System

Abstract

An object of a system according to an embodiment is to propose an optimal training method and nutrition management in accordance with the degree of growth of a user by utilizing data of a professional athlete.SOLUTION: A system includes a data collection unit, an analysis unit, a proposal unit, a feedback unit, and a purchase platform unit. The data collection unit collects data of a professional athlete. The analysis unit analyzes the data of the professional athlete collected by the data collection unit. The proposal unit proposes an optimal form, training method, frequency, nutrition, and reference book according to the degree of growth of the user on the basis of the data analyzed by the analysis unit. The feedback unit analyzes the training result of the user and provides feedback. The purchasing platform portion allows the user to purchase items needed for training.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to apply the training methods and nutritional management of professional athletes to ordinary users, making it difficult to provide optimal instruction tailored to each individual's level of growth.

[0005] The system according to the embodiment aims to utilize data on professional athletes to propose optimal training methods and nutritional management tailored to the user's level of growth. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a suggestion unit, a feedback unit, and a purchase platform unit. The data collection unit collects data on professional athletes. The analysis unit analyzes the data on professional athletes collected by the data collection unit. The suggestion unit suggests optimal form, training method, frequency, nutrition, and reference books based on the user's level of growth based on the data analyzed by the analysis unit. The feedback unit analyzes the user's training results and provides feedback. The purchase platform unit allows the user to purchase items necessary for training. [Effects of the Invention]

[0007] The system according to the embodiment can utilize data on professional athletes to suggest optimal training methods and nutritional management tailored to the user's level of growth. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The competitive level improvement service according to the embodiment of the present invention is a system that utilizes data on professional athletes to provide users with optimal training methods, thereby enabling the competitive level improvement service to improve the users' competitive level.

[0029] A competitive level improvement service according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, a feedback unit, and a purchase platform unit. The data collection unit collects data on professional athletes. For example, the data collection unit collects training videos of professional athletes. The data collection unit can also collect interview articles of professional athletes. The data collection unit can also collect data on the nutritional management of professional athletes. The analysis unit analyzes the data on professional athletes collected by the data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. The proposal unit proposes optimal form, training method, frequency, nutrition, and reference books tailored to the user's level of growth based on the data analyzed by the analysis unit. For example, the proposal unit proposes a training menu based on the user's age. The proposal unit can also propose a nutrition plan based on the user's physical strength. The proposal unit can also propose reference books based on the user's skill level. The feedback unit analyzes the user's training results and provides feedback. For example, the feedback unit may analyze a video of the user's form and provide specific advice. The feedback unit may also analyze the user's training results and evaluate the effectiveness of the training. The feedback unit may also analyze the user's training data chronologically and reflect long-term growth trends in the feedback. The purchasing platform unit may enable the user to purchase items necessary for training. For example, the purchasing platform unit may enable the user to purchase training equipment online. The purchasing platform unit may also enable the user to purchase sportswear. The purchasing platform unit may also enable the user to purchase nutritional supplements. In this way, the competitive level improvement service according to the embodiment can improve the user's competitive level. For example, the user can improve their competitive level by practicing optimal training methods based on data from professional athletes.Furthermore, by receiving feedback, users can maximize the effectiveness of their training. Furthermore, by easily purchasing the items they need, users can create a good training environment.

[0030] The data collection unit can collect training videos, interview articles, or nutritional management data of professional athletes. The data collection unit, for example, collects training videos of professional athletes. For example, the data collection unit collects footage of professional athletes practicing or playing. The data collection unit can also collect interview articles of professional athletes. For example, the data collection unit collects interviews with players and comments from coaches. The data collection unit can also collect data on the nutritional management of professional athletes. For example, the data collection unit collects food records and nutrient intakes of professional athletes. This allows for more accurate analysis by collecting a variety of data on professional athletes.

[0031] The suggestion unit can provide a training menu and a nutrition plan based on the user's age, physical strength, or skill level. The suggestion unit, for example, provides a training menu based on the user's age. For example, the suggestion unit can provide a training menu suitable for a teenage user during their growth period. The suggestion unit can also provide a nutrition plan based on the user's physical strength. For example, the suggestion unit can provide a nutrition plan for improving endurance. The suggestion unit can also provide reference books based on the user's skill level. For example, the suggestion unit can provide books on training theories for beginners. This allows for effective training by providing an optimal training menu and nutrition plan based on the user's individual characteristics.

[0032] The feedback unit can analyze videos of the user's training results or form and provide specific advice. The feedback unit, for example, analyzes the user's training results and provides specific advice. For example, the feedback unit points out areas for improvement in form based on the user's training data. The feedback unit can also analyze videos of the user's form and provide specific advice. For example, the feedback unit can use video analysis technology to detect errors in form and suggest correction methods. The feedback unit can also analyze the user's training results and evaluate the effectiveness of the training. For example, the feedback unit can evaluate the progress of training and suggest the next step. In this way, the effectiveness of the training is maximized by analyzing the user's training results and providing specific advice.

[0033] The purchasing platform unit may enable the user to purchase items necessary for training online. The purchasing platform unit may, for example, enable the user to purchase items necessary for training online. For example, the purchasing platform unit may enable the user to purchase training equipment online. The purchasing platform unit may also enable the user to purchase sportswear online. The purchasing platform unit may also enable the user to purchase nutritional supplements online. This allows the user to easily purchase items necessary for training, thereby creating a better training environment.

[0034] The analysis unit can collect the sleep patterns and stress levels of professional athletes and integrate them with training data for analysis. For example, the analysis unit collects the sleep patterns of professional athletes using a smartwatch or sleep tracker and integrates them with training data for analysis. For example, the analysis unit examines the correlation between sleep quality and training effectiveness. The analysis unit can also collect the heart rate variability and cortisol levels of professional athletes to measure stress levels and integrate them with training data for analysis. For example, the analysis unit can evaluate the impact of stress on training effectiveness. The analysis unit can also collect the sleep patterns and stress levels of professional athletes using diaries or questionnaires and integrate them with training data for analysis. For example, the analysis unit can evaluate training effectiveness using self-reported data. This allows for a more accurate analysis of training effectiveness by taking into account the sleep patterns and stress levels of professional athletes.

[0035] The analysis unit can record the professional athlete's diet in detail and analyze the correlation between nutrient intake and training effects. For example, the analysis unit records the professional athlete's diet using an app and analyzes nutrient intake. For example, the analysis unit examines the correlation between protein and carbohydrate intake and training effects. The analysis unit can also record the diet in photographs and automatically calculate nutrient intake using image analysis technology. For example, the analysis unit can analyze calories and nutrients from food photos to evaluate training effects. The analysis unit can also have a nutritionist supervise the professional athlete's diet and collect detailed nutrient data. For example, the analysis unit analyzes the impact of diet quality and balance on training effects. This enables more effective nutritional management by recording the professional athlete's diet in detail and analyzing the correlation between nutrient intake and training effects.

[0036] The data collection unit can collect biometric data of professional athletes and perform detailed analysis. For example, the data collection unit collects the professional athletes' heart rate and blood pressure in real time using a smartwatch and integrates the data with training data for analysis. For example, the data collection unit can examine the correlation between heart rate fluctuations and training effectiveness. In addition, the data collection unit can have the professional athletes wear wearable devices to measure their heart rate and blood pressure in order to collect biometric data. For example, the data collection unit can analyze physiological responses during training. The data collection unit can also periodically collect the professional athletes' biometric data and evaluate the long-term training effectiveness. For example, the data collection unit can adjust training plans based on changes in heart rate and blood pressure. In this way, collecting the professional athletes' biometric data enables more detailed analysis.

[0037] The data collection unit can collect data on professional athletes from different sports and perform cross-sport analysis. For example, the data collection unit collects training data from professional athletes from different sports and analyzes common training elements. For example, the data collection unit compares training methods for soccer and basketball. The data collection unit can also integrate data from professional athletes from different sports to perform cross-sport analysis and evaluate training effectiveness. For example, the data collection unit compares training menus for different sports. The data collection unit can also collect data from professional athletes from different sports and analyze common nutritional management and mental training elements. For example, the data collection unit compares nutrition plans for different sports. In this way, common training elements can be identified by collecting data from different sports and performing cross-sport analysis.

[0038] The suggestion unit can analyze the user's genetic information and make training suggestions based on the user's genetic characteristics. For example, the suggestion unit can analyze the user's genetic information and suggest a training menu based on muscle growth rate and endurance. For example, the suggestion unit can intensify strength training for a person who is genetically prone to building muscle. The suggestion unit can also evaluate the user's risk of injury based on the genetic information and suggest a safe training menu. For example, the suggestion unit can recommend low-impact training for a person with weak joints. The suggestion unit can also analyze the user's genetic information to optimize nutritional intake. For example, the suggestion unit can suggest a meal plan to supplement a person who is prone to deficiencies in certain vitamins or minerals. This makes it possible to make more effective training by making training suggestions based on the user's genetic information.

[0039] The suggestion unit can collect the user's lifestyle data (sleep, diet, stress) and make training suggestions based on the user's overall health condition. The suggestion unit, for example, analyzes the user's sleep data and suggests a training menu based on the quality of sleep. For example, the suggestion unit recommends light training when the user is sleep deprived. The suggestion unit can also collect dietary data and suggest a training menu based on nutritional balance. For example, the suggestion unit provides a menu that emphasizes energy replenishment when carbohydrates are lacking. The suggestion unit can also analyze the user's stress level and suggest a training menu that takes stress management into consideration. For example, the suggestion unit recommends a menu that incorporates relaxation when stress is high. In this way, by making training suggestions based on the user's lifestyle data, it becomes possible to perform training that takes the user's overall health condition into consideration.

[0040] The suggestion unit can suggest an optimal training method based on the user's learning style (visual, auditory, kinesthetic). For example, the suggestion unit analyzes the user's learning style and suggests video training to people who are good at visual learning. For example, the suggestion unit may train while watching a video of form. The suggestion unit can also suggest a training menu with audio guidance to people who are good at auditory learning. For example, the suggestion unit may use an app that gives training instructions by voice. The suggestion unit can also suggest a training menu that allows people who are good at kinesthetic learning to learn while actually moving their bodies. For example, the suggestion unit may use an interactive training game. This allows for more effective training by suggesting a training method based on the user's learning style.

[0041] The suggestion unit can propose a training menu suitable for a region by taking into account the user's cultural background and regional characteristics. The suggestion unit, for example, proposes sports and training methods unique to a region by taking into account the user's cultural background. For example, the suggestion unit provides soccer-focused training to a European user. The suggestion unit can also propose an appropriate training menu by taking into account the climate and environment of the region. For example, the suggestion unit recommends indoor training to a user in a cold region. The suggestion unit can also propose a meal plan and a nutritional supplementation method based on the user's cultural background. For example, the suggestion unit provides a rice-focused meal plan to an Asian user. This allows for training suitable for a region by proposing a training menu that takes into account the user's cultural background and regional characteristics.

[0042] The feedback unit can analyze the user's training data in chronological order and reflect long-term growth trends in the feedback. The feedback unit, for example, analyzes the user's training data in chronological order and visualizes the growth trends. For example, the feedback unit displays training progress in a graph based on past data. The feedback unit can also periodically analyze the user's training data to reflect long-term growth trends in the feedback. For example, the feedback unit calculates monthly growth rates and reflects them in the feedback. The feedback unit can also analyze the user's training data in chronological order and adjust the training menu based on the growth trends. For example, the feedback unit can suggest a new training method if growth has stagnated. In this way, the effectiveness of training is maximized by analyzing the user's training data in chronological order and reflecting long-term growth trends in the feedback.

[0043] The feedback unit can analyze the user's training environment and provide feedback appropriate to the environment. For example, the feedback unit collects data on the user's training environment and provides feedback appropriate to the temperature and humidity. For example, the feedback unit may advise the user to hydrate when training on a hot day. The feedback unit can also analyze data on the training location and provide feedback appropriate to the environment. For example, the feedback unit may recommend using sunscreen when training outdoors. The feedback unit can also suggest optimal training times and locations based on the user's training environment data. For example, the feedback unit may recommend indoor training on days with high humidity. In this way, the effectiveness of training is maximized by analyzing the user's training environment and providing feedback appropriate to the environment.

[0044] The feedback unit can provide the feedback content in the form of a visual graph or animation to make it easier to understand. For example, the feedback unit can display the user's training data in the form of a visual graph to make the feedback content easier to understand. For example, the feedback unit can show training progress in the form of a line graph. The feedback unit can also provide the feedback content in the form of an animation to make it easier for the user to understand intuitively. For example, the feedback unit can show areas for form improvement in the form of an animation. The feedback unit can also provide the feedback content in an easy-to-understand manner using a visual graph or animation. For example, the feedback unit can show the effect of training in the form of a bar graph. In this way, providing the feedback content in the form of a visual graph or animation makes it easier for the user to understand intuitively.

[0045] The feedback unit can provide feedback in real time through a voice assistant to encourage immediate improvement during training. The feedback unit can provide feedback in real time during training using, for example, a voice assistant. For example, the feedback unit can provide voice instructions on how to improve form. The feedback unit can also analyze the user's training data and provide feedback to encourage immediate improvement through the voice assistant. For example, the feedback unit can instruct the user to correct their posture during training. The feedback unit can also provide feedback in real time during training using a voice assistant to maximize the training effect. For example, the feedback unit can provide advice according to the progress of training. In this way, by providing feedback in real time through the voice assistant, it is possible to encourage immediate improvement during training.

[0046] The purchasing platform unit can analyze the user's training data and suggest the purchase of necessary items at the optimal time. The purchasing platform unit, for example, analyzes the user's training data and suggests the purchase of necessary items based on the user's training progress. For example, the purchasing platform unit suggests new training equipment when training results are being achieved. The purchasing platform unit can also suggest items the user needs at the optimal time based on the training data. For example, the purchasing platform unit suggests replenishing consumables when the user trains frequently. The purchasing platform unit can also analyze the user's training data and suggest items to maximize the effectiveness of training. For example, the purchasing platform unit suggests supplements based on the user's training progress. In this way, the effectiveness of training is maximized by analyzing the user's training data and suggesting necessary items at the optimal time.

[0047] The purchasing platform unit can analyze the user's past purchase history and make personalized item suggestions. The purchasing platform unit, for example, analyzes the user's past purchase history and makes personalized item suggestions. For example, the purchasing platform unit can suggest new items that match training wear previously purchased. The purchasing platform unit can also suggest items the user needs based on the purchase history. For example, the purchasing platform unit can suggest refills for supplements previously purchased. The purchasing platform unit can also analyze the user's purchase history and make item suggestions based on the user's training progress. For example, the purchasing platform unit can suggest new items that match training equipment previously purchased. In this way, by analyzing the user's past purchase history and making personalized item suggestions, it is possible to provide items that meet the user's needs.

[0048] The purchasing platform unit can display reviews or ratings from other users on the purchasing platform to help users make purchases. The purchasing platform unit, for example, displays reviews and ratings from other users on the purchasing platform to help users make purchases. For example, the purchasing platform unit displays reviews of training shoes. The purchasing platform unit can also select items for users to purchase based on ratings from other users. For example, the purchasing platform unit can suggest highly rated training equipment. The purchasing platform unit can also add a review function to the purchasing platform to enable users to post ratings of purchased items. For example, the purchasing platform unit collects feedback after purchases. This allows other users' reviews and ratings to be displayed on the purchasing platform to help users make purchases.

[0049] The purchasing platform unit can introduce an AI chatbot into the purchasing platform to answer user questions in real time. The purchasing platform unit, for example, introduces an AI chatbot into the purchasing platform to answer user questions in real time. For example, the purchasing platform unit answers questions about how to use training equipment. The purchasing platform unit can also use the AI ​​chatbot to resolve user questions about purchases. For example, the purchasing platform unit answers questions about product inventory and delivery information. The purchasing platform unit can also introduce an AI chatbot into the purchasing platform to quickly respond to user questions. For example, the purchasing platform unit answers questions about how to choose products and recommended items. In this way, by introducing an AI chatbot into the purchasing platform, user questions can be answered in real time.

[0050] The advertisement display unit can analyze the user's training data and display advertisements related to the training content. The advertisement display unit, for example, analyzes the user's training data and displays advertisements related to the training content. For example, if the user is performing strength training, the advertisement display unit displays an advertisement for protein. The advertisement display unit can also display advertisements for items the user needs based on the training data. For example, if the user is running, the advertisement display unit displays an advertisement for running shoes. The advertisement display unit can also analyze the user's training data and display advertisements according to the user's training progress. For example, the advertisement display unit displays an advertisement for new training equipment when the user is seeing results from their training. In this way, the effectiveness of the advertisements is maximized by analyzing the user's training data and displaying advertisements related to the training content.

[0051] The advertisement display unit can analyze the user's past behavioral history and display personalized advertisements. The advertisement display unit can, for example, analyze the user's past behavioral history and display personalized advertisements. For example, the advertisement display unit displays advertisements related to training wear purchased in the past. The advertisement display unit can also display advertisements for items that the user is interested in based on the behavioral history. For example, the advertisement display unit displays advertisements for training equipment searched for in the past. The advertisement display unit can also analyze the user's behavioral history and display advertisements according to the user's training progress. For example, the advertisement display unit displays advertisements suggesting refills of supplements purchased in the past. In this way, the effectiveness of advertisements is maximized by analyzing the user's past behavioral history and displaying personalized advertisements.

[0052] The advertisement display unit can adjust the timing of advertisement display according to the user's training progress. The advertisement display unit, for example, analyzes the user's training progress and displays advertisements at the optimal timing. For example, the advertisement display unit displays advertisements for new training equipment when training results are being achieved. The advertisement display unit can also display advertisements for items the user needs at the optimal timing based on training data. For example, the advertisement display unit displays advertisements suggesting the replenishment of consumables when the user trains frequently. The advertisement display unit can also analyze the user's training progress in real time and adjust the timing of advertisement display. For example, the advertisement display unit displays advertisements for supplements according to the user's training progress. In this way, the effectiveness of advertisements is maximized by adjusting the timing of advertisement display according to the user's training progress.

[0053] The advertisement display unit can customize advertisements based on the user's regional characteristics and cultural background. The advertisement display unit, for example, displays advertisements appropriate for the region, taking into account the user's regional characteristics. For example, the advertisement display unit displays advertisements for winter training wear to a user living in a cold climate. The advertisement display unit can also display advertisements for items of interest to the user based on the user's cultural background. For example, the advertisement display unit displays advertisements for rice-based meal plans to a user living in Asia. The advertisement display unit can also analyze the user's regional characteristics and cultural background and display personalized advertisements. For example, the advertisement display unit displays advertisements tailored to local events and trends. In this way, the effectiveness of advertisements is maximized by customizing advertisements based on the user's regional characteristics and cultural background.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can collect the sleep patterns and stress levels of professional athletes and integrate them with training data for analysis. For example, the analysis unit can collect the sleep patterns of professional athletes using a smartwatch or sleep tracker and integrate them with training data for analysis. For example, the analysis unit can examine the correlation between sleep quality and training effectiveness. The analysis unit can also collect the heart rate variability and cortisol levels of professional athletes to measure stress levels and integrate them with training data for analysis. For example, the analysis unit can evaluate the impact of stress on training effectiveness. The analysis unit can also collect the sleep patterns and stress levels of professional athletes using diaries or questionnaires and integrate them with training data for analysis. For example, the analysis unit can evaluate training effectiveness using self-reported data. In this way, the training effectiveness can be more accurately analyzed by taking into account the sleep patterns and stress levels of professional athletes.

[0056] The suggestion unit can analyze the user's genetic information and make training suggestions based on the user's genetic characteristics. For example, the suggestion unit can analyze the user's genetic information and suggest a training menu based on muscle growth rate and endurance. For example, the suggestion unit can intensify strength training for a person who is genetically prone to building muscle. The suggestion unit can also evaluate the user's risk of injury based on the genetic information and suggest a safe training menu. For example, the suggestion unit can recommend low-impact training for a person with weak joints. The suggestion unit can also analyze the user's genetic information to optimize nutritional intake. For example, the suggestion unit can suggest a meal plan to supplement a person who is prone to deficiencies in certain vitamins or minerals. This makes it possible to make more effective training by making training suggestions based on the user's genetic information.

[0057] The suggestion unit can suggest an optimal training method based on the user's learning style (visual, auditory, kinesthetic). For example, the suggestion unit analyzes the user's learning style and suggests video training to a person who is good at visual learning. For example, the suggestion unit may train while watching a video of form. The suggestion unit can also suggest a training menu with audio guidance to a person who is good at auditory learning. For example, the suggestion unit may use an app that gives training instructions by voice. The suggestion unit can also suggest a training menu that allows the user to learn while actually moving their body to a person who is good at kinesthetic learning. For example, the suggestion unit may use an interactive training game. This allows for more effective training by suggesting a training method based on the user's learning style.

[0058] The suggestion unit can propose a training menu suitable for a region, taking into account the user's cultural background and regional characteristics. For example, the suggestion unit proposes sports and training methods unique to the region, taking into account the user's cultural background. For example, the suggestion unit provides soccer-focused training to a European user. The suggestion unit can also propose an appropriate training menu, taking into account the region's climate and environment. For example, the suggestion unit recommends indoor training to a user in a cold region. The suggestion unit can also propose a meal plan and a nutritional supplementation method based on the user's cultural background. For example, the suggestion unit provides a rice-focused meal plan to an Asian user. This allows for training suitable for the region by proposing a training menu that takes into account the user's cultural background and regional characteristics.

[0059] The feedback unit can analyze the user's training environment and provide feedback appropriate to the environment. For example, the feedback unit collects data on the user's training environment and provides feedback appropriate to the temperature and humidity. For example, the feedback unit may advise the user to hydrate when training on a hot day. The feedback unit can also analyze data on the training location and provide feedback appropriate to the environment. For example, the feedback unit may recommend using sunscreen when training outdoors. The feedback unit can also suggest optimal training times and locations based on the user's training environment data. For example, the feedback unit may recommend indoor training on days with high humidity. In this way, the effectiveness of training is maximized by analyzing the user's training environment and providing feedback appropriate to the environment.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The data collection department collects data on professional athletes. For example, the data collection department collects training videos, interview articles, and nutritional management data on professional athletes. Step 2: The analysis unit analyzes the data of the professional athletes collected by the data collection unit. For example, the analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The suggestion unit suggests optimal form, training method, frequency, nutrition, and reference books based on the data analyzed by the analysis unit, according to the user's level of development. For example, the suggestion unit suggests training menus, nutrition plans, and reference books based on the user's age, physical strength, and skill level. Step 4: The feedback unit analyzes the user's training results and provides feedback. For example, the feedback unit analyzes videos of the user's form and provides specific advice. It also analyzes the training results, evaluates the effectiveness of the training, and reflects long-term growth trends in the feedback. Step 5: The purchasing platform allows the user to purchase items necessary for training. For example, the purchasing platform allows the user to purchase training equipment, sportswear, and nutritional supplements online.

[0062] (Example 2) The competitive level improvement service according to the embodiment of the present invention is a system that utilizes data on professional athletes to provide users with optimal training methods, thereby enabling the competitive level improvement service to improve the users' competitive level.

[0063] A competitive level improvement service according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, a feedback unit, and a purchase platform unit. The data collection unit collects data on professional athletes. For example, the data collection unit collects training videos of professional athletes. The data collection unit can also collect interview articles of professional athletes. The data collection unit can also collect data on the nutritional management of professional athletes. The analysis unit analyzes the data on professional athletes collected by the data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. The proposal unit proposes optimal form, training method, frequency, nutrition, and reference books tailored to the user's level of growth based on the data analyzed by the analysis unit. For example, the proposal unit proposes a training menu based on the user's age. The proposal unit can also propose a nutrition plan based on the user's physical strength. The proposal unit can also propose reference books based on the user's skill level. The feedback unit analyzes the user's training results and provides feedback. For example, the feedback unit may analyze a video of the user's form and provide specific advice. The feedback unit may also analyze the user's training results and evaluate the effectiveness of the training. The feedback unit may also analyze the user's training data chronologically and reflect long-term growth trends in the feedback. The purchasing platform unit may enable the user to purchase items necessary for training. For example, the purchasing platform unit may enable the user to purchase training equipment online. The purchasing platform unit may also enable the user to purchase sportswear. The purchasing platform unit may also enable the user to purchase nutritional supplements. In this way, the competitive level improvement service according to the embodiment can improve the user's competitive level. For example, the user can improve their competitive level by practicing optimal training methods based on data from professional athletes.Furthermore, by receiving feedback, users can maximize the effectiveness of their training. Furthermore, by easily purchasing the items they need, users can create a good training environment.

[0064] The data collection unit can collect training videos, interview articles, or nutritional management data of professional athletes. The data collection unit, for example, collects training videos of professional athletes. For example, the data collection unit collects footage of professional athletes practicing or playing. The data collection unit can also collect interview articles of professional athletes. For example, the data collection unit collects interviews with players and comments from coaches. The data collection unit can also collect data on the nutritional management of professional athletes. For example, the data collection unit collects food records and nutrient intakes of professional athletes. This allows for more accurate analysis by collecting a variety of data on professional athletes.

[0065] The suggestion unit can provide a training menu and a nutrition plan based on the user's age, physical strength, or skill level. The suggestion unit, for example, provides a training menu based on the user's age. For example, the suggestion unit can provide a training menu suitable for a teenage user during their growth period. The suggestion unit can also provide a nutrition plan based on the user's physical strength. For example, the suggestion unit can provide a nutrition plan for improving endurance. The suggestion unit can also provide reference books based on the user's skill level. For example, the suggestion unit can provide books on training theories for beginners. This allows for effective training by providing an optimal training menu and nutrition plan based on the user's individual characteristics.

[0066] The feedback unit can analyze videos of the user's training results or form and provide specific advice. The feedback unit, for example, analyzes the user's training results and provides specific advice. For example, the feedback unit points out areas for improvement in form based on the user's training data. The feedback unit can also analyze videos of the user's form and provide specific advice. For example, the feedback unit can use video analysis technology to detect errors in form and suggest correction methods. The feedback unit can also analyze the user's training results and evaluate the effectiveness of the training. For example, the feedback unit can evaluate the progress of training and suggest the next step. In this way, the effectiveness of the training is maximized by analyzing the user's training results and providing specific advice.

[0067] The purchasing platform unit may enable the user to purchase items necessary for training online. The purchasing platform unit may, for example, enable the user to purchase items necessary for training online. For example, the purchasing platform unit may enable the user to purchase training equipment online. The purchasing platform unit may also enable the user to purchase sportswear online. The purchasing platform unit may also enable the user to purchase nutritional supplements online. This allows the user to easily purchase items necessary for training, thereby creating a better training environment.

[0068] The analysis unit can estimate the emotional state of a professional athlete and analyze the effectiveness of training based on that emotion. The analysis unit, for example, analyzes the professional athlete's facial expressions and voice during training to estimate the emotional state. For example, the analysis unit analyzes the professional athlete's smile and tone of voice during training to analyze the impact of positive emotions on training effectiveness. The analysis unit can also monitor the professional athlete's emotional state in real time to evaluate the effectiveness of training. For example, the analysis unit measures heart rate and electrodermal activity to analyze the correlation between changes in emotion and training effectiveness. The analysis unit can also perform text analysis of the professional athlete's diary and interview content to quantify the intensity and type of emotion in order to analyze the effectiveness of training based on the emotional state. This allows for more accurate analysis of training effectiveness by taking the professional athlete's emotional state into account.

[0069] The analysis unit can collect the sleep patterns and stress levels of professional athletes and integrate them with training data for analysis. For example, the analysis unit collects the sleep patterns of professional athletes using a smartwatch or sleep tracker and integrates them with training data for analysis. For example, the analysis unit examines the correlation between sleep quality and training effectiveness. The analysis unit can also collect the heart rate variability and cortisol levels of professional athletes to measure stress levels and integrate them with training data for analysis. For example, the analysis unit can evaluate the impact of stress on training effectiveness. The analysis unit can also collect the sleep patterns and stress levels of professional athletes using diaries or questionnaires and integrate them with training data for analysis. For example, the analysis unit can evaluate training effectiveness using self-reported data. This allows for a more accurate analysis of training effectiveness by taking into account the sleep patterns and stress levels of professional athletes.

[0070] The analysis unit can record the professional athlete's diet in detail and analyze the correlation between nutrient intake and training effects. For example, the analysis unit records the professional athlete's diet using an app and analyzes nutrient intake. For example, the analysis unit examines the correlation between protein and carbohydrate intake and training effects. The analysis unit can also record the diet in photographs and automatically calculate nutrient intake using image analysis technology. For example, the analysis unit can analyze calories and nutrients from food photos to evaluate training effects. The analysis unit can also have a nutritionist supervise the professional athlete's diet and collect detailed nutrient data. For example, the analysis unit analyzes the impact of diet quality and balance on training effects. This enables more effective nutritional management by recording the professional athlete's diet in detail and analyzing the correlation between nutrient intake and training effects.

[0071] The data collection unit can collect biometric data of professional athletes and perform detailed analysis. For example, the data collection unit collects the professional athletes' heart rate and blood pressure in real time using a smartwatch and integrates the data with training data for analysis. For example, the data collection unit can examine the correlation between heart rate fluctuations and training effectiveness. In addition, the data collection unit can have the professional athletes wear wearable devices to measure their heart rate and blood pressure in order to collect biometric data. For example, the data collection unit can analyze physiological responses during training. The data collection unit can also periodically collect the professional athletes' biometric data and evaluate the long-term training effectiveness. For example, the data collection unit can adjust training plans based on changes in heart rate and blood pressure. In this way, collecting the professional athletes' biometric data enables more detailed analysis.

[0072] The data collection unit can collect data on professional athletes from different sports and perform cross-sport analysis. For example, the data collection unit collects training data from professional athletes from different sports and analyzes common training elements. For example, the data collection unit compares training methods for soccer and basketball. The data collection unit can also integrate data from professional athletes from different sports to perform cross-sport analysis and evaluate training effectiveness. For example, the data collection unit compares training menus for different sports. The data collection unit can also collect data from professional athletes from different sports and analyze common nutritional management and mental training elements. For example, the data collection unit compares nutrition plans for different sports. In this way, common training elements can be identified by collecting data from different sports and performing cross-sport analysis.

[0073] The analysis unit can use the emotion estimation function to monitor the emotional state of a professional athlete in real time and use the information to optimize training. For example, the analysis unit can use the emotion estimation function to monitor the emotional state of a professional athlete in real time and adjust the training menu. For example, the analysis unit can suggest relaxation training when stress is high. The analysis unit can also analyze the emotional state of a professional athlete in real time and provide feedback to maximize the effectiveness of training. For example, the analysis unit can perform high-intensity training when positive emotions are strong. The analysis unit can also use the emotion estimation function to monitor the emotional state of a professional athlete and suggest mental training based on the progress of training. For example, the analysis unit can send encouraging messages when motivation is low. In this way, training can be optimized by monitoring the emotional state of a professional athlete in real time.

[0074] The suggestion unit can estimate the user's emotional state and adjust a training menu based on that emotion. For example, the suggestion unit estimates the user's emotional state and suggests a high-intensity training menu when the user has strong positive emotions. For example, the suggestion unit performs interval training when the emotion score is high. The suggestion unit can also use the emotion estimation function to monitor the user's emotional state in real time and adjust the training menu. For example, the suggestion unit suggests relaxing yoga when stress is high. The suggestion unit can also analyze the user's emotional state and provide a menu to maximize the effectiveness of training. For example, the suggestion unit suggests lighter training when motivation is low. In this way, the training menu can be adjusted based on the user's emotional state to maximize the effectiveness of training.

[0075] The suggestion unit can analyze the user's genetic information and make training suggestions based on the user's genetic characteristics. For example, the suggestion unit can analyze the user's genetic information and suggest a training menu based on muscle growth rate and endurance. For example, the suggestion unit can intensify strength training for a person who is genetically prone to building muscle. The suggestion unit can also evaluate the user's risk of injury based on the genetic information and suggest a safe training menu. For example, the suggestion unit can recommend low-impact training for a person with weak joints. The suggestion unit can also analyze the user's genetic information to optimize nutritional intake. For example, the suggestion unit can suggest a meal plan to supplement a person who is prone to deficiencies in certain vitamins or minerals. This makes it possible to make more effective training by making training suggestions based on the user's genetic information.

[0076] The suggestion unit can collect the user's lifestyle data (sleep, diet, stress) and make training suggestions based on the user's overall health condition. The suggestion unit, for example, analyzes the user's sleep data and suggests a training menu based on the quality of sleep. For example, the suggestion unit recommends light training when the user is sleep deprived. The suggestion unit can also collect dietary data and suggest a training menu based on nutritional balance. For example, the suggestion unit provides a menu that emphasizes energy replenishment when carbohydrates are lacking. The suggestion unit can also analyze the user's stress level and suggest a training menu that takes stress management into consideration. For example, the suggestion unit recommends a menu that incorporates relaxation when stress is high. In this way, by making training suggestions based on the user's lifestyle data, it becomes possible to perform training that takes the user's overall health condition into consideration.

[0077] The suggestion unit can suggest an optimal training method based on the user's learning style (visual, auditory, kinesthetic). For example, the suggestion unit analyzes the user's learning style and suggests video training to people who are good at visual learning. For example, the suggestion unit may train while watching a video of form. The suggestion unit can also suggest a training menu with audio guidance to people who are good at auditory learning. For example, the suggestion unit may use an app that gives training instructions by voice. The suggestion unit can also suggest a training menu that allows people who are good at kinesthetic learning to learn while actually moving their bodies. For example, the suggestion unit may use an interactive training game. This allows for more effective training by suggesting a training method based on the user's learning style.

[0078] The suggestion unit can propose a training menu suitable for a region by taking into account the user's cultural background and regional characteristics. The suggestion unit, for example, proposes sports and training methods unique to a region by taking into account the user's cultural background. For example, the suggestion unit provides soccer-focused training to a European user. The suggestion unit can also propose an appropriate training menu by taking into account the climate and environment of the region. For example, the suggestion unit recommends indoor training to a user in a cold region. The suggestion unit can also propose a meal plan and a nutritional supplementation method based on the user's cultural background. For example, the suggestion unit provides a rice-focused meal plan to an Asian user. This allows for training suitable for a region by proposing a training menu that takes into account the user's cultural background and regional characteristics.

[0079] The suggestion unit can use the emotion estimation function to monitor the user's emotional state in real time and adjust suggestions according to the progress of the training. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and adjust the training menu. For example, the suggestion unit suggests relaxation training when stress is high. The suggestion unit can also analyze the user's emotional state in real time and provide feedback according to the progress of the training. For example, the suggestion unit sends an encouraging message when motivation is low. The suggestion unit can also use the emotion estimation function to monitor the user's emotional state and suggest mental training according to the progress of the training. For example, the suggestion unit performs high-intensity training when positive emotions are strong. In this way, the effect of the training is maximized by monitoring the user's emotional state in real time and making suggestions according to the progress of the training.

[0080] The feedback unit can estimate the user's emotional state and adjust the feedback content based on the emotion. For example, the feedback unit estimates the user's emotional state and provides encouraging feedback when the user has strong positive emotions. For example, the feedback unit sends a message praising the user's training results. The feedback unit can also monitor the user's emotional state in real time using the emotion estimation function and adjust the feedback content. For example, the feedback unit provides advice to relax when stress is high. The feedback unit can also analyze the user's emotional state and provide feedback to maximize the effectiveness of the training. For example, the feedback unit sends an encouraging message when motivation is low. In this way, the effectiveness of the training is maximized by adjusting the feedback content based on the user's emotional state.

[0081] The feedback unit can analyze the user's training data in chronological order and reflect long-term growth trends in the feedback. The feedback unit, for example, analyzes the user's training data in chronological order and visualizes the growth trends. For example, the feedback unit displays training progress in a graph based on past data. The feedback unit can also periodically analyze the user's training data to reflect long-term growth trends in the feedback. For example, the feedback unit calculates monthly growth rates and reflects them in the feedback. The feedback unit can also analyze the user's training data in chronological order and adjust the training menu based on the growth trends. For example, the feedback unit can suggest a new training method if growth has stagnated. In this way, the effectiveness of training is maximized by analyzing the user's training data in chronological order and reflecting long-term growth trends in the feedback.

[0082] The feedback unit can analyze the user's training environment and provide feedback appropriate to the environment. For example, the feedback unit collects data on the user's training environment and provides feedback appropriate to the temperature and humidity. For example, the feedback unit may advise the user to hydrate when training on a hot day. The feedback unit can also analyze data on the training location and provide feedback appropriate to the environment. For example, the feedback unit may recommend using sunscreen when training outdoors. The feedback unit can also suggest optimal training times and locations based on the user's training environment data. For example, the feedback unit may recommend indoor training on days with high humidity. In this way, the effectiveness of training is maximized by analyzing the user's training environment and providing feedback appropriate to the environment.

[0083] The feedback unit can provide the feedback content in the form of a visual graph or animation to make it easier to understand. For example, the feedback unit can display the user's training data in the form of a visual graph to make the feedback content easier to understand. For example, the feedback unit can show training progress in the form of a line graph. The feedback unit can also provide the feedback content in the form of an animation to make it easier for the user to understand intuitively. For example, the feedback unit can show areas for form improvement in the form of an animation. The feedback unit can also provide the feedback content in an easy-to-understand manner using a visual graph or animation. For example, the feedback unit can show the effect of training in the form of a bar graph. In this way, providing the feedback content in the form of a visual graph or animation makes it easier for the user to understand intuitively.

[0084] The feedback unit can provide feedback in real time through a voice assistant to encourage immediate improvement during training. The feedback unit can provide feedback in real time during training using, for example, a voice assistant. For example, the feedback unit can provide voice instructions on how to improve form. The feedback unit can also analyze the user's training data and provide feedback to encourage immediate improvement through the voice assistant. For example, the feedback unit can instruct the user to correct their posture during training. The feedback unit can also provide feedback in real time during training using a voice assistant to maximize the training effect. For example, the feedback unit can provide advice according to the progress of training. In this way, by providing feedback in real time through the voice assistant, it is possible to encourage immediate improvement during training.

[0085] The feedback unit can use the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. The feedback unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and provide feedback according to the emotion. For example, the feedback unit provides advice to relax when stress is high. The feedback unit can also analyze the user's emotional state in real time and provide feedback according to the emotion. For example, the feedback unit sends an encouraging message when the user has strong positive emotions. The feedback unit can also use the emotion estimation function to monitor the user's emotional state and provide feedback according to the progress of training. For example, the feedback unit sends an encouraging message when motivation is low. In this way, the effect of training is maximized by monitoring the user's emotional state in real time and providing feedback according to the emotion.

[0086] The purchasing platform unit can estimate the user's emotional state and suggest items to purchase based on that emotion. For example, the purchasing platform unit estimates the user's emotional state and suggests new training items when the user has strong positive emotions. For example, the purchasing platform unit suggests new training shoes when the emotion score is high. The purchasing platform unit can also use the emotion estimation function to monitor the user's emotional state in real time and suggest items to purchase. For example, the purchasing platform unit suggests relaxation products when the user is highly stressed. The purchasing platform unit can also analyze the user's emotional state and suggest items to purchase based on the emotion. For example, the purchasing platform unit suggests new training wear when motivation is low. In this way, by suggesting items to purchase based on the user's emotional state, items that meet the user's needs can be provided.

[0087] The purchasing platform unit can analyze the user's training data and suggest the purchase of necessary items at the optimal time. The purchasing platform unit, for example, analyzes the user's training data and suggests the purchase of necessary items based on the user's training progress. For example, the purchasing platform unit suggests new training equipment when training results are being achieved. The purchasing platform unit can also suggest items the user needs at the optimal time based on the training data. For example, the purchasing platform unit suggests replenishing consumables when the user trains frequently. The purchasing platform unit can also analyze the user's training data and suggest items to maximize the effectiveness of training. For example, the purchasing platform unit suggests supplements based on the user's training progress. In this way, the effectiveness of training is maximized by analyzing the user's training data and suggesting necessary items at the optimal time.

[0088] The purchasing platform unit can analyze the user's past purchase history and make personalized item suggestions. The purchasing platform unit, for example, analyzes the user's past purchase history and makes personalized item suggestions. For example, the purchasing platform unit can suggest new items that match training wear previously purchased. The purchasing platform unit can also suggest items the user needs based on the purchase history. For example, the purchasing platform unit can suggest refills for supplements previously purchased. The purchasing platform unit can also analyze the user's purchase history and make item suggestions based on the user's training progress. For example, the purchasing platform unit can suggest new items that match training equipment previously purchased. In this way, by analyzing the user's past purchase history and making personalized item suggestions, it is possible to provide items that meet the user's needs.

[0089] The purchasing platform unit can display reviews or ratings from other users on the purchasing platform to help users make purchases. The purchasing platform unit, for example, displays reviews and ratings from other users on the purchasing platform to help users make purchases. For example, the purchasing platform unit displays reviews of training shoes. The purchasing platform unit can also select items for users to purchase based on ratings from other users. For example, the purchasing platform unit can suggest highly rated training equipment. The purchasing platform unit can also add a review function to the purchasing platform to enable users to post ratings of purchased items. For example, the purchasing platform unit collects feedback after purchases. This allows other users' reviews and ratings to be displayed on the purchasing platform to help users make purchases.

[0090] The purchasing platform unit can introduce an AI chatbot into the purchasing platform to answer user questions in real time. The purchasing platform unit, for example, introduces an AI chatbot into the purchasing platform to answer user questions in real time. For example, the purchasing platform unit answers questions about how to use training equipment. The purchasing platform unit can also use the AI ​​chatbot to resolve user questions about purchases. For example, the purchasing platform unit answers questions about product inventory and delivery information. The purchasing platform unit can also introduce an AI chatbot into the purchasing platform to quickly respond to user questions. For example, the purchasing platform unit answers questions about how to choose products and recommended items. In this way, by introducing an AI chatbot into the purchasing platform, user questions can be answered in real time.

[0091] The purchasing platform unit can use the emotion estimation function to monitor the user's emotional state in real time and suggest items according to the emotion. The purchasing platform unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and suggest items according to the emotion. For example, the purchasing platform unit suggests relaxation goods when stress is high. The purchasing platform unit can also analyze the user's emotional state in real time and suggest items based on the emotion. For example, the purchasing platform unit suggests new training wear when the user is feeling strongly positive. The purchasing platform unit can also use the emotion estimation function to monitor the user's emotional state and suggest items according to the user's training progress. For example, the purchasing platform unit suggests new training shoes when motivation is low. In this way, by monitoring the user's emotional state in real time and suggesting items according to the emotion, items that meet the user's needs can be provided.

[0092] The advertisement display unit can estimate the user's emotional state and display optimal advertisements based on the user's emotions. For example, the advertisement display unit estimates the user's emotional state and displays relevant advertisements when the user has strong positive emotions. For example, the advertisement display unit displays advertisements for new training equipment when the emotion score is high. The advertisement display unit can also use the emotion estimation function to monitor the user's emotional state in real time and display optimal advertisements. For example, the advertisement display unit displays advertisements for relaxation goods when the user is highly stressed. The advertisement display unit can also analyze the user's emotional state and display advertisements based on the user's emotions. For example, the advertisement display unit displays advertisements for new training wear when the user's motivation is low. In this way, the effectiveness of the advertisements is maximized by displaying optimal advertisements based on the user's emotional state.

[0093] The advertisement display unit can analyze the user's training data and display advertisements related to the training content. The advertisement display unit, for example, analyzes the user's training data and displays advertisements related to the training content. For example, if the user is performing strength training, the advertisement display unit displays an advertisement for protein. The advertisement display unit can also display advertisements for items the user needs based on the training data. For example, if the user is running, the advertisement display unit displays an advertisement for running shoes. The advertisement display unit can also analyze the user's training data and display advertisements according to the user's training progress. For example, the advertisement display unit displays an advertisement for new training equipment when the user is seeing results from their training. In this way, the effectiveness of the advertisements is maximized by analyzing the user's training data and displaying advertisements related to the training content.

[0094] The advertisement display unit can analyze the user's past behavioral history and display personalized advertisements. The advertisement display unit can, for example, analyze the user's past behavioral history and display personalized advertisements. For example, the advertisement display unit displays advertisements related to training wear purchased in the past. The advertisement display unit can also display advertisements for items that the user is interested in based on the behavioral history. For example, the advertisement display unit displays advertisements for training equipment searched for in the past. The advertisement display unit can also analyze the user's behavioral history and display advertisements according to the user's training progress. For example, the advertisement display unit displays advertisements suggesting refills of supplements purchased in the past. In this way, the effectiveness of advertisements is maximized by analyzing the user's past behavioral history and displaying personalized advertisements.

[0095] The advertisement display unit can adjust the timing of advertisement display according to the user's training progress. The advertisement display unit, for example, analyzes the user's training progress and displays advertisements at the optimal timing. For example, the advertisement display unit displays advertisements for new training equipment when training results are being achieved. The advertisement display unit can also display advertisements for items the user needs at the optimal timing based on training data. For example, the advertisement display unit displays advertisements suggesting the replenishment of consumables when the user trains frequently. The advertisement display unit can also analyze the user's training progress in real time and adjust the timing of advertisement display. For example, the advertisement display unit displays advertisements for supplements according to the user's training progress. In this way, the effectiveness of advertisements is maximized by adjusting the timing of advertisement display according to the user's training progress.

[0096] The advertisement display unit can customize advertisements based on the user's regional characteristics and cultural background. The advertisement display unit, for example, displays advertisements appropriate for the region, taking into account the user's regional characteristics. For example, the advertisement display unit displays advertisements for winter training wear to a user living in a cold climate. The advertisement display unit can also display advertisements for items of interest to the user based on the user's cultural background. For example, the advertisement display unit displays advertisements for rice-based meal plans to a user living in Asia. The advertisement display unit can also analyze the user's regional characteristics and cultural background and display personalized advertisements. For example, the advertisement display unit displays advertisements tailored to local events and trends. In this way, the effectiveness of advertisements is maximized by customizing advertisements based on the user's regional characteristics and cultural background.

[0097] The advertisement display unit can use the emotion estimation function to monitor the user's emotional state in real time and display advertisements corresponding to the emotion. The advertisement display unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time and display advertisements corresponding to the emotion. For example, the advertisement display unit displays advertisements for relaxation goods when stress is high. The advertisement display unit can also analyze the user's emotional state in real time and display advertisements based on the emotion. For example, the advertisement display unit displays advertisements for new training wear when the user has strong positive emotions. The advertisement display unit can also monitor the user's emotional state using the emotion estimation function and display advertisements corresponding to the progress of training. For example, the advertisement display unit displays advertisements for new training shoes when motivation is low. In this way, the effectiveness of advertisements is maximized by monitoring the user's emotional state in real time and displaying advertisements corresponding to the emotion.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The analysis unit can collect the sleep patterns and stress levels of professional athletes and integrate them with training data for analysis. For example, the analysis unit can collect the sleep patterns of professional athletes using a smartwatch or sleep tracker and integrate them with training data for analysis. For example, the analysis unit can examine the correlation between sleep quality and training effectiveness. The analysis unit can also collect the heart rate variability and cortisol levels of professional athletes to measure stress levels and integrate them with training data for analysis. For example, the analysis unit can evaluate the impact of stress on training effectiveness. The analysis unit can also collect the sleep patterns and stress levels of professional athletes using diaries or questionnaires and integrate them with training data for analysis. For example, the analysis unit can evaluate training effectiveness using self-reported data. In this way, the training effectiveness can be more accurately analyzed by taking into account the sleep patterns and stress levels of professional athletes.

[0100] The suggestion unit can analyze the user's genetic information and make training suggestions based on the user's genetic characteristics. For example, the suggestion unit can analyze the user's genetic information and suggest a training menu based on muscle growth rate and endurance. For example, the suggestion unit can intensify strength training for a person who is genetically prone to building muscle. The suggestion unit can also evaluate the user's risk of injury based on the genetic information and suggest a safe training menu. For example, the suggestion unit can recommend low-impact training for a person with weak joints. The suggestion unit can also analyze the user's genetic information to optimize nutritional intake. For example, the suggestion unit can suggest a meal plan to supplement a person who is prone to deficiencies in certain vitamins or minerals. This makes it possible to make more effective training by making training suggestions based on the user's genetic information.

[0101] The suggestion unit can suggest an optimal training method based on the user's learning style (visual, auditory, kinesthetic). For example, the suggestion unit analyzes the user's learning style and suggests video training to a person who is good at visual learning. For example, the suggestion unit may train while watching a video of form. The suggestion unit can also suggest a training menu with audio guidance to a person who is good at auditory learning. For example, the suggestion unit may use an app that gives training instructions by voice. The suggestion unit can also suggest a training menu that allows the user to learn while actually moving their body to a person who is good at kinesthetic learning. For example, the suggestion unit may use an interactive training game. This allows for more effective training by suggesting a training method based on the user's learning style.

[0102] The suggestion unit can propose a training menu suitable for a region, taking into account the user's cultural background and regional characteristics. For example, the suggestion unit proposes sports and training methods unique to the region, taking into account the user's cultural background. For example, the suggestion unit provides soccer-focused training to a European user. The suggestion unit can also propose an appropriate training menu, taking into account the region's climate and environment. For example, the suggestion unit recommends indoor training to a user in a cold region. The suggestion unit can also propose a meal plan and a nutritional supplementation method based on the user's cultural background. For example, the suggestion unit provides a rice-focused meal plan to an Asian user. This allows for training suitable for the region by proposing a training menu that takes into account the user's cultural background and regional characteristics.

[0103] The feedback unit can analyze the user's training environment and provide feedback appropriate to the environment. For example, the feedback unit collects data on the user's training environment and provides feedback appropriate to the temperature and humidity. For example, the feedback unit may advise the user to hydrate when training on a hot day. The feedback unit can also analyze data on the training location and provide feedback appropriate to the environment. For example, the feedback unit may recommend using sunscreen when training outdoors. The feedback unit can also suggest optimal training times and locations based on the user's training environment data. For example, the feedback unit may recommend indoor training on days with high humidity. In this way, the effectiveness of training is maximized by analyzing the user's training environment and providing feedback appropriate to the environment.

[0104] The analysis unit can estimate the emotional state of a professional athlete and analyze the effectiveness of training based on that emotion. For example, the analysis unit analyzes the professional athlete's facial expressions and voice during training to estimate the emotional state. For example, the analysis unit analyzes the professional athlete's smile and tone of voice during training to analyze the impact of positive emotions on training effectiveness. The analysis unit can also monitor the professional athlete's emotional state in real time to evaluate the effectiveness of training. For example, the analysis unit measures heart rate and electrodermal activity to analyze the correlation between changes in emotion and training effectiveness. The analysis unit can also perform text analysis of the professional athlete's diary and interview content to quantify the intensity and type of emotion in order to analyze the effectiveness of training based on the emotional state. This allows for more accurate analysis of the effectiveness of training by taking the professional athlete's emotional state into consideration.

[0105] The suggestion unit can estimate the user's emotional state and adjust the training menu based on that emotion. For example, the suggestion unit estimates the user's emotional state and suggests a high-intensity training menu when the user has strong positive emotions. For example, the suggestion unit performs interval training when the emotion score is high. The suggestion unit can also use the emotion estimation function to monitor the user's emotional state in real time and adjust the training menu. For example, the suggestion unit suggests relaxing yoga when stress is high. The suggestion unit can also analyze the user's emotional state and provide a menu to maximize the effectiveness of the training. For example, the suggestion unit suggests lighter training when motivation is low. In this way, the training menu can be adjusted based on the user's emotional state to maximize the effectiveness of the training.

[0106] The feedback unit can estimate the user's emotional state and adjust the feedback content based on the emotion. For example, the feedback unit can estimate the user's emotional state and provide encouraging feedback when the user's emotion is strong. For example, the feedback unit can send a message praising the user's training results. The feedback unit can also use the emotion estimation function to monitor the user's emotional state in real time and adjust the feedback content. For example, the feedback unit can provide advice to relax when stress is high. The feedback unit can also analyze the user's emotional state and provide feedback to maximize the effectiveness of the training. For example, the feedback unit can send an encouraging message when motivation is low. In this way, the effectiveness of the training is maximized by adjusting the feedback content based on the user's emotional state.

[0107] The advertisement display unit can estimate the user's emotional state and display optimal advertisements based on the user's emotions. For example, the advertisement display unit estimates the user's emotional state and displays relevant advertisements when the user has strong positive emotions. For example, the advertisement display unit displays advertisements for new training equipment when the emotion score is high. The advertisement display unit can also use the emotion estimation function to monitor the user's emotional state in real time and display optimal advertisements. For example, the advertisement display unit displays advertisements for relaxation goods when the user is highly stressed. The advertisement display unit can also analyze the user's emotional state and display advertisements based on the user's emotions. For example, the advertisement display unit displays advertisements for new training wear when the user's motivation is low. In this way, the effectiveness of the advertisements is maximized by displaying optimal advertisements based on the user's emotional state.

[0108] The purchasing platform unit can estimate the user's emotional state and suggest items to purchase based on that emotion. For example, the purchasing platform unit estimates the user's emotional state and suggests new training items when the user has a strong positive emotion. For example, the purchasing platform unit suggests new training shoes when the emotion score is high. The purchasing platform unit can also use the emotion estimation function to monitor the user's emotional state in real time and suggest items to purchase. For example, the purchasing platform unit suggests relaxation products when the user is highly stressed. The purchasing platform unit can also analyze the user's emotional state and suggest items to purchase based on the emotion. For example, the purchasing platform unit suggests new training wear when motivation is low. In this way, by suggesting items to purchase based on the user's emotional state, items that meet the user's needs can be provided.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The data collection department collects data on professional athletes. For example, the data collection department collects training videos, interview articles, and nutritional management data on professional athletes. Step 2: The analysis unit analyzes the data of the professional athletes collected by the data collection unit. For example, the analysis unit analyzes the data using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The suggestion unit suggests optimal form, training method, frequency, nutrition, and reference books based on the data analyzed by the analysis unit, according to the user's level of development. For example, the suggestion unit suggests training menus, nutrition plans, and reference books based on the user's age, physical strength, and skill level. Step 4: The feedback unit analyzes the user's training results and provides feedback. For example, the feedback unit analyzes videos of the user's form and provides specific advice. It also analyzes the training results, evaluates the effectiveness of the training, and reflects long-term growth trends in the feedback. Step 5: The purchasing platform allows the user to purchase items necessary for training. For example, the purchasing platform allows the user to purchase training equipment, sportswear, and nutritional supplements online.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 AI 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.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on professional athletes; an analysis unit that analyzes the data of the professional athletes collected by the data collection unit; a suggestion unit that suggests optimal form, training method, frequency, nutrition, and reference books according to the user's level of growth based on the data analyzed by the analysis unit; a feedback unit that analyzes the user's training results and provides feedback; a purchasing platform unit where the user can purchase items necessary for training. A system characterized by:

2. The data collection unit Collect training videos, interview articles, or nutritional management data of said professional athletes 2. The system of claim 1.

3. The proposal unit Providing training and nutritional plans based on the user's age, fitness, or skill level 2. The system of claim 1.

4. The feedback unit Analyzing the user's training results or videos of the user's form and providing specific advice 2. The system of claim 1.

5. The purchasing platform unit: Allowing the user to purchase items needed for training online 2. The system of claim 1.

6. The analysis unit The emotional state of the professional athlete is estimated, and the effectiveness of training is analyzed based on the estimated emotional state.

2. The system of claim 1.

7. The analysis unit The sleep patterns and stress levels of the professional athletes are collected and analyzed in conjunction with their training data.

2. The system of claim 1.

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