identify subjects the child excels at, analyzes interest data to identify areas of interest, and analyzes personality trait data to identify future potential. Step 3: The service provider provides advice based on the results identified by the analysis provider. For example, they provide parents with advice on areas of expertise, future potential, and education and extracurricular activities. Step 4: The reception department handles registration for parents to use the service. For example, it enables parents to use the service through a smartphone app or website, and handles registration procedures and inquiries.

[0055] (Example of form 2) The guide system according to an embodiment of the present invention is a system that uses generative AI to teach and guide children about their strengths and future potential. In this guide system, the generative AI accumulates and analyzes data on children and provides advice to parents through a chatbot. This allows parents to make optimal choices regarding their children's extracurricular activities and education without hesitation. The target audience is parents who are struggling with parenting, especially working parents who have little time to spend with their children. By leveraging the strengths of generative AI, it is possible to maximize a child's potential. For example, the data collected by the generative AI in the guide system could include a child's academic performance, interests, personality traits, etc. By analyzing this data, the system identifies the child's strengths and future potential. The chatbot provides advice based on the results of the generative AI's analysis in response to questions from parents. Parents can access this service through a smartphone app or website. In this way, the guide system can support a child's growth by identifying their strengths and future potential and providing advice to parents.

[0056] The guide system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a data reception unit. The data collection unit collects data such as a child's academic performance, interests, and personality traits. For example, the data collection unit collects school report cards and test scores. The data collection unit can also collect data on a child's hobbies, favorite subjects, and activities. Furthermore, the data collection unit can also collect data on a child's personality traits. For example, the data collection unit collects data on a child's sociability, introversion, and curiosity. The analysis unit analyzes the data collected by the data collection unit to identify a child's strengths and future potential. For example, the analysis unit analyzes the collected academic performance data to identify a child's strong subjects. Furthermore, the analysis unit can analyze the collected interest data to identify areas of interest for a child. Furthermore, the analysis unit can analyze the collected personality trait data to identify a child's future potential based on their personality. The data provision unit provides advice based on the results identified by the analysis unit. For example, the data provision unit provides advice to parents regarding their child's strengths based on the analysis results. Furthermore, the service provider can provide parents with advice on their child's future potential based on the analysis results. The service provider can also provide parents with advice on their child's education and extracurricular activities based on the analysis results. The reception department handles registration for parents to use this service. The reception department enables parents to use the service, for example, through a smartphone app or website. The reception department can also handle the registration process when parents use the service. Furthermore, the reception department can handle inquiries from parents when they use the service. In this way, the guide system according to this embodiment can support a child's growth by identifying their strengths and future potential and providing advice to parents.

[0057] The data collection department collects data on children's academic performance, interests, personality traits, and more. Specifically, it collects school report cards and test scores, digitizing and storing this data. Report cards include evaluations for each subject and overall grades, while test scores include results from regular tests and mock exams. The data collection department can also collect data on children's hobbies, favorite subjects, and activities. For example, it can use questionnaires and interviews to understand what activities children are interested in and which subjects they like. Furthermore, the data collection department can collect data on children's personality traits. For example, it can use psychological tests and observation records to collect data on children's sociability, introversion, and curiosity. This data is often collected based on expert evaluations and feedback from parents. The data collection department centrally manages this diverse data and stores it in a database. The database is securely managed, and personal information is thoroughly protected. The data collection department regularly updates the data to maintain the most up-to-date information. The data collection department can also flexibly adjust the data collection methods and frequency. For example, it can collect academic data every semester and update interest data monthly. This ensures that the data collection unit always maintains the latest data that reflects the child's growth and changes, making it available to the analysis and data provision units.

[0058] The analysis unit analyzes the data collected by the data collection unit to identify children's strengths and future potential. Specifically, it analyzes collected academic performance data to identify children's strong subjects. For example, a child who consistently performs well in mathematics is judged to have a high level of understanding of and interest in mathematics. The analysis unit can also analyze collected interest data to identify areas of interest for children. For example, if a child is interested in science experiments or nature observation, they are judged to have a high level of interest in science and biology. Furthermore, the analysis unit can analyze collected personality trait data to identify future potential based on a child's personality. For example, a child who is highly sociable and exhibits leadership qualities is judged to be suited to occupations that require leadership in the future. The analysis unit uses AI to analyze this data and identify patterns and trends. The AI ​​uses machine learning algorithms to learn from past data and make predictions for the future. For example, it predicts changes in a child's future performance and interests based on past performance data and interest data. The AI ​​also uses anomaly detection algorithms to detect unusual patterns and abnormal data, and can issue warnings early. This allows the analysis unit to quickly and accurately analyze the collected data and identify the child's strengths and future potential.

[0059] The service provider offers advice based on the results identified by the analysis team. Specifically, based on the analysis results, they provide parents with advice on their child's strengths. For example, if a child is identified as being good at mathematics, they recommend reinforcement learning in mathematics or related extracurricular activities to the parents. The service provider can also provide parents with advice on their child's future potential based on the analysis results. For example, if a child has personality traits that indicate leadership potential, they recommend activities and programs to foster leadership. Furthermore, the service provider can also provide parents with advice on their child's education and extracurricular activities based on the analysis results. For example, if a child is interested in science, they recommend science classes or experiment classes to the parents. To make this advice easy for parents to understand, the service provider can provide information in the form of reports and presentations. The service provider also supports parents in implementing the advice. For example, they assist with the application process for extracurricular activities or programs selected based on the advice. In addition, the service provider can collect feedback from parents and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide parents with appropriate advice and support their child's growth.

[0060] The reception department handles registration for parents to use this service. Specifically, it enables parents to use the service through a smartphone app or website. The reception department can also handle the registration process when parents use the service. For example, it assists parents in downloading the app and creating an account by entering the necessary information. The reception department can also handle inquiries from parents using the service. For example, it provides prompt and courteous answers to questions about how to use the service and pricing. Furthermore, the reception department also troubleshoots issues when parents use the service. For example, it provides technical support to resolve problems such as app malfunctions or login issues. The reception department can provide 24 / 7 support to ensure parents can use the service smoothly. In addition, the reception department collects feedback from parents and uses it to improve the service. For example, it considers improvement measures to enhance the functionality and usability of the service based on parents' opinions and requests. In this way, the reception department can improve the convenience and satisfaction of parents using the service.

[0061] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can obtain natural data by collecting data during times when the child is relaxed. The data collection unit can also temporarily suspend data collection if the child is stressed and resume it after the child has relaxed. Furthermore, the data collection unit can obtain accurate data by collecting data during times when the child is focused. By adjusting the timing of data collection according to the child's emotions, more natural and accurate data can be collected. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's facial expression data into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0062] The data collection unit can analyze a child's past academic performance and changes in interests and select the optimal data collection method. For example, the data collection unit can analyze a child's past performance and focus on collecting data related to their strong subjects. It can also track changes in a child's interests and collect data based on their current interests. Furthermore, the data collection unit can analyze fluctuations in a child's performance and focus on collecting data from periods when their performance improved. This allows for optimal data collection based on current interests and strengths by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's past performance data into a generating AI and have the generating AI select the optimal data collection method.

[0063] The data collection unit can filter data based on the child's current living situation and environment during data collection. For example, the data collection unit can collect data on academic performance while the child is at school. It can also collect data on the home environment while the child is at home. Furthermore, it can collect data on extracurricular activities while the child is attending them. This allows for the collection of more relevant data by tailoring data collection to the child's living situation and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's living situation into a generating AI and have the generating AI perform data filtering.

[0064] The data collection unit can estimate a child's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a child is excited, the data collection unit may prioritize collecting data related to their interests. Similarly, if a child is calm, it may prioritize collecting data related to their academic performance. Furthermore, if a child is tired, it may prioritize collecting data related to their personality traits. This allows for the priority collection of important data by prioritizing data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input child facial expression data into a generative AI and have the generative AI determine the data prioritization.

[0065] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location during data collection. For example, if the child is at school, the data collection unit can prioritize the collection of data related to academic performance. Similarly, if the child is at home, the data collection unit can prioritize the collection of data related to the home environment. Furthermore, if the child is attending extracurricular activities, the data collection unit can prioritize the collection of data related to those activities. This allows for the priority collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into a generating AI and have the generating AI determine the data prioritization.

[0066] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data on topics that a child has shown interest in on social media. The data collection unit can also analyze the content of accounts that a child follows on social media and collect relevant data. Furthermore, the data collection unit can analyze content that a child has shared on social media and collect data on their interests. In this way, data on a child's interests can be collected by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a child's social media data into a generating AI and have the generating AI collect relevant data.

[0067] The analysis unit can estimate the child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. If the child is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the child is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the child's emotions, more appropriate analysis results can be provided. 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 without AI. For example, the analysis unit can input the child's facial expression data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0068] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. By adjusting the level of detail of the analysis according to the importance of the data, efficient analysis becomes possible. 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 importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0069] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm for improving academic performance to data related to academic performance. It can also apply an analysis algorithm to measure the depth of interest to data related to interests. Furthermore, it can apply an analysis algorithm for personality diagnosis to data related to personality traits. This allows for more accurate analysis by applying analysis algorithms appropriate to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0070] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can perform a detailed analysis. If the child is stressed, the analysis unit can also perform a concise analysis. Furthermore, if the child is excited, the analysis unit can perform a visually appealing analysis. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. 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 without AI. For example, the analysis unit can input the child's facial expression data into the generative AI and have the generative AI adjust the length of the analysis.

[0071] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, the analysis unit can prioritize the analysis of current data while also considering past data. Furthermore, the analysis unit can focus its analysis on data collected during a specific period. This allows for analysis that prioritizes the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0072] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can evaluate the relevance of the data and perform the analysis in the optimal order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0073] The service provider can estimate the parent's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the parent is relaxed, the service provider can provide detailed advice. If the parent is stressed, the service provider can also provide concise advice. Furthermore, if the parent is agitated, the service provider can provide visually appealing advice. This allows for more appropriate advice to be provided by adjusting the way advice is expressed 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 service provider may be performed using AI or not using AI. For example, the service provider can input the parent's facial expression data into the generative AI and have the generative AI adjust the way advice is expressed.

[0074] The advice provider can adjust the level of detail in the advice based on the importance of the child's strengths and potential. For example, the provider can provide detailed advice for strengths of high importance. It can also provide concise advice for strengths of low importance. Furthermore, it can provide advice of moderate level of detail for strengths of moderate importance. By adjusting the level of detail in the advice according to the importance of the child's strengths and potential, more effective advice can be provided. Some or all of the above processing in the advice provider may be performed using AI, for example, or without AI. For example, the advice provider can input the child's strengths data into a generating AI and have the generating AI perform the adjustment of the level of detail in the advice.

[0075] The service provider can apply different advice algorithms depending on the parent's area of ​​interest when providing advice. For example, if the parent is interested in education, the service provider can apply an advice algorithm related to education. It can also apply an advice algorithm related to sports if the parent is interested in sports. Furthermore, if the parent is interested in art, it can apply an advice algorithm related to art. This allows for more appropriate advice to be provided by applying an advice algorithm tailored to the parent's area of ​​interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input parent's area of ​​interest data into a generating AI and have the generating AI apply the advice algorithm.

[0076] The service provider can estimate the parent's emotions and adjust the length of the advice based on the estimated emotions. For example, if the parent is relaxed, the service provider can provide detailed advice. If the parent is stressed, the service provider can also provide concise advice. Furthermore, if the parent is agitated, the service provider can provide visually appealing advice. By adjusting the length of the advice according to the parent's emotions, more appropriate advice can be provided. 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 processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input the parent's facial expression data into the generative AI and have the generative AI adjust the length of the advice.

[0077] The advice delivery unit can prioritize advice based on the child's developmental stage. For example, it might prioritize basic advice for preschoolers. It could also prioritize academic advice for elementary school students. Furthermore, it could prioritize career guidance for middle school students. By prioritizing advice based on the child's developmental stage, it can provide more appropriate advice. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not. For example, the advice delivery unit could input child developmental stage data into a generating AI and have the generating AI determine the priority of advice.

[0078] The advice provider can adjust the order of advice by referring to the parent's past feedback when providing advice. For example, the provider may prioritize advice that the parent has received favorably in the past. It can also postpone advice that the parent has received negatively in the past. Furthermore, the provider can analyze the parent's past feedback and provide advice in the optimal order. This allows for the provision of advice in a more appropriate order by referring to the parent's past feedback. Some or all of the above processing in the advice provider may be performed using AI, for example, or not using AI. For example, the provider can input the parent's past feedback data into a generating AI and have the generating AI adjust the order of advice.

[0079] The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated emotions. For example, if the parent is relaxed, the reception unit can provide a detailed reception procedure. If the parent is stressed, it can also provide a concise reception procedure. Furthermore, if the parent is agitated, it can provide a visually appealing reception procedure. This allows for more appropriate reception by adjusting the reception method 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 reception unit may be performed using AI or not using AI. For example, the reception unit can input the parent's facial expression data into the generative AI and have the generative AI adjust the reception method.

[0080] The reception desk can select the optimal reception method by referring to the parent's past usage history at the time of reception. For example, the reception desk may prioritize suggesting reception methods that the parent has used in the past. The reception desk can also select the optimal reception method based on the parent's past usage history. Furthermore, the reception desk can analyze the parent's past usage history and suggest the most efficient reception method. In this way, the optimal reception method can be selected by referring to the parent's past usage history. 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 the parent's past usage history data into a generating AI and have the generating AI perform the selection of the optimal reception method.

[0081] The reception unit can estimate the parent's emotions and determine the priority of reception based on the estimated emotions. For example, if the parent is relaxed, the reception unit can provide detailed reception instructions. If the parent is stressed, the reception unit can also provide concise reception instructions. Furthermore, if the parent is agitated, the reception unit can provide visually appealing reception instructions. This allows for more appropriate reception by determining the priority of reception according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input parent facial expression data into a generative AI and have the generative AI determine the priority of reception.

[0082] The reception unit can select the optimal reception method at the time of reception, taking into account the parent's device information. For example, if the parent is using a smartphone, the reception unit can provide a reception method that matches the screen size. Furthermore, if the parent is using a tablet, the reception unit can provide a reception method optimized for a larger screen. Additionally, if the parent is using a computer, the reception unit can provide a reception method that allows for the input of detailed information. This allows the reception unit to select the optimal reception method by considering the parent's device 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 parent's device information into a generating AI and have the generating AI select the optimal reception method.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The analysis unit can collect and analyze data on children's academic performance and interests, as well as their health data. For example, by collecting and analyzing data such as children's sleep patterns, diet, and exercise habits, it is possible to understand the child's health status. Furthermore, based on the health data, it can identify factors that influence a child's academic performance and interests, and provide parents with advice on health management. This allows for the provision of comprehensive advice that takes the child's health status into consideration.

[0085] The system can estimate the parent's emotions and adjust the timing of advice based on those emotions. For example, if the parent is relaxed, it can select a time to provide detailed advice. If the parent is stressed, it can select a time to provide concise advice. Furthermore, if the parent is agitated, it can select a time to provide visually appealing advice. By adjusting the timing of advice according to the parent's emotions, the system can provide more appropriate advice.

[0086] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their friendships. For example, it can collect data on the types of friends children interact with, the quality and frequency of these friendships. This allows for an understanding of children's social skills and interpersonal abilities, and enables the provision of advice to parents regarding friendships. Furthermore, based on the friendship data, it can identify factors influencing children's interests and personality traits, and provide comprehensive advice.

[0087] The analysis unit can estimate the child's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the child is relaxed, it can perform detailed analysis frequently. If the child is stressed, it can reduce the frequency of analysis and perform a more concise analysis. Furthermore, if the child is excited, it can increase the frequency of visually engaging analysis. By adjusting the frequency of analysis according to the child's emotions, it can provide more appropriate analysis results.

[0088] The system can estimate the parent's emotions and adjust the advice based on that estimation. For example, if the parent is relaxed, it can provide detailed advice. If the parent is stressed, it can provide concise advice. Furthermore, if the parent is agitated, it can provide visually appealing advice. By adjusting the advice according to the parent's emotions, it can provide more appropriate advice.

[0089] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their hobbies and special skills. For example, it can collect data on what hobbies and special skills a child has, the frequency of their activities, and their results. This allows for a more accurate identification of a child's strengths and future potential, and enables the provision of advice to parents regarding hobbies and special skills. Furthermore, based on the data on hobbies and special skills, it can identify factors that influence a child's interests and personality traits, and provide comprehensive advice.

[0090] The analysis unit can estimate a child's emotions and adjust the visualization method of the analysis based on the estimated emotions. For example, if the child is relaxed, it can provide analysis results using detailed graphs and charts. If the child is stressed, it can provide concise text-based analysis results. Furthermore, if the child is agitated, it can provide analysis results using animations and interactive visuals. This allows for more appropriate analysis results by adjusting the visualization method according to the child's emotions.

[0091] The system can estimate the parent's emotions and adjust the advice format based on that estimation. For example, if the parent is relaxed, it can provide detailed report-style advice. If the parent is stressed, it can provide concise bullet-point advice. Furthermore, if the parent is agitated, it can provide advice using visuals or videos. By adjusting the advice format according to the parent's emotions, the system can provide more appropriate advice.

[0092] The data collection unit can collect and analyze data on children's academic performance and interests, as well as data on their home environment. For example, it can collect data on the family's economic situation, parents' educational policies, and the quality of communication within the family. This allows for the identification of factors in the home environment that influence a child's development and enables the provision of advice to parents regarding the home environment. Furthermore, based on the data on the home environment, it can also identify factors that influence a child's academic performance and interests and provide comprehensive advice.

[0093] The analysis unit can estimate the child's emotions and adjust the feedback method based on the estimated emotions. For example, if the child is relaxed, it can provide detailed feedback. If the child is stressed, it can provide concise feedback. Furthermore, if the child is excited, it can provide visually appealing feedback. By adjusting the feedback method according to the child's emotions, more appropriate feedback can be provided.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection department collects data on children's academic performance, interests, personality traits, etc. For example, it collects school report cards and test scores, data on children's hobbies and favorite subjects and activities, and data on children's personality traits such as sociability, introversion, and curiosity. Step 2: The analysis unit analyzes the data collected by the collection unit to identify the child's strengths and future potential. For example, it analyzes academic performance data to identify subjects the child excels at, analyzes interest data to identify areas of interest, and analyzes personality trait data to identify future potential. Step 3: The service provider provides advice based on the results identified by the analysis provider. For example, they provide parents with advice on areas of expertise, future potential, and education and extracurricular activities. Step 4: The reception department handles registration for parents to use the service. For example, it enables parents to use the service through a smartphone app or website, and handles registration procedures and inquiries.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and reception unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the child's strengths and future potential. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and provides advice to the parents based on the analysis results. The reception unit is implemented, for example, in the control unit 46A of the smart device 14, and enables parents to use the service through a smartphone app or website. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and reception unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the child's strengths and future potential. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and provides advice to the parents based on the analysis results. The reception unit is implemented, for example, in the control unit 46A of the smart glasses 214, and enables parents to use the service through a smartphone app or website. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and reception unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing unit 12 by the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the child's strengths and future potential. The provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides advice to the parents based on the analysis results. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314, and enables parents to use the service through a smartphone app or website. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and reception unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 238 of the robot 414, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the child's strengths and future potential. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and provides advice to the parents based on the analysis results. The reception unit is implemented, for example, in the control unit 46A of the robot 414, and enables parents to use the service through a smartphone app or website. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A data collection department that collects data on children's academic performance, interests, personality traits, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit to identify the child's strengths and future potential, A provision unit provides advice based on the results identified by the aforementioned analysis unit, It includes a reception area for parents to use this service. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the child's past academic performance and changes in interests and concerns to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the child's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the length of the 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 analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the parent's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the child's strengths and potential. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the parents' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the parent's emotions and adjusts the length of the advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, prioritize the advice based on the child's developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, refer to past feedback from parents to adjust the order of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is We estimate the parents' emotions and adjust the reception method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is At the time of registration, the system will refer to the parent's past usage history to select the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is The system estimates the parents' emotions and determines the priority of registration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is During registration, the system will select the most suitable registration method by considering the parent's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 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 data collection department collects data on children's academic performance, interests, personality traits, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit to identify the child's strengths and future potential, A provision unit provides advice based on the results identified by the aforementioned analysis unit, It includes a reception area for parents to use this service. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the child's past academic performance and changes in interests and concerns to select the most suitable data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting data, filtering is performed based on the child's current living situation and environment. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the child's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the child's emotions and adjust the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system according to feature 1.

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