System

Abstract

An object of a system according to an embodiment is to efficiently propose a sport most suitable for a child.SOLUTION: A system according to an embodiment includes a physical characteristics analysis unit, an interest analysis unit, and a sport suggestion unit. The physical characteristics analysis unit analyzes the physical characteristics and athletic ability of the child. The interest analysis unit analyzes the interest and concern of the child. The sport suggestion unit suggests an optimum sport on the basis of analysis results of the physical feature analysis unit and the interest analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technology had the problem that the process of finding a suitable sport for a child relied on trial and error, making it inefficient.

[0005] The system according to the embodiment aims to efficiently suggest the most suitable sports for children. [Means for solving the problem]

[0006] The system according to the embodiment includes a physical characteristic analysis unit, an interest analysis unit, and a sports suggestion unit. The physical characteristic analysis unit analyzes a child's physical characteristics and athletic ability. The interest analysis unit analyzes the child's interests and concerns. The sports suggestion unit suggests an optimal sport based on the analysis results of the physical characteristic analysis unit and the interest analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest the most suitable sports for children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sports suggestion system according to an embodiment of the present invention analyzes a child's physical characteristics, athletic ability, and interests, and suggests the most suitable sports. This allows children to find a sport that suits them early on and develop while having fun.

[0029] The sports suggestion system according to the embodiment includes a physical characteristic analysis unit, an interest analysis unit, and a sports suggestion unit. The physical characteristic analysis unit analyzes a child's physical characteristics and athletic ability. For example, data such as height, weight, muscle strength, flexibility, and endurance are collected, and a generation AI analyzes this data. The physical characteristic analysis unit can also perform analysis based on the results of muscle strength tests and flexibility tests. For example, growth predictions are made based on height and weight data, and appropriate sports are suggested. The interest analysis unit analyzes a child's interests and concerns. For example, data such as a child's favorite sports and activities and past experiences are collected, and a generation AI analyzes this data. The interest analysis unit can also perform analysis based on a child's hobbies and topics of interest. For example, interests are identified based on data on sporting events and club activities the child has participated in in the past. The sports suggestion unit suggests optimal sports based on the analysis results of the physical characteristic analysis unit and the interest analysis unit. For example, sports such as soccer, basketball, and track and field are suggested by comprehensively considering the physical characteristics, athletic ability, and interests. The sports suggestion unit can also collect feedback on the results of the suggested sports and suggest further optimal sports. For example, data is collected on whether a child is enjoying or improving at a suggested sport as a result of playing the sport, and the generation AI analyzes this data. As a result, the sports suggestion system according to the embodiment can suggest the best sport for a child. For example, a child can find a sport that suits them early on and grow while having fun. Furthermore, playing the suggested sport can improve a child's motor skills and physical characteristics, promoting healthy growth.

[0030] When analyzing a child's physical characteristics, the physical characteristic analysis unit can collect additional genetic information and perform an analysis based on genetic factors. For example, when analyzing a child's physical characteristics, the physical characteristic analysis unit collects the parents' genetic information, and the generation AI performs the analysis taking genetic factors into account. For example, data on the parents' height, weight, and athletic ability can be input to predict the child's future physical characteristics. Genetic information can also be used to identify children with genetic characteristics suited to specific sports. For example, genetic data related to endurance and muscle strength can be analyzed to find children suited to marathons or weightlifting. Genetic information can also be used to predict a child's growth and suggest sports suitable for the future. For example, basketball or volleyball can be suggested for a child who has a genetic potential to grow tall. This allows for more accurate sport suggestions by taking genetic factors into account during analysis.

[0031] When analyzing a child's athletic ability, the physical characteristic analysis unit collects athletic data in real time, and the generation AI can perform movement analysis. For example, when a child exercises, the physical characteristic analysis unit collects athletic data in real time using a wearable device, and the generation AI performs movement analysis. For example, it analyzes heart rate, step count, movement speed, etc. In addition, to perform movement analysis, a video camera can be used to record the child's exercise, and the generation AI can analyze the video data. For example, it can analyze running form and jump height to evaluate athletic ability. Furthermore, based on the athletic data collected in real time, the generation AI can propose a training plan to support the improvement of athletic ability. For example, it can provide a strength training or stretching program. In this way, by collecting athletic data in real time and performing movement analysis, a more accurate evaluation of athletic ability is possible.

[0032] The physical feature analysis unit can integrate the analysis results of physical features and athletic ability with other health data to evaluate overall health status. For example, the physical feature analysis unit can integrate the analysis results of physical features and athletic ability with dietary data, and the generation AI can evaluate overall health status. For example, health status can be evaluated taking into account nutritional balance and calorie intake. Sleep pattern data can also be collected and integrated with the analysis results of physical features and athletic ability. For example, sleep quality and duration can be taken into account to suggest rest necessary to improve athletic ability. The generation AI can also integrate other health data (e.g., stress level and heart rate variability) to evaluate overall health status. For example, it can make suggestions for stress management and relaxation. This allows for an evaluation of overall health status, enabling more appropriate sports recommendations.

[0033] The physical feature analysis unit can perform similar analyses on children of different age groups and genders, and suggest the most suitable sports for each age and gender. For example, the physical feature analysis unit analyzes the physical features and athletic abilities of children of different age groups, and suggests the most suitable sports for each age. For example, it can suggest exercises that develop a sense of balance to young children. It can also analyze physical features and athletic abilities according to gender, and suggest sports that are suitable for each gender. For example, it can suggest sports that make use of flexibility to girls. It can also predict growth according to age and gender, and suggest sports that will be suitable for the future. For example, it can suggest exercises that increase bone density to children in their growth period. This makes it possible to suggest the most suitable sports according to age and gender, enabling more personalized sports suggestions.

[0034] When analyzing a child's interests and concerns, the interest analysis unit collects social media activity history, and the generation AI can analyze changes in interests. For example, the interest analysis unit collects a child's social media activity history, and the generation AI analyzes changes in interests. For example, it tracks changes in interests based on the content of posts and the history of "likes." It can also analyze social media activity history to identify what sports or activities a child is interested in. For example, it analyzes sports-related posts and accounts followed. It can also analyze changes in a child's interests based on social media data and suggest sports that the child has maintained an interest in. For example, it prioritizes sports that the child has been interested in for a long time. In this way, by analyzing social media activity history, it is possible to understand changes in a child's interests and suggest more appropriate sports.

[0035] When analyzing a child's interests and concerns, the interest analysis unit collects feedback from parents and teachers, and the generation AI can perform a multifaceted analysis. The interest analysis unit, for example, collects feedback from parents and teachers, and the generation AI analyzes the child's interests and concerns from multiple angles. For example, the interests and behavior of the child observed by parents and teachers are used as input data. The generation AI can also analyze the child's interests and concerns based on feedback from parents and teachers and suggest the most appropriate sports. For example, it can take into account the sports recommended by parents and teachers. It is also possible to build a system in which feedback from parents and teachers is collected and the generation AI analyzes the child's interests and concerns from multiple angles. For example, it uses a questionnaire filled out by parents and teachers. This allows for a multifaceted analysis of feedback from parents and teachers, making it possible to suggest more appropriate sports.

[0036] The interest analysis unit can integrate the results of interest and concern analysis with educational data to provide comprehensive support for growth. For example, the interest analysis unit integrates the results of interest and concern analysis with academic performance data, and the generation AI provides comprehensive support for growth. For example, academic performance can be linked to interest in sports to support balanced growth. Learning style data can also be collected and integrated with the results of interest and concern analysis. For example, visual sports can be suggested for visual learners. The generation AI can also integrate other educational data (for example, learning progress and task completion level) to provide comprehensive support for growth. For example, a program can be suggested to help balance studies and sports. By integrating this with educational data, it is possible to support a child's comprehensive growth.

[0037] The interest analysis unit can perform similar analyses on children from different cultural spheres and regions, and suggest the most appropriate sports for each culture and region. For example, the interest analysis unit analyzes the interests and concerns of children from different cultural spheres and suggests the most appropriate sports for each culture. For example, it takes into account traditional sports and sports unique to each region. It can also take into account the sports environment of each region and suggest the most appropriate sports based on the results of the interest and concern analysis. For example, it selects sports taking into account the local climate and facilities. It is also possible to build a system that collects data from different cultural spheres and regions and has a generation AI suggest the most appropriate sports for each culture and region. For example, it takes into account information about local sporting events and tournaments. This allows for more personalized sports suggestions by suggesting the most appropriate sports for each culture and region.

[0038] When selecting sports to suggest, the sports suggestion unit refers to data on success cases, allowing the generation AI to suggest sports with a high probability of success. For example, the sports suggestion unit collects data on past success cases, and the generation AI uses that data to suggest sports with a high probability of success. For example, the unit may preferentially suggest sports in which children with the same physical characteristics or interests have been successful. The success case data can also be analyzed to identify the factors behind success in a specific sport. For example, if a specific training method or environment contributes to success, the unit will suggest that sport. It is also possible to build a system in which the generation AI suggests sports with a high probability of success based on data on past success cases. For example, statistical data on success cases can be used to improve the accuracy of suggestions. This makes it possible to suggest sports with a high probability of success by referring to the success case data.

[0039] The sports suggestion unit takes growth predictions into consideration when selecting sports to suggest, allowing the generative AI to suggest sports that are suitable in the long term. For example, the sports suggestion unit uses growth prediction data for a child to suggest sports that are suitable in the long term. For example, it selects sports that are suitable in the future by taking into consideration growth predictions for height and weight. It can also analyze growth prediction data to predict future performance in a specific sport. For example, it can suggest sports by taking into consideration changes in muscle strength and endurance during the growth period. It can also build a system in which the generative AI suggests sports that are suitable in the long term based on a child's growth predictions. For example, it updates growth prediction data in real time to suggest the optimal sport. In this way, it is possible to suggest sports that are suitable in the long term by taking growth predictions into consideration.

[0040] When selecting suggested sports, the sports suggestion unit allows the generation AI to make comprehensive lifestyle suggestions based on compatibility with other hobbies and activities. The sports suggestion unit, for example, collects data on a child's other hobbies and activities, and the generation AI makes comprehensive lifestyle suggestions based on that data. For example, it can suggest sports that go well with hobbies such as music and art. It is also possible to build a system in which the generation AI makes comprehensive lifestyle suggestions by taking compatibility with other hobbies and activities into consideration. For example, hobby and activity data can be integrated to improve the accuracy of suggestions. The generation AI can also suggest the optimal sports by taking into consideration the child's overall lifestyle. For example, it can select sports taking into account balance with academics and home environment. This makes it possible to make comprehensive lifestyle suggestions by taking compatibility with other hobbies and activities into consideration.

[0041] The sports suggestion unit can suggest sports according to different seasons and weather conditions, and provide sports that can be enjoyed throughout the year. For example, the sports suggestion unit has the generation AI suggest sports that can be enjoyed throughout the year based on season and weather data. For example, it suggests swimming and surfing in the summer, and skiing and snowboarding in the winter. It is also possible to build a system that suggests sports according to seasons and weather conditions. For example, it collects weather data in real time and suggests the most suitable sport. It is also possible for the generation AI to suggest sports that can be enjoyed throughout the year, taking seasons and weather conditions into consideration. For example, it may suggest a combination of indoor and outdoor sports. In this way, it is possible to provide sports that can be enjoyed throughout the year by suggesting sports according to seasons and weather conditions.

[0042] When analyzing the results of the proposed sport, the sports suggestion unit can use video analysis to collect details of movements, and the generation AI can suggest technical improvements. For example, the sports suggestion unit uses a video camera to record movements while the proposed sport is being played, and the generation AI analyzes the video data. For example, it analyzes running form and shooting movements and suggests technical improvements. It is also possible to build a system that uses video analysis to collect details of movements during sport playing, and the generation AI suggests technical improvements. For example, it analyzes the speed and angle of movements. It is also possible to analyze video of the results of the proposed sport playing, and the generation AI can suggest technical improvements. For example, it can suggest corrections to form or improvements to training methods. In this way, technical improvements can be suggested using video analysis.

[0043] The sports suggestion unit collects biometric data when analyzing the results of the proposed sports, and the generation AI can manage physical condition. For example, the sports suggestion unit collects biometric data using a wearable device while the proposed sports are being played, and the generation AI manages physical condition. For example, it analyzes heart rate and oxygen intake. It is also possible to build a system in which the generation AI manages physical condition while playing sports based on the biometric data. For example, it adjusts exercise intensity and rest time. It is also possible to analyze the results of the proposed sports using biometric data, and the generation AI manages physical condition. For example, it evaluates fatigue level and recovery state and adjusts the training plan. In this way, physical condition can be managed by collecting biometric data.

[0044] The sports suggestion unit collects biometric data when analyzing the results of the proposed sports, and the generation AI can manage physical condition. For example, the sports suggestion unit collects biometric data using a wearable device while the proposed sports are being played, and the generation AI manages physical condition. For example, it analyzes heart rate and oxygen intake. It is also possible to build a system in which the generation AI manages physical condition while playing sports based on the biometric data. For example, it adjusts exercise intensity and rest time. It is also possible to analyze the results of the proposed sports using biometric data, and the generation AI manages physical condition. For example, it evaluates fatigue level and recovery state and adjusts the training plan. In this way, physical condition can be managed by collecting biometric data.

[0045] The sports suggestion unit can compare the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. The sports suggestion unit, for example, compares the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. For example, performance is evaluated based on data from children of the same age and gender. It is also possible to build a system in which data from other children is collected and the generative AI provides a benchmark. For example, athletic ability and achievements are compared and evaluated. The generative AI can also compare the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. For example, training progress and results are evaluated. This makes it possible to provide a benchmark by comparing with data from other children.

[0046] The sports suggestion unit can share the results of the suggested sports with parents and teachers, thereby strengthening the support system at home and school. The sports suggestion unit, for example, can share the results of the suggested sports with parents and teachers, thereby strengthening the support system at home and school. For example, the results can be provided in the form of a report. A system for sharing the results of sports with parents and teachers can also be established to strengthen the support system. For example, the results can be shared through an online platform. The results of the suggested sports can also be shared with parents and teachers, thereby strengthening the support system at home and school. For example, a system can be introduced that allows parents and teachers to provide feedback. In this way, the support system at home and school can be strengthened by sharing the results of the sports.

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

[0048] The sports suggestion system can further include a music analysis unit. The music analysis unit analyzes the genre and rhythm of music a child likes, and the generation AI uses that information to suggest the most suitable sport. For example, it can suggest sports such as dance or gymnastics, which require a sense of rhythm. The music analysis unit can also analyze the music a child listens to while exercising and suggest music to improve athletic performance. For example, recommending fast-paced music can improve running or cycling performance. The music analysis unit can also suggest training plans that incorporate music based on the child's musical preferences. For example, it can provide exercises and rhythmic training that are timed to music. This allows children to enjoy exercising by utilizing music.

[0049] The sports suggestion system can further include an environmental analysis unit. The environmental analysis unit collects environmental data about the area where a child lives, and the generation AI uses that data to suggest the most suitable sports. For example, it can suggest outdoor or indoor sports taking into account the climate, topography, air quality, etc. The environmental analysis unit can also collect information about local sports facilities and clubs to suggest sports that are easy for children to participate in. For example, it can introduce nearby soccer clubs or swimming schools. The environmental analysis unit can also suggest sports events that children can participate in based on information about local events and tournaments. For example, it can suggest local marathons or basketball tournaments. This makes it possible to suggest sports that take the local environment into consideration.

[0050] The sports suggestion system can further include a nutritional analysis unit. The nutritional analysis unit collects a child's dietary data, and the generation AI uses that data to suggest the most suitable sports. For example, it can suggest sports that consume a lot of energy, taking into account nutritional balance and calorie intake. The nutritional analysis unit can also analyze a child's eating habits and suggest a meal plan to improve athletic performance. For example, it can recommend appropriate nutritional supplementation before and after exercise. The nutritional analysis unit can also take into account a child's allergy information and suggest sports and meal plans that are allergy-friendly. For example, it can provide a meal plan that avoids certain ingredients. This can support a child's sports activities from a nutritional perspective.

[0051] The sports suggestion system can further include a community analysis unit. The community analysis unit collects data on the community to which a child belongs, and the generation AI uses that data to suggest the most suitable sports. For example, by suggesting sports that friends and classmates are participating in, the system can encourage the child to participate more actively. The community analysis unit can also collect information on local sports clubs and teams to suggest sports that the child is likely to participate in. For example, it can introduce nearby soccer teams or volleyball clubs. The community analysis unit can also take into account other activities and events in which the child is participating and suggest sports that fit into the child's schedule. For example, it can select sports that do not overlap with school club activities or local events. This makes it possible to suggest sports that utilize community information.

[0052] The sports suggestion system can further include a virtual reality (VR) analysis unit. The VR analysis unit collects data on a child's sports experiences in a virtual reality environment, and the generation AI uses that data to suggest the most suitable sports. For example, the exercise data in the VR environment can be analyzed to evaluate the child's athletic ability and interests. The VR analysis unit can also provide a system that allows a child to try out a sport in a virtual environment before actually experiencing it. For example, the VR analysis unit can allow the child to try out virtual soccer or virtual basketball. The VR analysis unit can also suggest real sports based on the sports a child enjoys in the virtual environment. For example, the VR analysis unit can suggest real tennis to a child who enjoys virtual tennis. This makes it possible to suggest sports using virtual reality.

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

[0054] Step 1: The physical characteristics analysis unit analyzes the child's physical characteristics and athletic ability. For example, data such as height, weight, muscle strength, flexibility, and endurance is collected, and the generation AI analyzes this data. The physical characteristics analysis unit can also perform analysis based on the results of muscle strength and flexibility tests. For example, based on height and weight data, it predicts growth and suggests suitable sports. Step 2: The interest analysis unit analyzes the child's interests. For example, data such as the child's favorite sports and activities, past experiences, etc. are collected, and the generation AI analyzes this data. The interest analysis unit can also perform analysis based on the child's hobbies and topics of interest. For example, interests can be identified based on data on the sporting events and club activities the child has participated in in the past. Step 3: The sports suggestion unit suggests the most suitable sport based on the analysis results of the physical characteristics analysis unit and the interest analysis unit. For example, it suggests sports such as soccer, basketball, and track and field by comprehensively considering physical characteristics, athletic ability, and interests. The sports suggestion unit can also collect feedback on the results of playing the suggested sports and suggest further suitable sports. For example, data such as whether a child is enjoying playing the suggested sports and whether they are improving is collected, and the generation AI analyzes this data.

[0055] (Example 2) The sports suggestion system according to an embodiment of the present invention analyzes a child's physical characteristics, athletic ability, and interests, and suggests the most suitable sports. This allows children to find a sport that suits them early on and develop while having fun.

[0056] The sports suggestion system according to the embodiment includes a physical characteristic analysis unit, an interest analysis unit, and a sports suggestion unit. The physical characteristic analysis unit analyzes a child's physical characteristics and athletic ability. For example, data such as height, weight, muscle strength, flexibility, and endurance are collected, and a generation AI analyzes this data. The physical characteristic analysis unit can also perform analysis based on the results of muscle strength tests and flexibility tests. For example, growth predictions are made based on height and weight data, and appropriate sports are suggested. The interest analysis unit analyzes a child's interests and concerns. For example, data such as a child's favorite sports and activities and past experiences are collected, and a generation AI analyzes this data. The interest analysis unit can also perform analysis based on a child's hobbies and topics of interest. For example, interests are identified based on data on sporting events and club activities the child has participated in in the past. The sports suggestion unit suggests optimal sports based on the analysis results of the physical characteristic analysis unit and the interest analysis unit. For example, sports such as soccer, basketball, and track and field are suggested by comprehensively considering the physical characteristics, athletic ability, and interests. The sports suggestion unit can also collect feedback on the results of the suggested sports and suggest further optimal sports. For example, data is collected on whether a child is enjoying or improving at a suggested sport as a result of playing the sport, and the generation AI analyzes this data. As a result, the sports suggestion system according to the embodiment can suggest the best sport for a child. For example, a child can find a sport that suits them early on and grow while having fun. Furthermore, playing the suggested sport can improve a child's motor skills and physical characteristics, promoting healthy growth.

[0057] When analyzing a child's physical characteristics, the physical characteristic analysis unit can collect additional genetic information and perform an analysis based on genetic factors. For example, when analyzing a child's physical characteristics, the physical characteristic analysis unit collects the parents' genetic information, and the generation AI performs the analysis taking genetic factors into account. For example, data on the parents' height, weight, and athletic ability can be input to predict the child's future physical characteristics. Genetic information can also be used to identify children with genetic characteristics suited to specific sports. For example, genetic data related to endurance and muscle strength can be analyzed to find children suited to marathons or weightlifting. Genetic information can also be used to predict a child's growth and suggest sports suitable for the future. For example, basketball or volleyball can be suggested for a child who has a genetic potential to grow tall. This allows for more accurate sport suggestions by taking genetic factors into account during analysis.

[0058] When analyzing a child's athletic ability, the physical characteristic analysis unit collects athletic data in real time, and the generation AI can perform movement analysis. For example, when a child exercises, the physical characteristic analysis unit collects athletic data in real time using a wearable device, and the generation AI performs movement analysis. For example, it analyzes heart rate, step count, movement speed, etc. In addition, to perform movement analysis, a video camera can be used to record the child's exercise, and the generation AI can analyze the video data. For example, it can analyze running form and jump height to evaluate athletic ability. Furthermore, based on the athletic data collected in real time, the generation AI can propose a training plan to support the improvement of athletic ability. For example, it can provide a strength training or stretching program. In this way, by collecting athletic data in real time and performing movement analysis, a more accurate evaluation of athletic ability is possible.

[0059] The physical feature analysis unit can use the emotion estimation function to analyze a child's emotions while exercising and identify exercise patterns that elicit positive emotions. For example, the physical feature analysis unit analyzes a child's facial expressions and voice while exercising and uses the emotion estimation function to identify exercise patterns that elicit positive emotions. For example, it can detect smiles and happy voices and recommend those exercise patterns. The emotion estimation function can also be used to monitor a child's emotional changes while exercising in real time and identify exercise patterns that enhance positive emotions. For example, the intensity and type of exercise can be adjusted. Furthermore, based on the emotional data during exercise, the generative AI can suggest an exercise program that elicits positive emotions. For example, it could incorporate game-style exercise that children can enjoy. By identifying exercise patterns that elicit positive emotions, children can enjoy exercising.

[0060] The physical feature analysis unit can integrate the analysis results of physical features and athletic ability with other health data to evaluate overall health status. For example, the physical feature analysis unit can integrate the analysis results of physical features and athletic ability with dietary data, and the generation AI can evaluate overall health status. For example, health status can be evaluated taking into account nutritional balance and calorie intake. Sleep pattern data can also be collected and integrated with the analysis results of physical features and athletic ability. For example, sleep quality and duration can be taken into account to suggest rest necessary to improve athletic ability. The generation AI can also integrate other health data (e.g., stress level and heart rate variability) to evaluate overall health status. For example, it can make suggestions for stress management and relaxation. This allows for an evaluation of overall health status, enabling more appropriate sports recommendations.

[0061] The physical feature analysis unit can perform similar analyses on children of different age groups and genders, and suggest the most suitable sports for each age and gender. For example, the physical feature analysis unit analyzes the physical features and athletic abilities of children of different age groups, and suggests the most suitable sports for each age. For example, it can suggest exercises that develop a sense of balance to young children. It can also analyze physical features and athletic abilities according to gender, and suggest sports that are suitable for each gender. For example, it can suggest sports that make use of flexibility to girls. It can also predict growth according to age and gender, and suggest sports that will be suitable for the future. For example, it can suggest exercises that increase bone density to children in their growth period. This makes it possible to suggest the most suitable sports according to age and gender, enabling more personalized sports suggestions.

[0062] When analyzing a child's interests and concerns, the interest analysis unit collects social media activity history, and the generation AI can analyze changes in interests. For example, the interest analysis unit collects a child's social media activity history, and the generation AI analyzes changes in interests. For example, it tracks changes in interests based on the content of posts and the history of "likes." It can also analyze social media activity history to identify what sports or activities a child is interested in. For example, it analyzes sports-related posts and accounts followed. It can also analyze changes in a child's interests based on social media data and suggest sports that the child has maintained an interest in. For example, it prioritizes sports that the child has been interested in for a long time. In this way, by analyzing social media activity history, it is possible to understand changes in a child's interests and suggest more appropriate sports.

[0063] When analyzing a child's interests and concerns, the interest analysis unit collects feedback from parents and teachers, and the generation AI can perform a multifaceted analysis. The interest analysis unit, for example, collects feedback from parents and teachers, and the generation AI analyzes the child's interests and concerns from multiple angles. For example, the interests and behavior of the child observed by parents and teachers are used as input data. The generation AI can also analyze the child's interests and concerns based on feedback from parents and teachers and suggest the most appropriate sports. For example, it can take into account the sports recommended by parents and teachers. It is also possible to build a system in which feedback from parents and teachers is collected and the generation AI analyzes the child's interests and concerns from multiple angles. For example, it uses a questionnaire filled out by parents and teachers. This allows for a multifaceted analysis of feedback from parents and teachers, making it possible to suggest more appropriate sports.

[0064] The interest analysis unit can use the emotion estimation function to analyze emotions during activities that interest a child and identify activities that elicit positive emotions. The interest analysis unit can, for example, use the emotion estimation function to analyze emotions during activities that interest a child and identify activities that elicit positive emotions. For example, it can detect smiles and happy voices. The generation AI can also identify activities that elicit positive emotions based on emotion data during activities that interest a child. For example, it can analyze the time of day and the content of activities that a child is enjoying. The emotion estimation function can also be used to monitor emotional changes during activities that interest a child in real time and identify activities that strengthen positive emotions. For example, it can adjust the intensity or type of activity. In this way, by identifying activities that elicit positive emotions, a child can enjoy the activity.

[0065] The interest analysis unit can integrate the results of interest and concern analysis with educational data to provide comprehensive support for growth. For example, the interest analysis unit integrates the results of interest and concern analysis with academic performance data, and the generation AI provides comprehensive support for growth. For example, academic performance can be linked to interest in sports to support balanced growth. Learning style data can also be collected and integrated with the results of interest and concern analysis. For example, visual sports can be suggested for visual learners. The generation AI can also integrate other educational data (for example, learning progress and task completion level) to provide comprehensive support for growth. For example, a program can be suggested to help balance studies and sports. By integrating this with educational data, it is possible to support a child's comprehensive growth.

[0066] The interest analysis unit can perform similar analyses on children from different cultural spheres and regions, and suggest the most appropriate sports for each culture and region. For example, the interest analysis unit analyzes the interests and concerns of children from different cultural spheres and suggests the most appropriate sports for each culture. For example, it takes into account traditional sports and sports unique to each region. It can also take into account the sports environment of each region and suggest the most appropriate sports based on the results of the interest and concern analysis. For example, it selects sports taking into account the local climate and facilities. It is also possible to build a system that collects data from different cultural spheres and regions and has a generation AI suggest the most appropriate sports for each culture and region. For example, it takes into account information about local sporting events and tournaments. This allows for more personalized sports suggestions by suggesting the most appropriate sports for each culture and region.

[0067] The interest analysis unit can use the emotion estimation function to monitor the child's emotions in real time during interest and interest analysis and track changes in the child's interests and interests according to the emotions. The interest analysis unit, for example, uses the emotion estimation function to monitor the child's emotions in real time during interest and interest analysis and track changes in the child's interests and interests according to the emotions. For example, it identifies activities that increase interest. The generation AI can also analyze changes in the child's interests and interests in real time based on the child's emotion data. For example, it can prioritize and suggest activities that increase positive emotions. The emotion estimation function can also track changes in the child's emotions during interest and interest analysis and the generation AI can make suggestions according to changes in the child's interests and interests. For example, it can exclude activities in which the child's interest has waned. This allows for more appropriate sports suggestions by tracking changes in the child's interests and interests according to the emotions.

[0068] When selecting sports to suggest, the sports suggestion unit refers to data on success cases, allowing the generation AI to suggest sports with a high probability of success. For example, the sports suggestion unit collects data on past success cases, and the generation AI uses that data to suggest sports with a high probability of success. For example, the unit may preferentially suggest sports in which children with the same physical characteristics or interests have been successful. The success case data can also be analyzed to identify the factors behind success in a specific sport. For example, if a specific training method or environment contributes to success, the unit will suggest that sport. It is also possible to build a system in which the generation AI suggests sports with a high probability of success based on data on past success cases. For example, statistical data on success cases can be used to improve the accuracy of suggestions. This makes it possible to suggest sports with a high probability of success by referring to the success case data.

[0069] The sports suggestion unit takes growth predictions into consideration when selecting sports to suggest, allowing the generative AI to suggest sports that are suitable in the long term. For example, the sports suggestion unit uses growth prediction data for a child to suggest sports that are suitable in the long term. For example, it selects sports that are suitable in the future by taking into consideration growth predictions for height and weight. It can also analyze growth prediction data to predict future performance in a specific sport. For example, it can suggest sports by taking into consideration changes in muscle strength and endurance during the growth period. It can also build a system in which the generative AI suggests sports that are suitable in the long term based on a child's growth predictions. For example, it updates growth prediction data in real time to suggest the optimal sport. In this way, it is possible to suggest sports that are suitable in the long term by taking growth predictions into consideration.

[0070] The sports suggestion unit can use the emotion estimation function to analyze a child's emotional response to the suggested sports and identify sports that elicit positive emotions. The sports suggestion unit can, for example, use the emotion estimation function to analyze a child's emotional response to the suggested sports and identify sports that elicit positive emotions. For example, it can detect smiles and happy voices. The generation AI can also identify sports that elicit positive emotions based on the child's emotional data. For example, it can analyze the time of day and activities that the child is enjoying. The emotion estimation function can also be used to monitor changes in the child's emotions toward the suggested sports in real time and identify sports that will enhance positive emotions. For example, it can adjust the intensity or type of sport. In this way, it is possible to identify sports that elicit positive emotions by analyzing emotional responses.

[0071] When selecting suggested sports, the sports suggestion unit allows the generation AI to make comprehensive lifestyle suggestions based on compatibility with other hobbies and activities. The sports suggestion unit, for example, collects data on a child's other hobbies and activities, and the generation AI makes comprehensive lifestyle suggestions based on that data. For example, it can suggest sports that go well with hobbies such as music and art. It is also possible to build a system in which the generation AI makes comprehensive lifestyle suggestions by taking compatibility with other hobbies and activities into consideration. For example, hobby and activity data can be integrated to improve the accuracy of suggestions. The generation AI can also suggest the optimal sports by taking into consideration the child's overall lifestyle. For example, it can select sports taking into account balance with academics and home environment. This makes it possible to make comprehensive lifestyle suggestions by taking compatibility with other hobbies and activities into consideration.

[0072] The sports suggestion unit can suggest sports according to different seasons and weather conditions, and provide sports that can be enjoyed throughout the year. For example, the sports suggestion unit has the generation AI suggest sports that can be enjoyed throughout the year based on season and weather data. For example, it suggests swimming and surfing in the summer, and skiing and snowboarding in the winter. It is also possible to build a system that suggests sports according to seasons and weather conditions. For example, it collects weather data in real time and suggests the most suitable sport. It is also possible for the generation AI to suggest sports that can be enjoyed throughout the year, taking seasons and weather conditions into consideration. For example, it may suggest a combination of indoor and outdoor sports. In this way, it is possible to provide sports that can be enjoyed throughout the year by suggesting sports according to seasons and weather conditions.

[0073] The sports suggestion unit can use the emotion estimation function to analyze the emotional reactions of parents and teachers to the suggested sports and strengthen the support system. The sports suggestion unit, for example, uses the emotion estimation function to analyze the emotional reactions of parents and teachers to the suggested sports and strengthen the support system. For example, it can prioritize suggesting sports for which parents and teachers have positive feelings. In addition, based on the emotional data of parents and teachers, the generative AI can make suggestions to strengthen the support system. For example, it can select sports that parents and teachers can actively support. In addition, the emotion estimation function can be used to monitor changes in parents' and teachers' emotions toward the suggested sports in real time and strengthen the support system. For example, it can adjust the sports based on feedback from parents and teachers. In this way, the support system can be strengthened by analyzing the emotional reactions of parents and teachers.

[0074] When analyzing the results of the proposed sport, the sports suggestion unit can use video analysis to collect details of movements, and the generation AI can suggest technical improvements. For example, the sports suggestion unit uses a video camera to record movements while the proposed sport is being played, and the generation AI analyzes the video data. For example, it analyzes running form and shooting movements and suggests technical improvements. It is also possible to build a system that uses video analysis to collect details of movements during sport playing, and the generation AI suggests technical improvements. For example, it analyzes the speed and angle of movements. It is also possible to analyze video of the results of the proposed sport playing, and the generation AI can suggest technical improvements. For example, it can suggest corrections to form or improvements to training methods. In this way, technical improvements can be suggested using video analysis.

[0075] The sports suggestion unit collects biometric data when analyzing the results of the proposed sports, and the generation AI can manage physical condition. For example, the sports suggestion unit collects biometric data using a wearable device while the proposed sports are being played, and the generation AI manages physical condition. For example, it analyzes heart rate and oxygen intake. It is also possible to build a system in which the generation AI manages physical condition while playing sports based on the biometric data. For example, it adjusts exercise intensity and rest time. It is also possible to analyze the results of the proposed sports using biometric data, and the generation AI manages physical condition. For example, it evaluates fatigue level and recovery state and adjusts the training plan. In this way, physical condition can be managed by collecting biometric data.

[0076] The sports suggestion unit collects biometric data when analyzing the results of the proposed sports, and the generation AI can manage physical condition. For example, the sports suggestion unit collects biometric data using a wearable device while the proposed sports are being played, and the generation AI manages physical condition. For example, it analyzes heart rate and oxygen intake. It is also possible to build a system in which the generation AI manages physical condition while playing sports based on the biometric data. For example, it adjusts exercise intensity and rest time. It is also possible to analyze the results of the proposed sports using biometric data, and the generation AI manages physical condition. For example, it evaluates fatigue level and recovery state and adjusts the training plan. In this way, physical condition can be managed by collecting biometric data.

[0077] The sports suggestion unit can use the emotion estimation function to analyze a child's emotions while playing sports and suggest a training plan that will elicit positive emotions. The sports suggestion unit can, for example, use the emotion estimation function to analyze a child's emotions while playing sports and suggest a training plan that will elicit positive emotions. For example, it can detect smiles and happy voices. Furthermore, based on the child's emotion data, the generation AI can suggest a training plan that will elicit positive emotions. For example, it can analyze the time of day and the activities that the child is enjoying. Furthermore, it can use the emotion estimation function to monitor changes in a child's emotions while playing sports in real time and suggest a training plan that will enhance positive emotions. For example, it can adjust the intensity and type of training. In this way, it is possible to suggest a training plan that will elicit positive emotions by analyzing emotions.

[0078] The sports suggestion unit can compare the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. The sports suggestion unit, for example, compares the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. For example, performance is evaluated based on data from children of the same age and gender. It is also possible to build a system in which data from other children is collected and the generative AI provides a benchmark. For example, athletic ability and achievements are compared and evaluated. The generative AI can also compare the results of the proposed sports with data from other children, allowing the generative AI to provide a benchmark. For example, training progress and results are evaluated. This makes it possible to provide a benchmark by comparing with data from other children.

[0079] The sports suggestion unit can share the results of the suggested sports with parents and teachers, thereby strengthening the support system at home and school. The sports suggestion unit, for example, can share the results of the suggested sports with parents and teachers, thereby strengthening the support system at home and school. For example, the results can be provided in the form of a report. A system for sharing the results of sports with parents and teachers can also be established to strengthen the support system. For example, the results can be shared through an online platform. The results of the suggested sports can also be shared with parents and teachers, thereby strengthening the support system at home and school. For example, a system can be introduced that allows parents and teachers to provide feedback. In this way, the support system at home and school can be strengthened by sharing the results of the sports.

[0080] The sports suggestion unit can use the emotion estimation function to monitor a child's emotions after playing sports in real time and provide feedback according to the emotions. The sports suggestion unit can, for example, use the emotion estimation function to monitor a child's emotions after playing sports in real time and provide feedback according to the emotions. For example, feedback that strengthens positive emotions can be provided. It is also possible to build a system in which a generative AI provides feedback after playing sports based on the child's emotion data. For example, appropriate feedback can be provided by analyzing emotional changes. It is also possible to use the emotion estimation function to monitor a child's emotional changes after playing sports in real time and provide feedback according to the emotions. For example, the intensity and type of training can be adjusted. In this way, emotions can be monitored in real time and feedback according to the emotions can be provided.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The sports suggestion system can further include a music analysis unit. The music analysis unit analyzes the genre and rhythm of music a child likes, and the generation AI uses that information to suggest the most suitable sport. For example, it can suggest sports such as dance or gymnastics, which require a sense of rhythm. The music analysis unit can also analyze the music a child listens to while exercising and suggest music to improve athletic performance. For example, recommending fast-paced music can improve running or cycling performance. The music analysis unit can also suggest training plans that incorporate music based on the child's musical preferences. For example, it can provide exercises and rhythmic training that are timed to music. This allows children to enjoy exercising by utilizing music.

[0083] The sports suggestion system can further include an environmental analysis unit. The environmental analysis unit collects environmental data about the area where a child lives, and the generation AI uses that data to suggest the most suitable sports. For example, it can suggest outdoor or indoor sports taking into account the climate, topography, air quality, etc. The environmental analysis unit can also collect information about local sports facilities and clubs to suggest sports that are easy for children to participate in. For example, it can introduce nearby soccer clubs or swimming schools. The environmental analysis unit can also suggest sports events that children can participate in based on information about local events and tournaments. For example, it can suggest local marathons or basketball tournaments. This makes it possible to suggest sports that take the local environment into consideration.

[0084] The sports suggestion system can further include a nutritional analysis unit. The nutritional analysis unit collects a child's dietary data, and the generation AI uses that data to suggest the most suitable sports. For example, it can suggest sports that consume a lot of energy, taking into account nutritional balance and calorie intake. The nutritional analysis unit can also analyze a child's eating habits and suggest a meal plan to improve athletic performance. For example, it can recommend appropriate nutritional supplementation before and after exercise. The nutritional analysis unit can also take into account a child's allergy information and suggest sports and meal plans that are allergy-friendly. For example, it can provide a meal plan that avoids certain ingredients. This can support a child's sports activities from a nutritional perspective.

[0085] The sports suggestion system can further include a community analysis unit. The community analysis unit collects data on the community to which a child belongs, and the generation AI uses that data to suggest the most suitable sports. For example, by suggesting sports that friends and classmates are participating in, the system can encourage the child to participate more actively. The community analysis unit can also collect information on local sports clubs and teams to suggest sports that the child is likely to participate in. For example, it can introduce nearby soccer teams or volleyball clubs. The community analysis unit can also take into account other activities and events in which the child is participating and suggest sports that fit into the child's schedule. For example, it can select sports that do not overlap with school club activities or local events. This makes it possible to suggest sports that utilize community information.

[0086] The sports suggestion system can further include a virtual reality (VR) analysis unit. The VR analysis unit collects data on a child's sports experiences in a virtual reality environment, and the generation AI uses that data to suggest the most suitable sports. For example, the exercise data in the VR environment can be analyzed to evaluate the child's athletic ability and interests. The VR analysis unit can also provide a system that allows a child to try out a sport in a virtual environment before actually experiencing it. For example, the VR analysis unit can allow the child to try out virtual soccer or virtual basketball. The VR analysis unit can also suggest real sports based on the sports a child enjoys in the virtual environment. For example, the VR analysis unit can suggest real tennis to a child who enjoys virtual tennis. This makes it possible to suggest sports using virtual reality.

[0087] The sports suggestion system can also use an emotion estimation function to analyze a child's motivation when playing sports and make suggestions to increase their motivation. For example, it can analyze a child's facial expressions and voice when playing sports to identify factors that increase motivation. The emotion estimation function can also be used to suggest actions to increase a child's motivation if their motivation is declining. For example, it can send encouraging messages or set goals. The emotion estimation function can also be used to monitor a child's motivation in real time and suggest sports or training plans that will increase their motivation. For example, it can incorporate game-style training or team play. This makes it possible to make suggestions to increase motivation.

[0088] The sports suggestion system can further use the emotion estimation function to analyze a child's stress level when playing sports and make suggestions to reduce stress. For example, it can analyze a child's facial expressions and voice when playing sports to identify factors that increase stress. The emotion estimation function can also be used to suggest actions to reduce stress when a child's stress level is high. For example, it can recommend relaxation exercises or rest. The emotion estimation function can also be used to monitor a child's stress level in real time and suggest sports or training plans that will reduce stress. For example, it can incorporate yoga or meditation. This makes it possible to make suggestions to reduce stress.

[0089] The sports suggestion system can further use the emotion estimation function to analyze a child's level of concentration when playing sports and make suggestions to improve that level. For example, the system can analyze a child's facial expressions and voice when playing sports to identify factors that increase concentration. The emotion estimation function can also be used to suggest actions to improve a child's concentration if it is declining. For example, it can recommend breathing techniques or mental training to improve concentration. The emotion estimation function can also be used to monitor a child's concentration in real time and suggest sports or training plans that will improve concentration. For example, it can incorporate sports that require concentration or game-style training. This makes it possible to make suggestions to improve concentration.

[0090] The sports suggestion system can further use an emotion estimation function to analyze the enjoyment a child feels when playing sports and make suggestions to maximize enjoyment. For example, it can analyze a child's facial expressions and voice when playing sports to identify factors that increase enjoyment. The emotion estimation function can also be used to suggest actions to increase a child's enjoyment if the child's enjoyment is declining. For example, it can recommend game-style training or sports to play with friends. The emotion estimation function can also be used to monitor a child's enjoyment in real time and suggest sports or training plans that will increase enjoyment. For example, it can combine training that incorporates the child's favorite music or activities that the child finds enjoyable. This makes it possible to make suggestions to maximize enjoyment.

[0091] The sports suggestion system can also use the emotion estimation function to analyze a child's confidence when playing sports and make suggestions to increase it. For example, it can analyze a child's facial expressions and voice when playing sports to identify factors that increase confidence. The emotion estimation function can also be used to suggest actions to increase a child's confidence if it is low. For example, it can set small goals to help children accumulate successful experiences and provide positive feedback. The emotion estimation function can also be used to monitor a child's confidence in real time and suggest sports and training plans that will increase their confidence. For example, it can incorporate sports that make use of the skills the child is good at or training that will help them feel successful. This makes it possible to make suggestions to increase confidence.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The physical characteristics analysis unit analyzes the child's physical characteristics and athletic ability. For example, data such as height, weight, muscle strength, flexibility, and endurance is collected, and the generation AI analyzes this data. The physical characteristics analysis unit can also perform analysis based on the results of muscle strength and flexibility tests. For example, based on height and weight data, it predicts growth and suggests suitable sports. Step 2: The interest analysis unit analyzes the child's interests. For example, data such as the child's favorite sports and activities, past experiences, etc. are collected, and the generation AI analyzes this data. The interest analysis unit can also perform analysis based on the child's hobbies and topics of interest. For example, interests can be identified based on data on the sporting events and club activities the child has participated in in the past. Step 3: The sports suggestion unit suggests the most suitable sport based on the analysis results of the physical characteristics analysis unit and the interest analysis unit. For example, it suggests sports such as soccer, basketball, and track and field by comprehensively considering physical characteristics, athletic ability, and interests. The sports suggestion unit can also collect feedback on the results of playing the suggested sports and suggest further suitable sports. For example, data such as whether a child is enjoying playing the suggested sports and whether they are improving is collected, and the generation AI analyzes this data.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a physical characteristic analysis unit that analyzes the child's physical characteristics and motor abilities; an interest analysis unit that analyzes the interests and concerns of children; a sports suggestion unit that suggests an optimal sport based on the analysis results of the physical feature analysis unit and the interest analysis unit. A system characterized by:

2. The physical feature analysis unit When analyzing a child's athletic ability, movement data is collected in real time and generated by AI to analyze the movement.

2. The system of claim 1.

3. The interest analysis unit When analyzing children's interests and concerns, social media activity history is collected and the generation AI analyzes changes in interests.

2. The system of claim 1.

4. The sports suggestion unit When selecting sports to suggest, the AI ​​will refer to data on successful cases and suggest sports with a high probability of success.

2. The system of claim 1.

5. The physical feature analysis unit Analyzing children's emotions during exercise and identifying exercise patterns that elicit positive emotions 2. The system of claim 1.

6. The interest analysis unit Analyzing emotions during activities that interest children and identifying activities that elicit positive emotions 2. The system of claim 1.

7. The sports suggestion unit To analyze children's emotional responses to proposed sports and identify sports that elicit positive emotions.

2. The system of claim 1.

8. The sports suggestion unit Analyzing children's emotions while playing sports and proposing training plans that bring out positive emotions 2. The system of claim 1.

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