system

Abstract

The system according to the embodiment aims to provide an optimal nutrition plan based on the individual physical data of the athlete. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a provision unit, a feedback collection unit, and an improvement unit. The collection unit collects physical data of an athlete. The generation unit analyzes the data collected by the collection unit and generates a nutrition plan tailored to the athlete's needs. The provision unit provides the nutrition plan generated by the generation unit. The feedback collection unit collects feedback from the athlete based on the nutrition plan provided by the provision unit. The improvement unit improves the nutrition plan based on the feedback collected by the feedback collection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal nutrition plans based on individual athlete physical data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal nutrition plan based on the individual physical data of the athlete. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a provision unit, a feedback collection unit, and an improvement unit. The collection unit collects physical data of the athlete. The generation unit analyzes the data collected by the collection unit and generates a nutrition plan tailored to the athlete's needs. The provision unit provides the nutrition plan generated by the generation unit. The feedback collection unit collects feedback from the athlete based on the nutrition plan provided by the provision unit. The improvement unit improves the nutrition plan based on the feedback collected by the feedback collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal nutrition plan based on the athlete's individual physical data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A nutrition plan providing system according to an embodiment of the present invention integrates an athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan. The nutrition plan providing system collects the athlete's physical data, training information, and lifestyle habits, and a generation AI analyzes the data to generate and provide an optimal nutrition plan. The system also collects the athlete's feedback and improves the nutrition plan based on the athlete's feedback. For example, the nutrition plan providing system collects the athlete's physical data (e.g., weight, height, body fat percentage, etc.). Next, it collects training information (e.g., type, intensity, duration, etc. of exercise). It also collects lifestyle habits (e.g., sleep time, meal timing, stress level, etc.). These data are input into the generation AI, which analyzes the input data and generates an optimal nutrition plan tailored to the athlete's needs. For example, it may suggest meal recipes containing nutrients needed after a specific training session or recommend specific nutritional supplements. The generation AI constantly learns the latest medical data and makes suggestions to optimize the athlete's health and performance. This makes the nutrition plan providing system a powerful tool for optimizing an athlete's health and performance. This allows the nutrition plan providing system to integrate an athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan. For example, an athlete can receive an individual nutrition plan based on their physical data, training information, and lifestyle habits. This allows athletes to perform at their best and live a healthy life.

[0029] A nutrition plan providing system according to an embodiment includes a collection unit, a generation unit, a provision unit, a feedback collection unit, and an improvement unit. The collection unit collects the athlete's physical data. The athlete's physical data includes, but is not limited to, weight, height, body fat percentage, heart rate, body temperature, and blood pressure. The collection unit collects the athlete's physical data, for example, through a wearable device or a smartphone app. The collection unit can also collect training information from a training log or a fitness tracker. The collection unit can also collect lifestyle habits through a diary or questionnaire entered by the athlete. For example, the collection unit can monitor heart rate and body temperature in real time using a wearable device and collect data. The collection unit can also record the athlete's diet and sleep time using a smartphone app and collect data. The collection unit can also analyze the training log and record the type, intensity, and duration of exercise. The generation unit uses a generation AI to analyze the data collected by the collection unit and generate a nutrition plan tailored to the athlete's needs. The generation AI analyzes data using techniques such as neural networks and reinforcement learning to generate an optimal nutrition plan. For example, the generation unit allows the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health and performance. The generation unit can also allow the generation AI to learn the athlete's feedback and continuously improve the nutrition plan. The provision unit provides the nutrition plan generated by the generation unit to the athlete. For example, the provision unit provides the nutrition plan generated by the generation AI to the athlete. The provision unit can also provide the nutrition plan in an optimal format taking into account the athlete's device information. For example, the provision unit provides the nutrition plan in a format optimized for smartphones. The provision unit can also adjust the timing of provision based on the athlete's schedule. For example, the provision unit provides the nutrition plan after training. The feedback collection unit collects the athlete's feedback based on the nutrition plan provided by the provision unit.The feedback collection unit collects feedback using, for example, questionnaires, interviews, sensor data, etc. The feedback collection unit can also estimate the athlete's emotions and adjust the timing of feedback collection based on the emotions. For example, the feedback collection unit collects feedback when the athlete is relaxed. The improvement unit improves the nutrition plan based on the feedback collected by the feedback collection unit. For example, the improvement unit modifies the plan or adjusts the algorithm based on the feedback. The improvement unit can also estimate the athlete's emotions and adjust the improvement method based on the emotions. For example, the improvement unit suggests an improvement method that has a relaxing effect when the athlete is feeling stressed. In this way, the nutrition plan provision system according to the embodiment can integrate the athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan.

[0030] The collection unit can collect the athlete's physical data through a wearable device or a smartphone app. Examples of wearable devices include, but are not limited to, smartwatches and fitness trackers. For example, the collection unit can use a smartwatch to monitor heart rate and body temperature in real time and collect data. The collection unit can also use a fitness tracker to record steps and calories burned and collect data. The collection unit can also use a smartphone app to record the athlete's diet and sleep time and collect data. For example, the collection unit can use a smartphone app to record the athlete's diet with photos and collect data. The collection unit can also use a smartphone app to record the athlete's sleep time and collect data. This allows the athlete's physical data to be efficiently collected through the wearable device or smartphone app. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input heart rate data acquired by the smartwatch into the generation AI and have the generation AI analyze the data.

[0031] The collection unit can collect training information from a training log or a fitness tracker. The training log includes, but is not limited to, the type, time, and intensity of exercise. For example, the collection unit analyzes the training log and records the type, time, and intensity of exercise. The collection unit can also collect training information using a fitness tracker. For example, the collection unit records the type, time, and intensity of exercise using a fitness tracker. The collection unit can also record the athlete's training history using the training log. For example, the collection unit analyzes the athlete's training history using the training log and collects data. This allows for efficient collection of training information from the training log or fitness tracker. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input training data acquired by the fitness tracker into a generation AI and have the generation AI analyze the data.

[0032] The collection unit can collect lifestyle habits through a diary or questionnaire entered by the athlete. Diaries include, but are not limited to, dietary details, exercise records, and changes in physical condition. For example, the collection unit analyzes the diary entered by the athlete to collect lifestyle habits. The collection unit can also collect lifestyle habits of the athlete using a questionnaire. For example, the collection unit can use a questionnaire to collect the athlete's dietary details, exercise records, and changes in physical condition. The collection unit can also record the athlete's lifestyle habits using the diary. For example, the collection unit can use the diary to record the athlete's dietary details, exercise records, and changes in physical condition. This allows lifestyle habits to be efficiently collected through the diary or questionnaire entered by the athlete. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input diary data entered by the athlete into a generation AI and have the generation AI analyze the data.

[0033] The generation unit can use the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health or performance. Examples of the generation AI include, but are not limited to, neural networks and reinforcement learning. The generation unit can, for example, use the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health or performance. The generation AI can learn the latest medical data, such as clinical trial data and research papers, and generate a nutrition plan tailored to the athlete's needs. The generation unit can also have the generation AI learn the athlete's feedback and continuously improve the nutrition plan. For example, the generation unit can have the generation AI learn the athlete's feedback and reflect it in the next proposal. In this way, the generation AI can generate a nutrition plan to optimize the athlete's health or performance. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the latest medical data into the generation AI and have the generation AI learn the data and generate a nutrition plan.

[0034] The providing unit can provide the nutrition plan generated by the generation AI to the athlete. Examples of the generation AI include, but are not limited to, neural networks and reinforcement learning. For example, the providing unit can provide the nutrition plan generated by the generation AI to the athlete. The providing unit can also provide the nutrition plan in an optimal format taking into account the athlete's device information. For example, the providing unit can provide the nutrition plan in a format optimized for smartphones. The providing unit can also adjust the timing of provision based on the athlete's schedule. For example, the providing unit can provide the nutrition plan after training. In this way, by providing the athlete with the nutrition plan generated by the generation AI, the athlete can receive the optimal nutrition plan. Some or all of the above-mentioned processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can send the nutrition plan generated by the generation AI to the athlete's smartphone so that the athlete can check it at any time.

[0035] The feedback collection unit collects athlete feedback and allows the generation AI to learn from the feedback and improve the nutritional plan. Examples of feedback include, but are not limited to, questionnaires, interviews, sensor data, etc. The feedback collection unit may collect athlete feedback using, for example, questionnaires. The feedback collection unit may also collect athlete feedback through interviews. The feedback collection unit may also collect athlete feedback using sensor data. For example, the feedback collection unit may collect athlete feedback using questionnaires. The feedback collection unit may also collect athlete opinions on the nutritional plan through interviews. The feedback collection unit may also monitor the athlete's physical condition and collect feedback using sensor data. This allows the generation AI to collect athlete feedback, learn from the feedback, and improve the nutritional plan, thereby consistently providing an optimal nutritional plan. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback collection unit may input athlete feedback data into the generation AI and have the generation AI analyze and learn from the data.

[0036] The collection unit can analyze sensor data from the wearable device in real time and issue an alert if an abnormal value is detected. Sensor data includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the collection unit can issue an alert to the athlete if the heart rate is abnormally high. The collection unit can also issue an alert to the athlete if the body temperature is abnormally elevated. The collection unit can also issue an alert to the athlete if the blood pressure is abnormally low. For example, the collection unit can issue an alert to the athlete if the heart rate is abnormally high. The collection unit can also issue an alert to the athlete if the body temperature is abnormally elevated. The collection unit can also issue an alert to the athlete if the blood pressure is abnormally low. In this way, by analyzing sensor data from the wearable device in real time and issuing an alert if an abnormal value is detected, the safety of the athlete can be ensured. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input sensor data acquired by a wearable device into the generation AI and have the generation AI analyze the data and detect outliers.

[0037] When collecting training information, the collection unit can optimize the collection method by referring to the athlete's past performance data. Past performance data includes, but is not limited to, training records, match results, etc. For example, the collection unit can determine the optimal collection timing based on the past training data. The collection unit can also select the type of data to collect based on the past performance data. Furthermore, the collection unit can customize the collection method based on the past training history. For example, the collection unit can determine the optimal collection timing based on the past training data. The collection unit can also select the type of data to collect based on the past performance data. Furthermore, the collection unit can customize the collection method based on the past training history. In this way, optimizing the collection method by referring to the athlete's past performance data enables more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data to a generation AI and cause the generation AI to analyze the data and optimize the collection method.

[0038] The collection unit may monitor the athlete's stress level when collecting lifestyle habits and collect additional data if the stress level is high. Measurements of stress levels include, but are not limited to, heart rate variability and self-reporting. For example, the collection unit may collect additional sleep data if the stress level is high. Furthermore, the collection unit may collect additional detailed dietary data if the stress level is high. Furthermore, the collection unit may collect additional exercise amount data if the stress level is high. For example, the collection unit may collect additional sleep data if the stress level is high. Furthermore, the collection unit may collect additional detailed dietary data if the stress level is high. Furthermore, the collection unit may collect additional exercise amount data if the stress level is high. In this way, by monitoring the athlete's stress level and collecting additional data if the stress level is high, more detailed data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the athlete's stress level data into the generation AI and cause the generation AI to analyze the data and collect additional data.

[0039] The collection unit can synchronize data from the wearable device to the cloud and collect and integrate data from multiple devices. Use of the cloud can include, but is not limited to, security measures such as data encryption and access control. For example, the collection unit can synchronize data from a smartwatch and a fitness tracker to the cloud. The collection unit can also synchronize data from a heart rate monitor and a thermometer to the cloud. Furthermore, the collection unit can synchronize data from a GPS device and an accelerometer to the cloud. For example, the collection unit can synchronize data from a smartwatch and a fitness tracker to the cloud. The collection unit can also synchronize data from a heart rate monitor and a thermometer to the cloud. Furthermore, the collection unit can also synchronize data from a GPS device and an accelerometer to the cloud. This allows for more comprehensive data collection by synchronizing data from wearable devices to the cloud and collecting and integrating data from multiple devices. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data synchronized to the cloud to a generation AI and have the generation AI integrate and analyze the data.

[0040] When collecting training information, the collection unit can collect environmental conditions based on the athlete's geographical location information. Examples of collection of geographical location information include, but are not limited to, GPS data, location information services, etc. The collection unit can, for example, collect the temperature of the training location. The collection unit can also collect the humidity of the training location. The collection unit can also collect the altitude of the training location. For example, the collection unit can collect the temperature of the training location. The collection unit can also collect the humidity of the training location. The collection unit can also collect the altitude of the training location. In this way, by collecting environmental conditions taking the athlete's geographical location information into consideration, more accurate training information can be obtained. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect and analyze environmental conditions.

[0041] When collecting lifestyle habits, the collection unit can collect related data based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the collection unit can analyze food posts on social media to collect food data. The collection unit can also analyze exercise posts on social media to collect exercise data. The collection unit can also analyze sleep posts on social media to collect sleep data. For example, the collection unit can analyze food posts on social media to collect food data. The collection unit can also analyze exercise posts on social media to collect exercise data. The collection unit can also analyze sleep posts on social media to collect sleep data. In this way, by analyzing the athlete's social media activity and collecting related data, more detailed lifestyle habit data can be obtained. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media post data into the generation AI and have the generation AI analyze and collect the data.

[0042] When generating a nutrition plan, the generation unit can optimize the plan by referring to the athlete's past nutritional intake history. Past nutritional intake history includes, but is not limited to, food records and supplement usage. For example, the generation unit optimizes nutritional balance based on the past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the past nutritional intake history. Furthermore, the generation unit can generate a plan that takes allergies into account based on the past nutritional intake history. For example, the generation unit optimizes nutritional balance based on the past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the past nutritional intake history. Furthermore, the generation unit can generate a plan that takes allergies into account based on the past nutritional intake history. In this way, by optimizing the plan by referring to the athlete's past nutritional intake history, a more personalized nutrition plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past nutritional intake history data into the generation AI and have the generation AI analyze the data and optimize the plan.

[0043] When generating a nutrition plan, the generation unit can generate a safe plan based on the athlete's allergy information. Examples of allergy information include, but are not limited to, medical records and self-reporting. For example, if the athlete has a nut allergy, the generation unit can generate a nut-free plan. Also, if the athlete has a dairy allergy, the generation unit can generate a dairy-free plan. Furthermore, if the athlete has a gluten allergy, the generation unit can generate a gluten-free plan. For example, if the athlete has a nut allergy, the generation unit can generate a nut-free plan. Also, if the athlete has a dairy allergy, the generation unit can generate a dairy-free plan. Furthermore, if the athlete has a gluten allergy, the generation unit can generate a gluten-free plan. This allows the athlete's health to be protected by generating a safe plan taking the athlete's allergy information into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the athlete's allergy information into the generation AI and have the generation AI analyze the data and generate a plan.

[0044] When generating a nutrition plan, the generation unit can adjust meal timing based on the athlete's training schedule. The collected training schedule information includes, but is not limited to, the type, duration, and frequency of training. For example, the generation unit can suggest a pre-training meal if energy replenishment is required before training. The generation unit can also suggest a post-training meal if recovery is required after training. The generation unit can also suggest the timing of a snack if a snack is required between training sessions. For example, the generation unit can suggest a pre-training meal if energy replenishment is required before training. The generation unit can also suggest a post-training meal if recovery is required after training. The generation unit can also suggest the timing of a snack if a snack is required between training sessions. This allows for more effective nutritional support by adjusting meal timing based on the athlete's training schedule. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the athlete's training schedule data into the generation AI and have the generation AI analyze the data and adjust the meal timing.

[0045] When generating a nutrition plan, the generation unit can use regional ingredients based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the athlete is in Japan, the generation unit can use Japanese cuisine ingredients. If the athlete is in Italy, the generation unit can use Italian cuisine ingredients. If the athlete is in Mexico, the generation unit can use Mexican cuisine ingredients. For example, if the athlete is in Japan, the generation unit can use Japanese cuisine ingredients. If the athlete is in Italy, the generation unit can use Italian cuisine ingredients. If the athlete is in Mexico, the generation unit can use Mexican cuisine ingredients. This allows for a more appropriate nutrition plan by using regional ingredients in consideration of the athlete's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select ingredients.

[0046] When generating a nutrition plan, the generation unit can customize the plan based on the athlete's cultural background. Examples of cultural background information include, but are not limited to, food culture and religious restrictions. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. Furthermore, if the athlete requires halal food, the generation unit can generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can generate a kosher meal plan. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. Furthermore, if the athlete requires halal food, the generation unit can generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can generate a kosher meal plan. This allows the plan to be customized taking into account the athlete's cultural background, thereby providing a more personalized nutrition plan. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the athlete's cultural background data into the generation AI and have the generation AI analyze the data and customize the plan.

[0047] When generating a nutrition plan, the generation unit can update the plan based on the athlete's feedback in real time. Examples of collected feedback include, but are not limited to, questionnaires, interviews, and sensor data. For example, if the athlete is dissatisfied with the taste of a meal, the generation unit can adjust the taste in the next plan. Furthermore, if the athlete has an allergic reaction to a particular ingredient, the generation unit can exclude that ingredient from the next plan. Furthermore, if the athlete feels an energy deficiency, the generation unit can increase energy replenishment in the next plan. For example, if the athlete is dissatisfied with the taste of a meal, the generation unit can adjust the taste in the next plan. Furthermore, if the athlete has an allergic reaction to a particular ingredient, the generation unit can exclude that ingredient from the next plan. Furthermore, if the athlete feels an energy deficiency, the generation unit can increase energy replenishment in the next plan. This allows the plan to be updated in real time to reflect the athlete's feedback, thereby always providing an optimal nutrition plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input athlete feedback data into the generation AI and have the generation AI analyze the data and update the plan.

[0048] When providing a nutrition plan, the provision unit can optimize the provision method based on the athlete's past feedback. Examples of past feedback collected include, but are not limited to, questionnaire results and interview records. For example, the provision unit provides the nutrition plan in a format preferred by the athlete based on the past feedback. The provision unit can also provide the nutrition plan in language easily understood by the athlete based on the past feedback. Furthermore, the provision unit can provide the nutrition plan at a time preferred by the athlete based on the past feedback. For example, the provision unit provides the nutrition plan in a format preferred by the athlete based on the past feedback. The provision unit can also provide the nutrition plan in language easily understood by the athlete based on the past feedback. Furthermore, the provision unit can provide the nutrition plan at a time preferred by the athlete based on the past feedback. This makes it possible to provide a more effective nutrition plan by optimizing the provision method with reference to the athlete's past feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or without AI. For example, the provision unit may input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and optimize the provision method.

[0049] When providing a nutrition plan, the providing unit can provide it in an optimal format based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the providing unit can provide the nutrition plan in a format optimized for the smartphone. Furthermore, if the athlete uses a tablet, the providing unit can also provide the nutrition plan in a format optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the providing unit can also provide the nutrition plan in a format optimized for the smartwatch. For example, if the athlete uses a smartphone, the providing unit can provide the nutrition plan in a format optimized for the smartphone. Furthermore, if the athlete uses a tablet, the providing unit can also provide the nutrition plan in a format optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the providing unit can also provide the nutrition plan in a format optimized for the smartwatch. This allows for a more visible nutrition plan by providing the nutrition plan in the optimal format taking into account the athlete's device information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provider can input the athlete's device information into the generation AI and have the generation AI analyze the data and select the optimal format.

[0050] When providing the nutrition plan, the providing unit can adjust the timing of provision based on the athlete's schedule. Examples of collected schedules include, but are not limited to, training schedules and daily plans. For example, the providing unit provides the nutrition plan immediately after training so that the athlete receives it after training. The providing unit can also provide the nutrition plan before breakfast so that the athlete receives it before breakfast. Furthermore, the providing unit can also provide the nutrition plan before bedtime so that the athlete receives it before bedtime. For example, the providing unit provides the nutrition plan immediately after training so that the athlete receives it after training. The providing unit can also provide the nutrition plan before breakfast so that the athlete receives it before breakfast. Furthermore, the providing unit can also provide the nutrition plan before bedtime so that the athlete receives it before bedtime. By adjusting the timing of provision based on the athlete's schedule, the nutrition plan can be provided at a more appropriate time. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the athlete's schedule data into the generation AI and have the generation AI analyze the data and adjust the timing of provision.

[0051] When providing a nutrition plan, the providing unit can select the optimal delivery method based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the athlete is at home, the providing unit can provide the nutrition plan via email. If the athlete is out, the providing unit can also provide the nutrition plan via a smartphone notification. Furthermore, if the athlete is at a training facility, the providing unit can also provide the nutrition plan to a device at the facility. For example, if the athlete is at home, the providing unit can provide the nutrition plan via email. If the athlete is out, the providing unit can also provide the nutrition plan via a smartphone notification. Furthermore, if the athlete is at a training facility, the providing unit can also provide the nutrition plan to a device at the facility. This enables the provision of a more effective nutrition plan by selecting the optimal delivery method taking the athlete's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select a delivery method.

[0052] When providing a nutrition plan, the providing unit can provide relevant information based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit can provide a relevant nutrition plan based on food posts shared by the athlete on social media. The providing unit can also provide a relevant nutrition plan based on exercise posts posted by the athlete on social media. Furthermore, the providing unit can also provide a relevant nutrition plan based on sleep posts posted by the athlete on social media. For example, the providing unit can provide a relevant nutrition plan based on food posts shared by the athlete on social media. The providing unit can also provide a relevant nutrition plan based on exercise posts posted by the athlete on social media. Furthermore, the providing unit can provide a relevant nutrition plan based on sleep posts posted by the athlete on social media. In this way, by analyzing the athlete's social media activity and providing relevant information, a more personalized nutrition plan can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input an athlete's social media data into the generating AI and have the generating AI analyze the data and provide related information.

[0053] When providing a nutrition plan, the provider can provide a multilingual plan based on the athlete's language setting. Examples of language setting collection include, but are not limited to, the device's language setting and user selection. For example, the provider can automatically set the nutrition plan based on the athlete's device's language setting. The provider can also provide a language switching function if the athlete speaks multiple languages. Furthermore, the provider can provide the nutrition plan in a specific language if the athlete selects that language. For example, the provider can automatically set the nutrition plan based on the athlete's device's language setting. The provider can also provide a language switching function if the athlete speaks multiple languages. Furthermore, the provider can provide the nutrition plan in a specific language if the athlete selects that language. This allows the provision of a multilingual plan based on the athlete's language setting, making it easier to understand. Some or all of the above-described processing by the provider can be performed using, or without, AI. For example, the provider can input the athlete's language setting data into a generation AI and have the generation AI analyze the data and generate a multilingual plan.

[0054] When collecting feedback, the feedback collection unit can optimize the collection method based on the athlete's past feedback history. Past feedback history includes, but is not limited to, questionnaire results, interview records, etc. For example, the feedback collection unit determines the optimal collection timing based on the past feedback history. The feedback collection unit can also select the type of feedback to collect based on the past feedback history. Furthermore, the feedback collection unit can customize the collection method based on the past feedback history. For example, the feedback collection unit determines the optimal collection timing based on the past feedback history. The feedback collection unit can also select the type of feedback to collect based on the past feedback history. Furthermore, the feedback collection unit can customize the collection method based on the past feedback history. In this way, by optimizing the collection method with reference to the athlete's past feedback history, more effective feedback collection is possible. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, AI. For example, the feedback collection unit may input the athlete's past feedback history data into a generation AI and cause the generation AI to analyze the data and optimize the collection method.

[0055] When collecting feedback, the feedback collection unit can select the optimal collection means based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the feedback collection unit collects feedback via the smartphone. Furthermore, if the athlete uses a tablet, the feedback collection unit can also collect feedback via the tablet. Furthermore, if the athlete uses a smartwatch, the feedback collection unit can also collect feedback via the smartwatch. For example, if the athlete uses a smartphone, the feedback collection unit collects feedback via the smartphone. Furthermore, if the athlete uses a tablet, the feedback collection unit can also collect feedback via the tablet. Furthermore, if the athlete uses a smartwatch, the feedback collection unit can also collect feedback via the smartwatch. This allows for more effective feedback collection by selecting the optimal collection means in consideration of the athlete's device information. Some or all of the above-described processing by the feedback collection unit may be performed using, or without, AI. For example, the feedback collection unit may input the athlete's device information into the generation AI and have the generation AI analyze the data and select the optimal collection means.

[0056] When collecting feedback, the feedback collection unit can adjust the collection timing based on the athlete's schedule. Examples of schedule collection include, but are not limited to, training schedules and daily plans. For example, the feedback collection unit collects feedback immediately after training so that the athlete provides feedback after training. The feedback collection unit can also collect feedback before breakfast so that the athlete provides feedback before breakfast. The feedback collection unit can also collect feedback before bedtime so that the athlete provides feedback before bedtime. For example, the feedback collection unit collects feedback immediately after training so that the athlete provides feedback after training. The feedback collection unit can also collect feedback before breakfast so that the athlete provides feedback before breakfast. The feedback collection unit can also collect feedback before bedtime so that the athlete provides feedback before bedtime. In this way, by adjusting the collection timing based on the athlete's schedule, feedback can be collected at a more appropriate time. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the athlete's schedule data into the generation AI and cause the generation AI to analyze the data and adjust the collection timing.

[0057] When collecting feedback, the feedback collection unit can prioritize collection of highly relevant feedback based on the athlete's geographical location information. Examples of collection of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the athlete is at a training facility, the feedback collection unit can prioritize feedback related to training. Furthermore, when the athlete is at home, the feedback collection unit can prioritize feedback related to lifestyle habits. Furthermore, when the athlete is out and about, the feedback collection unit can prioritize feedback related to activities while away from home. For example, when the athlete is at a training facility, the feedback collection unit can prioritize feedback related to training. Furthermore, when the athlete is at home, the feedback collection unit can prioritize feedback related to lifestyle habits. Furthermore, when the athlete is out and about, the feedback collection unit can prioritize feedback related to activities while away from home. This enables more effective feedback collection by prioritizing collection of highly relevant feedback taking into account the athlete's geographical location information. Some or all of the above-described processing by the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the athlete's geographic location information into the generation AI and have the generation AI analyze the data and determine the priority of the feedback.

[0058] During feedback collection, the feedback collection unit can collect relevant feedback based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the feedback collection unit can analyze food posts on social media and collect feedback related to the food. The feedback collection unit can also analyze exercise posts on social media and collect feedback related to the exercise. Furthermore, the feedback collection unit can analyze sleep posts on social media and collect feedback related to the sleep. For example, the feedback collection unit can analyze food posts on social media and collect feedback related to the food. The feedback collection unit can also analyze exercise posts on social media and collect feedback related to the exercise. Furthermore, the feedback collection unit can analyze sleep posts on social media and collect feedback related to the sleep. In this way, by analyzing the athlete's social media activity and collecting relevant feedback, more detailed feedback can be obtained. Some or all of the above-described processing by the feedback collection unit may be performed, for example, using AI or without using AI. For example, the feedback collection unit can input social media posting data into the generation AI and have the generation AI analyze the data and collect feedback.

[0059] When collecting feedback, the feedback collection unit can customize the collection method based on the athlete's past feedback. Past feedback includes, for example, questionnaire results, interview records, etc., but is not limited to these examples. For example, the feedback collection unit selects a collection method preferred by the athlete based on the past feedback. The feedback collection unit can also collect feedback in language that the athlete can easily understand based on the past feedback. Furthermore, the feedback collection unit can collect feedback at a timing preferred by the athlete based on the past feedback. For example, the feedback collection unit selects a collection method preferred by the athlete based on the past feedback. The feedback collection unit can also collect feedback in language that the athlete can easily understand based on the past feedback. Furthermore, the feedback collection unit can collect feedback at a timing preferred by the athlete based on the past feedback. In this way, customizing the collection method to reflect the athlete's past feedback enables more effective feedback collection. Some or all of the above-mentioned processing by the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input an athlete's past feedback data into the generation AI and have the generation AI analyze the data and customize the collection method.

[0060] During improvement, the improvement unit can optimize the improvement method based on the athlete's past feedback. Past feedback includes, but is not limited to, questionnaire results, interview records, etc. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. This enables more effective improvement by optimizing the improvement method with reference to the athlete's past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and optimize the improvement method.

[0061] When making improvements, the improvement unit can select optimal improvement measures based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the improvement unit can suggest improvement measures optimized for the smartphone. Furthermore, if the athlete uses a tablet, the improvement unit can suggest improvement measures optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the improvement unit can suggest improvement measures optimized for the smartwatch. For example, if the athlete uses a smartphone, the improvement unit can suggest improvement measures optimized for the smartphone. Furthermore, if the athlete uses a tablet, the improvement unit can suggest improvement measures optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the improvement unit can suggest improvement measures optimized for the smartwatch. This enables more effective improvement by selecting optimal improvement measures taking the athlete's device information into consideration. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input the athlete's device information into the generation AI and have the generation AI analyze the data and select optimal improvement measures.

[0062] During improvement, the improvement unit can adjust the timing of improvement based on the athlete's schedule. Examples of collected schedules include, but are not limited to, training schedules and daily plans. For example, the improvement unit may suggest that the athlete receive the improvement method immediately after training. The improvement unit may also suggest that the athlete receive the improvement method before breakfast. The improvement unit may also suggest that the athlete receive the improvement method before bedtime. For example, the improvement unit may suggest that the athlete receive the improvement method immediately after training. The improvement unit may also suggest that the athlete receive the improvement method before breakfast. The improvement unit may also suggest that the athlete receive the improvement method before bedtime. By adjusting the timing of improvement based on the athlete's schedule, the improvement method can be provided at a more appropriate time. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit may input the athlete's schedule data into the generation AI and have the generation AI analyze the data and adjust the timing of improvement.

[0063] During improvement, the improvement unit can select the optimal improvement method based on the athlete's geographical location information. Examples of geographical location information collection include, but are not limited to, GPS data and location information services. For example, if the athlete is at home, the improvement unit can suggest improvement methods that can be done at home. Also, if the athlete is at a training facility, the improvement unit can suggest improvement methods that can be done at the facility. Furthermore, if the athlete is out and about, the improvement unit can suggest improvement methods that can be done while away from home. For example, if the athlete is at home, the improvement unit can suggest improvement methods that can be done at home. Also, if the athlete is at a training facility, the improvement unit can suggest improvement methods that can be done at the facility. Furthermore, if the athlete is out and about, the improvement unit can suggest improvement methods that can be done while away from home. This enables more effective improvement by selecting the optimal improvement method taking the athlete's geographical location information into consideration. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select an improvement method.

[0064] During improvement, the improvement unit can suggest relevant improvement measures based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the improvement unit can analyze food posts on social media and suggest food-related improvement measures. The improvement unit can also analyze exercise posts on social media and suggest exercise-related improvement measures. The improvement unit can also analyze sleep posts on social media and suggest sleep-related improvement measures. For example, the improvement unit can analyze food posts on social media and suggest food-related improvement measures. The improvement unit can also analyze exercise posts on social media and suggest exercise-related improvement measures. The improvement unit can also analyze sleep posts on social media and suggest sleep-related improvement measures. This enables more personalized improvement by analyzing the athlete's social media activity and suggesting relevant improvement measures. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input social media post data into the generation AI and have the generation AI analyze the data and suggest improvement measures.

[0065] When improving, the improvement unit can customize the improvement method based on the athlete's past feedback. Past feedback includes, but is not limited to, questionnaire results, interview records, etc. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. This enables more effective improvement by customizing the improvement method to reflect the athlete's past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and customize the improvement method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] When collecting an athlete's physical data, the collection unit can optimize the collection method by referring to the athlete's past health checkup data. For example, the collection unit may collect data with an emphasis on specific health indicators based on the past health checkup data. The collection unit may also adjust the frequency of data collection based on the past health checkup data. Furthermore, the collection unit may select the type of data to collect based on the past health checkup data. In this way, by optimizing the collection method by referring to the athlete's past health checkup data, more effective data collection is possible.

[0068] When collecting physical data of an athlete, the collection unit can optimize the collection method by referring to the athlete's genetic information. For example, the collection unit collects data related to a specific genetic risk based on the genetic information. The collection unit can also adjust the frequency of data collection based on the genetic information. Furthermore, the collection unit can select the type of data to collect based on the genetic information. In this way, by optimizing the collection method by referring to the athlete's genetic information, more effective data collection is possible.

[0069] When collecting the athlete's physical data, the collection unit can monitor the athlete's dietary content in real time and adjust the type of data to be collected based on the dietary content. For example, if the athlete eats a high-calorie meal, the collection unit can collect data on calories burned. Also, if the athlete eats a meal that is high in a particular nutrient, the collection unit can collect data related to that nutrient. Furthermore, if the athlete does not eat, the collection unit can collect data related to energy consumption. This allows for more effective data collection by adjusting the type of data to be collected based on the athlete's dietary content.

[0070] When generating a nutritional plan for an athlete, the generation unit can optimize the plan by referring to the athlete's past nutritional intake history. For example, the generation unit can optimize the nutritional balance based on the athlete's past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the athlete's past nutritional intake history. Furthermore, the generation unit can also generate a plan that takes allergies into consideration based on the athlete's past nutritional intake history. In this way, by optimizing the plan by referring to the athlete's past nutritional intake history, a more personalized nutritional plan can be provided.

[0071] When generating an athlete's nutrition plan, the generation unit can adjust meal timing based on the athlete's training schedule. For example, if an athlete needs to replenish energy before training, the generation unit can suggest a pre-training meal. In addition, if an athlete needs to recover after training, the generation unit can also suggest a post-training meal. Furthermore, if an athlete needs a snack between training sessions, the generation unit can also suggest the timing of a snack. This allows for more effective nutritional replenishment by adjusting meal timing based on the athlete's training schedule.

[0072] When generating the nutrition plan for an athlete, the generation unit can use ingredients specific to the region based on the athlete's geographical location information. For example, the generation unit can use ingredients for Japanese cuisine when the athlete is in Japan. The generation unit can also use ingredients for Italian cuisine when the athlete is in Italy. Furthermore, the generation unit can use ingredients for Mexican cuisine when the athlete is in Mexico. This allows for a more appropriate nutrition plan to be provided by using ingredients specific to the region in consideration of the athlete's geographical location information.

[0073] When generating a nutrition plan for an athlete, the generation unit can customize the plan based on the athlete's cultural background. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. If the athlete requires halal food, the generation unit can also generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can also generate a kosher meal plan. This makes it possible to provide a more personalized nutrition plan by customizing the plan taking into account the athlete's cultural background.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection unit collects the athlete's physical data. The athlete's physical data includes weight, height, body fat percentage, heart rate, body temperature, blood pressure, etc. The collection unit collects this data through wearable devices and smartphone apps. It can also collect training information from training logs and fitness trackers, and lifestyle habits through diaries and questionnaires filled out by the athlete. Step 2: The generation unit analyzes the data collected by the collection unit and generates a nutrition plan tailored to the athlete's needs. The generation unit analyzes the data using generative AI and generates an optimal nutrition plan. The generative AI uses techniques such as neural networks and reinforcement learning to generate a nutrition plan to optimize the athlete's health and performance. Step 3: The provision unit provides the nutrition plan generated by the generation unit to the athlete. The provision unit provides the nutrition plan generated by the generation AI to the athlete and provides the nutrition plan in an optimal format taking into account the athlete's device information. For example, the nutrition plan may be provided in a format optimized for smartphones and the timing of delivery may be adjusted based on the athlete's schedule. Step 4: The feedback collection unit collects feedback from the athlete based on the nutrition plan provided by the provision unit. The feedback collection unit collects feedback using questionnaires, interviews, sensor data, etc., estimates the athlete's emotions, and adjusts the timing of feedback collection. Step 5: The Improvement Team improves the nutrition plan based on the feedback collected by the Feedback Collection Team. Based on the feedback, the Improvement Team modifies the plan and adjusts the algorithm, and adjusts the improvement method by estimating the athlete's emotions.

[0076] (Example 2) A nutrition plan providing system according to an embodiment of the present invention integrates an athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan. The nutrition plan providing system collects the athlete's physical data, training information, and lifestyle habits, and a generation AI analyzes the data to generate and provide an optimal nutrition plan. The system also collects the athlete's feedback and improves the nutrition plan based on the athlete's feedback. For example, the nutrition plan providing system collects the athlete's physical data (e.g., weight, height, body fat percentage, etc.). Next, it collects training information (e.g., type, intensity, duration, etc. of exercise). It also collects lifestyle habits (e.g., sleep time, meal timing, stress level, etc.). These data are input into the generation AI, which analyzes the input data and generates an optimal nutrition plan tailored to the athlete's needs. For example, it may suggest meal recipes containing nutrients needed after a specific training session or recommend specific nutritional supplements. The generation AI constantly learns the latest medical data and makes suggestions to optimize the athlete's health and performance. This makes the nutrition plan providing system a powerful tool for optimizing an athlete's health and performance. This allows the nutrition plan providing system to integrate an athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan. For example, an athlete can receive an individual nutrition plan based on their physical data, training information, and lifestyle habits. This allows athletes to perform at their best and live a healthy life.

[0077] A nutrition plan providing system according to an embodiment includes a collection unit, a generation unit, a provision unit, a feedback collection unit, and an improvement unit. The collection unit collects the athlete's physical data. The athlete's physical data includes, but is not limited to, weight, height, body fat percentage, heart rate, body temperature, and blood pressure. The collection unit collects the athlete's physical data, for example, through a wearable device or a smartphone app. The collection unit can also collect training information from a training log or a fitness tracker. The collection unit can also collect lifestyle habits through a diary or questionnaire entered by the athlete. For example, the collection unit can monitor heart rate and body temperature in real time using a wearable device and collect data. The collection unit can also record the athlete's diet and sleep time using a smartphone app and collect data. The collection unit can also analyze the training log and record the type, intensity, and duration of exercise. The generation unit uses a generation AI to analyze the data collected by the collection unit and generate a nutrition plan tailored to the athlete's needs. The generation AI analyzes data using techniques such as neural networks and reinforcement learning to generate an optimal nutrition plan. For example, the generation unit allows the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health and performance. The generation unit can also allow the generation AI to learn the athlete's feedback and continuously improve the nutrition plan. The provision unit provides the nutrition plan generated by the generation unit to the athlete. For example, the provision unit provides the nutrition plan generated by the generation AI to the athlete. The provision unit can also provide the nutrition plan in an optimal format taking into account the athlete's device information. For example, the provision unit provides the nutrition plan in a format optimized for smartphones. The provision unit can also adjust the timing of provision based on the athlete's schedule. For example, the provision unit provides the nutrition plan after training. The feedback collection unit collects the athlete's feedback based on the nutrition plan provided by the provision unit.The feedback collection unit collects feedback using, for example, questionnaires, interviews, sensor data, etc. The feedback collection unit can also estimate the athlete's emotions and adjust the timing of feedback collection based on the emotions. For example, the feedback collection unit collects feedback when the athlete is relaxed. The improvement unit improves the nutrition plan based on the feedback collected by the feedback collection unit. For example, the improvement unit modifies the plan or adjusts the algorithm based on the feedback. The improvement unit can also estimate the athlete's emotions and adjust the improvement method based on the emotions. For example, the improvement unit suggests an improvement method that has a relaxing effect when the athlete is feeling stressed. In this way, the nutrition plan provision system according to the embodiment can integrate the athlete's individual physical data, training information, and lifestyle habits to provide an optimal daily nutrition plan.

[0078] The collection unit can collect the athlete's physical data through a wearable device or a smartphone app. Examples of wearable devices include, but are not limited to, smartwatches and fitness trackers. For example, the collection unit can use a smartwatch to monitor heart rate and body temperature in real time and collect data. The collection unit can also use a fitness tracker to record steps and calories burned and collect data. The collection unit can also use a smartphone app to record the athlete's diet and sleep time and collect data. For example, the collection unit can use a smartphone app to record the athlete's diet with photos and collect data. The collection unit can also use a smartphone app to record the athlete's sleep time and collect data. This allows the athlete's physical data to be efficiently collected through the wearable device or smartphone app. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input heart rate data acquired by the smartwatch into the generation AI and have the generation AI analyze the data.

[0079] The collection unit can collect training information from a training log or a fitness tracker. The training log includes, but is not limited to, the type, time, and intensity of exercise. For example, the collection unit analyzes the training log and records the type, time, and intensity of exercise. The collection unit can also collect training information using a fitness tracker. For example, the collection unit records the type, time, and intensity of exercise using a fitness tracker. The collection unit can also record the athlete's training history using the training log. For example, the collection unit analyzes the athlete's training history using the training log and collects data. This allows for efficient collection of training information from the training log or fitness tracker. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input training data acquired by the fitness tracker into a generation AI and have the generation AI analyze the data.

[0080] The collection unit can collect lifestyle habits through a diary or questionnaire entered by the athlete. Diaries include, but are not limited to, dietary details, exercise records, and changes in physical condition. For example, the collection unit analyzes the diary entered by the athlete to collect lifestyle habits. The collection unit can also collect lifestyle habits of the athlete using a questionnaire. For example, the collection unit can use a questionnaire to collect the athlete's dietary details, exercise records, and changes in physical condition. The collection unit can also record the athlete's lifestyle habits using the diary. For example, the collection unit can use the diary to record the athlete's dietary details, exercise records, and changes in physical condition. This allows lifestyle habits to be efficiently collected through the diary or questionnaire entered by the athlete. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input diary data entered by the athlete into a generation AI and have the generation AI analyze the data.

[0081] The generation unit can use the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health or performance. Examples of the generation AI include, but are not limited to, neural networks and reinforcement learning. The generation unit can, for example, use the generation AI to learn the latest medical data and generate a nutrition plan to optimize the athlete's health or performance. The generation AI can learn the latest medical data, such as clinical trial data and research papers, and generate a nutrition plan tailored to the athlete's needs. The generation unit can also have the generation AI learn the athlete's feedback and continuously improve the nutrition plan. For example, the generation unit can have the generation AI learn the athlete's feedback and reflect it in the next proposal. In this way, the generation AI can generate a nutrition plan to optimize the athlete's health or performance. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the latest medical data into the generation AI and have the generation AI learn the data and generate a nutrition plan.

[0082] The providing unit can provide the nutrition plan generated by the generation AI to the athlete. Examples of the generation AI include, but are not limited to, neural networks and reinforcement learning. For example, the providing unit can provide the nutrition plan generated by the generation AI to the athlete. The providing unit can also provide the nutrition plan in an optimal format taking into account the athlete's device information. For example, the providing unit can provide the nutrition plan in a format optimized for smartphones. The providing unit can also adjust the timing of provision based on the athlete's schedule. For example, the providing unit can provide the nutrition plan after training. In this way, by providing the athlete with the nutrition plan generated by the generation AI, the athlete can receive the optimal nutrition plan. Some or all of the above-mentioned processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can send the nutrition plan generated by the generation AI to the athlete's smartphone so that the athlete can check it at any time.

[0083] The feedback collection unit collects athlete feedback and allows the generation AI to learn from the feedback and improve the nutritional plan. Examples of feedback include, but are not limited to, questionnaires, interviews, sensor data, etc. The feedback collection unit may collect athlete feedback using, for example, questionnaires. The feedback collection unit may also collect athlete feedback through interviews. The feedback collection unit may also collect athlete feedback using sensor data. For example, the feedback collection unit may collect athlete feedback using questionnaires. The feedback collection unit may also collect athlete opinions on the nutritional plan through interviews. The feedback collection unit may also monitor the athlete's physical condition and collect feedback using sensor data. This allows the generation AI to collect athlete feedback, learn from the feedback, and improve the nutritional plan, thereby consistently providing an optimal nutritional plan. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback collection unit may input athlete feedback data into the generation AI and have the generation AI analyze and learn from the data.

[0084] The collection unit can estimate the athlete's emotions and adjust the timing of collecting physical data based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the collection unit collects physical data when the athlete is relaxed. Furthermore, if the athlete is concentrating, the collection unit can also collect physical data after training. Furthermore, if the athlete is tired, the collection unit can also collect physical data after resting. For example, if the athlete is feeling stressed, the collection unit collects heart rate and body temperature when the athlete is relaxed. Furthermore, if the athlete is concentrating, the collection unit can also collect blood pressure and body fat percentage after training. Furthermore, if the athlete is tired, the collection unit can also collect weight and height after resting. By adjusting the timing of collecting physical data based on the athlete's emotions, data can be collected at more appropriate times. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input facial expression data of an athlete into the generation AI and cause the generation AI to estimate emotions and adjust the timing of collection.

[0085] The collection unit can analyze sensor data from the wearable device in real time and issue an alert if an abnormal value is detected. Sensor data includes, but is not limited to, heart rate, body temperature, and blood pressure. For example, the collection unit can issue an alert to the athlete if the heart rate is abnormally high. The collection unit can also issue an alert to the athlete if the body temperature is abnormally elevated. The collection unit can also issue an alert to the athlete if the blood pressure is abnormally low. For example, the collection unit can issue an alert to the athlete if the heart rate is abnormally high. The collection unit can also issue an alert to the athlete if the body temperature is abnormally elevated. The collection unit can also issue an alert to the athlete if the blood pressure is abnormally low. In this way, by analyzing sensor data from the wearable device in real time and issuing an alert if an abnormal value is detected, the safety of the athlete can be ensured. Some or all of the above-mentioned processing by the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input sensor data acquired by a wearable device into the generation AI and have the generation AI analyze the data and detect outliers.

[0086] When collecting training information, the collection unit can optimize the collection method by referring to the athlete's past performance data. Past performance data includes, but is not limited to, training records, match results, etc. For example, the collection unit can determine the optimal collection timing based on the past training data. The collection unit can also select the type of data to collect based on the past performance data. Furthermore, the collection unit can customize the collection method based on the past training history. For example, the collection unit can determine the optimal collection timing based on the past training data. The collection unit can also select the type of data to collect based on the past performance data. Furthermore, the collection unit can customize the collection method based on the past training history. In this way, optimizing the collection method by referring to the athlete's past performance data enables more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past performance data to a generation AI and cause the generation AI to analyze the data and optimize the collection method.

[0087] The collection unit may monitor the athlete's stress level when collecting lifestyle habits and collect additional data if the stress level is high. Measurements of stress levels include, but are not limited to, heart rate variability and self-reporting. For example, the collection unit may collect additional sleep data if the stress level is high. Furthermore, the collection unit may collect additional detailed dietary data if the stress level is high. Furthermore, the collection unit may collect additional exercise amount data if the stress level is high. For example, the collection unit may collect additional sleep data if the stress level is high. Furthermore, the collection unit may collect additional detailed dietary data if the stress level is high. Furthermore, the collection unit may collect additional exercise amount data if the stress level is high. In this way, by monitoring the athlete's stress level and collecting additional data if the stress level is high, more detailed data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the athlete's stress level data into the generation AI and cause the generation AI to analyze the data and collect additional data.

[0088] The collection unit can estimate the athlete's emotions and prioritize the data to be collected based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is relaxed, the collection unit can prioritize collecting physical data. Furthermore, if the athlete is concentrating, the collection unit can prioritize collecting training data. Furthermore, if the athlete is tired, the collection unit can prioritize collecting lifestyle habit data. For example, if the athlete is relaxed, the collection unit can prioritize collecting physical data. Furthermore, if the athlete is concentrating, the collection unit can prioritize collecting training data. Furthermore, if the athlete is tired, the collection unit can prioritize collecting lifestyle habit data. By prioritizing the data to be collected based on the athlete's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input facial expression data of athletes to the generation AI and have the generation AI estimate emotions and determine the priority of the data.

[0089] The collection unit can synchronize data from the wearable device to the cloud and collect and integrate data from multiple devices. Use of the cloud can include, but is not limited to, security measures such as data encryption and access control. For example, the collection unit can synchronize data from a smartwatch and a fitness tracker to the cloud. The collection unit can also synchronize data from a heart rate monitor and a thermometer to the cloud. Furthermore, the collection unit can synchronize data from a GPS device and an accelerometer to the cloud. For example, the collection unit can synchronize data from a smartwatch and a fitness tracker to the cloud. The collection unit can also synchronize data from a heart rate monitor and a thermometer to the cloud. Furthermore, the collection unit can also synchronize data from a GPS device and an accelerometer to the cloud. This allows for more comprehensive data collection by synchronizing data from wearable devices to the cloud and collecting and integrating data from multiple devices. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data synchronized to the cloud to a generation AI and have the generation AI integrate and analyze the data.

[0090] When collecting training information, the collection unit can collect environmental conditions based on the athlete's geographical location information. Examples of collection of geographical location information include, but are not limited to, GPS data, location information services, etc. The collection unit can, for example, collect the temperature of the training location. The collection unit can also collect the humidity of the training location. The collection unit can also collect the altitude of the training location. For example, the collection unit can collect the temperature of the training location. The collection unit can also collect the humidity of the training location. The collection unit can also collect the altitude of the training location. In this way, by collecting environmental conditions taking the athlete's geographical location information into consideration, more accurate training information can be obtained. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect and analyze environmental conditions.

[0091] When collecting lifestyle habits, the collection unit can collect related data based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the collection unit can analyze food posts on social media to collect food data. The collection unit can also analyze exercise posts on social media to collect exercise data. The collection unit can also analyze sleep posts on social media to collect sleep data. For example, the collection unit can analyze food posts on social media to collect food data. The collection unit can also analyze exercise posts on social media to collect exercise data. The collection unit can also analyze sleep posts on social media to collect sleep data. In this way, by analyzing the athlete's social media activity and collecting related data, more detailed lifestyle habit data can be obtained. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input social media post data into the generation AI and have the generation AI analyze and collect the data.

[0092] The generation unit can estimate the athlete's emotions and adjust the nutrition plan content based on the estimated emotions of the athlete. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is stressed, the generation unit can generate a nutrition plan including ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the generation unit can generate a nutrition plan including nutrients to maintain concentration. Furthermore, if the athlete is tired, the generation unit can generate a nutrition plan including nutrients to promote recovery. For example, if the athlete is stressed, the generation unit can generate a nutrition plan including ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the generation unit can generate a nutrition plan including nutrients to maintain concentration. Furthermore, if the athlete is tired, the generation unit can generate a nutrition plan including nutrients to promote recovery. This allows the nutrition plan content to be adjusted based on the athlete's emotions, thereby providing a more appropriate nutrition plan. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input emotional data of the athlete into the generation AI and cause the generation AI to estimate the emotion and adjust the nutrition plan.

[0093] When generating a nutrition plan, the generation unit can optimize the plan by referring to the athlete's past nutritional intake history. Past nutritional intake history includes, but is not limited to, food records and supplement usage. For example, the generation unit optimizes nutritional balance based on the past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the past nutritional intake history. Furthermore, the generation unit can generate a plan that takes allergies into account based on the past nutritional intake history. For example, the generation unit optimizes nutritional balance based on the past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the past nutritional intake history. Furthermore, the generation unit can generate a plan that takes allergies into account based on the past nutritional intake history. In this way, by optimizing the plan by referring to the athlete's past nutritional intake history, a more personalized nutrition plan can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past nutritional intake history data into the generation AI and have the generation AI analyze the data and optimize the plan.

[0094] When generating a nutrition plan, the generation unit can generate a safe plan based on the athlete's allergy information. Examples of allergy information include, but are not limited to, medical records and self-reporting. For example, if the athlete has a nut allergy, the generation unit can generate a nut-free plan. Also, if the athlete has a dairy allergy, the generation unit can generate a dairy-free plan. Furthermore, if the athlete has a gluten allergy, the generation unit can generate a gluten-free plan. For example, if the athlete has a nut allergy, the generation unit can generate a nut-free plan. Also, if the athlete has a dairy allergy, the generation unit can generate a dairy-free plan. Furthermore, if the athlete has a gluten allergy, the generation unit can generate a gluten-free plan. This allows the athlete's health to be protected by generating a safe plan taking the athlete's allergy information into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the athlete's allergy information into the generation AI and have the generation AI analyze the data and generate a plan.

[0095] When generating a nutrition plan, the generation unit can adjust meal timing based on the athlete's training schedule. The collected training schedule information includes, but is not limited to, the type, duration, and frequency of training. For example, the generation unit can suggest a pre-training meal if energy replenishment is required before training. The generation unit can also suggest a post-training meal if recovery is required after training. The generation unit can also suggest the timing of a snack if a snack is required between training sessions. For example, the generation unit can suggest a pre-training meal if energy replenishment is required before training. The generation unit can also suggest a post-training meal if recovery is required after training. The generation unit can also suggest the timing of a snack if a snack is required between training sessions. This allows for more effective nutritional support by adjusting meal timing based on the athlete's training schedule. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the athlete's training schedule data into the generation AI and have the generation AI analyze the data and adjust the meal timing.

[0096] The generation unit can estimate the athlete's emotions and prioritize the nutrition plan based on the estimated emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is stressed, the generation unit prioritizes ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the generation unit can prioritize nutrients that help maintain concentration. Furthermore, if the athlete is tired, the generation unit can prioritize nutrients that promote recovery. For example, if the athlete is stressed, the generation unit prioritizes ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the generation unit can prioritize nutrients that help maintain concentration. Furthermore, if the athlete is tired, the generation unit can prioritize nutrients that promote recovery. Thus, by prioritizing the nutrition plan based on the athlete's emotions, it is possible to provide more important nutrition plans first. Emotion estimation is realized using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input emotional data of the athlete to the generation AI and cause the generation AI to estimate the emotions and determine the priority of the nutrition plan.

[0097] When generating a nutrition plan, the generation unit can use regional ingredients based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the athlete is in Japan, the generation unit can use Japanese cuisine ingredients. If the athlete is in Italy, the generation unit can use Italian cuisine ingredients. If the athlete is in Mexico, the generation unit can use Mexican cuisine ingredients. For example, if the athlete is in Japan, the generation unit can use Japanese cuisine ingredients. If the athlete is in Italy, the generation unit can use Italian cuisine ingredients. If the athlete is in Mexico, the generation unit can use Mexican cuisine ingredients. This allows for a more appropriate nutrition plan by using regional ingredients in consideration of the athlete's geographical location information. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select ingredients.

[0098] When generating a nutrition plan, the generation unit can customize the plan based on the athlete's cultural background. Examples of cultural background information include, but are not limited to, food culture and religious restrictions. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. Furthermore, if the athlete requires halal food, the generation unit can generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can generate a kosher meal plan. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. Furthermore, if the athlete requires halal food, the generation unit can generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can generate a kosher meal plan. This allows the plan to be customized taking into account the athlete's cultural background, thereby providing a more personalized nutrition plan. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the athlete's cultural background data into the generation AI and have the generation AI analyze the data and customize the plan.

[0099] When generating a nutrition plan, the generation unit can update the plan based on the athlete's feedback in real time. Examples of collected feedback include, but are not limited to, questionnaires, interviews, and sensor data. For example, if the athlete is dissatisfied with the taste of a meal, the generation unit can adjust the taste in the next plan. Furthermore, if the athlete has an allergic reaction to a particular ingredient, the generation unit can exclude that ingredient from the next plan. Furthermore, if the athlete feels an energy deficiency, the generation unit can increase energy replenishment in the next plan. For example, if the athlete is dissatisfied with the taste of a meal, the generation unit can adjust the taste in the next plan. Furthermore, if the athlete has an allergic reaction to a particular ingredient, the generation unit can exclude that ingredient from the next plan. Furthermore, if the athlete feels an energy deficiency, the generation unit can increase energy replenishment in the next plan. This allows the plan to be updated in real time to reflect the athlete's feedback, thereby always providing an optimal nutrition plan. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input athlete feedback data into the generation AI and have the generation AI analyze the data and update the plan.

[0100] The provision unit can estimate the athlete's emotions and adjust the method of providing the nutrition plan based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the provision unit can provide the nutrition plan along with relaxing music. Furthermore, if the athlete is concentrating, the provision unit can provide the nutrition plan in a simple, highly visible format. Furthermore, if the athlete is tired, the provision unit can provide the nutrition plan in a less visually stimulating format. For example, if the athlete is feeling stressed, the provision unit can provide the nutrition plan along with relaxing music. Furthermore, if the athlete is concentrating, the provision unit can provide the nutrition plan in a simple, highly visible format. Furthermore, if the athlete is tired, the provision unit can provide the nutrition plan in a less visually stimulating format. This allows the nutrition plan to be provided in a more appropriate manner by adjusting the method of providing the nutrition plan based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the athlete's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the providing method.

[0101] When providing a nutrition plan, the provision unit can optimize the provision method based on the athlete's past feedback. Examples of past feedback collected include, but are not limited to, questionnaire results and interview records. For example, the provision unit provides the nutrition plan in a format preferred by the athlete based on the past feedback. The provision unit can also provide the nutrition plan in language easily understood by the athlete based on the past feedback. Furthermore, the provision unit can provide the nutrition plan at a time preferred by the athlete based on the past feedback. For example, the provision unit provides the nutrition plan in a format preferred by the athlete based on the past feedback. The provision unit can also provide the nutrition plan in language easily understood by the athlete based on the past feedback. Furthermore, the provision unit can provide the nutrition plan at a time preferred by the athlete based on the past feedback. This makes it possible to provide a more effective nutrition plan by optimizing the provision method with reference to the athlete's past feedback. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or without AI. For example, the provision unit may input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and optimize the provision method.

[0102] When providing a nutrition plan, the providing unit can provide it in an optimal format based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the providing unit can provide the nutrition plan in a format optimized for the smartphone. Furthermore, if the athlete uses a tablet, the providing unit can also provide the nutrition plan in a format optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the providing unit can also provide the nutrition plan in a format optimized for the smartwatch. For example, if the athlete uses a smartphone, the providing unit can provide the nutrition plan in a format optimized for the smartphone. Furthermore, if the athlete uses a tablet, the providing unit can also provide the nutrition plan in a format optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the providing unit can also provide the nutrition plan in a format optimized for the smartwatch. This allows for a more visible nutrition plan by providing the nutrition plan in the optimal format taking into account the athlete's device information. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the provider can input the athlete's device information into the generation AI and have the generation AI analyze the data and select the optimal format.

[0103] When providing the nutrition plan, the providing unit can adjust the timing of provision based on the athlete's schedule. Examples of collected schedules include, but are not limited to, training schedules and daily plans. For example, the providing unit provides the nutrition plan immediately after training so that the athlete receives it after training. The providing unit can also provide the nutrition plan before breakfast so that the athlete receives it before breakfast. Furthermore, the providing unit can also provide the nutrition plan before bedtime so that the athlete receives it before bedtime. For example, the providing unit provides the nutrition plan immediately after training so that the athlete receives it after training. The providing unit can also provide the nutrition plan before breakfast so that the athlete receives it before breakfast. Furthermore, the providing unit can also provide the nutrition plan before bedtime so that the athlete receives it before bedtime. By adjusting the timing of provision based on the athlete's schedule, the nutrition plan can be provided at a more appropriate time. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the athlete's schedule data into the generation AI and have the generation AI analyze the data and adjust the timing of provision.

[0104] The providing unit can estimate the athlete's emotions and prioritize the nutrition plans to provide based on those emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the providing unit can prioritize a nutrition plan including ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the providing unit can prioritize a nutrition plan including nutrients to maintain concentration. Furthermore, if the athlete is tired, the providing unit can prioritize a nutrition plan including nutrients to promote recovery. For example, if the athlete is feeling stressed, the providing unit can prioritize a nutrition plan including ingredients with a relaxing effect. Furthermore, if the athlete is concentrating, the providing unit can prioritize a nutrition plan including nutrients to maintain concentration. Furthermore, if the athlete is tired, the providing unit can prioritize a nutrition plan including nutrients to promote recovery. By prioritizing the nutrition plans to provide based on the athlete's emotions, more important nutrition plans can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the athlete's emotional data into the generation AI and cause the generation AI to estimate the emotions and determine the priority of the nutrition plan.

[0105] When providing a nutrition plan, the providing unit can select the optimal delivery method based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the athlete is at home, the providing unit can provide the nutrition plan via email. If the athlete is out, the providing unit can also provide the nutrition plan via a smartphone notification. Furthermore, if the athlete is at a training facility, the providing unit can also provide the nutrition plan to a device at the facility. For example, if the athlete is at home, the providing unit can provide the nutrition plan via email. If the athlete is out, the providing unit can also provide the nutrition plan via a smartphone notification. Furthermore, if the athlete is at a training facility, the providing unit can also provide the nutrition plan to a device at the facility. This enables the provision of a more effective nutrition plan by selecting the optimal delivery method taking the athlete's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select a delivery method.

[0106] When providing a nutrition plan, the providing unit can provide relevant information based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the providing unit can provide a relevant nutrition plan based on food posts shared by the athlete on social media. The providing unit can also provide a relevant nutrition plan based on exercise posts posted by the athlete on social media. Furthermore, the providing unit can also provide a relevant nutrition plan based on sleep posts posted by the athlete on social media. For example, the providing unit can provide a relevant nutrition plan based on food posts shared by the athlete on social media. The providing unit can also provide a relevant nutrition plan based on exercise posts posted by the athlete on social media. Furthermore, the providing unit can provide a relevant nutrition plan based on sleep posts posted by the athlete on social media. In this way, by analyzing the athlete's social media activity and providing relevant information, a more personalized nutrition plan can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input an athlete's social media data into the generating AI and have the generating AI analyze the data and provide related information.

[0107] When providing a nutrition plan, the provider can provide a multilingual plan based on the athlete's language setting. Examples of language setting collection include, but are not limited to, the device's language setting and user selection. For example, the provider can automatically set the nutrition plan based on the athlete's device's language setting. The provider can also provide a language switching function if the athlete speaks multiple languages. Furthermore, the provider can provide the nutrition plan in a specific language if the athlete selects that language. For example, the provider can automatically set the nutrition plan based on the athlete's device's language setting. The provider can also provide a language switching function if the athlete speaks multiple languages. Furthermore, the provider can provide the nutrition plan in a specific language if the athlete selects that language. This allows the provision of a multilingual plan based on the athlete's language setting, making it easier to understand. Some or all of the above-described processing by the provider can be performed using, or without, AI. For example, the provider can input the athlete's language setting data into a generation AI and have the generation AI analyze the data and generate a multilingual plan.

[0108] The feedback collection unit can estimate the athlete's emotions and adjust the timing of feedback collection based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the feedback collection unit collects feedback when the athlete is relaxed. Furthermore, the feedback collection unit can collect feedback after training when the athlete is focused. Furthermore, the feedback collection unit can collect feedback after rest when the athlete is tired. For example, the feedback collection unit collects feedback when the athlete is relaxed. Furthermore, the feedback collection unit can collect feedback after training when the athlete is focused. Furthermore, the feedback collection unit can collect feedback after rest when the athlete is tired. In this way, by adjusting the timing of feedback collection based on the athlete's emotions, feedback can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the athlete's emotional data into the generation AI and cause the generation AI to estimate the emotions and adjust the timing of feedback collection.

[0109] When collecting feedback, the feedback collection unit can optimize the collection method based on the athlete's past feedback history. Past feedback history includes, but is not limited to, questionnaire results, interview records, etc. For example, the feedback collection unit determines the optimal collection timing based on the past feedback history. The feedback collection unit can also select the type of feedback to collect based on the past feedback history. Furthermore, the feedback collection unit can customize the collection method based on the past feedback history. For example, the feedback collection unit determines the optimal collection timing based on the past feedback history. The feedback collection unit can also select the type of feedback to collect based on the past feedback history. Furthermore, the feedback collection unit can customize the collection method based on the past feedback history. In this way, by optimizing the collection method with reference to the athlete's past feedback history, more effective feedback collection is possible. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, AI. For example, the feedback collection unit may input the athlete's past feedback history data into a generation AI and cause the generation AI to analyze the data and optimize the collection method.

[0110] When collecting feedback, the feedback collection unit can select the optimal collection means based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the feedback collection unit collects feedback via the smartphone. Furthermore, if the athlete uses a tablet, the feedback collection unit can also collect feedback via the tablet. Furthermore, if the athlete uses a smartwatch, the feedback collection unit can also collect feedback via the smartwatch. For example, if the athlete uses a smartphone, the feedback collection unit collects feedback via the smartphone. Furthermore, if the athlete uses a tablet, the feedback collection unit can also collect feedback via the tablet. Furthermore, if the athlete uses a smartwatch, the feedback collection unit can also collect feedback via the smartwatch. This allows for more effective feedback collection by selecting the optimal collection means in consideration of the athlete's device information. Some or all of the above-described processing by the feedback collection unit may be performed using, or without, AI. For example, the feedback collection unit may input the athlete's device information into the generation AI and have the generation AI analyze the data and select the optimal collection means.

[0111] When collecting feedback, the feedback collection unit can adjust the collection timing based on the athlete's schedule. Examples of schedule collection include, but are not limited to, training schedules and daily plans. For example, the feedback collection unit collects feedback immediately after training so that the athlete provides feedback after training. The feedback collection unit can also collect feedback before breakfast so that the athlete provides feedback before breakfast. The feedback collection unit can also collect feedback before bedtime so that the athlete provides feedback before bedtime. For example, the feedback collection unit collects feedback immediately after training so that the athlete provides feedback after training. The feedback collection unit can also collect feedback before breakfast so that the athlete provides feedback before breakfast. The feedback collection unit can also collect feedback before bedtime so that the athlete provides feedback before bedtime. In this way, by adjusting the collection timing based on the athlete's schedule, feedback can be collected at a more appropriate time. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the athlete's schedule data into the generation AI and cause the generation AI to analyze the data and adjust the collection timing.

[0112] The feedback collection unit can estimate the athlete's emotions and prioritize the feedback to be collected based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the feedback collection unit can prioritize stress-related feedback. Furthermore, if the athlete is focused, the feedback collection unit can prioritize training-related feedback. Furthermore, if the athlete is tired, the feedback collection unit can prioritize recovery-related feedback. For example, if the athlete is feeling stressed, the feedback collection unit can prioritize stress-related feedback. Furthermore, if the athlete is focused, the feedback collection unit can prioritize training-related feedback. Furthermore, if the athlete is tired, the feedback collection unit can prioritize recovery-related feedback. By prioritizing the feedback to be collected based on the athlete's emotions, more important feedback can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit may input the athlete's emotional data into the generation AI and cause the generation AI to estimate the emotions and determine the priority of the feedback.

[0113] When collecting feedback, the feedback collection unit can prioritize collection of highly relevant feedback based on the athlete's geographical location information. Examples of collection of geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the athlete is at a training facility, the feedback collection unit can prioritize feedback related to training. Furthermore, when the athlete is at home, the feedback collection unit can prioritize feedback related to lifestyle habits. Furthermore, when the athlete is out and about, the feedback collection unit can prioritize feedback related to activities while away from home. For example, when the athlete is at a training facility, the feedback collection unit can prioritize feedback related to training. Furthermore, when the athlete is at home, the feedback collection unit can prioritize feedback related to lifestyle habits. Furthermore, when the athlete is out and about, the feedback collection unit can prioritize feedback related to activities while away from home. This enables more effective feedback collection by prioritizing collection of highly relevant feedback taking into account the athlete's geographical location information. Some or all of the above-described processing by the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the athlete's geographic location information into the generation AI and have the generation AI analyze the data and determine the priority of the feedback.

[0114] During feedback collection, the feedback collection unit can collect relevant feedback based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the feedback collection unit can analyze food posts on social media and collect feedback related to the food. The feedback collection unit can also analyze exercise posts on social media and collect feedback related to the exercise. Furthermore, the feedback collection unit can analyze sleep posts on social media and collect feedback related to the sleep. For example, the feedback collection unit can analyze food posts on social media and collect feedback related to the food. The feedback collection unit can also analyze exercise posts on social media and collect feedback related to the exercise. Furthermore, the feedback collection unit can analyze sleep posts on social media and collect feedback related to the sleep. In this way, by analyzing the athlete's social media activity and collecting relevant feedback, more detailed feedback can be obtained. Some or all of the above-described processing by the feedback collection unit may be performed, for example, using AI or without using AI. For example, the feedback collection unit can input social media posting data into the generation AI and have the generation AI analyze the data and collect feedback.

[0115] When collecting feedback, the feedback collection unit can customize the collection method based on the athlete's past feedback. Past feedback includes, for example, questionnaire results, interview records, etc., but is not limited to these examples. For example, the feedback collection unit selects a collection method preferred by the athlete based on the past feedback. The feedback collection unit can also collect feedback in language that the athlete can easily understand based on the past feedback. Furthermore, the feedback collection unit can collect feedback at a timing preferred by the athlete based on the past feedback. For example, the feedback collection unit selects a collection method preferred by the athlete based on the past feedback. The feedback collection unit can also collect feedback in language that the athlete can easily understand based on the past feedback. Furthermore, the feedback collection unit can collect feedback at a timing preferred by the athlete based on the past feedback. In this way, customizing the collection method to reflect the athlete's past feedback enables more effective feedback collection. Some or all of the above-mentioned processing by the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input an athlete's past feedback data into the generation AI and have the generation AI analyze the data and customize the collection method.

[0116] The improvement unit can estimate the athlete's emotions and adjust the improvement method based on the emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the improvement unit can suggest an improvement method that has a relaxing effect. Furthermore, if the athlete is concentrating, the improvement unit can suggest an improvement method to maintain concentration. Furthermore, if the athlete is tired, the improvement unit can suggest an improvement method to promote recovery. For example, if the athlete is feeling stressed, the improvement unit can suggest an improvement method that has a relaxing effect. Furthermore, if the athlete is concentrating, the improvement unit can suggest an improvement method to maintain concentration. Furthermore, if the athlete is tired, the improvement unit can suggest an improvement method to promote recovery. In this way, by adjusting the improvement method based on the athlete's emotions, a more appropriate improvement method can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the athlete's emotional data into the generation AI and have the generation AI estimate the emotions and adjust the improvement method.

[0117] During improvement, the improvement unit can optimize the improvement method based on the athlete's past feedback. Past feedback includes, but is not limited to, questionnaire results, interview records, etc. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. This enables more effective improvement by optimizing the improvement method with reference to the athlete's past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and optimize the improvement method.

[0118] When making improvements, the improvement unit can select optimal improvement measures based on the athlete's device information. Device information collection includes, but is not limited to, the type of device and usage status. For example, if the athlete uses a smartphone, the improvement unit can suggest improvement measures optimized for the smartphone. Furthermore, if the athlete uses a tablet, the improvement unit can suggest improvement measures optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the improvement unit can suggest improvement measures optimized for the smartwatch. For example, if the athlete uses a smartphone, the improvement unit can suggest improvement measures optimized for the smartphone. Furthermore, if the athlete uses a tablet, the improvement unit can suggest improvement measures optimized for the tablet. Furthermore, if the athlete uses a smartwatch, the improvement unit can suggest improvement measures optimized for the smartwatch. This enables more effective improvement by selecting optimal improvement measures taking the athlete's device information into consideration. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input the athlete's device information into the generation AI and have the generation AI analyze the data and select optimal improvement measures.

[0119] During improvement, the improvement unit can adjust the timing of improvement based on the athlete's schedule. Examples of collected schedules include, but are not limited to, training schedules and daily plans. For example, the improvement unit may suggest that the athlete receive the improvement method immediately after training. The improvement unit may also suggest that the athlete receive the improvement method before breakfast. The improvement unit may also suggest that the athlete receive the improvement method before bedtime. For example, the improvement unit may suggest that the athlete receive the improvement method immediately after training. The improvement unit may also suggest that the athlete receive the improvement method before breakfast. The improvement unit may also suggest that the athlete receive the improvement method before bedtime. By adjusting the timing of improvement based on the athlete's schedule, the improvement method can be provided at a more appropriate time. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit may input the athlete's schedule data into the generation AI and have the generation AI analyze the data and adjust the timing of improvement.

[0120] The improvement unit can estimate the athlete's emotions and determine improvement priorities based on those emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, if the athlete is feeling stressed, the improvement unit prioritizes improvement methods that have a relaxing effect. Furthermore, if the athlete is concentrating, the improvement unit can prioritize improvement methods that help maintain concentration. Furthermore, if the athlete is tired, the improvement unit can prioritize improvement methods that promote recovery. For example, if the athlete is feeling stressed, the improvement unit prioritizes improvement methods that have a relaxing effect. Furthermore, if the athlete is concentrating, the improvement unit can prioritize improvement methods that help maintain concentration. Furthermore, if the athlete is tired, the improvement unit can prioritize improvement methods that promote recovery. Thus, by determining improvement priorities based on the athlete's emotions, more important improvement methods can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit may input the athlete's emotional data into the generation AI and have the generation AI estimate the emotions and determine the priority of improvements.

[0121] During improvement, the improvement unit can select the optimal improvement method based on the athlete's geographical location information. Examples of geographical location information collection include, but are not limited to, GPS data and location information services. For example, if the athlete is at home, the improvement unit can suggest improvement methods that can be done at home. Also, if the athlete is at a training facility, the improvement unit can suggest improvement methods that can be done at the facility. Furthermore, if the athlete is out and about, the improvement unit can suggest improvement methods that can be done while away from home. For example, if the athlete is at home, the improvement unit can suggest improvement methods that can be done at home. Also, if the athlete is at a training facility, the improvement unit can suggest improvement methods that can be done at the facility. Furthermore, if the athlete is out and about, the improvement unit can suggest improvement methods that can be done while away from home. This enables more effective improvement by selecting the optimal improvement method taking the athlete's geographical location information into consideration. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input the athlete's geographical location information into the generation AI and have the generation AI analyze the data and select an improvement method.

[0122] During improvement, the improvement unit can suggest relevant improvement measures based on the athlete's social media activity. Analysis of social media activity includes, but is not limited to, the content of posts, the number of likes, and comments. For example, the improvement unit can analyze food posts on social media and suggest food-related improvement measures. The improvement unit can also analyze exercise posts on social media and suggest exercise-related improvement measures. The improvement unit can also analyze sleep posts on social media and suggest sleep-related improvement measures. For example, the improvement unit can analyze food posts on social media and suggest food-related improvement measures. The improvement unit can also analyze exercise posts on social media and suggest exercise-related improvement measures. The improvement unit can also analyze sleep posts on social media and suggest sleep-related improvement measures. This enables more personalized improvement by analyzing the athlete's social media activity and suggesting relevant improvement measures. Some or all of the above-described processing by the improvement unit may be performed using, for example, AI, or without AI. For example, the improvement unit can input social media post data into the generation AI and have the generation AI analyze the data and suggest improvement measures.

[0123] When improving, the improvement unit can customize the improvement method based on the athlete's past feedback. Past feedback includes, but is not limited to, questionnaire results, interview records, etc. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. For example, the improvement unit selects an improvement method preferred by the athlete based on the past feedback. The improvement unit can also propose an improvement method in language that the athlete can easily understand based on the past feedback. Furthermore, the improvement unit can also propose an improvement method at a timing preferred by the athlete based on the past feedback. This enables more effective improvement by customizing the improvement method to reflect the athlete's past feedback. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the athlete's past feedback data into the generation AI and have the generation AI analyze the data and customize the improvement method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, provision unit, feedback collection unit, and improvement unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the athlete's physical data using a wearable device or smartphone app of the smart device 14. The generation unit analyzes the data using a generation AI by the specific processing unit 290 of the data processing device 12 to generate an optimal nutrition plan. The provision unit provides the athlete with the nutrition plan generated by the control unit 46A of the smart device 14. The feedback collection unit collects feedback using sensor data and questionnaires from the smart device 14. The improvement unit improves the nutrition plan based on the feedback by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, provision unit, feedback collection unit, and improvement unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the athlete's physical data using a wearable device or a smartphone app in the smart glasses 214. The generation unit analyzes the data using a generation AI by the specific processing unit 290 of the data processing device 12 to generate an optimal nutrition plan. The provision unit provides the athlete with the nutrition plan generated by the control unit 46A of the smart glasses 214. The feedback collection unit collects feedback using sensor data or a questionnaire in the smart glasses 214. The improvement unit improves the nutrition plan based on the feedback by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, provision unit, feedback collection unit, and improvement unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the athlete's physical data using a wearable device or a smartphone app in the headset-type terminal 314. The generation unit analyzes the data using a generation AI by the specific processing unit 290 of the data processing device 12 to generate an optimal nutrition plan. The provision unit provides the athlete with the nutrition plan generated by the control unit 46A of the headset-type terminal 314. The feedback collection unit collects feedback using sensor data and questionnaires in the headset-type terminal 314. The improvement unit improves the nutrition plan based on the feedback by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, provision unit, feedback collection unit, and improvement 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 physical data of the athlete using a wearable device or smartphone app of the robot 414. The generation unit analyzes the data using a generation AI by the specific processing unit 290 of the data processing device 12 to generate an optimal nutrition plan. The provision unit provides the nutrition plan generated by the control unit 46A of the robot 414 to the athlete. The feedback collection unit collects feedback using sensor data and questionnaires of the robot 414. The improvement unit improves the nutrition plan based on the feedback by the specific processing unit 290 of the data processing device 12.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] When collecting an athlete's physical data, the collection unit can optimize the collection method by referring to the athlete's past health checkup data. For example, the collection unit may collect data with an emphasis on specific health indicators based on the past health checkup data. The collection unit may also adjust the frequency of data collection based on the past health checkup data. Furthermore, the collection unit may select the type of data to collect based on the past health checkup data. In this way, by optimizing the collection method by referring to the athlete's past health checkup data, more effective data collection is possible.

[0126] When collecting physical data of an athlete, the collection unit can optimize the collection method by referring to the athlete's genetic information. For example, the collection unit collects data related to a specific genetic risk based on the genetic information. The collection unit can also adjust the frequency of data collection based on the genetic information. Furthermore, the collection unit can select the type of data to collect based on the genetic information. In this way, by optimizing the collection method by referring to the athlete's genetic information, more effective data collection is possible.

[0127] When collecting the athlete's physical data, the collection unit can monitor the athlete's psychological state and adjust the collection timing based on the athlete's psychological state. For example, the collection unit collects data when the athlete is relaxed. The collection unit can also collect data when the athlete is concentrating. Furthermore, the collection unit can refrain from collecting data when the athlete is feeling stressed. In this way, by adjusting the collection timing based on the athlete's psychological state, data can be collected at more appropriate times.

[0128] When collecting the athlete's physical data, the collection unit can monitor the athlete's dietary content in real time and adjust the type of data to be collected based on the dietary content. For example, if the athlete eats a high-calorie meal, the collection unit can collect data on calories burned. Also, if the athlete eats a meal that is high in a particular nutrient, the collection unit can collect data related to that nutrient. Furthermore, if the athlete does not eat, the collection unit can collect data related to energy consumption. This allows for more effective data collection by adjusting the type of data to be collected based on the athlete's dietary content.

[0129] When collecting the athlete's physical data, the collection unit can monitor the athlete's sleep patterns and adjust the collection timing based on the sleep patterns. For example, the collection unit collects data when the athlete is in deep sleep. The collection unit can also collect data when the athlete is in light sleep. Furthermore, the collection unit can refrain from collecting data when the athlete is sleep-deprived. In this way, by adjusting the collection timing based on the athlete's sleep patterns, data can be collected at more appropriate times.

[0130] When generating a nutritional plan for an athlete, the generation unit can optimize the plan by referring to the athlete's past nutritional intake history. For example, the generation unit can optimize the nutritional balance based on the athlete's past nutritional intake history. The generation unit can also generate a plan tailored to the athlete's preferences based on the athlete's past nutritional intake history. Furthermore, the generation unit can also generate a plan that takes allergies into consideration based on the athlete's past nutritional intake history. In this way, by optimizing the plan by referring to the athlete's past nutritional intake history, a more personalized nutritional plan can be provided.

[0131] When generating a nutritional plan for an athlete, the generation unit can estimate the athlete's emotions and adjust the content of the plan based on the estimated emotions. For example, if the athlete is feeling stressed, the generation unit can generate a plan that includes ingredients that have a relaxing effect. Also, if the athlete is concentrating, the generation unit can generate a plan that includes nutrients to help maintain concentration. Furthermore, if the athlete is tired, the generation unit can generate a plan that includes nutrients that promote recovery. In this way, by adjusting the content of the plan based on the athlete's emotions, a more appropriate nutritional plan can be provided.

[0132] When generating an athlete's nutrition plan, the generation unit can adjust meal timing based on the athlete's training schedule. For example, if an athlete needs to replenish energy before training, the generation unit can suggest a pre-training meal. In addition, if an athlete needs to recover after training, the generation unit can also suggest a post-training meal. Furthermore, if an athlete needs a snack between training sessions, the generation unit can also suggest the timing of a snack. This allows for more effective nutritional replenishment by adjusting meal timing based on the athlete's training schedule.

[0133] When generating the nutrition plan for an athlete, the generation unit can use ingredients specific to the region based on the athlete's geographical location information. For example, the generation unit can use ingredients for Japanese cuisine when the athlete is in Japan. The generation unit can also use ingredients for Italian cuisine when the athlete is in Italy. Furthermore, the generation unit can use ingredients for Mexican cuisine when the athlete is in Mexico. This allows for a more appropriate nutrition plan to be provided by using ingredients specific to the region in consideration of the athlete's geographical location information.

[0134] When generating a nutrition plan for an athlete, the generation unit can customize the plan based on the athlete's cultural background. For example, if the athlete is vegetarian, the generation unit can generate a vegetarian plan. If the athlete requires halal food, the generation unit can also generate a halal meal plan. Furthermore, if the athlete requires kosher food, the generation unit can also generate a kosher meal plan. This makes it possible to provide a more personalized nutrition plan by customizing the plan taking into account the athlete's cultural background.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects the athlete's physical data. The athlete's physical data includes weight, height, body fat percentage, heart rate, body temperature, blood pressure, etc. The collection unit collects this data through wearable devices and smartphone apps. It can also collect training information from training logs and fitness trackers, and lifestyle habits through diaries and questionnaires filled out by the athlete. Step 2: The generation unit analyzes the data collected by the collection unit and generates a nutrition plan tailored to the athlete's needs. The generation unit analyzes the data using generative AI and generates an optimal nutrition plan. The generative AI uses techniques such as neural networks and reinforcement learning to generate a nutrition plan to optimize the athlete's health and performance. Step 3: The provision unit provides the nutrition plan generated by the generation unit to the athlete. The provision unit provides the nutrition plan generated by the generation AI to the athlete and provides the nutrition plan in an optimal format taking into account the athlete's device information. For example, the nutrition plan may be provided in a format optimized for smartphones and the timing of delivery may be adjusted based on the athlete's schedule. Step 4: The feedback collection unit collects feedback from the athlete based on the nutrition plan provided by the provision unit. The feedback collection unit collects feedback using questionnaires, interviews, sensor data, etc., estimates the athlete's emotions, and adjusts the timing of feedback collection. Step 5: The Improvement Team improves the nutrition plan based on the feedback collected by the Feedback Collection Team. Based on the feedback, the Improvement Team modifies the plan and adjusts the algorithm, and adjusts the improvement method by estimating the athlete's emotions.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] 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.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] 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.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] 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.

[0182] 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.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] 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.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] 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.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 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 physical data of athletes; A generating unit that analyzes the data collected by the collecting unit and generates a nutrition plan tailored to the needs of the athlete; a providing unit that provides the nutrition plan generated by the generating unit; a feedback collection unit that collects feedback from athletes based on the nutrition plan provided by the provision unit; an improvement unit that improves the nutrition plan based on the feedback collected by the feedback collection unit. A system characterized by:

2. The collecting unit Collecting athletes' physical data through wearable devices or smartphone apps 2. The system of claim 1.

3. The collecting unit Collect training information from your training log or fitness tracker 2. The system of claim 1.

4. The collecting unit Collect lifestyle information through a diary or questionnaire completed by the athlete 2. The system of claim 1.

5. The generation unit Using generative AI to learn from the latest medical data and generate nutrition plans to optimize an athlete's health or performance 2. The system of claim 1.

6. The providing unit Providing athletes with nutrition plans generated by generative AI 2. The system of claim 1.

7. The feedback collection unit: Collect athlete feedback and let generative AI learn from it to improve nutrition plans 2. The system of claim 1.

8. The collecting unit Estimate the athlete's emotions and adjust the timing of physical data collection based on those emotions 2. The system of claim 1.

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