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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately recommend optimal sports competitions 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 an 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 result obtained by the analysis unit. The provision unit provides a training plan and advice based on the sport recommended by the recommendation unit. Effect of the Invention

[0007] The system according to the embodiment can recommend the most suitable sports competition based on an individual's physical characteristics and growth process. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The sports competition recommendation system according to the embodiment of the present invention is a system that recommends the most suitable sports competition based on the physical characteristics of the user and provides training plans and advice. This system starts with the user inputting data on their own physical characteristics and growth process. For example, data such as height, weight, muscle mass, bone density, and flexibility are input. This data is input into AI. Next, AI analyzes the input data. Based on the past database, AI analyzes the success factors in each sports competition and compares it with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into consideration. This identifies the sports competition that is most suitable for the user's physical characteristics. Furthermore, AI evaluates the possibility of success in the identified sports competition. For example, if it is determined that the user is suitable for basketball, it evaluates the possibility of success in that competition and provides specific training plans and advice. This system allows the user to find the sports competition that is most suitable for their physical characteristics, and even players who are buried in sports with a large number of participants can find the possibility of leading the world in other competitions. For example, even players who were not successful in baseball or soccer may be successful in track and field or swimming. This enables the sports recommendation system to recommend the most suitable sports based on the user's physical characteristics and provide training plans and advice.

[0029] The sports competition recommendation 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 user's physical characteristics include, for example, height, weight, muscle mass, bone density, flexibility, etc., but are not limited to such examples. The collection unit collects, for example, data input by the user. The collection unit can also collect data using a sensor or a measuring device. For example, the collection unit measures height and weight using a height gauge or a weight scale and collects data. Furthermore, 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 the success factors in each sports competition based on a past database and compares it with the user's data. The analysis unit analyzes, for example, that in basketball, height is an important factor, and weight and muscle mass are also taken into consideration. The recommendation unit identifies the sports competition that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend an optimal sport based on the physical characteristics of the user. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides a training plan and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide a specific training plan and advice to the user. For example, the provision unit provides a training menu, dietary advice, mental support, etc. suitable for basketball. As a result, the sports competition recommendation system according to the embodiment can recommend an optimal sports competition based on the physical characteristics of the user and provide a training plan and advice.

[0030] The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, flexibility, and the like. The collection unit collects, for example, data input by the user. The collection unit can also collect data using sensors and measuring devices. For example, the collection unit measures height and weight using a height gauge or a weight scale and collects data. Furthermore, the collection unit can use dedicated devices for measuring muscle mass and bone density. Specifically, the user can input his / her own physical data using a smartphone or tablet. This includes daily changes in weight and height, diet, amount of exercise, and the like. The collection unit can also collect data in real time using a wearable device. For example, a smart watch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows the user's physical condition to be understood in more detail. Furthermore, the collection unit can also import measurement results from regular health checkups and sports clinics. This includes blood test results and doctor's diagnosis results. This allows a comprehensive evaluation of the user's health condition and potential risks. The collection unit centrally manages the information obtained from these various data sources and makes it accessible to the analysis unit and recommendation unit. This allows the collection unit to grasp the user's physical characteristics and growth process in detail and accurately, 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 the success factors in each sport based on the past database and compares it with the user's data. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's physical data and predict performance in a specific sport. 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 for. In addition, the analysis unit can also evaluate the possibility of future performance improvement by taking into account the user's growth process and training history. For example, it predicts how much performance improvement the user can expect by performing specific training while maintaining their current physical characteristics. In addition, the analysis unit provides basic data for evaluating the user's health condition and potential risks and providing appropriate training plans and advice. As a result, the analysis unit can provide data for evaluating the user's physical characteristics in detail and recommending the most suitable sport. In addition, the analysis unit can incorporate user feedback and continuously improve the accuracy of the analysis algorithm. This enables the analysis unit to always use the latest data and technology to recommend the most suitable sports events to the user.

[0032] The recommendation unit identifies the sports competition that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the most suitable sports competition based on the user's physical characteristics. For example, the recommendation unit determines that the user is suitable for basketball and recommends that competition. Specifically, the AI ​​compares the user's physical data with past success cases to identify the most suitable sports competition. For example, if the user is tall and has a lot of muscle mass, it determines that basketball or volleyball is suitable. Also, if the user is flexible and has a good sense of balance, it determines that gymnastics or figure skating is suitable. The recommendation unit recommends a specific sports competition to the user based on this information. Furthermore, the recommendation unit can recommend the most suitable sports competition by taking into account the user's interests and preferences. For example, if the user is interested in a specific sport, it preferentially recommends that sport. In addition, the recommendation unit can also suggest an appropriate training plan according to the user's lifestyle and schedule. As a result, the recommendation unit can comprehensively consider the user's physical characteristics and interests and recommend the most suitable sports competition. Furthermore, the recommendation unit can incorporate user feedback and continuously improve the accuracy of the recommendation algorithm, so that the recommendation unit can always use the latest data and technology to recommend the most suitable sports events to the user.

[0033] The providing unit provides a training plan and advice based on the sports competition recommended by the recommending unit. The providing unit uses AI to provide the user with a specific training plan and advice. For example, the providing unit provides a training menu, 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 competition. For example, a training menu suitable for basketball includes plyometric training to improve jumping power and aerobic exercise to improve endurance. In addition, the dietary advice recommends a high-protein diet to increase muscle mass and the intake of carbohydrates to replenish energy. Furthermore, the mental support provides relaxation techniques to relieve tension before a game and mental training to improve concentration. The providing unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the providing unit can incorporate user feedback and continuously improve the content of the training plan and advice. As a result, the providing unit can provide the user with optimal training plans and advice to support success in sports competitions. Furthermore, the providing unit can monitor the user's progress and adjust the training plan as necessary. This enables 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 the user. The collection unit measures height and weight using, for example, a height gauge or a weight scale, and collects data. The collection unit can also use a dedicated device for measuring muscle mass. For example, the collection unit uses a bioimpedance method for measuring muscle mass. Furthermore, the collection unit can also use a dual energy X-ray absorptiometry (DXA) for measuring bone density. For example, the collection unit measures bone density using DXA and collects data. The collection unit can also use a dedicated device for measuring flexibility. For example, the collection unit performs a stretch test to measure flexibility and collects data. This allows the collection unit to collect detailed data on the user's body. Part 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 inputs data input by the user into AI, and the AI ​​can collect data.

[0035] The analysis unit can analyze the success factors in each sport based on the past database and compare it with the user's data. The analysis unit, for example, analyzes the success factors in each sport based on the past database. The analysis unit can analyze that, for example, in basketball, height is an important factor, and 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 the user's data based on these success factors. 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. Part 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 past database into AI, and the AI ​​can analyze the success factors.

[0036] The recommendation unit can identify a sport that is suitable for the physical characteristics of the user. The recommendation unit identifies a sport that is most suitable for the physical characteristics of the user based on, for example, the analysis results obtained by the analysis unit. For example, the recommendation unit determines that the user is suitable for basketball and recommends that sport. The recommendation unit can also determine that the user is suitable for soccer and recommend that sport. Furthermore, the recommendation unit can determine that the user is suitable for track and field and recommend that sport. The recommendation unit suggests the most suitable sport for the user based on these recommendations. This allows the recommendation unit to identify the most suitable sport based on the physical characteristics of the user. Part 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 analysis results obtained by the analysis unit to AI, and the AI ​​can identify the most suitable sport.

[0037] The providing unit can evaluate the probability of success in the identified sports competition and provide a specific training plan and advice. The providing unit, for example, evaluates the probability of success in the sports competition identified by the recommending unit. For example, if the user is determined to be suitable for basketball, the providing unit evaluates the possibility of success in the competition. In addition, if the user is determined to be suitable for soccer, the providing unit can also evaluate the possibility of success in the competition. Furthermore, if the user is determined to be suitable for track and field, the providing unit can also evaluate the possibility of success in the competition. The providing unit provides a specific training plan and advice based on these evaluation results. For example, the providing unit provides a training menu, dietary advice, mental support, etc. suitable for basketball. In addition, 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. As a result, the providing unit can increase the possibility of success in the sports competition by providing the user with a specific training plan and advice. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the probability of success in a sporting event identified by the recommendation 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. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past physical data into AI, which can select 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, when the user is in good health, the collection unit collects detailed data. For example, when 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 diet record, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. A part 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, and the AI ​​can perform filtering.

[0040] The collection unit can collect data with high relevance based on the geographical location information of the user at the time of data collection. For example, if the user lives at high altitude, the collection unit collects data related to exercise at high altitude with high priority. For example, if the user lives in an urban area, the collection unit can also collect data related to exercise in urban areas with high priority. In addition, if the user lives at the seaside, the collection unit can also collect data related to swimming and beach sports with high priority. This enables the collection unit to collect data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can collect data with high relevance with high priority.

[0041] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit collects data related to exercises that the user frequently posts 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 in which the user shows interest on social media. This enables the collection unit to collect data based on the user's social media activity. 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 social media activity into AI, and the AI ​​can collect related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the physical data during the analysis. For example, when height is an important factor, the analysis unit performs a detailed analysis of height. For example, when muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, when 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the physical data to an AI, which can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of physical data during analysis. 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of physical data to AI, and the AI ​​can apply different analysis algorithms.

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

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, correlates and analyzes data on height and weight. The analysis unit can also correlate and analyze data on muscle mass and flexibility. The analysis unit can also correlate 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 data. A part 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 data to AI, which can adjust the order of analysis.

[0046] The recommendation unit can improve the accuracy of the recommendation by considering the interrelationship of the physical data when making a recommendation. The recommendation unit makes a recommendation by considering, for example, the interrelationship between height and weight. The recommendation unit can also make a recommendation by considering, for example, the interrelationship between muscle mass and flexibility. The recommendation unit can also make a recommendation by considering the interrelationship between bone density and athletic ability. This allows the recommendation unit to make a highly accurate recommendation by considering the interrelationship between the physical data. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the interrelationship between the physical data to AI, which can improve the accuracy of the recommendation.

[0047] The recommendation unit can make a recommendation taking into consideration the attribute information of the user when making a recommendation. The recommendation unit can make a recommendation taking into consideration, for example, the age of the user. The recommendation unit can also make a recommendation taking into consideration, for example, the gender of the user. The recommendation unit can also make a recommendation taking into consideration the exercise experience of the user. This enables the recommendation unit to make a recommendation based on the attribute information of the user. A part or all of the above-mentioned 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 attribute information of the user to an AI, which can then make a recommendation.

[0048] The recommendation unit can make recommendations taking into consideration the geographical distribution of the user when making recommendations. For example, if the user lives at high altitude, the recommendation unit makes recommendations related to exercise at high altitude. For example, if the user lives in an urban area, the recommendation unit can also make recommendations related to exercise in urban areas. Also, if the user lives by the sea, the recommendation unit can make recommendations related to swimming and beach sports. This enables the recommendation unit to make recommendations based on the geographical distribution of the user. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the geographical distribution of the user to AI, and the AI ​​can make recommendations.

[0049] The recommendation unit can improve the accuracy of the recommendation by referring to related literature when making a recommendation. The recommendation unit, for example, makes a recommendation by referring to the latest research papers. The recommendation unit can also make a recommendation by referring to past success cases. The recommendation unit can also make a recommendation by referring to expert opinions. As a result, the recommendation unit improves the accuracy of the recommendation by referring to related literature. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may 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] The providing unit can provide an optimal plan by referring to the user's past training history at the time of providing. 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 from, 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 including improvements. As a result, the providing unit provides an optimal plan based on the past training history, thereby enabling effective training. A part 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, and the AI ​​can provide an optimal training plan.

[0051] The providing unit can customize the training plan based on the user's current living situation at the time of providing. For example, when the user is busy, the providing unit provides an effective training plan in a short time. For example, when 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. A part 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 living situation into AI, and the AI ​​can customize the training plan.

[0052] The providing unit can provide an optimal plan taking into consideration the geographical location information of the user at the time of providing. For example, if the user lives at high altitude, the providing unit provides 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 an urban area. In addition, 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 geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can provide an optimal plan.

[0053] The providing unit can analyze the user's social media activity at the time of providing and propose a training plan or advice. The providing unit, for example, provides a training plan related to an exercise that the user frequently posts on social media. The providing unit can also provide a training plan related to an exercise that the user's social media friends are doing. The providing unit can also provide a training plan related to a sport in which the user is interested on social media. This enables the providing unit to propose a training plan or advice based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using 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 can propose a training plan or 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 content and nutritional intake and combining this with the physical data for analysis, it is possible to recommend sports competitions with higher accuracy. Furthermore, the analysis unit can provide dietary advice to supplement a specific nutrient based on the user's dietary data if the user is lacking in that nutrient. This allows the user to find the most suitable sports competition not only in terms of physical characteristics but also in terms of 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 recommend a more comprehensive sporting event. Furthermore, the collection unit can provide advice for improving sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the optimal sporting event not only in terms of physical characteristics but also in terms of sleep.

[0057] The recommendation unit can recommend sports events taking into consideration 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 events related to that sport. Furthermore, the recommendation unit can 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 on not only physical characteristics but also 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 and analyzing this with the physical data, it is possible to recommend sports competitions with higher accuracy. 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 most suitable sports competition not only in terms of physical characteristics but also in terms of 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, the user's living environment, work environment, commuting time, etc. can be collected and combined with the physical data for analysis, thereby recommending a sport that is suitable for the living environment. Furthermore, the collection unit can provide training plans and advice suitable for the living environment based on the user's living environment data. This allows the user to find a sport that is optimal not only for the user's physical characteristics but also for the living environment.

[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, the recommendation unit can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the selection of sports events based on the user's social background. This allows the user to find the most suitable sports event not only in terms of physical characteristics but also in terms of social background.

[0061] The process flow of the first embodiment will be briefly described 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, flexibility, etc. 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 measuring device or a weight measuring device, 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 consideration. Step 3: The recommendation unit identifies the sport that is most suitable for 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 suitable for basketball and recommends that sport. Step 4: The provision unit provides a training plan and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. For example, it provides training menus, dietary advice, and mental support suitable for basketball.

[0063] (Example 2) The sports competition recommendation system according to the embodiment of the present invention is a system that recommends the most suitable sports competition based on the physical characteristics of the user and provides training plans and advice. This system starts with the user inputting data on their own physical characteristics and growth process. For example, data such as height, weight, muscle mass, bone density, and flexibility are input. This data is input into AI. Next, AI analyzes the input data. Based on the past database, AI analyzes the success factors in each sports competition and compares it with the user's data. For example, height is an important factor in basketball, and weight and muscle mass are also taken into consideration. This identifies the sports competition that is most suitable for the user's physical characteristics. Furthermore, AI evaluates the possibility of success in the identified sports competition. For example, if it is determined that the user is suitable for basketball, it evaluates the possibility of success in that competition and provides specific training plans and advice. This system allows the user to find the sports competition that is most suitable for their physical characteristics, and even players who are buried in sports with a large number of participants can find the possibility of leading the world in other competitions. For example, even players who were not successful in baseball or soccer may be successful in track and field or swimming. This enables the sports recommendation system to recommend the most suitable sports based on the user's physical characteristics and provide training plans and advice.

[0064] The sports competition recommendation 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 user's physical characteristics include, for example, height, weight, muscle mass, bone density, flexibility, etc., but are not limited to such examples. The collection unit collects, for example, data input by the user. The collection unit can also collect data using a sensor or a measuring device. For example, the collection unit measures height and weight using a height gauge or a weight scale and collects data. Furthermore, 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 the success factors in each sports competition based on a past database and compares it with the user's data. The analysis unit analyzes, for example, that in basketball, height is an important factor, and weight and muscle mass are also taken into consideration. The recommendation unit identifies the sports competition that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend an optimal sport based on the physical characteristics of the user. For example, the recommendation unit determines that the user is suited to basketball and recommends that sport. The provision unit provides a training plan and advice based on the sport recommended by the recommendation unit. The provision unit uses AI to provide a specific training plan and advice to the user. For example, the provision unit provides a training menu, dietary advice, mental support, etc. suitable for basketball. As a result, the sports competition recommendation system according to the embodiment can recommend an optimal sports competition based on the physical characteristics of the user and provide a training plan and advice.

[0065] The collection unit collects data on the user's physical characteristics and growth process. The user's physical characteristics include, but are not limited to, height, weight, muscle mass, bone density, flexibility, and the like. The collection unit collects, for example, data input by the user. The collection unit can also collect data using sensors and measuring devices. For example, the collection unit measures height and weight using a height gauge or a weight scale and collects data. Furthermore, the collection unit can use dedicated devices for measuring muscle mass and bone density. Specifically, the user can input his / her own physical data using a smartphone or tablet. This includes daily changes in weight and height, diet, amount of exercise, and the like. The collection unit can also collect data in real time using a wearable device. For example, a smart watch or fitness tracker can be used to collect data such as heart rate, number of steps, calories burned, and sleep patterns. This allows the user's physical condition to be understood in more detail. Furthermore, the collection unit can also import measurement results from regular health checkups and sports clinics. This includes blood test results and doctor's diagnosis results. This allows a comprehensive evaluation of the user's health condition and potential risks. The collection unit centrally manages the information obtained from these various data sources and makes it accessible to the analysis unit and recommendation unit. This allows the collection unit to grasp the user's physical characteristics and growth process in detail and accurately, 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 the success factors in each sport based on the past database and compares it with the user's data. Specifically, the AI ​​uses a machine learning algorithm to analyze the user's physical data and predict performance in a specific sport. 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 for. In addition, the analysis unit can also evaluate the possibility of future performance improvement by taking into account the user's growth process and training history. For example, it predicts how much performance improvement the user can expect by performing specific training while maintaining their current physical characteristics. In addition, the analysis unit provides basic data for evaluating the user's health condition and potential risks and providing appropriate training plans and advice. As a result, the analysis unit can provide data for evaluating the user's physical characteristics in detail and recommending the most suitable sport. In addition, the analysis unit can incorporate user feedback and continuously improve the accuracy of the analysis algorithm. This enables the analysis unit to always use the latest data and technology to recommend the most suitable sports events to the user.

[0067] The recommendation unit identifies the sports competition that is most suitable for the user's physical characteristics based on the analysis results obtained by the analysis unit. The recommendation unit uses AI to recommend the most suitable sports competition based on the user's physical characteristics. For example, the recommendation unit determines that the user is suitable for basketball and recommends that competition. Specifically, the AI ​​compares the user's physical data with past success cases to identify the most suitable sports competition. For example, if the user is tall and has a lot of muscle mass, it determines that basketball or volleyball is suitable. Also, if the user is flexible and has a good sense of balance, it determines that gymnastics or figure skating is suitable. The recommendation unit recommends a specific sports competition to the user based on this information. Furthermore, the recommendation unit can recommend the most suitable sports competition by taking into account the user's interests and preferences. For example, if the user is interested in a specific sport, it preferentially recommends that sport. In addition, the recommendation unit can also suggest an appropriate training plan according to the user's lifestyle and schedule. As a result, the recommendation unit can comprehensively consider the user's physical characteristics and interests and recommend the most suitable sports competition. Furthermore, the recommendation unit can incorporate user feedback and continuously improve the accuracy of the recommendation algorithm, so that the recommendation unit can always use the latest data and technology to recommend the most suitable sports events to the user.

[0068] The providing unit provides a training plan and advice based on the sports competition recommended by the recommending unit. The providing unit uses AI to provide the user with a specific training plan and advice. For example, the providing unit provides a training menu, 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 competition. For example, a training menu suitable for basketball includes plyometric training to improve jumping power and aerobic exercise to improve endurance. In addition, the dietary advice recommends a high-protein diet to increase muscle mass and the intake of carbohydrates to replenish energy. Furthermore, the mental support provides relaxation techniques to relieve tension before a game and mental training to improve concentration. The providing unit continuously provides these training plans and advice to the user to support the user's performance improvement. Furthermore, the providing unit can incorporate user feedback and continuously improve the content of the training plan and advice. As a result, the providing unit can provide the user with optimal training plans and advice to support success in sports competitions. Furthermore, the providing unit can monitor the user's progress and adjust the training plan as necessary. This enables 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 the user. The collection unit measures height and weight using, for example, a height gauge or a weight scale, and collects data. The collection unit can also use a dedicated device for measuring muscle mass. For example, the collection unit uses a bioimpedance method for measuring muscle mass. Furthermore, the collection unit can also use a dual energy X-ray absorptiometry (DXA) for measuring bone density. For example, the collection unit measures bone density using DXA and collects data. The collection unit can also use a dedicated device for measuring flexibility. For example, the collection unit performs a stretch test to measure flexibility and collects data. This allows the collection unit to collect detailed data on the user's body. Part 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 inputs data input by the user into AI, and the AI ​​can collect data.

[0070] The analysis unit can analyze the success factors in each sport based on the past database and compare it with the user's data. The analysis unit, for example, analyzes the success factors in each sport based on the past database. The analysis unit can analyze that, for example, in basketball, height is an important factor, and 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 the user's data based on these success factors. 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. Part 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 past database into AI, and the AI ​​can analyze the success factors.

[0071] The recommendation unit can identify a sport that is suitable for the physical characteristics of the user. The recommendation unit identifies a sport that is most suitable for the physical characteristics of the user based on, for example, the analysis results obtained by the analysis unit. For example, the recommendation unit determines that the user is suitable for basketball and recommends that sport. The recommendation unit can also determine that the user is suitable for soccer and recommend that sport. Furthermore, the recommendation unit can determine that the user is suitable for track and field and recommend that sport. The recommendation unit suggests the most suitable sport for the user based on these recommendations. This allows the recommendation unit to identify the most suitable sport based on the physical characteristics of the user. Part 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 analysis results obtained by the analysis unit to AI, and the AI ​​can identify the most suitable sport.

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

[0073] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. For example, when the user is relaxed, the collection unit immediately collects data and acquires detailed information. Also, when the user is 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 emotion, thereby enabling more accurate data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 a user's emotional 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. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past physical data into AI, which can select 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, when the user is in good health, the collection unit collects detailed data. For example, when 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 diet record, exercise habits, and sleep patterns. This enables the collection unit to collect data according to the user's health condition and lifestyle habits. A part 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, and the AI ​​can perform filtering.

[0076] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit preferentially collects detailed data. In addition, when the user is stressed, the collection unit can also preferentially collect basic data. Furthermore, when the user is excited, the collection unit can also preferentially collect data that can be collected quickly. This allows the collection unit to determine the priority of data according to the user's emotion, thereby enabling efficient data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part 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 the user's emotion data to the generation AI, and the generation AI can determine the priority of the data.

[0077] The collection unit can collect data with high relevance based on the geographical location information of the user at the time of data collection. For example, if the user lives at high altitude, the collection unit collects data related to exercise at high altitude with high priority. For example, if the user lives in an urban area, the collection unit can also collect data related to exercise in urban areas with high priority. In addition, if the user lives at the seaside, the collection unit can also collect data related to swimming and beach sports with high priority. This enables the collection unit to collect data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can collect data with high relevance with high priority.

[0078] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit collects data related to exercises that the user frequently posts 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 in which the user shows interest on social media. This enables the collection unit to collect data based on the user's social media activity. 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 social media activity into AI, and the AI ​​can collect related data.

[0079] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. The analysis unit provides a detailed analysis result when the user is relaxed, for example. The analysis unit can also provide a concise analysis result when the user is stressed. Furthermore, the analysis unit can provide a visually attractive analysis result when the user is excited. This enables the analysis unit to provide an analysis result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the expression method of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the physical data during the analysis. For example, when height is an important factor, the analysis unit performs a detailed analysis of height. For example, when muscle mass is an important factor, the analysis unit performs a detailed analysis of muscle mass. Furthermore, when 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the physical data to an AI, which can adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of physical data during analysis. 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, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of physical data to 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. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. The analysis unit, for example, performs a detailed analysis when the user is relaxed. The analysis unit can also 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 an appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the length of the analysis.

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

[0084] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, correlates and analyzes data on height and weight. The analysis unit can also correlate and analyze data on muscle mass and flexibility. The analysis unit can also correlate 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 data. A part 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 data to AI, which can adjust the order of analysis.

[0085] The recommendation unit can estimate the user's emotion and adjust the recommendation criteria based on the estimated user's emotion. The recommendation unit can, for example, estimate the user's emotion and adjust the recommendation criteria based on the estimated user's emotion. The recommendation unit can, for example, make detailed recommendations when the user is relaxed. The recommendation unit can also make concise recommendations when the user is stressed. Furthermore, the recommendation unit can make visually appealing recommendations when the user is excited. This enables the recommendation unit to adjust the recommendation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI, and the generation AI can adjust the recommendation criteria.

[0086] The recommendation unit can improve the accuracy of the recommendation by considering the interrelationship of the physical data when making a recommendation. The recommendation unit makes a recommendation by considering, for example, the interrelationship between height and weight. The recommendation unit can also make a recommendation by considering, for example, the interrelationship between muscle mass and flexibility. The recommendation unit can also make a recommendation by considering the interrelationship between bone density and athletic ability. This allows the recommendation unit to make a highly accurate recommendation by considering the interrelationship between the physical data. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the interrelationship between the physical data to AI, which can improve the accuracy of the recommendation.

[0087] The recommendation unit can make a recommendation taking into consideration the attribute information of the user when making a recommendation. The recommendation unit can make a recommendation taking into consideration, for example, the age of the user. The recommendation unit can also make a recommendation taking into consideration, for example, the gender of the user. The recommendation unit can also make a recommendation taking into consideration the exercise experience of the user. This enables the recommendation unit to make a recommendation based on the attribute information of the user. A part or all of the above-mentioned 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 attribute information of the user to an AI, which can then make a recommendation.

[0088] The recommendation unit can estimate the user's emotion and adjust the order in which the recommended results are displayed based on the estimated user's emotion. The recommendation unit, for example, estimates the user's emotion and adjusts the order in which the recommended results are displayed based on the estimated user's emotion. For example, when the user is relaxed, the recommendation unit can preferentially display detailed recommended results. In addition, when the user is stressed, the recommendation unit can also preferentially display concise recommended results. Furthermore, when the user is excited, the recommendation unit can also preferentially display visually attractive recommended results. In this way, the recommendation unit can provide more appropriate recommended results by adjusting the display order of the recommended results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and adjust the order in which the generation AI displays the recommended results.

[0089] The recommendation unit can make recommendations taking into consideration the geographical distribution of the user when making recommendations. For example, if the user lives at high altitude, the recommendation unit makes recommendations related to exercise at high altitude. For example, if the user lives in an urban area, the recommendation unit can also make recommendations related to exercise in urban areas. Also, if the user lives by the sea, the recommendation unit can make recommendations related to swimming and beach sports. This enables the recommendation unit to make recommendations based on the geographical distribution of the user. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may be performed without using AI. For example, the recommendation unit can input the geographical distribution of the user to AI, and the AI ​​can make recommendations.

[0090] The recommendation unit can improve the accuracy of the recommendation by referring to related literature when making a recommendation. The recommendation unit, for example, makes a recommendation by referring to the latest research papers. The recommendation unit can also make a recommendation by referring to past success cases. The recommendation unit can also make a recommendation by referring to expert opinions. As a result, the recommendation unit improves the accuracy of the recommendation by referring to related literature. A part or all of the above-mentioned processing in the recommendation unit may be performed, for example, using AI, or may 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 emotion and adjust the method of providing the training plan and advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the training plan and advice based on the estimated user's emotion. The providing unit, for example, provides a detailed training plan when the user is relaxed. The providing unit can also provide a concise training plan when the user is stressed. Furthermore, the providing unit can also provide a visually attractive 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 emotion, thereby enabling more appropriate support. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can adjust the method of providing the training plan and advice.

[0092] The providing unit can provide an optimal plan by referring to the user's past training history at the time of providing. 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 from, 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 including improvements. As a result, the providing unit provides an optimal plan based on the past training history, thereby enabling effective training. A part 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, and the AI ​​can provide an optimal training plan.

[0093] The providing unit can customize the training plan based on the user's current living situation at the time of providing. For example, when the user is busy, the providing unit provides an effective training plan in a short time. For example, when 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. A part 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 living situation into AI, and the AI ​​can customize the training plan.

[0094] The providing unit can estimate the user's emotion and determine the priority of the training plan and advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of the training plan and advice based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide a detailed training plan preferentially. In addition, when the user is stressed, the providing unit can also provide a brief training plan preferentially. Furthermore, when the user is excited, the providing unit can also provide a visually attractive training plan preferentially. This enables the providing unit to provide training plans and advice in a priority order according to the user's emotion, thereby enabling more effective support. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority order of the training plan and advice.

[0095] The providing unit can provide an optimal plan taking into consideration the geographical location information of the user at the time of providing. For example, if the user lives at high altitude, the providing unit provides 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 an urban area. In addition, 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 geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can provide an optimal plan.

[0096] The providing unit can analyze the user's social media activity at the time of providing and propose a training plan or advice. The providing unit, for example, provides a training plan related to an exercise that the user frequently posts on social media. The providing unit can also provide a training plan related to an exercise that the user's social media friends are doing. The providing unit can also provide a training plan related to a sport in which the user is interested on social media. This enables the providing unit to propose a training plan or advice based on the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using 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 can propose a training plan or 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 content and nutritional intake and combining this with the physical data for analysis, it is possible to recommend sports competitions with higher accuracy. Furthermore, the analysis unit can provide dietary advice to supplement a specific nutrient based on the user's dietary data if the user is lacking in that nutrient. This allows the user to find the most suitable sports competition not only in terms of physical characteristics but also in terms of 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 recommend a more comprehensive sporting event. Furthermore, the collection unit can provide advice for improving sleep if the user's sleep quality is declining based on the user's sleep data. This allows the user to find the optimal sporting event not only in terms of physical characteristics but also in terms of sleep.

[0100] The analysis unit can include the user's stress level in the analysis in addition to the user's physical data. 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 conditions. Furthermore, if the user's stress level is high, the analysis unit can provide relaxation methods and mental support to reduce stress. This allows the user to find the most suitable sports not only in terms of physical characteristics but also in terms of stress.

[0101] The recommendation unit can recommend sports events taking into consideration 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 events related to that sport. Furthermore, the recommendation unit can 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 on not only physical characteristics but also personal interests and hobbies.

[0102] The providing unit 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, a more challenging training plan can be provided, and if the user is less motivated, a simple and easy-to-achieve training plan can be provided. Furthermore, the providing unit can provide mental support and encouraging messages to maintain the user's motivation. This allows the user to receive an optimal training plan not only in terms of physical characteristics but also in terms of 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 and analyzing this with the physical data, it is possible to recommend sports competitions with higher accuracy. 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 most suitable sports competition not only in terms of physical characteristics but also in terms of 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, the user's living environment, work environment, commuting time, etc. can be collected and combined with the physical data for analysis, thereby recommending a sport that is suitable for the living environment. Furthermore, the collection unit can provide training plans and advice suitable for the living environment based on the user's living environment data. This allows the user to find a sport that is optimal not only for the user's physical characteristics but also for the living environment.

[0105] The analysis unit can include the user's emotional data in the analysis in addition to the user's physical data. For example, based on the user's emotional data, it can recommend a sport that can be played in a state where the user's emotions fluctuate less. Furthermore, based on the user's emotional data, the analysis unit can provide mental support and relaxation methods to stabilize emotions. This allows the user to find the most suitable sport not only in terms of physical characteristics but also in terms of 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, the recommendation unit can recommend sports events that are suitable for the user's occupation, family environment, and social role. Furthermore, the recommendation unit can expand the selection of sports events based on the user's social background. This allows the user to find the most suitable sports event not only in terms of physical characteristics but also in terms of social background.

[0107] The providing unit can provide a training plan and advice taking into consideration the user's emotional data in addition to the user's physical data. For example, a training plan that can be carried out in a state where the user's emotions fluctuate less can be provided based on the user's emotional data. Furthermore, the providing unit can provide mental support and relaxation methods for stabilizing emotions based on the user's emotional data. This allows the user to receive an optimal training plan not only based on physical characteristics but also based on emotions.

[0108] The process flow of the second embodiment will be briefly described 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, flexibility, etc. 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 measuring device or a weight measuring device, 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 consideration. Step 3: The recommendation unit identifies the sport that is most suitable for 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 suitable for basketball and recommends that sport. Step 4: The provision unit provides a training plan and advice based on the sports recommended by the recommendation unit. The provision unit uses AI to provide the user with specific training plans and advice. 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice 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<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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such 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 above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned collection unit, analysis unit, recommendation unit, and provision unit 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 competition based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides a training plan and advice based on the recommended sports competition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned collection unit, analysis unit, recommendation unit, and provision unit 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 competition based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides a training plan and advice based on the recommended sports competition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, recommendation unit, and provision unit 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 a sensor or a measuring device 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 competition based on the analysis results. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides a training plan and advice based on the recommended sports competition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of 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 performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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 above-mentioned collection unit, analysis unit, recommendation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's physical data 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 competition based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides a training plan and advice based on the recommended sports competition. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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 inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," 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 Universal Serial Bus (USB) 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] In addition, 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 upon 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] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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 configuration using one 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 configuration using a processor that realizes the functions of the 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, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and 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 result obtained by the analysis unit; A providing unit that provides a training plan and advice based on the sports event recommended by the recommending unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on height, weight, muscle mass, bone density, and flexibility 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the success factors of each sport based on a past database and compare it with the user's data 2. The system of claim 1, (Appendix 4) The recommendation unit is Identifying a sport suitable for a user's physical characteristics 2. The system of claim 1, (Appendix 5) The providing unit is 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 includes: 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 includes: Analyze the user's past physical data and select the optimal collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting data, filter it based on the user's current health status and lifestyle habits 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is 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 is 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 is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The recommendation unit is Infer user sentiment and adjust recommendation criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The recommendation unit is 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 is When making recommendations, user attribute information is taken into account. 2. The system of claim 1, (Appendix 21) The recommendation unit is 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 is While making recommendations, the geographical distribution of users is taken into account 2. The system of claim 1, (Appendix 23) The recommendation unit is When making recommendations, improve the accuracy of the recommendations based on related literature 2. The system of claim 1, (Appendix 24) The providing unit is Inferring a 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 is 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 is At the time of delivery, customize training plans based on the user's current life situation 2. The system of claim 1, (Appendix 27) The providing unit is 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 is 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 is 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 result obtained by the analysis unit; A providing unit that provides a training plan and advice based on the sports event recommended by the recommending unit. A system characterized by:

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

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

4. The recommendation unit is Identifying a sport suitable for a user's physical characteristics 2. The system of claim 1 .

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

6. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze the user's past physical data and select the optimal collection method 2. The system of claim 1 .

8. The collecting unit includes: When collecting data, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1 .

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