system

Abstract

To provide a system according to an embodiment which recommends the optimum sporting competition on the basis of individual physical features or growth processes.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data of physical features or growth processes of a user. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends a proper sport competition on the basis of the result of the analysis obtained by the analysis unit. The provision unit provides a training plan or advice on the basis of the sport competition recommended by the recommendation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend optimal sports based on an individual's physical characteristics and growth process, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend the most suitable sports competition based on the individual's physical characteristics and growth process. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on the user's physical characteristics and growth process. The analysis unit analyzes the data collected by the collection unit. The recommendation unit recommends an appropriate sport based on the analysis results obtained by the analysis unit. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable sports competition based on the individual's physical characteristics and growth process. [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) A sports recommendation system according to an embodiment of the present invention recommends optimal sports based on a user's physical characteristics and provides training plans and advice. The system begins with the user inputting data about their physical characteristics and growth process, such as height, weight, muscle mass, bone density, and flexibility. This data is then input into an AI. The AI ​​then analyzes the input data. Based on a historical database, the AI ​​analyzes success factors for each sport and compares them with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into account. This identifies the sports that best suit the user's physical characteristics. Furthermore, the AI ​​evaluates the user's potential for success in the identified sports. For example, if the user is determined to be suited to basketball, the AI ​​evaluates the user's potential for success in that sport and provides specific training plans and advice. This system allows users to find the sports that best suit their physical characteristics, thereby enabling even athletes who are overlooked in popular sports to discover their potential to lead the world in other sports. For example, athletes who are not successful in baseball or soccer may be successful in track and field or swimming. This allows the sports event recommendation system to recommend the most suitable sports events based on the user's physical characteristics and provide training plans and advice.

[0029] A sports competition recommendation system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on a user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or a bathroom scale and collect the data. The collection unit can also use dedicated devices for measuring muscle mass and bone density. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using AI and evaluates the user's physical characteristics. For example, the analysis unit analyzes success factors in each sports competition based on a past database and compares the results with the user's data. For example, the analysis unit analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The recommendation unit identifies a sports competition that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. For example, the provision unit provides training menus, dietary advice, mental support, etc. that are suitable for basketball. As a result, the sport recommendation system according to the embodiment can recommend the optimal sport based on the user's physical characteristics and provide training plans and advice.

[0030] The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or a bathroom scale and collect the data. The collection unit can also use dedicated devices for measuring muscle mass and bone density. Specifically, the user can input their own physical data using a smartphone or tablet. This data includes daily changes in weight and height, diet, and exercise volume. The collection unit can also collect data in real time using a wearable device. For example, a smartwatch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows for a more detailed understanding of the user's physical condition. The collection unit can also import measurement results from regular health checkups or sports clinics. This data includes blood test results and doctor's diagnoses. This allows for a comprehensive assessment of the user's health status and potential risks. The collection unit centrally manages information obtained from these diverse data sources and makes it accessible to the analysis and recommendation units. This allows the collection unit to gain a detailed and accurate understanding of the user's physical characteristics and growth process, improving the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, the analysis unit analyzes success factors for each sport based on a past database and compares them with the user's data. Specifically, the AI ​​uses machine learning algorithms to analyze the user's physical data and predict performance in specific sports. For example, it analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The AI ​​comprehensively evaluates these factors and determines which sport the user is best suited to. Furthermore, the analysis unit can evaluate the user's growth process and training history to assess the potential for future performance improvement. For example, it predicts the extent to which performance improvement can be expected if the user maintains their current physical characteristics and engages in specific training. The analysis unit also evaluates the user's health status and potential risks, providing basic data for providing appropriate training plans and advice. This allows the analysis unit to perform a detailed evaluation of the user's physical characteristics and provide data to recommend the most appropriate sports. Furthermore, the analysis unit can incorporate user feedback to continuously improve the accuracy of the analysis algorithm. This allows the analysis unit to always use the latest data and technology to recommend the most suitable sports competitions to the user.

[0032] The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. Specifically, the AI ​​compares the user's physical data with past success stories to identify the most suitable sport. For example, if the user is tall and has a lot of muscle mass, it may determine that basketball or volleyball is suitable. Alternatively, if the user is flexible and has a good sense of balance, it may determine that gymnastics or figure skating is suitable. The recommendation unit recommends specific sports to the user based on this information. Furthermore, the recommendation unit may also consider the user's interests and preferences to recommend the optimal sport. For example, if the user is interested in a particular sport, it may preferentially recommend that sport. The recommendation unit may also suggest an appropriate training plan tailored to the user's lifestyle and schedule. This allows the recommendation unit to recommend the optimal sport by comprehensively considering the user's physical characteristics and interests. Furthermore, the recommendation unit can incorporate user feedback to continuously improve the accuracy of the recommendation algorithm, allowing the recommendation unit to always use the latest data and technology to recommend the most suitable sports events to the user.

[0033] The provision unit provides training plans and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, the provision unit provides training menus, dietary advice, and mental support suitable for basketball. Specifically, the AI ​​creates an individually customized training plan based on the user's physical data and the recommended sports. For example, a training menu suitable for basketball may include plyometric training to improve jumping power and aerobic exercise to increase endurance. Furthermore, the dietary advice may recommend a high-protein diet to increase muscle mass and carbohydrate intake to replenish energy. Furthermore, the mental support may provide relaxation techniques to relieve pre-game tension and mental training to improve concentration. The provision unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the provision unit can incorporate user feedback and continuously improve the content of the training plans and advice. As a result, the provision unit can provide the user with optimal training plans and advice and support success in sports competitions. Furthermore, the provision unit can monitor the user's progress and adjust the training plan as needed. This allows the providing unit to support the user in achieving their goals and improve their performance in sports competitions.

[0034] The collection unit can collect data on height, weight, muscle mass, bone density, and flexibility. The collection unit collects, for example, data on height, weight, muscle mass, bone density, and flexibility input by a user. The collection unit measures height and weight using, for example, a height gauge or a bathroom scale and collects the data. The collection unit can also use dedicated equipment for measuring muscle mass. For example, the collection unit uses bioimpedance to measure muscle mass. The collection unit can also use dual-energy X-ray absorptiometry (DXA) to measure bone density. For example, the collection unit measures bone density using DXA and collects the data. The collection unit can also use dedicated equipment for measuring flexibility. For example, the collection unit performs a stretching test to measure flexibility and collects the data. This allows the collection unit to collect detailed data on the user's body. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data input by the user into AI, which then collects the data.

[0035] The analysis unit can analyze success factors in each sport based on a past database and compare them with user data. The analysis unit, for example, analyzes success factors in each sport based on a past database. For example, the analysis unit can analyze that height is an important factor in basketball, and that weight and muscle mass are also taken into consideration. The analysis unit can also analyze that speed and endurance are important factors in soccer. Furthermore, the analysis unit can analyze that muscle strength and flexibility are important factors in track and field. The analysis unit compares these success factors with user data. For example, the analysis unit evaluates the possibility of success in each sport based on the user's height, weight, muscle mass, bone density, and flexibility data. This allows the analysis unit to perform highly accurate analysis based on the user's physical characteristics. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the past database into AI, which then analyzes success factors.

[0036] The recommendation unit can identify a sport that is appropriate for the user's physical characteristics. The recommendation unit can identify a sport that is most suitable for the user's physical characteristics based on, for example, the analysis results obtained by the analysis unit. For example, the recommendation unit can determine that the user is suited to basketball and recommend that sport. The recommendation unit can also determine that the user is suited to soccer and recommend that sport. The recommendation unit can also determine that the user is suited to track and field and recommend that sport. Based on these recommendations, the recommendation unit can suggest the optimal sport for the user. This allows the recommendation unit to identify the optimal sport based on the user's physical characteristics. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into AI, which can identify the optimal sport.

[0037] The providing unit can evaluate the probability of success in the identified sport and provide a specific training plan and advice. For example, the providing unit evaluates the probability of success in the sport identified by the recommending unit. For example, if the providing unit determines that the user is suited to basketball, the providing unit evaluates the possibility of success in the sport. Furthermore, if the providing unit determines that the user is suited to soccer, the providing unit can evaluate the possibility of success in the sport. Furthermore, if the providing unit determines that the user is suited to track and field, the providing unit can evaluate the possibility of success in the sport. The providing unit provides a specific training plan and advice based on these evaluation results. For example, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for basketball. Furthermore, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for soccer. Furthermore, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for track and field. In this way, the providing unit can increase the possibility of success in the sport by providing the user with a specific training plan and advice. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the probability of success in a sporting event identified by the recommending unit into the AI, which can then provide specific training plans and advice.

[0038] The collection unit can analyze the user's past physical data and select the optimal collection method. The collection unit selects the optimal measurement method based on, for example, the user's past height and weight data. The collection unit selects the optimal muscle mass measurement method based on, for example, the user's past muscle mass data. The collection unit can also select the optimal flexibility measurement method based on the user's past flexibility data. This allows the collection unit to select the optimal collection method based on the past physical data, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past physical data into AI, which then selects the optimal collection method.

[0039] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in good health, the collection unit collects detailed data. For example, if the user is in poor health, the collection unit collects only basic data. The collection unit can also select an appropriate data collection method based on the user's lifestyle habits. For example, the collection unit selects an appropriate data collection method based on data such as the user's dietary records, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and lifestyle habits into AI, which then performs filtering.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to exercise at high altitude. For example, if the user lives in an urban area, the collection unit can also prioritize collecting data related to exercise in urban areas. Furthermore, if the user lives near the sea, the collection unit can also prioritize collecting data related to swimming and beach sports. This enables the collection unit to collect data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0041] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to exercises that the user frequently posts about on social media. For example, the collection unit can also collect data related to exercises performed by the user's social media friends. The collection unit can also collect data related to sports that the user shows interest in on social media. This enables the collection unit to collect data based on the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about the user's social media activities into AI, which then collects related data.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the physical data. For example, if height is an important factor, the analysis unit performs a detailed analysis of height. For example, if muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, if flexibility is an important factor, the analysis unit can also perform a detailed analysis of flexibility. This enables the analysis unit to perform a detailed analysis according to the importance of the physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the physical data into AI, which can adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of physical data. For example, the analysis unit applies a growth prediction algorithm to height data. For example, the analysis unit applies a muscle strength evaluation algorithm to muscle mass data. The analysis unit can also apply a flexibility evaluation algorithm to flexibility data. This enables the analysis unit to perform appropriate analysis depending on the category of physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of physical data into AI, and the AI ​​can apply different analysis algorithms.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also perform analysis while referring to past data, for example. The analysis unit can also focus on analyzing data from a specific period. This allows the analysis unit to perform analysis in priority order based on the time when the data was collected, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit associates and analyzes data on height and weight. For example, the analysis unit can also associate and analyze data on muscle mass and flexibility. The analysis unit can also associate and analyze data on bone density and athletic ability. This enables the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0046] The recommendation unit can improve the accuracy of the recommendation by taking into account the interrelationships of physical data when making a recommendation. The recommendation unit makes a recommendation by taking into account, for example, the interrelationships between height and weight. The recommendation unit can also make a recommendation by taking into account, for example, the interrelationships between muscle mass and flexibility. The recommendation unit can also make a recommendation by taking into account the interrelationships between bone density and athletic ability. This enables the recommendation unit to make highly accurate recommendations by taking into account the interrelationships of physical data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the interrelationships of physical data into AI, which can improve the accuracy of the recommendation.

[0047] The recommendation unit can make recommendations taking into consideration the user's attribute information when making recommendations. The recommendation unit can make recommendations taking into consideration, for example, the user's age. The recommendation unit can also make recommendations taking into consideration, for example, the user's gender. The recommendation unit can also make recommendations taking into consideration the user's exercise experience. This enables the recommendation unit to make recommendations based on the user's attribute information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's attribute information into AI, and the AI ​​can make recommendations.

[0048] The recommendation unit can make recommendations taking into account the geographical distribution of the user. For example, if the user lives in a high altitude, the recommendation unit can make recommendations related to exercise at high altitudes. For example, if the user lives in an urban area, the recommendation unit can make recommendations related to exercise in urban areas. Furthermore, if the user lives by the sea, the recommendation unit can make recommendations related to swimming or beach sports. This enables the recommendation unit to make recommendations based on the geographical distribution of the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the geographical distribution of the user into AI, which then makes recommendations.

[0049] The recommendation unit can improve the accuracy of the recommendation by referring to related literature when making a recommendation. The recommendation unit can make a recommendation by referring to, for example, the latest research papers. The recommendation unit can also make a recommendation by referring to, for example, past success stories. The recommendation unit can also make a recommendation by referring to expert opinions. In this way, the recommendation unit can improve the accuracy of the recommendation by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input related literature into AI, which can improve the accuracy of the recommendation.

[0050] When providing the plan, the providing unit can provide an optimal plan by referring to the user's past training history. The providing unit provides an optimal training plan based on, for example, the user's past training history. The providing unit can also suggest an effective training method based on, for example, the user's past training history. The providing unit can also analyze the user's past training history and provide a training plan that includes areas for improvement. This allows the providing unit to provide an optimal plan based on the past training history, enabling effective training. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past training history into AI, which can then provide an optimal training plan.

[0051] The providing unit can customize the training plan based on the user's current living situation when providing the plan. For example, if the user is busy, the providing unit can provide a short and effective training plan. For example, if the user has time, the providing unit can also provide a detailed training plan. The providing unit can also customize an appropriate training plan based on the user's living habits. This enables the providing unit to customize a training plan according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation into AI, which can then customize the training plan.

[0052] The providing unit can provide an optimal plan taking into consideration the user's geographical location information when providing the plan. For example, if the user lives at high altitude, the providing unit can provide a plan suitable for training at high altitude. For example, if the user lives in an urban area, the providing unit can also provide a plan suitable for training in urban areas. Furthermore, if the user lives by the sea, the providing unit can also provide a plan suitable for swimming or beach sports. This enables the providing unit to provide an optimal plan based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then provide an optimal plan.

[0053] At the time of providing, the providing unit can analyze the user's social media activity and propose training plans and advice. The providing unit, for example, provides training plans related to exercises that the user frequently posts about on social media. The providing unit can also provide training plans related to exercises that the user's social media friends are doing. The providing unit can also provide training plans related to sports that the user is interested in on social media. This enables the providing unit to propose training plans and advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's social media activity into AI, which then proposes training plans and advice.

[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 include the user's dietary data in addition to the user's physical data in the analysis. For example, by collecting the user's dietary details and nutritional intake and analyzing them in combination with the physical data, more accurate sports competition recommendations can be made. Furthermore, the analysis unit can provide dietary advice to supplement specific nutrients based on the user's dietary data if the user is lacking in certain nutrients. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their diet.

[0056] The collection unit can collect the user's sleep data and provide it to the analysis unit. For example, by measuring the user's sleep time and sleep quality and combining and analyzing this with physical data, it is possible to make more comprehensive recommendations for sports competitions. Furthermore, the collection unit can provide advice on improving sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also in terms of sleep.

[0057] The recommendation unit can recommend sports events taking into account the user's hobbies and interests in addition to the user's physical data. For example, if the user is interested in a particular sport, it can preferentially recommend sports related to that sport. Furthermore, the recommendation unit can also expand the selection of sports events based on the user's hobbies and interests. This allows the user to find the most suitable sports event based not only on their physical characteristics but also on their personal interests and hobbies.

[0058] The collection unit can collect the user's exercise history in addition to the user's physical data and provide it to the analysis unit. For example, by collecting the type, frequency, and intensity of past exercise and combining this with the physical data and analyzing it, more accurate sports competition recommendations can be made. Furthermore, the collection unit can identify training methods and approaches that have been successful in the past based on the user's exercise history and provide a training plan based on them. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also their exercise history.

[0059] The collection unit can collect the user's living environment data in addition to the user's physical data and provide it to the analysis unit. For example, by collecting the user's living environment, work environment, commuting time, etc. and combining this with the physical data and analyzing it, it is possible to recommend sports events that are suitable for the user's living environment. Furthermore, the collection unit can provide training plans and advice that are suitable for the user's living environment based on the user's living environment data. This allows the user to find the sports event that is best suited to the user's living environment, not just their physical characteristics.

[0060] The recommendation unit can recommend sports events taking into account the user's social background in addition to the user's physical data. For example, it can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the options for sports events based on the user's social background. This allows the user to find the most suitable sports event not only based on their physical characteristics but also on their social background.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, for example, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data using data entered by the user, sensors, and measuring devices. For example, height and weight can be measured using a height chart or a weight scale, and dedicated devices for measuring muscle mass and bone density can also be used. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, based on a past database, it analyzes the success factors for each sport and compares them with the user's data. It analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into account. Step 3: The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. Step 4: The provision unit provides training plans and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, it provides training menus, dietary advice, and mental support suitable for basketball.

[0063] (Example 2) A sports recommendation system according to an embodiment of the present invention recommends optimal sports based on a user's physical characteristics and provides training plans and advice. The system begins with the user inputting data about their physical characteristics and growth process, such as height, weight, muscle mass, bone density, and flexibility. This data is then input into an AI. The AI ​​then analyzes the input data. Based on a historical database, the AI ​​analyzes success factors for each sport and compares them with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into account. This identifies the sports that best suit the user's physical characteristics. Furthermore, the AI ​​evaluates the user's potential for success in the identified sports. For example, if the user is determined to be suited to basketball, the AI ​​evaluates the user's potential for success in that sport and provides specific training plans and advice. This system allows users to find the sports that best suit their physical characteristics, thereby enabling even athletes who are overlooked in popular sports to discover their potential to lead the world in other sports. For example, athletes who are not successful in baseball or soccer may be successful in track and field or swimming. This allows the sports event recommendation system to recommend the most suitable sports events based on the user's physical characteristics and provide training plans and advice.

[0064] A sports competition recommendation system according to an embodiment includes a collection unit, an analysis unit, a recommendation unit, and a provision unit. The collection unit collects data on a user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or a bathroom scale and collect the data. The collection unit can also use dedicated devices for measuring muscle mass and bone density. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using AI and evaluates the user's physical characteristics. For example, the analysis unit analyzes success factors in each sports competition based on a past database and compares the results with the user's data. For example, the analysis unit analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The recommendation unit identifies a sports competition that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides training plans and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. For example, the provision unit provides training menus, dietary advice, mental support, etc. that are suitable for basketball. As a result, the sport recommendation system according to the embodiment can recommend the optimal sport based on the user's physical characteristics and provide training plans and advice.

[0065] The collection unit collects data on the user's physical characteristics and growth process. Examples of the user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data entered by the user. The collection unit can also collect data using sensors or measuring devices. For example, the collection unit may measure height and weight using a height chart or a bathroom scale and collect the data. The collection unit can also use dedicated devices for measuring muscle mass and bone density. Specifically, the user can input their own physical data using a smartphone or tablet. This data includes daily changes in weight and height, diet, and exercise volume. The collection unit can also collect data in real time using a wearable device. For example, a smartwatch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows for a more detailed understanding of the user's physical condition. The collection unit can also import measurement results from regular health checkups or sports clinics. This data includes blood test results and doctor's diagnoses. This allows for a comprehensive assessment of the user's health status and potential risks. The collection unit centrally manages information obtained from these diverse data sources and makes it accessible to the analysis and recommendation units. This allows the collection unit to gain a detailed and accurate understanding of the user's physical characteristics and growth process, improving the accuracy and reliability of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, the analysis unit analyzes success factors for each sport based on a past database and compares them with the user's data. Specifically, the AI ​​uses machine learning algorithms to analyze the user's physical data and predict performance in specific sports. For example, it analyzes that height is an important factor in basketball, and weight and muscle mass are also taken into consideration. The AI ​​comprehensively evaluates these factors and determines which sport the user is best suited to. Furthermore, the analysis unit can evaluate the user's growth process and training history to assess the potential for future performance improvement. For example, it predicts the extent to which performance improvement can be expected if the user maintains their current physical characteristics and engages in specific training. The analysis unit also evaluates the user's health status and potential risks, providing basic data for providing appropriate training plans and advice. This allows the analysis unit to perform a detailed evaluation of the user's physical characteristics and provide data to recommend the most appropriate sports. Furthermore, the analysis unit can incorporate user feedback to continuously improve the accuracy of the analysis algorithm. This allows the analysis unit to always use the latest data and technology to recommend the most suitable sports competitions to the user.

[0067] The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. Specifically, the AI ​​compares the user's physical data with past success stories to identify the most suitable sport. For example, if the user is tall and has a lot of muscle mass, it may determine that basketball or volleyball is suitable. Alternatively, if the user is flexible and has a good sense of balance, it may determine that gymnastics or figure skating is suitable. The recommendation unit recommends specific sports to the user based on this information. Furthermore, the recommendation unit may also consider the user's interests and preferences to recommend the optimal sport. For example, if the user is interested in a particular sport, it may preferentially recommend that sport. The recommendation unit may also suggest an appropriate training plan tailored to the user's lifestyle and schedule. This allows the recommendation unit to recommend the optimal sport by comprehensively considering the user's physical characteristics and interests. Furthermore, the recommendation unit can incorporate user feedback to continuously improve the accuracy of the recommendation algorithm, allowing the recommendation unit to always use the latest data and technology to recommend the most suitable sports events to the user.

[0068] The provision unit provides training plans and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, the provision unit provides training menus, dietary advice, and mental support suitable for basketball. Specifically, the AI ​​creates an individually customized training plan based on the user's physical data and the recommended sports. For example, a training menu suitable for basketball may include plyometric training to improve jumping power and aerobic exercise to increase endurance. Furthermore, the dietary advice may recommend a high-protein diet to increase muscle mass and carbohydrate intake to replenish energy. Furthermore, the mental support may provide relaxation techniques to relieve pre-game tension and mental training to improve concentration. The provision unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the provision unit can incorporate user feedback and continuously improve the content of the training plans and advice. As a result, the provision unit can provide the user with optimal training plans and advice and support success in sports competitions. Furthermore, the provision unit can monitor the user's progress and adjust the training plan as needed. This allows the providing unit to support the user in achieving their goals and improve their performance in sports competitions.

[0069] The collection unit can collect data on height, weight, muscle mass, bone density, and flexibility. The collection unit collects, for example, data on height, weight, muscle mass, bone density, and flexibility input by a user. The collection unit measures height and weight using, for example, a height gauge or a bathroom scale and collects the data. The collection unit can also use dedicated equipment for measuring muscle mass. For example, the collection unit uses bioimpedance to measure muscle mass. The collection unit can also use dual-energy X-ray absorptiometry (DXA) to measure bone density. For example, the collection unit measures bone density using DXA and collects the data. The collection unit can also use dedicated equipment for measuring flexibility. For example, the collection unit performs a stretching test to measure flexibility and collects the data. This allows the collection unit to collect detailed data on the user's body. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data input by the user into AI, which then collects the data.

[0070] The analysis unit can analyze success factors in each sport based on a past database and compare them with user data. The analysis unit, for example, analyzes success factors in each sport based on a past database. For example, the analysis unit can analyze that height is an important factor in basketball, and that weight and muscle mass are also taken into consideration. The analysis unit can also analyze that speed and endurance are important factors in soccer. Furthermore, the analysis unit can analyze that muscle strength and flexibility are important factors in track and field. The analysis unit compares these success factors with user data. For example, the analysis unit evaluates the possibility of success in each sport based on the user's height, weight, muscle mass, bone density, and flexibility data. This allows the analysis unit to perform highly accurate analysis based on the user's physical characteristics. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the past database into AI, which then analyzes success factors.

[0071] The recommendation unit can identify a sport that is appropriate for the user's physical characteristics. The recommendation unit can identify a sport that is most suitable for the user's physical characteristics based on, for example, the analysis results obtained by the analysis unit. For example, the recommendation unit can determine that the user is suited to basketball and recommend that sport. The recommendation unit can also determine that the user is suited to soccer and recommend that sport. The recommendation unit can also determine that the user is suited to track and field and recommend that sport. Based on these recommendations, the recommendation unit can suggest the optimal sport for the user. This allows the recommendation unit to identify the optimal sport based on the user's physical characteristics. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into AI, which can identify the optimal sport.

[0072] The providing unit can evaluate the probability of success in the identified sport and provide a specific training plan and advice. For example, the providing unit evaluates the probability of success in the sport identified by the recommending unit. For example, if the providing unit determines that the user is suited to basketball, the providing unit evaluates the possibility of success in the sport. Furthermore, if the providing unit determines that the user is suited to soccer, the providing unit can evaluate the possibility of success in the sport. Furthermore, if the providing unit determines that the user is suited to track and field, the providing unit can evaluate the possibility of success in the sport. The providing unit provides a specific training plan and advice based on these evaluation results. For example, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for basketball. Furthermore, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for soccer. Furthermore, the providing unit can provide a training menu, dietary advice, mental support, etc. suitable for track and field. In this way, the providing unit can increase the possibility of success in the sport by providing the user with a specific training plan and advice. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the probability of success in a sporting event identified by the recommending unit into the AI, which can then provide specific training plans and advice.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit immediately collects data and acquires detailed information. Furthermore, when the user is feeling stressed, the collection unit can postpone data collection and wait until the user is relaxed. Furthermore, when the user is excited, the collection unit can quickly collect data and acquire necessary information in a short time. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby enabling more accurate data collection. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI, which can then adjust the timing of data collection.

[0074] The collection unit can analyze the user's past physical data and select the optimal collection method. The collection unit selects the optimal measurement method based on, for example, the user's past height and weight data. The collection unit selects the optimal muscle mass measurement method based on, for example, the user's past muscle mass data. The collection unit can also select the optimal flexibility measurement method based on the user's past flexibility data. This allows the collection unit to select the optimal collection method based on the past physical data, thereby enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past physical data into AI, which then selects the optimal collection method.

[0075] The collection unit can perform filtering based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in good health, the collection unit collects detailed data. For example, if the user is in poor health, the collection unit collects only basic data. The collection unit can also select an appropriate data collection method based on the user's lifestyle habits. For example, the collection unit selects an appropriate data collection method based on data such as the user's dietary records, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's health condition and lifestyle habits into AI, which then performs filtering.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit can prioritize collecting detailed data. Furthermore, when the user is stressed, the collection unit can prioritize collecting basic data. Furthermore, when the user is excited, the collection unit can prioritize collecting data that can be collected quickly. This enables the collection unit to prioritize data according to the user's emotions, thereby enabling efficient data collection. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then prioritize the data.

[0077] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to exercise at high altitude. For example, if the user lives in an urban area, the collection unit can also prioritize collecting data related to exercise in urban areas. Furthermore, if the user lives near the sea, the collection unit can also prioritize collecting data related to swimming and beach sports. This enables the collection unit to collect data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0078] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to exercises that the user frequently posts about on social media. For example, the collection unit can also collect data related to exercises performed by the user's social media friends. The collection unit can also collect data related to sports that the user shows interest in on social media. This enables the collection unit to collect data based on the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data about the user's social media activities into AI, which then collects related data.

[0079] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. This enables the analysis unit to provide analysis results according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the physical data. For example, if height is an important factor, the analysis unit performs a detailed analysis of height. For example, if muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, if flexibility is an important factor, the analysis unit can also perform a detailed analysis of flexibility. This enables the analysis unit to perform a detailed analysis according to the importance of the physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the physical data into AI, which can adjust the level of detail of the analysis.

[0081] During analysis, the analysis unit can apply different analysis algorithms depending on the category of physical data. For example, the analysis unit applies a growth prediction algorithm to height data. For example, the analysis unit applies a muscle strength evaluation algorithm to muscle mass data. The analysis unit can also apply a flexibility evaluation algorithm to flexibility data. This enables the analysis unit to perform appropriate analysis depending on the category of physical data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the category of physical data into AI, and the AI ​​can apply different analysis algorithms.

[0082] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can perform a detailed analysis when the user is relaxed. Furthermore, the analysis unit can perform a concise analysis when the user is stressed. Furthermore, the analysis unit can perform a visually appealing analysis when the user is excited. This allows the analysis unit to provide appropriate analysis results by adjusting the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can adjust the length of the analysis.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also perform analysis while referring to past data, for example. The analysis unit can also focus on analyzing data from a specific period. This allows the analysis unit to perform analysis in priority order based on the time when the data was collected, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the priority of analysis.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit associates and analyzes data on height and weight. For example, the analysis unit can also associate and analyze data on muscle mass and flexibility. The analysis unit can also associate and analyze data on bone density and athletic ability. This enables the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can then adjust the order of analysis.

[0085] The recommendation unit can estimate the user's emotions and adjust the recommendation criteria based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the recommendation criteria based on the estimated user emotions. For example, the recommendation unit can provide detailed recommendations when the user is relaxed. The recommendation unit can also provide concise recommendations when the user is stressed. Furthermore, the recommendation unit can provide visually appealing recommendations when the user is excited. This enables the recommendation unit to adjust the recommendation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recommendation unit can be performed using an AI, for example, or without an AI. For example, the recommendation unit can input the user's emotion data into the generation AI, which can then adjust the recommendation criteria.

[0086] The recommendation unit can improve the accuracy of the recommendation by taking into account the interrelationships of physical data when making a recommendation. The recommendation unit makes a recommendation by taking into account, for example, the interrelationships between height and weight. The recommendation unit can also make a recommendation by taking into account, for example, the interrelationships between muscle mass and flexibility. The recommendation unit can also make a recommendation by taking into account the interrelationships between bone density and athletic ability. This enables the recommendation unit to make highly accurate recommendations by taking into account the interrelationships of physical data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the interrelationships of physical data into AI, which can improve the accuracy of the recommendation.

[0087] The recommendation unit can make recommendations taking into consideration the user's attribute information when making recommendations. The recommendation unit can make recommendations taking into consideration, for example, the user's age. The recommendation unit can also make recommendations taking into consideration, for example, the user's gender. The recommendation unit can also make recommendations taking into consideration the user's exercise experience. This enables the recommendation unit to make recommendations based on the user's attribute information. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's attribute information into AI, and the AI ​​can make recommendations.

[0088] The recommendation unit can estimate the user's emotions and adjust the order in which recommended results are displayed based on the estimated user emotions. For example, the recommendation unit can estimate the user's emotions and adjust the order in which recommended results are displayed based on the estimated user emotions. For example, when the user is relaxed, the recommendation unit can prioritize displaying detailed recommended results. Furthermore, when the user is stressed, the recommendation unit can prioritize displaying concise recommended results. Furthermore, when the user is excited, the recommendation unit can prioritize displaying visually appealing recommended results. This allows the recommendation unit to provide more appropriate recommended results by adjusting the display order of recommended results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and adjust the order in which the generation AI displays recommended results.

[0089] The recommendation unit can make recommendations taking into account the geographical distribution of the user. For example, if the user lives in a high altitude, the recommendation unit can make recommendations related to exercise at high altitudes. For example, if the user lives in an urban area, the recommendation unit can make recommendations related to exercise in urban areas. Furthermore, if the user lives by the sea, the recommendation unit can make recommendations related to swimming or beach sports. This enables the recommendation unit to make recommendations based on the geographical distribution of the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the geographical distribution of the user into AI, which then makes recommendations.

[0090] The recommendation unit can improve the accuracy of the recommendation by referring to related literature when making a recommendation. The recommendation unit can make a recommendation by referring to, for example, the latest research papers. The recommendation unit can also make a recommendation by referring to, for example, past success stories. The recommendation unit can also make a recommendation by referring to expert opinions. In this way, the recommendation unit can improve the accuracy of the recommendation by referring to related literature. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, AI, or can be performed without using AI. For example, the recommendation unit can input related literature into AI, which can improve the accuracy of the recommendation.

[0091] The providing unit can estimate the user's emotions and adjust the method of providing the training plan and advice based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the method of providing the training plan and advice based on the estimated user's emotions. For example, the providing unit can provide a detailed training plan when the user is relaxed. Furthermore, the providing unit can provide a concise training plan when the user is stressed. Furthermore, the providing unit can provide a visually appealing training plan when the user is excited. This enables the providing unit to adjust the method of providing the training plan and advice according to the user's emotions, thereby enabling more appropriate support. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then adjust the method of providing the training plan and advice.

[0092] When providing the plan, the providing unit can provide an optimal plan by referring to the user's past training history. The providing unit provides an optimal training plan based on, for example, the user's past training history. The providing unit can also suggest an effective training method based on, for example, the user's past training history. The providing unit can also analyze the user's past training history and provide a training plan that includes areas for improvement. This allows the providing unit to provide an optimal plan based on the past training history, enabling effective training. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past training history into AI, which can then provide an optimal training plan.

[0093] The providing unit can customize the training plan based on the user's current living situation when providing the plan. For example, if the user is busy, the providing unit can provide a short and effective training plan. For example, if the user has time, the providing unit can also provide a detailed training plan. The providing unit can also customize an appropriate training plan based on the user's living habits. This enables the providing unit to customize a training plan according to the user's living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation into AI, which can then customize the training plan.

[0094] The providing unit can estimate the user's emotions and determine the priority of training plans and advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of training plans and advice based on the estimated user emotions. For example, when the user is relaxed, the providing unit can prioritize providing detailed training plans. Furthermore, when the user is stressed, the providing unit can prioritize providing concise training plans. Furthermore, when the user is excited, the providing unit can prioritize providing visually appealing training plans. This enables the providing unit to provide training plans and advice in order of priority based on the user's emotions, thereby enabling more effective support. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then determine the priority of training plans and advice.

[0095] The providing unit can provide an optimal plan taking into consideration the user's geographical location information when providing the plan. For example, if the user lives at high altitude, the providing unit can provide a plan suitable for training at high altitude. For example, if the user lives in an urban area, the providing unit can also provide a plan suitable for training in urban areas. Furthermore, if the user lives by the sea, the providing unit can also provide a plan suitable for swimming or beach sports. This enables the providing unit to provide an optimal plan based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can then provide an optimal plan.

[0096] At the time of providing, the providing unit can analyze the user's social media activity and propose training plans and advice. The providing unit, for example, provides training plans related to exercises that the user frequently posts about on social media. The providing unit can also provide training plans related to exercises that the user's social media friends are doing. The providing unit can also provide training plans related to sports that the user is interested in on social media. This enables the providing unit to propose training plans and advice based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's social media activity into AI, which then proposes training plans and advice.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can include the user's dietary data in addition to the user's physical data in the analysis. For example, by collecting the user's dietary details and nutritional intake and analyzing them in combination with the physical data, more accurate sports competition recommendations can be made. Furthermore, the analysis unit can provide dietary advice to supplement specific nutrients based on the user's dietary data if the user is lacking in certain nutrients. This allows the user to find the sports competition that is best suited to them, not only in terms of their physical characteristics but also their diet.

[0099] The collection unit can collect the user's sleep data and provide it to the analysis unit. For example, by measuring the user's sleep time and sleep quality and combining and analyzing this with physical data, it is possible to make more comprehensive recommendations for sports competitions. Furthermore, the collection unit can provide advice on improving sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also in terms of sleep.

[0100] In addition to the user's physical data, the analysis unit can also include the user's stress level in the analysis. For example, by measuring the user's stress level and analyzing it in combination with the physical data, it is possible to recommend sports that can be played under low stress. Furthermore, if the user's stress level is high, the analysis unit can also provide relaxation methods and mental support to reduce stress. This allows the user to find the sports that are best suited to them not only in terms of their physical characteristics but also in terms of stress.

[0101] The recommendation unit can recommend sports events taking into account the user's hobbies and interests in addition to the user's physical data. For example, if the user is interested in a particular sport, it can preferentially recommend sports related to that sport. Furthermore, the recommendation unit can also expand the selection of sports events based on the user's hobbies and interests. This allows the user to find the most suitable sports event based not only on their physical characteristics but also on their personal interests and hobbies.

[0102] The provider can provide training plans and advice taking into account the user's motivation level in addition to the user's physical data. For example, if the user is highly motivated, the provider can provide a more challenging training plan, and if the user is less motivated, the provider can provide a simpler, more achievable training plan. Furthermore, the provider can provide mental support and encouraging messages to maintain the user's motivation. This allows the user to receive a training plan that is optimal not only for their physical characteristics but also for their motivation.

[0103] The collection unit can collect the user's exercise history in addition to the user's physical data and provide it to the analysis unit. For example, by collecting the type, frequency, and intensity of past exercise and combining this with the physical data and analyzing it, more accurate sports competition recommendations can be made. Furthermore, the collection unit can identify training methods and approaches that have been successful in the past based on the user's exercise history and provide a training plan based on them. This allows the user to find the sports competition that is best suited to them not only in terms of their physical characteristics but also their exercise history.

[0104] The collection unit can collect the user's living environment data in addition to the user's physical data and provide it to the analysis unit. For example, by collecting the user's living environment, work environment, commuting time, etc. and combining this with the physical data and analyzing it, it is possible to recommend sports events that are suitable for the user's living environment. Furthermore, the collection unit can provide training plans and advice that are suitable for the user's living environment based on the user's living environment data. This allows the user to find the sports event that is best suited to the user's living environment, not just their physical characteristics.

[0105] The analysis unit can include the user's emotional data in addition to the user's physical data in the analysis. For example, based on the user's emotional data, it can recommend a sport that can be played with minimal emotional fluctuations. Furthermore, based on the user's emotional data, the analysis unit can also provide mental support and relaxation methods to stabilize emotions. This allows the user to find the sport that is best suited to them not only in terms of their physical characteristics but also their emotions.

[0106] The recommendation unit can recommend sports events taking into account the user's social background in addition to the user's physical data. For example, it can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the options for sports events based on the user's social background. This allows the user to find the most suitable sports event not only based on their physical characteristics but also on their social background.

[0107] The providing unit can provide training plans and advice taking into account the user's emotional data in addition to the user's physical data. For example, the providing unit can provide a training plan that can be carried out with minimal emotional fluctuations based on the user's emotional data. Furthermore, the providing unit can also provide mental support and relaxation methods to stabilize emotions based on the user's emotional data. This allows the user to receive a training plan that is optimal not only for their physical characteristics but also for their emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, for example, height, weight, muscle mass, bone density, and flexibility. The collection unit collects data using data entered by the user, sensors, and measuring devices. For example, height and weight can be measured using a height chart or a weight scale, and dedicated devices for measuring muscle mass and bone density can also be used. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and evaluate the user's physical characteristics. For example, based on a past database, it analyzes the success factors for each sport and compares them with the user's data. It analyzes that height is an important factor in basketball, and that weight and muscle mass are also taken into account. Step 3: The recommendation unit identifies the sport that best suits the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the optimal sport based on the user's physical characteristics. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. Step 4: The provision unit provides training plans and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide specific training plans and advice to the user. For example, it provides training menus, dietary advice, and mental support suitable for basketball.

[0110] 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.

[0111] 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> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] 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.

[0113] Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[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] Each of the multiple elements, including the collection unit, analysis unit, recommendation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's physical data using sensors and measuring devices of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] Each of the multiple elements including the collection unit, analysis unit, recommendation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects physical data of the user using sensors and measuring devices of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The recommendation unit is realized by the specific processing unit 290 of the data processing device 12 and recommends an optimal sports event based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides training plans and advice based on the recommended sports event. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Appendix 1) a collection unit that collects data on the user's physical characteristics and growth process; an analysis unit that analyzes the data collected by the collection unit; a recommendation unit that recommends an appropriate sport based on the analysis results obtained by the analysis unit; a providing unit that provides training plans and advice based on the sports competition recommended by the recommending unit. A system characterized by: (Appendix 2) The collecting unit Collect data on height, weight, muscle mass, bone density, and flexibility 2. The system of claim 1. (Appendix 3) The analysis unit Analyze the success factors of each sport based on a past database and compare them with user data 2. The system of claim 1. (Appendix 4) The recommendation unit Identifying sports that are suitable for the user's physical characteristics 2. The system of claim 1. (Appendix 5) The providing unit Evaluate your chances of success in a specific sport and provide specific training plans and advice 2. The system of claim 1. (Appendix 6) The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1. (Appendix 7) The collecting unit Analyze the user's past physical data and select the optimal collection method 2. The system of claim 1. (Appendix 8) The collecting unit When collecting data, filtering is performed based on the user's current health status and lifestyle habits. 2. The system of claim 1. (Appendix 9) The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1. (Appendix 10) The collecting unit When collecting data, prioritize the collection of relevant data based on the user's geographic location 2. The system of claim 1. (Appendix 11) The collecting unit When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1. (Appendix 12) The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1. (Appendix 13) The analysis unit During analysis, adjust the level of detail based on the importance of the body data. 2. The system of claim 1. (Appendix 14) The analysis unit During analysis, different analysis algorithms are applied depending on the category of body data. 2. The system of claim 1. (Appendix 15) The analysis unit Inferring user sentiment and adjusting the length of the analysis based on the estimated user sentiment 2. The system of claim 1. (Appendix 16) The analysis unit During analysis, prioritize analysis based on when the data was collected. 2. The system of claim 1. (Appendix 17) The analysis unit During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1. (Appendix 18) The recommendation unit Inferring user sentiment and adjusting recommendation criteria based on the inferred sentiment 2. The system of claim 1. (Appendix 19) The recommendation unit When making recommendations, correlations between body data are taken into account to improve the accuracy of recommendations. 2. The system of claim 1. (Appendix 20) The recommendation unit When making recommendations, user attribute information is taken into consideration. 2. The system of claim 1. (Appendix 21) The recommendation unit Infer user sentiment and adjust the order in which recommendation results are displayed based on the inferred user sentiment 2. The system of claim 1. (Appendix 22) The recommendation unit When making recommendations, consider the geographical distribution of users. 2. The system of claim 1. (Appendix 23) The recommendation unit When making recommendations, improve the accuracy of the recommendations based on relevant literature 2. The system of claim 1. (Appendix 24) The providing unit Inferring the user's emotions and adjusting the training plan and advice provided based on the inferred user emotions 2. The system of claim 1. (Appendix 25) The providing unit When providing a plan, the system will refer to the user's past training history to provide the optimal plan. 2. The system of claim 1. (Appendix 26) The providing unit At the time of delivery, customize your training plan based on your current life situation 2. The system of claim 1. (Appendix 27) The providing unit Estimate the user's emotions and prioritize training plans and advice based on the estimated user emotions. 2. The system of claim 1. (Appendix 28) The providing unit At the time of provision, the optimal plan is provided taking into account the user's geographic location information. 2. The system of claim 1. (Appendix 29) The providing unit When provided, analyze users' social media activity to suggest training plans and advice 2. The system of claim 1. [Explanation of symbols]

[0182] 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 collection unit that collects data on the user's physical characteristics and growth process; an analysis unit that analyzes the data collected by the collection unit; a recommendation unit that recommends an appropriate sport based on the analysis results obtained by the analysis unit; a providing unit that provides training plans and advice based on the sports competition recommended by the recommending unit, The analysis unit estimates the user's emotion, and provides an analysis result when the estimated user's emotion is a relaxed state, provides a more concise analysis result than the analysis result provided when the estimated user's emotion is a stressed state, and adjusts a method of presenting the analysis so as to provide a visually appealing analysis result when the estimated user's emotion is excited. A system characterized by:

2. The collecting unit Collect data on height, weight, muscle mass, bone density, and flexibility 2. The system of claim 1.

3. The analysis unit Analyze the success factors of each sport based on a past database and compare them with user data 2. The system of claim 1.

4. The recommendation unit Identifying sports that are suitable for the user's physical characteristics 2. The system of claim 1.

5. The providing unit Evaluate your chances of success in a specific sport and provide specific training plans and advice 2. The system of claim 1.

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