system

Abstract

The system according to the embodiment aims to provide an optimal training plan for each individual user. [Solution] According to an embodiment, the system includes a collection unit, a generation unit, a monitoring unit, and an adjustment unit. The collection unit collects health and fitness data of a user. The generation unit learns the data collected by the collection unit and generates a training plan for each individual user. The monitoring unit monitors training progress based on the training plan generated by the generation unit. The adjustment unit adjusts the training plan based on the results monitored by the monitoring 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] With conventional technology, it is difficult to provide an optimal training plan for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal training plan for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a monitoring unit, and an adjustment unit. The collection unit collects health and fitness data of a user. The generation unit learns the data collected by the collection unit and generates a training plan for each individual user. The monitoring unit monitors training progress based on the training plan generated by the generation unit. The adjustment unit adjusts the training plan based on the results monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training plan for each individual user. [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 training plan provision system according to an embodiment of the present invention uses AI technology to learn meaningful healthcare data and provide optimal muscle training and rehabilitation training plans for individuals. This training plan provision system is characterized by its ability to provide training anywhere, anytime, and at a price one-tenth the price of a personal trainer at a facility. First, the system collects the user's health and fitness data, including the user's age, gender, weight, height, exercise history, and health status. The system then uses AI to learn from the collected data and generate an optimal training plan for each individual user. For example, a user who needs strength training might be suggested exercises to train specific muscle groups, and a user who needs rehabilitation might be suggested exercises to promote injury recovery. The generated training plan is displayed on the user's smartphone or tablet, allowing the user to follow the plan. Furthermore, the system monitors the user's training progress in real time and adjusts the plan as needed. For example, if the user is struggling with a particular exercise, the AI ​​can change it to an easier one. This system allows users to train at their own pace and is less expensive than a personal trainer at a facility. Furthermore, because training can be done anywhere, anytime, it allows users to continue training even within their busy daily lives. This allows the training plan providing system to provide optimal training plans based on the user's health and fitness data, monitor progress, and make adjustments as needed.

[0029] A training plan providing system according to an embodiment includes a collection unit, a generation unit, a monitoring unit, and an adjustment unit. The collection unit collects health and fitness data of a user. The user's health data includes, for example, heart rate, blood pressure, and body temperature. The fitness data includes, for example, exercise time, calories burned, and exercise intensity. The collection unit can collect this data using, for example, sensors in a wearable device or smartphone. The generation unit generates an optimal training plan for each user based on the collected data. The generation unit analyzes the data using, for example, AI and suggests exercises based on the user's health condition and fitness level. For example, the generation unit suggests exercises to train specific muscle groups for a user who needs strength training. Also, for a user who needs rehabilitation, the generation unit suggests exercises to promote recovery from an injury. The monitoring unit monitors the training progress in real time based on the generated training plan. For example, the monitoring unit collects the user's exercise data in real time and evaluates the training progress. The adjustment unit adjusts the training plan based on the results of monitoring by the monitoring unit. For example, if the user is having difficulty with a particular exercise, the adjustment unit can change the exercise to an easier one. This allows the training plan providing system according to the embodiment to provide an optimal training plan based on the user's health and fitness data, monitor progress, and make adjustments as needed.

[0030] The collection unit can collect data on the user's age, gender, weight, height, exercise history, and health condition. The collection unit collects, for example, data on the user's age, gender, weight, height, exercise history, and health condition. This data is collected based on, for example, information entered by the user or information obtained from the wearable device. For example, the collection unit collects information on the user's age, gender, weight, and height entered into an application by the user. The collection unit can also collect exercise history and health condition data obtained from the wearable device. For example, the collection unit collects past exercise time and calories burned as the user's exercise history. Furthermore, the collection unit can collect heart rate and blood pressure data as the user's health condition. In this way, the collection unit can collect detailed health data on the user and provide a more accurate training plan.

[0031] The generation unit can suggest exercises for training specific muscle groups to users who need strength training based on the collected data. For example, the generation unit can suggest exercises for training specific muscle groups to users who need strength training based on the collected data. For example, the generation unit can analyze the user's health data and fitness data and suggest exercises such as push-ups and dumbbell curls to users who need upper body strength training. The generation unit can also suggest exercises such as squats and leg presses to users who need lower body strength training. Furthermore, the generation unit can suggest exercises such as planks and sit-ups to users who need core strength training. In this way, the generation unit can provide exercises that meet the user's strength training needs.

[0032] The generation unit can suggest exercises to promote recovery from an injury to a user who needs rehabilitation based on the collected data. For example, the generation unit suggests exercises to promote recovery from an injury to a user who needs rehabilitation based on the collected data. For example, the generation unit analyzes the user's health data and fitness data and suggests exercises specialized for knee rehabilitation to a user with a knee injury. For example, the generation unit suggests exercises such as knee flexion and extension exercises and light stretching. The generation unit can also suggest exercises specialized for shoulder rehabilitation to a user with a shoulder injury. For example, the generation unit suggests shoulder rotation exercises and light dumbbell exercises. Furthermore, the generation unit can suggest exercises specialized for waist rehabilitation to a user with a waist injury. For example, the generation unit suggests waist stretches and light core training. In this way, the generation unit can provide appropriate exercises to users who need rehabilitation.

[0033] The monitoring unit can monitor the progress of training in real time. For example, the monitoring unit monitors the progress of training in real time. The monitoring unit collects the user's exercise data in real time and evaluates the progress of training. For example, the monitoring unit collects data such as the user's heart rate, exercise time, and calories burned in real time to monitor the progress of training. The monitoring unit can also collect data such as the number of times the user exercises and the number of sets to evaluate the progress of training. Furthermore, the monitoring unit can monitor the user's training form and the accuracy of their movements. For example, the monitoring unit captures the user's movements with a camera and evaluates the accuracy of the movements using AI. In this way, the monitoring unit can provide appropriate feedback by monitoring the progress of training in real time.

[0034] The adjustment unit can change a specific exercise to an easier one if the user is unable to complete it. For example, if the user is unable to complete a specific exercise, the adjustment unit changes the exercise to an easier one. The adjustment unit analyzes the user's motion data and adjusts the difficulty of the exercise. For example, if the user is unable to complete push-ups, the adjustment unit can change the exercise to kneeling push-ups. Furthermore, if the user is unable to complete squats, the adjustment unit can change the exercise to chair squats. Furthermore, if the user is unable to complete planks, the adjustment unit can change the exercise to kneeling planks. In this way, the adjustment unit can maximize the effectiveness of training by adjusting the exercises according to the user's abilities.

[0035] The collection unit can analyze the user's past health data and select the optimal data collection method. For example, the collection unit analyzes the user's past health data and selects the optimal data collection method. The collection unit selects the most effective data collection method based on the user's past health data. For example, the collection unit selects the most effective data collection method from the user's past health data. The collection unit can also adjust the frequency of data collection based on the user's past health data. Furthermore, the collection unit can analyze the user's past health data and collect data during specific time periods. This allows the collection unit to select the optimal data collection method based on the past health data, enabling efficient data collection.

[0036] The collection unit can perform filtering based on the user's current living situation and activity level when collecting data. For example, the collection unit performs filtering based on the user's current living situation and activity level when collecting data. The collection unit filters the type of data to be collected based on the user's living situation and activity level. For example, the collection unit temporarily suspends data collection when the user is at a high activity level. Furthermore, the collection unit can collect detailed data when the user is at a low activity level. Furthermore, the collection unit can filter the type of data to be collected according to the user's living situation. For example, the collection unit collects work-related data when the user is at work, and collects vacation-related data when the user is on vacation. This enables the collection unit to collect data according to the user's living situation and activity level.

[0037] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information when collecting data. The collection unit collects highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. Furthermore, when the user is traveling, the collection unit can collect data related to health risks at the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to daily life. In this way, the collection unit can obtain more useful data by collecting highly relevant data based on the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting data. The collection unit collects related data based on the user's social media activities. For example, if the user posts about health on social media, the collection unit collects data related to the content. Also, if the user shows interest in a particular exercise on social media, the collection unit can collect data related to the exercise. Furthermore, if the user shows that they are feeling stressed on social media, the collection unit can collect stress-related data. This allows the collection unit to collect related data based on the user's social media activities, enabling more comprehensive data collection.

[0039] The generation unit can adjust the level of detail of the training plan based on the user's health condition when generating the training plan. For example, the generation unit adjusts the level of detail of the plan based on the user's health condition when generating the training plan. The generation unit adjusts the level of detail of the plan based on the user's health data. For example, the generation unit provides a detailed training plan when the user is healthy. Furthermore, the generation unit can provide a simple training plan when the user is in poor health. Furthermore, the generation unit can also provide a training plan specialized for rehabilitation when the user is undergoing rehabilitation. In this way, the generation unit can provide a training plan that suits the user's health condition, enabling more appropriate training.

[0040] The generation unit can apply different algorithms depending on the user's fitness level when generating a training plan. For example, the generation unit applies different algorithms depending on the user's fitness level when generating a training plan. The generation unit selects an appropriate algorithm based on the user's fitness level. For example, the generation unit can provide a beginner user with a training plan that includes basic exercises. The generation unit can also provide an intermediate user with a training plan that includes slightly more difficult exercises. Furthermore, the generation unit can also provide an advanced user with a training plan that includes advanced exercises. In this way, the generation unit can provide a training plan that matches the user's fitness level, enabling effective training.

[0041] The generation unit can determine the priority of the plan based on the user's exercise history when generating a training plan. For example, the generation unit determines the priority of the plan based on the user's exercise history when generating a training plan. The generation unit suggests exercises that should be performed with priority based on the user's exercise history. For example, the generation unit suggests exercises that should be performed with priority based on exercises the user has performed in the past. The generation unit can also preferentially suggest effective exercises based on the user's exercise history. Furthermore, the generation unit can analyze the user's exercise history and provide the most effective training plan. In this way, the generation unit can provide an effective training plan by determining the priority based on the user's exercise history.

[0042] The generation unit can adjust the order of the plan based on the user's related data when generating a training plan. For example, the generation unit adjusts the order of the plan based on the user's related data when generating a training plan. The generation unit suggests exercises in an optimal order based on the user's health data and fitness data. For example, the generation unit suggests exercises in an optimal order based on the user's health data. The generation unit can also suggest exercises in an effective order based on the user's fitness data. Furthermore, the generation unit can also suggest exercises in an efficient order based on the user's exercise history. In this way, the generation unit can adjust the order of the plan based on the user's related data, enabling effective training.

[0043] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. For example, the monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. The monitoring unit improves the accuracy of monitoring based on the interrelationships between trainings. For example, the monitoring unit performs effective monitoring by taking into account the interrelationships between trainings. The monitoring unit can also analyze the interrelationships between trainings and improve the accuracy of monitoring. Furthermore, the monitoring unit can propose an optimal monitoring method based on the interrelationships between trainings. In this way, the monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings.

[0044] The monitoring unit can perform monitoring taking into account the user's attribute information. For example, the monitoring unit performs monitoring taking into account the user's attribute information. The monitoring unit performs appropriate monitoring based on the user's attribute information. For example, the monitoring unit performs appropriate monitoring taking into account the user's age and gender. The monitoring unit can also adjust the monitoring criteria taking into account the user's health condition. Furthermore, the monitoring unit can also improve the accuracy of monitoring taking into account the user's exercise history. This enables the monitoring unit to perform appropriate monitoring based on the user's attribute information.

[0045] The monitoring unit can perform monitoring taking into account the geographical distribution of users during monitoring. For example, the monitoring unit performs monitoring taking into account the geographical distribution of users during monitoring. The monitoring unit performs appropriate monitoring based on the geographical distribution of users. For example, when a user is in a specific area, the monitoring unit monitors health risks associated with that area. Also, when a user is traveling, the monitoring unit can monitor health risks at the travel destination. Furthermore, when a user is at home, the monitoring unit can also monitor health risks associated with daily life. This enables the monitoring unit to perform appropriate monitoring based on the geographical distribution of users.

[0046] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the user during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user during monitoring. The monitoring unit improves the accuracy of monitoring by referring to literature related to the user's health condition, exercise history, and fitness data. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user's health condition. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's exercise history. Furthermore, the monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's fitness data. In this way, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user.

[0047] The adjustment unit can analyze the user's past training data and select the optimal adjustment method during adjustment. For example, the adjustment unit analyzes the user's past training data and selects the optimal adjustment method during adjustment. The adjustment unit suggests optimal exercises based on the user's past training data. For example, the adjustment unit suggests optimal exercises based on the user's past training data. The adjustment unit can also analyze the user's past training data and select an effective adjustment method. Furthermore, the adjustment unit can also determine the frequency of adjustment with reference to the user's past training data. This allows the adjustment unit to select the optimal adjustment method based on the past training data, thereby enabling effective training.

[0048] The adjustment unit can customize the adjustment means based on the user's current health condition during adjustment. For example, the adjustment unit customizes the adjustment means based on the user's current health condition during adjustment. The adjustment unit suggests appropriate exercises based on the user's health condition. For example, the adjustment unit suggests effective exercises when the user is healthy. Furthermore, the adjustment unit can suggest low-impact exercises when the user is in poor health. Furthermore, the adjustment unit can also suggest exercises specialized for rehabilitation when the user is undergoing rehabilitation. This enables the adjustment unit to make adjustments according to the user's health condition.

[0049] The adjustment unit can select the optimal adjustment method taking into account the user's geographical location information during adjustment. For example, the adjustment unit selects the optimal adjustment method taking into account the user's geographical location information during adjustment. The adjustment unit suggests appropriate exercises based on the user's geographical location information. For example, if the user is in a specific area, the adjustment unit suggests exercises that are suitable for the local environment. Furthermore, if the user is traveling, the adjustment unit can suggest exercises that can be done at the user's travel destination. Furthermore, if the user is at home, the adjustment unit can also suggest exercises that can be done at home. This enables the adjustment unit to make appropriate adjustments based on the user's geographical location information.

[0050] The adjustment unit can analyze the user's social media activity and suggest adjustment measures during adjustment. For example, the adjustment unit analyzes the user's social media activity and suggests adjustment measures during adjustment. The adjustment unit suggests appropriate exercises based on the user's social media activity. For example, if the user has shown interest in a particular exercise on social media, the adjustment unit can suggest the exercise. Furthermore, if the user has posted about health on social media, the adjustment unit can suggest exercises based on the content of the posts. Furthermore, if the user has shown on social media that they are feeling stressed, the adjustment unit can also suggest stress-reducing exercises. This enables the adjustment unit to make appropriate adjustments based on the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The training plan providing system can also collect the user's dietary data and reflect it in the training plan. For example, the collection unit can collect data on the calories and nutrients the user has ingested, and the generation unit can adjust the training plan based on this data. If the user has eaten a high-calorie meal, the generation unit can suggest exercises that promote calorie consumption. Also, if the user is deficient in a particular nutrient, the generation unit can suggest exercises to supplement that nutrient. This allows the training plan providing system to provide a more comprehensive training plan that takes the user's dietary data into consideration.

[0053] The collection unit can collect the user's sleep data and reflect it in the training plan. For example, the collection unit can collect the user's sleep time and sleep quality, and the generation unit can adjust the training plan based on this data. If the user is not getting enough sleep, the generation unit can suggest lighter exercises. Also, if the user is getting good quality sleep, the generation unit can suggest more intense exercises. In this way, the collection unit can provide a training plan that takes the user's sleep data into consideration.

[0054] The generation unit can collect user feedback and reflect it in the training plan. For example, the generation unit can collect feedback provided by the user after an exercise and reflect it in the next training plan. If the user finds a particular exercise difficult, the generation unit can change the exercise to an easier one. Also, if the user enjoys a particular exercise, the generation unit can increase the number of times of that exercise. In this way, the generation unit can adjust the training plan based on the user feedback.

[0055] The collection unit can collect environmental data about the user and reflect it in the training plan. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the user, and the generation unit can adjust the training plan based on this data. If the user is in a hot and humid environment, the generation unit can suggest light exercises. Also, if the user is in a quiet environment, the generation unit can suggest exercises to improve concentration. In this way, the collection unit can provide a training plan that takes the user's environmental data into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects the user's health and fitness data. Health data includes heart rate, blood pressure, body temperature, etc., while fitness data includes exercise time, calories burned, exercise intensity, etc. The collection unit collects this data using sensors in wearable devices and smartphones. Step 2: The generator generates an optimal training plan for each individual user based on the collected data. The generator uses AI to analyze the data and suggest exercises based on the user's health and fitness level. For example, a user who needs strength training might be suggested exercises to train specific muscle groups, while a user who needs rehabilitation might be suggested exercises to promote recovery from an injury. Step 3: The monitoring unit monitors the training progress in real time based on the generated training plan. The monitoring unit collects the user's exercise data in real time and evaluates the training progress. Step 4: The adjustment unit adjusts the training plan based on the results monitored by the monitoring unit. For example, if the user is not good at a particular exercise, the exercise can be changed to an easier one.

[0058] (Example 2) A training plan provision system according to an embodiment of the present invention uses AI technology to learn meaningful healthcare data and provide optimal muscle training and rehabilitation training plans for individuals. This training plan provision system is characterized by its ability to provide training anywhere, anytime, and at a price one-tenth the price of a personal trainer at a facility. First, the system collects the user's health and fitness data, including the user's age, gender, weight, height, exercise history, and health status. The system then uses AI to learn from the collected data and generate an optimal training plan for each individual user. For example, a user who needs strength training might be suggested exercises to train specific muscle groups, and a user who needs rehabilitation might be suggested exercises to promote injury recovery. The generated training plan is displayed on the user's smartphone or tablet, allowing the user to follow the plan. Furthermore, the system monitors the user's training progress in real time and adjusts the plan as needed. For example, if the user is struggling with a particular exercise, the AI ​​can change it to an easier one. This system allows users to train at their own pace and is less expensive than a personal trainer at a facility. Furthermore, because training can be done anywhere, anytime, it allows users to continue training even within their busy daily lives. This allows the training plan providing system to provide optimal training plans based on the user's health and fitness data, monitor progress, and make adjustments as needed.

[0059] A training plan providing system according to an embodiment includes a collection unit, a generation unit, a monitoring unit, and an adjustment unit. The collection unit collects health and fitness data of a user. The user's health data includes, for example, heart rate, blood pressure, and body temperature. The fitness data includes, for example, exercise time, calories burned, and exercise intensity. The collection unit can collect this data using, for example, sensors in a wearable device or smartphone. The generation unit generates an optimal training plan for each user based on the collected data. The generation unit analyzes the data using, for example, AI and suggests exercises based on the user's health condition and fitness level. For example, the generation unit suggests exercises to train specific muscle groups for a user who needs strength training. Also, for a user who needs rehabilitation, the generation unit suggests exercises to promote recovery from an injury. The monitoring unit monitors the training progress in real time based on the generated training plan. For example, the monitoring unit collects the user's exercise data in real time and evaluates the training progress. The adjustment unit adjusts the training plan based on the results of monitoring by the monitoring unit. For example, if the user is having difficulty with a particular exercise, the adjustment unit can change the exercise to an easier one. This allows the training plan providing system according to the embodiment to provide an optimal training plan based on the user's health and fitness data, monitor progress, and make adjustments as needed.

[0060] The collection unit can collect data on the user's age, gender, weight, height, exercise history, and health condition. The collection unit collects, for example, data on the user's age, gender, weight, height, exercise history, and health condition. This data is collected based on, for example, information entered by the user or information obtained from the wearable device. For example, the collection unit collects information on the user's age, gender, weight, and height entered into an application by the user. The collection unit can also collect exercise history and health condition data obtained from the wearable device. For example, the collection unit collects past exercise time and calories burned as the user's exercise history. Furthermore, the collection unit can collect heart rate and blood pressure data as the user's health condition. In this way, the collection unit can collect detailed health data on the user and provide a more accurate training plan.

[0061] The generation unit can suggest exercises for training specific muscle groups to users who need strength training based on the collected data. For example, the generation unit can suggest exercises for training specific muscle groups to users who need strength training based on the collected data. For example, the generation unit can analyze the user's health data and fitness data and suggest exercises such as push-ups and dumbbell curls to users who need upper body strength training. The generation unit can also suggest exercises such as squats and leg presses to users who need lower body strength training. Furthermore, the generation unit can suggest exercises such as planks and sit-ups to users who need core strength training. In this way, the generation unit can provide exercises that meet the user's strength training needs.

[0062] The generation unit can suggest exercises to promote recovery from an injury to a user who needs rehabilitation based on the collected data. For example, the generation unit suggests exercises to promote recovery from an injury to a user who needs rehabilitation based on the collected data. For example, the generation unit analyzes the user's health data and fitness data and suggests exercises specialized for knee rehabilitation to a user with a knee injury. For example, the generation unit suggests exercises such as knee flexion and extension exercises and light stretching. The generation unit can also suggest exercises specialized for shoulder rehabilitation to a user with a shoulder injury. For example, the generation unit suggests shoulder rotation exercises and light dumbbell exercises. Furthermore, the generation unit can suggest exercises specialized for waist rehabilitation to a user with a waist injury. For example, the generation unit suggests waist stretches and light core training. In this way, the generation unit can provide appropriate exercises to users who need rehabilitation.

[0063] The monitoring unit can monitor the progress of training in real time. For example, the monitoring unit monitors the progress of training in real time. The monitoring unit collects the user's exercise data in real time and evaluates the progress of training. For example, the monitoring unit collects data such as the user's heart rate, exercise time, and calories burned in real time to monitor the progress of training. The monitoring unit can also collect data such as the number of times the user exercises and the number of sets to evaluate the progress of training. Furthermore, the monitoring unit can monitor the user's training form and the accuracy of their movements. For example, the monitoring unit captures the user's movements with a camera and evaluates the accuracy of the movements using AI. In this way, the monitoring unit can provide appropriate feedback by monitoring the progress of training in real time.

[0064] The adjustment unit can change a specific exercise to an easier one if the user is unable to complete it. For example, if the user is unable to complete a specific exercise, the adjustment unit changes the exercise to an easier one. The adjustment unit analyzes the user's motion data and adjusts the difficulty of the exercise. For example, if the user is unable to complete push-ups, the adjustment unit can change the exercise to kneeling push-ups. Furthermore, if the user is unable to complete squats, the adjustment unit can change the exercise to chair squats. Furthermore, if the user is unable to complete planks, the adjustment unit can change the exercise to kneeling planks. In this way, the adjustment unit can maximize the effectiveness of training by adjusting the exercises according to the user's abilities.

[0065] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. The collection unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can collect data immediately and obtain highly accurate data. Furthermore, if the user is in a hurry, the collection unit can adjust the collection timing to collect necessary data in a short amount of time. In this way, the collection unit can collect more accurate data by adjusting the timing of data collection according to the user's emotions.

[0066] The collection unit can analyze the user's past health data and select the optimal data collection method. For example, the collection unit analyzes the user's past health data and selects the optimal data collection method. The collection unit selects the most effective data collection method based on the user's past health data. For example, the collection unit selects the most effective data collection method from the user's past health data. The collection unit can also adjust the frequency of data collection based on the user's past health data. Furthermore, the collection unit can analyze the user's past health data and collect data during specific time periods. This allows the collection unit to select the optimal data collection method based on the past health data, enabling efficient data collection.

[0067] The collection unit can perform filtering based on the user's current living situation and activity level when collecting data. For example, the collection unit performs filtering based on the user's current living situation and activity level when collecting data. The collection unit filters the type of data to be collected based on the user's living situation and activity level. For example, the collection unit temporarily suspends data collection when the user is at a high activity level. Furthermore, the collection unit can collect detailed data when the user is at a low activity level. Furthermore, the collection unit can filter the type of data to be collected according to the user's living situation. For example, the collection unit collects work-related data when the user is at work, and collects vacation-related data when the user is on vacation. This enables the collection unit to collect data according to the user's living situation and activity level.

[0068] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. The collection unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. Also, if the user is relaxed, the collection unit can collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting the most important data. In this way, the collection unit can prioritize collecting important data by determining the priority of data according to the user's emotions.

[0069] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information when collecting data. The collection unit collects highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting health data related to that area. Furthermore, when the user is traveling, the collection unit can collect data related to health risks at the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to daily life. In this way, the collection unit can obtain more useful data by collecting highly relevant data based on the user's geographical location information.

[0070] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting data. The collection unit collects related data based on the user's social media activities. For example, if the user posts about health on social media, the collection unit collects data related to the content. Also, if the user shows interest in a particular exercise on social media, the collection unit can collect data related to the exercise. Furthermore, if the user shows that they are feeling stressed on social media, the collection unit can collect stress-related data. This allows the collection unit to collect related data based on the user's social media activities, enabling more comprehensive data collection.

[0071] The generation unit can estimate the user's emotions and adjust the presentation method of the training plan based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the presentation method of the training plan based on the estimated user's emotions. The generation unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, if the user is relaxed, the generation unit can provide a training plan with detailed explanations. Also, if the user is in a hurry, the generation unit can provide a concise training plan that focuses on the main points. Furthermore, if the user is excited, the generation unit can provide a training plan with visually stimulating effects. In this way, the generation unit can provide a more effective training plan by adjusting the presentation method of the training plan according to the user's emotions.

[0072] The generation unit can adjust the level of detail of the training plan based on the user's health condition when generating the training plan. For example, the generation unit adjusts the level of detail of the plan based on the user's health condition when generating the training plan. The generation unit adjusts the level of detail of the plan based on the user's health data. For example, the generation unit provides a detailed training plan when the user is healthy. Furthermore, the generation unit can provide a simple training plan when the user is in poor health. Furthermore, the generation unit can also provide a training plan specialized for rehabilitation when the user is undergoing rehabilitation. In this way, the generation unit can provide a training plan that suits the user's health condition, enabling more appropriate training.

[0073] The generation unit can apply different algorithms depending on the user's fitness level when generating a training plan. For example, the generation unit applies different algorithms depending on the user's fitness level when generating a training plan. The generation unit selects an appropriate algorithm based on the user's fitness level. For example, the generation unit can provide a beginner user with a training plan that includes basic exercises. The generation unit can also provide an intermediate user with a training plan that includes slightly more difficult exercises. Furthermore, the generation unit can also provide an advanced user with a training plan that includes advanced exercises. In this way, the generation unit can provide a training plan that matches the user's fitness level, enabling effective training.

[0074] The generation unit can estimate the user's emotions and adjust the length of the training plan based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the training plan based on the estimated user's emotions. The generation unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, the generation unit can provide a longer training plan if the user is relaxed. Furthermore, the generation unit can provide a shorter training plan if the user is in a hurry. Furthermore, the generation unit can also provide a training plan of an appropriate length if the user is excited. In this way, the generation unit can adjust the length of the training plan according to the user's emotions, enabling more effective training.

[0075] The generation unit can determine the priority of the plan based on the user's exercise history when generating a training plan. For example, the generation unit determines the priority of the plan based on the user's exercise history when generating a training plan. The generation unit suggests exercises that should be performed with priority based on the user's exercise history. For example, the generation unit suggests exercises that should be performed with priority based on exercises the user has performed in the past. The generation unit can also preferentially suggest effective exercises based on the user's exercise history. Furthermore, the generation unit can analyze the user's exercise history and provide the most effective training plan. In this way, the generation unit can provide an effective training plan by determining the priority based on the user's exercise history.

[0076] The generation unit can adjust the order of the plan based on the user's related data when generating a training plan. For example, the generation unit adjusts the order of the plan based on the user's related data when generating a training plan. The generation unit suggests exercises in an optimal order based on the user's health data and fitness data. For example, the generation unit suggests exercises in an optimal order based on the user's health data. The generation unit can also suggest exercises in an effective order based on the user's fitness data. Furthermore, the generation unit can also suggest exercises in an efficient order based on the user's exercise history. In this way, the generation unit can adjust the order of the plan based on the user's related data, enabling effective training.

[0077] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated user's emotions. For example, the monitoring unit estimates the user's emotions and adjusts the monitoring criteria based on the estimated user's emotions. The monitoring unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, the monitoring unit can reduce the frequency of monitoring if the user is feeling stressed. Also, the monitoring unit can increase the frequency of monitoring if the user is relaxed. Furthermore, the monitoring unit can relax the monitoring criteria if the user is in a hurry. This allows the monitoring unit to adjust the monitoring criteria according to the user's emotions, enabling more appropriate monitoring.

[0078] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. For example, the monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings during monitoring. The monitoring unit improves the accuracy of monitoring based on the interrelationships between trainings. For example, the monitoring unit performs effective monitoring by taking into account the interrelationships between trainings. The monitoring unit can also analyze the interrelationships between trainings and improve the accuracy of monitoring. Furthermore, the monitoring unit can propose an optimal monitoring method based on the interrelationships between trainings. In this way, the monitoring unit improves the accuracy of monitoring by taking into account the interrelationships between trainings.

[0079] The monitoring unit can perform monitoring taking into account the user's attribute information. For example, the monitoring unit performs monitoring taking into account the user's attribute information. The monitoring unit performs appropriate monitoring based on the user's attribute information. For example, the monitoring unit performs appropriate monitoring taking into account the user's age and gender. The monitoring unit can also adjust the monitoring criteria taking into account the user's health condition. Furthermore, the monitoring unit can also improve the accuracy of monitoring taking into account the user's exercise history. This enables the monitoring unit to perform appropriate monitoring based on the user's attribute information.

[0080] The monitoring unit can estimate the user's emotions and adjust the display order of the monitoring results based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the display order of the monitoring results based on the estimated user emotions. The monitoring unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important monitoring results. Also, if the user is relaxed, the monitoring unit can display overall monitoring results in a balanced manner. Furthermore, if the user is in a hurry, the monitoring unit can prioritize displaying monitoring results that highlight the main points. In this way, the monitoring unit can prioritize displaying important information by adjusting the display order of the monitoring results according to the user's emotions.

[0081] The monitoring unit can perform monitoring taking into account the geographical distribution of users during monitoring. For example, the monitoring unit performs monitoring taking into account the geographical distribution of users during monitoring. The monitoring unit performs appropriate monitoring based on the geographical distribution of users. For example, when a user is in a specific area, the monitoring unit monitors health risks associated with that area. Also, when a user is traveling, the monitoring unit can monitor health risks at the travel destination. Furthermore, when a user is at home, the monitoring unit can also monitor health risks associated with daily life. This enables the monitoring unit to perform appropriate monitoring based on the geographical distribution of users.

[0082] The monitoring unit can improve the accuracy of monitoring by referring to literature related to the user during monitoring. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user during monitoring. The monitoring unit improves the accuracy of monitoring by referring to literature related to the user's health condition, exercise history, and fitness data. For example, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user's health condition. The monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's exercise history. Furthermore, the monitoring unit can also improve the accuracy of monitoring by referring to literature related to the user's fitness data. In this way, the monitoring unit improves the accuracy of monitoring by referring to literature related to the user.

[0083] The adjustment unit can estimate the user's emotions and determine an adjustment method based on the estimated user's emotions. For example, the adjustment unit estimates the user's emotions and determines an adjustment method based on the estimated user's emotions. The adjustment unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, the adjustment unit can suggest relaxing exercises when the user is feeling stressed. Furthermore, the adjustment unit can suggest effective exercises when the user is relaxed. Furthermore, the adjustment unit can also suggest effective exercises that can be completed in a short amount of time when the user is in a hurry. In this way, the adjustment unit can determine an adjustment method according to the user's emotions, enabling more effective training.

[0084] The adjustment unit can analyze the user's past training data and select the optimal adjustment method during adjustment. For example, the adjustment unit analyzes the user's past training data and selects the optimal adjustment method during adjustment. The adjustment unit suggests optimal exercises based on the user's past training data. For example, the adjustment unit suggests optimal exercises based on the user's past training data. The adjustment unit can also analyze the user's past training data and select an effective adjustment method. Furthermore, the adjustment unit can also determine the frequency of adjustment with reference to the user's past training data. This allows the adjustment unit to select the optimal adjustment method based on the past training data, thereby enabling effective training.

[0085] The adjustment unit can customize the adjustment means based on the user's current health condition during adjustment. For example, the adjustment unit customizes the adjustment means based on the user's current health condition during adjustment. The adjustment unit suggests appropriate exercises based on the user's health condition. For example, the adjustment unit suggests effective exercises when the user is healthy. Furthermore, the adjustment unit can suggest low-impact exercises when the user is in poor health. Furthermore, the adjustment unit can also suggest exercises specialized for rehabilitation when the user is undergoing rehabilitation. This enables the adjustment unit to make adjustments according to the user's health condition.

[0086] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated user's emotions. For example, the adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated user's emotions. The adjustment unit analyzes data such as the user's facial expressions, voice, and heart rate to estimate emotions. For example, if the user is feeling stressed, the adjustment unit can suggest exercises that prioritize stress reduction. Also, if the user is relaxed, the adjustment unit can suggest exercises that improve overall health. Furthermore, if the user is in a hurry, the adjustment unit can prioritize suggesting effective exercises that can be completed in a short amount of time. In this way, the adjustment unit can determine the priority of adjustments according to the user's emotions, enabling more effective training.

[0087] The adjustment unit can select the optimal adjustment method taking into account the user's geographical location information during adjustment. For example, the adjustment unit selects the optimal adjustment method taking into account the user's geographical location information during adjustment. The adjustment unit suggests appropriate exercises based on the user's geographical location information. For example, if the user is in a specific area, the adjustment unit suggests exercises that are suitable for the local environment. Furthermore, if the user is traveling, the adjustment unit can suggest exercises that can be done at the user's travel destination. Furthermore, if the user is at home, the adjustment unit can also suggest exercises that can be done at home. This enables the adjustment unit to make appropriate adjustments based on the user's geographical location information.

[0088] The adjustment unit can analyze the user's social media activity and suggest adjustment measures during adjustment. For example, the adjustment unit analyzes the user's social media activity and suggests adjustment measures during adjustment. The adjustment unit suggests appropriate exercises based on the user's social media activity. For example, if the user has shown interest in a particular exercise on social media, the adjustment unit can suggest the exercise. Furthermore, if the user has posted about health on social media, the adjustment unit can suggest exercises based on the content of the posts. Furthermore, if the user has shown on social media that they are feeling stressed, the adjustment unit can also suggest stress-reducing exercises. This enables the adjustment unit to make appropriate adjustments based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, monitoring unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's health data and fitness data using sensors in the smart device 14 or a wearable device. The generation unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and generates an optimal training plan using AI based on the collected data. The monitoring unit is realized, for example, by the control unit 46A in the smart device 14 and monitors the progress of training in real time. The adjustment unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and adjusts the training plan based on the monitoring results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, generation unit, monitoring unit, and adjustment 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 health data and fitness data of the user using sensors in the smart glasses 214 or a wearable device. The generation unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and generates an optimal training plan using AI based on the collected data. The monitoring unit is realized, for example, by the control unit 46A in the smart glasses 214 and monitors the progress of training in real time. The adjustment unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and adjusts the training plan based on the monitoring results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, monitoring unit, and adjustment unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's health data and fitness data using sensors or wearable devices in the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and generates an optimal training plan using AI based on the collected data. The monitoring unit is realized, for example, by the control unit 46A in the headset-type terminal 314 and monitors the progress of training in real time. The adjustment unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and adjusts the training plan based on the monitoring results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, monitoring unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects health data and fitness data of the user using sensors and wearable devices of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal training plan using AI based on the collected data. The monitoring unit is realized, for example, by the control unit 46A of the robot 414 and monitors the progress of training in real time. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adjusts the training plan based on the monitoring results.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The training plan providing system can also collect the user's dietary data and reflect it in the training plan. For example, the collection unit can collect data on the calories and nutrients the user has ingested, and the generation unit can adjust the training plan based on this data. If the user has eaten a high-calorie meal, the generation unit can suggest exercises that promote calorie consumption. Also, if the user is deficient in a particular nutrient, the generation unit can suggest exercises to supplement that nutrient. This allows the training plan providing system to provide a more comprehensive training plan that takes the user's dietary data into consideration.

[0091] The collection unit can collect the user's sleep data and reflect it in the training plan. For example, the collection unit can collect the user's sleep time and sleep quality, and the generation unit can adjust the training plan based on this data. If the user is not getting enough sleep, the generation unit can suggest lighter exercises. Also, if the user is getting good quality sleep, the generation unit can suggest more intense exercises. In this way, the collection unit can provide a training plan that takes the user's sleep data into consideration.

[0092] The generation unit can estimate the user's emotions and adjust the motivational elements of the training plan based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can suggest exercises that have a relaxing effect. If the user is feeling motivated, the generation unit can suggest challenging exercises. Furthermore, if the user is tired, the generation unit can suggest exercises that emphasize recovery. In this way, the generation unit can provide a training plan that includes motivational elements according to the user's emotions.

[0093] The generation unit can collect user feedback and reflect it in the training plan. For example, the generation unit can collect feedback provided by the user after an exercise and reflect it in the next training plan. If the user finds a particular exercise difficult, the generation unit can change the exercise to an easier one. Also, if the user enjoys a particular exercise, the generation unit can increase the number of times of that exercise. In this way, the generation unit can adjust the training plan based on the user feedback.

[0094] The monitoring unit can estimate the user's emotions and evaluate the training progress based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can evaluate the training progress gently. If the user is relaxed, the monitoring unit can also evaluate the training progress strictly. Furthermore, if the user is excited, the monitoring unit can also evaluate the training progress aggressively. In this way, the monitoring unit can evaluate the progress according to the user's emotions.

[0095] The adjustment unit can estimate the user's emotions and adjust the difficulty level of the training plan based on the estimated user's emotions. For example, the adjustment unit can lower the difficulty level of the training plan when the user is feeling stressed. Also, the adjustment unit can increase the difficulty level of the training plan when the user is relaxed. Furthermore, the adjustment unit can appropriately adjust the difficulty level of the training plan when the user is excited. In this way, the adjustment unit can provide a training plan with a difficulty level that corresponds to the user's emotions.

[0096] The collection unit can collect environmental data about the user and reflect it in the training plan. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the user, and the generation unit can adjust the training plan based on this data. If the user is in a hot and humid environment, the generation unit can suggest light exercises. Also, if the user is in a quiet environment, the generation unit can suggest exercises to improve concentration. In this way, the collection unit can provide a training plan that takes the user's environmental data into consideration.

[0097] The generation unit can estimate the user's emotions and adjust the interactive elements of the training plan based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can add interactive elements that have a relaxing effect. If the user is feeling motivated, the generation unit can add challenging interactive elements. Furthermore, if the user is tired, the generation unit can add interactive elements that emphasize recovery. In this way, the generation unit can provide a training plan that includes interactive elements that correspond to the user's emotions.

[0098] The monitoring unit can estimate the user's emotions and adjust training feedback based on the estimated user's emotions. For example, the monitoring unit can provide positive feedback when the user is feeling stressed. Also, the monitoring unit can provide detailed feedback when the user is relaxed. Furthermore, the monitoring unit can provide motivational feedback when the user is excited. In this way, the monitoring unit can provide feedback according to the user's emotions.

[0099] The adjustment unit can estimate the user's emotions and adjust the schedule of the training plan based on the estimated user's emotions. For example, the adjustment unit can loosen the schedule of the training plan when the user is feeling stressed. Alternatively, the adjustment unit can tighten the schedule of the training plan when the user is relaxed. Furthermore, the adjustment unit can moderately adjust the schedule of the training plan when the user is excited. In this way, the adjustment unit can provide a schedule of the training plan that corresponds to the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects the user's health and fitness data. Health data includes heart rate, blood pressure, body temperature, etc., while fitness data includes exercise time, calories burned, exercise intensity, etc. The collection unit collects this data using sensors in wearable devices and smartphones. Step 2: The generator generates an optimal training plan for each individual user based on the collected data. The generator uses AI to analyze the data and suggest exercises based on the user's health and fitness level. For example, a user who needs strength training might be suggested exercises to train specific muscle groups, while a user who needs rehabilitation might be suggested exercises to promote recovery from an injury. Step 3: The monitoring unit monitors the training progress in real time based on the generated training plan. The monitoring unit collects the user's exercise data in real time and evaluates the training progress. Step 4: The adjustment unit adjusts the training plan based on the results monitored by the monitoring unit. For example, if the user is not good at a particular exercise, the exercise can be changed to an easier one.

[0102] 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.

[0103] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 health and fitness data of a user; a generation unit that learns the data collected by the collection unit and generates a training plan for each individual user; a monitoring unit that monitors the progress of training based on the training plan generated by the generation unit; an adjustment unit that adjusts the training plan based on the results monitored by the monitoring unit; A system characterized by:

2. The collecting unit Collecting data on the user's age, gender, weight, height, exercise history, and health status 2. The system of claim 1.

3. The generation unit Based on the collected data, the system suggests exercises to train specific muscle groups for users who need strength training.

2. The system of claim 1.

4. The generation unit Based on the collected data, the system suggests exercises to promote recovery for users who need rehabilitation.

2. The system of claim 1.

5. The monitoring unit Monitor your training progress in real time 2. The system of claim 1.

6. The adjustment unit If a user is unable to complete a particular exercise, change the exercise to an easier one 2. The system of claim 1.

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

8. The collecting unit Analyze users' past health data and select the optimal data collection method 2. The system of claim 1.

Images