System

Abstract

To create personalized workout plans tailored to each user's fitness goals and deliver real-time feedback.SOLUTION: A system according to an embodiment comprises a reception unit, a collection unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives input of a user's fitness goals. The collection unit collects data from a body composition monitor and a wearable device. The generation unit creates a personalized workout plan based on the data collected by the collection unit and the fitness goals received by the reception unit. The analysis unit performs real-time analysis of performance data during exercise. The provision unit provides feedback based on the results obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the drawback of being difficult to efficiently create a personalized exercise plan based on a user's individual fitness goals and provide feedback in real time.

[0005] In accordance with an embodiment, the system aims to create a personalized exercise plan based on a user's individual fitness goals and provide feedback in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a generation unit, an analysis unit, and a provision unit. The reception unit inputs a fitness goal of a user. The collection unit collects data from the body composition scale and the wearable device. The generation unit creates a personalized exercise plan based on the data collected by the collection unit and the fitness goal accepted by the reception unit. The analysis unit analyzes performance data during exercise in real time. The provision unit provides feedback based on the results obtained by the analysis unit. Effect of the Invention

[0007] In accordance with an embodiment, the system can create a personalized exercise plan based on a user's individual fitness goals and provide feedback in real time. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personalized fitness system according to the embodiment of the present invention is a system that uses a generation AI to create a personalized exercise plan based on the user's individual fitness goals, abilities, and specific body metrics (weight, muscle mass, body fat percentage, etc.) obtained from a body composition scale. This system works in conjunction with a body composition scale and a wearable device to grasp the user's current physical condition and provide an optimal exercise plan. For example, the user inputs a fitness goal and collects specific body metrics obtained from a body composition scale and a wearable device. The generation AI creates an exercise plan according to the user's fitness goal based on these data. During exercise, the generation AI analyzes performance data obtained from the wearable device in real time and determines whether the exercise is appropriate. Based on the analysis results, the generation AI provides feedback such as changing the intensity of the exercise or correcting the form through a voice service. For example, it gives instructions such as "Let's pick up the pace a little more" or "Please correct your form" in real time. This mechanism allows the user to execute the exercise plan that is optimal for him / her and effectively achieve his / her fitness goal. In addition, real-time feedback can improve the quality of exercise and train safely. This enables the personalized fitness system to provide an optimal exercise plan based on the user's individual fitness goals and provide feedback in real time.

[0029] The personalized fitness system according to the embodiment includes a reception unit, a collection unit, a generation unit, an analysis unit, and a provision unit. The reception unit inputs a fitness goal of the user. The fitness goal of the user includes, for example, weight loss, muscle strength, and endurance improvement, but is not limited to such examples. The collection unit collects data from a body composition scale and a wearable device. The collected data includes, for example, body metrics such as weight, muscle mass, and body fat percentage, but is not limited to such examples. The generation unit uses a generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goal accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the fitness goal and body metrics of the user. The analysis unit analyzes performance data during exercise in real time. The performance data includes, for example, heart rate, exercise intensity, and duration, but is not limited to such examples. The provision unit provides feedback based on the results obtained by the analysis unit. The feedback is provided, for example, through a voice service. Thereby, the personalized fitness system according to the embodiment can create a personalized exercise plan based on the fitness goal of the user and provide feedback in real time. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can create an exercise plan using a generation AI model that inputs the fitness goal and body metrics of the user and outputs a personalized exercise plan. A part or all of the above-mentioned processing in the analysis unit can be performed using an AI or without an AI. For example, the analysis unit can analyze the performance data using an AI model that inputs performance data obtained from a wearable device and outputs analysis results in real time. A part or all of the above-mentioned processing in the provision unit can be performed using an AI or without an AI. For example, the provision unit can provide feedback using an AI model that inputs the analysis results obtained by the analysis unit and outputs feedback.

[0030] The reception unit inputs the user's fitness goals. The user's fitness goals include, but are not limited to, weight loss, muscle strength, and endurance. Specifically, the reception unit allows the user to set his or her goals in detail through a user interface. For example, a user aiming to lose weight can input a target weight and a period of time to achieve the weight. A user aiming to increase muscle strength can set specific muscle group strengthening and specific weight goals. A user aiming to increase endurance can input distance and time goals for running and cycling. In addition, the reception unit can collect additional information such as the user's lifestyle, exercise history, and dietary habits. This allows for more accurate fitness goal setting according to the user's individual situation. The reception unit centrally manages this information and serves to provide it to the subsequent processing department.

[0031] The collection unit collects data from the body composition scale and the wearable device. The collected data includes, but is not limited to, body metrics such as weight, muscle mass, and body fat percentage. Specifically, the body composition scale measures weight, body fat percentage, muscle mass, water content, and the like by the user simply standing on the scale, and transmits the data to the collection unit via wireless communication. The wearable device continuously collects data such as heart rate, steps, calories burned, and sleep patterns by the user wearing it daily. These devices transmit data to the collection unit via Bluetooth (registered trademark) or Wi-Fi, and are updated in real time. The collection unit centrally manages these data, and organizes and stores them for each user. Furthermore, the collection unit also has a function of detecting abnormal values ​​and correcting data to ensure the accuracy and reliability of the data. For example, if a sudden change in weight or an abnormal value of the heart rate is detected, the collection unit rechecks the data and notifies the user as necessary. In this way, the collection unit provides accurate and reliable data to assist in subsequent analysis and generation processes.

[0032] The generation unit uses the generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goals accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the user's fitness goals and body metrics. Specifically, the generation AI uses a deep learning model to compare past data with the user's current condition and propose an optimal exercise program. For example, a program combining aerobic exercise and strength training is proposed to a user who aims to lose weight. A set and number of repetitions of weight training targeting specific muscle groups are proposed to a user who aims to increase muscle strength. A program that gradually increases the distance and time of running and cycling is proposed to a user who aims to improve endurance. The generation AI can continuously adjust and optimize the program based on the user's feedback and exercise history. For example, if a user finds a certain exercise difficult, the generation AI adjusts the intensity of that exercise so that the user can continue without difficulty. The generation AI also monitors the user's progress and provides advice and motivation to achieve the goal. As a result, the generation unit provides the optimal exercise plan for the user and helps achieve effective fitness.

[0033] The analysis unit analyzes performance data during exercise in real time. Performance data includes, for example, heart rate, exercise intensity, duration, etc., but is not limited to such examples. Specifically, the analysis unit receives data transmitted from the wearable device in real time and performs analysis using an AI algorithm. For example, the analysis unit analyzes heart rate fluctuations and evaluates whether the user is training at an appropriate exercise intensity. Based on the exercise intensity and duration, the analysis unit calculates calories burned and the effect of exercise. Furthermore, the analysis unit can monitor the user's exercise form and posture, and provide correction instructions as necessary. For example, if the user is running with an inappropriate form, the analysis unit detects that information and provides advice to correct to an appropriate form. In this way, the analysis unit optimizes the user's exercise performance and supports effective training. In addition, the analysis unit can accumulate long-term data and evaluate the user's progress and training effect. This allows the user to realize his or her own growth and maintain motivation.

[0034] The providing unit provides feedback based on the results obtained by the analyzing unit. The feedback is provided, for example, through a voice service. Specifically, the providing unit transmits feedback to the user's smartphone or wearable device, and provides exercise advice and motivation in real time. For example, if the user's heart rate exceeds the target range during exercise, the providing unit notifies the user by voice, "Your heart rate is too high. Slow down a little." After exercise, the providing unit provides feedback on calories burned and the effects of exercise, and gives advice for the next training. Furthermore, the providing unit can evaluate the user's progress and goal achievement, and provide rewards and praise for the achieved goals. For example, when the user achieves a set goal, the providing unit notifies the user, "Congratulations! You have achieved your goal," and encourages the user to set the next goal. This allows the providing unit to provide continuous motivation to the user and maximize the effect of fitness. Furthermore, the providing unit can collect user feedback and use it to improve the entire system. For example, the user evaluates the satisfaction level with the feedback provided, and adjusts the feedback content based on the result. This allows the providing unit to provide optimal feedback for the user and improve the performance of the entire system.

[0035] The collection unit can collect specific body metrics from the body composition scale and the wearable device. The body metrics include, for example, body weight, body fat percentage, muscle mass, etc., but are not limited to such examples. For example, the collection unit measures body weight and body fat percentage using a body composition scale and collects data. The collection unit can also measure heart rate and exercise intensity using a wearable device and collect data. For example, the collection unit can input data obtained from the body composition scale to the generation AI, which can analyze the data and extract body metrics. In this way, by collecting specific body metrics, the user's current physical condition can be accurately grasped. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data obtained from the body composition scale and the wearable device to the generation AI, which can analyze the data and extract body metrics.

[0036] The generation unit can create a personalized exercise plan based on the user's fitness goals and body metrics. The generation unit uses a generation AI to analyze the user's fitness goals and body metrics to create an optimal exercise plan. For example, the generation unit proposes a plan centered on aerobic exercise to a user who wants to lose weight, and proposes a plan centered on strength training to a user who wants to increase muscle mass. The generation AI can create an exercise plan using, for example, a generation AI model that inputs the user's fitness goals and body metrics and outputs a personalized exercise plan. This makes it possible to provide an optimal exercise plan based on the user's individual goals and physical condition. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can create an exercise plan using a generation AI model that inputs the user's fitness goals and body metrics and outputs a personalized exercise plan.

[0037] The analysis unit can analyze the performance data obtained from the wearable device in real time. The analysis unit uses the generation AI to analyze the performance data during exercise in real time. The performance data includes, for example, heart rate, exercise intensity, duration, etc., but is not limited to such examples. The generation AI can analyze the performance data using, for example, a generation AI model that inputs performance data obtained from the wearable device and outputs analysis results in real time. This makes it possible to provide appropriate feedback immediately by analyzing the performance data in real time. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs performance data obtained from the wearable device and outputs analysis results in real time.

[0038] The providing unit can provide feedback based on the analysis result through a voice output device. The providing unit uses a generating AI to provide feedback based on the result obtained by the analyzing unit. The feedback is provided, for example, through a voice service. The generating AI can provide feedback, for example, using a generating AI model that takes the analysis result obtained by the analyzing unit as input and outputs feedback. Thereby, by providing feedback through a voice service, the user can receive instructions in real time during exercise. A part or all of the above-mentioned processing in the providing unit is performed using a generating AI. For example, the providing unit can provide feedback using a generating AI model that takes the analysis result obtained by the analyzing unit as input and outputs feedback.

[0039] The reception unit can analyze the user's past fitness goal history and propose an appropriate goal setting method. The reception unit uses a generation AI to analyze the user's past fitness goal history and propose an optimal goal setting method. For example, fitness goals set by the user in the past can be automatically displayed as candidates. In addition, goal setting methods used by the user in the past (specific numerical goals, time goals, etc.) can be preferentially proposed. Furthermore, a specific goal setting method can be predicted and proposed from the user's past goal achievement history. This makes it possible to support the user in achieving their goals by proposing an optimal goal setting method based on the past history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit can propose a goal setting method using a generation AI model that inputs the user's past fitness goal history and outputs an optimal goal setting method.

[0040] The reception unit can filter the fitness goal based on the user's current health condition and lifestyle habits when inputting the fitness goal using the generation AI. For example, the reception unit proposes a realistic goal in consideration of the user's current health condition (weight, body fat percentage, etc.). Also, it can propose an achievable goal based on the user's lifestyle habits (exercise frequency, dietary habits, etc.). Furthermore, it can set a reasonable goal by referring to the user's past health data. This makes it possible to set a realistic goal based on the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can propose a fitness goal using a generation AI model that inputs data on the user's health condition and lifestyle habits and performs filtering.

[0041] The reception unit can preferentially suggest highly relevant goals based on the user's geographical location information when a fitness goal is input. The reception unit uses the generation AI to preferentially suggest highly relevant goals based on the user's geographical location information when a fitness goal is input. For example, appropriate exercise goals can be suggested taking into account the climate and topography of the area where the user lives. Also, available exercise goals can be suggested based on fitness facilities and parks near the user. Furthermore, area-specific exercise events and activities can be suggested based on the user's geographical location information. As a result, goals suitable for the user can be suggested by taking into account the geographical location information. Part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can suggest goals using a generation AI model that inputs the user's geographical location information and suggests highly relevant goals.

[0042] The reception unit can analyze the user's social media activity when the fitness goal is input, and suggest related goals. The reception unit uses the generation AI to analyze the user's social media activity when the fitness goal is input, and suggest related goals. For example, based on the fitness goals and achievements shared by the user on social media, related goals can be suggested. In addition, from the user's social media activity, exercises and activities in which the user is interested can be analyzed and goals can be suggested. Furthermore, related goals can be suggested with reference to the fitness goals of the user's friends and followers on social media. In this way, by analyzing social media activity, goals based on the user's interests and concerns can be suggested. Part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can suggest goals using a generation AI model that inputs the user's social media activity data and suggests related goals.

[0043] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit uses the generation AI to analyze the user's past data collection history and select the optimal collection method. For example, the optimal data collection method is proposed based on the device and app used by the user in the past. The most effective collection timing can also be predicted and proposed from the user's past data collection history. Furthermore, the user's past data collection history can be analyzed and a method for improving the accuracy of the data can be proposed. As a result, the accuracy of the data can be improved by selecting the optimal collection method based on the past history. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can propose a collection method using a generation AI model that inputs the user's past data collection history and outputs the optimal collection method.

[0044] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. The collection unit uses the generation AI to perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit proposes an appropriate data collection method taking into account the user's current living situation (work, family, etc.). In addition, related data can be preferentially collected based on the user's areas of interest (sports, health, etc.). Furthermore, the timing of data collection can be adjusted to match the user's life rhythm. This makes it possible to collect appropriate data based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can propose a data collection method using a generation AI model that inputs data on the user's living situation and areas of interest and performs filtering.

[0045] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. The collection unit uses the generation AI to prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, appropriate data is collected in consideration of the climate and environment of the place where the user is exercising. In addition, based on the user's geographical location information, it is also possible to preferentially collect region-specific health data. Furthermore, based on the user's geographical location information, it is also possible to collect data suitable for the exercise environment. In this way, by taking the geographical location information into consideration, data suitable for the user can be collected. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can collect data using a generation AI model that inputs the user's geographical location information and collects highly relevant data.

[0046] The collection unit can analyze the user's social media activities at the time of data collection and collect related data. The collection unit uses the generation AI to analyze the user's social media activities at the time of data collection and collect related data. For example, the collection unit collects related data based on exercise data and results shared by the user on social media. In addition, data related to exercises and activities in which the user is interested can be collected from the user's social media activities. Furthermore, related data can be collected with reference to the exercise data of the user's friends and followers on social media. In this way, data based on the user's interests and concerns can be collected by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can collect data using a generation AI model that inputs the user's social media activity data and collects related data.

[0047] The generating unit can adjust the specificity of the plan based on the importance of the user's fitness goal when generating the exercise plan. The generating unit adjusts the level of detail of the plan based on the importance of the user's fitness goal when generating the exercise plan using the generating AI. For example, if the user's fitness goal is high, a detailed exercise plan is generated. Also, if the user's fitness goal is low, a basic exercise plan can be generated. Furthermore, the level of detail of the plan can be adjusted based on the deadline for achieving the user's fitness goal. As a result, the level of detail of the plan can be adjusted based on the importance of the fitness goal, thereby providing the user with an optimal exercise plan. A part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the importance of the user's fitness goal and adjusts the level of detail of the plan.

[0048] The generating unit can apply different generation algorithms according to the user's body metric category when generating an exercise plan. The generating unit uses a generating AI to apply different generation algorithms according to the user's body metric category when generating an exercise plan. For example, an appropriate exercise plan is generated according to the user's weight category. In addition, an exercise plan centered on muscle training can be generated according to the user's muscle mass category. Furthermore, an exercise plan centered on aerobic exercise can be generated according to the user's body fat percentage category. This makes it possible to provide an optimal exercise plan according to the body metric category. A part or all of the above-mentioned processing in the generating unit is performed using a generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the user's body metric category and applies different generation algorithms.

[0049] The generating unit can determine the order of the plans based on the submission time of the user's fitness goals when generating the exercise plan. The generating unit uses the generating AI to determine the priority of the plans based on the submission time of the user's fitness goals when generating the exercise plan. For example, an exercise plan that preferentially reflects the fitness goal most recently submitted by the user is generated. In addition, when the user submits a long-term goal, an exercise plan based on the goal can be generated. Furthermore, when the user submits multiple goals, the priority can be determined based on the submission time. As a result, the optimal exercise plan can be provided to the user by determining the priority of the plans based on the submission time of the fitness goals. Part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the submission time of the user's fitness goals and determines the priority of the plans.

[0050] The generating unit can adjust the order of the plan based on the relevance of the user's body metrics when generating the exercise plan. The generating unit adjusts the order of the plan based on the relevance of the user's body metrics when generating the exercise plan using the generating AI. For example, the order of the exercise plan is adjusted taking into account the balance between the user's weight and muscle mass. The order of the exercise plan can also be adjusted based on the relevance between the user's body fat percentage and cardiopulmonary function. Furthermore, the optimal order of the exercise plan can be generated taking into account the overall balance of the user's body metrics. As a result, a more effective exercise plan can be provided by adjusting the order of the plan based on the relevance of the body metrics. A part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the relevance of the user's body metrics and adjusts the order of the plan.

[0051] The analysis unit can improve the accuracy of the analysis based on the user's exercise history when analyzing the performance data. The analysis unit uses the generation AI to improve the accuracy of the analysis by taking into account the user's exercise history when analyzing the performance data. For example, the current performance data is compared and analyzed based on the user's past exercise history. In addition, a specific pattern can be extracted from the user's exercise history to improve the accuracy of the analysis. Furthermore, individual feedback can be provided by taking into account the user's exercise history. In this way, the accuracy of the analysis can be improved by taking into account the exercise history. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that takes the user's exercise history as input and improves the accuracy of the analysis.

[0052] The analysis unit can perform the analysis taking into account the attribute information of the user when analyzing the performance data. The analysis unit performs the analysis taking into account the attribute information of the user using the generation AI when analyzing the performance data. For example, the performance data is analyzed based on attribute information such as the user's age, sex, and weight. In addition, appropriate analysis criteria can be set taking into account the fitness level of the user. Furthermore, individual feedback can be provided based on the attribute information of the user. In this way, more individualized feedback can be provided by taking the attribute information into account. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs the attribute information of the user and performs analysis.

[0053] The analysis unit can perform analysis based on the geographical distribution of the user when analyzing the performance data. The analysis unit uses the generation AI to perform analysis taking into account the geographical distribution of the user when analyzing the performance data. For example, the performance data is analyzed taking into account the climate and environment of the area where the user is exercising. In addition, region-specific health data can be analyzed based on the geographical distribution of the user. Furthermore, analysis suitable for the exercise environment can be performed based on the geographical distribution of the user. As a result, analysis based on region-specific health data can be performed by taking the geographical distribution into account. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs the geographical distribution data of the user and performs analysis.

[0054] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the performance data. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to related literature when analyzing the performance data. For example, the analysis criteria for the performance data can be updated by referring to the latest fitness research. In addition, the user's performance data can be compared and analyzed based on the related literature. Furthermore, individual feedback can be provided by referring to the related literature. This makes it possible to perform analysis based on the latest knowledge by referring to the related literature. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that uses the related literature as input and improves the accuracy of the analysis.

[0055] The providing unit can select an appropriate providing method by referring to the user's past feedback history when providing feedback. The providing unit uses the generation AI to select the optimal providing method by referring to the user's past feedback history when providing feedback. For example, the optimal providing method is proposed based on the feedback the user has received in the past. In addition, a specific providing method can be predicted and proposed from the user's past feedback history. Furthermore, it is also possible to analyze the user's past feedback history and propose a method for improving the accuracy of the feedback. As a result, more appropriate feedback can be provided by referring to the past feedback history. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can provide feedback using a generation AI model that inputs the user's past feedback history and selects the optimal providing method.

[0056] The providing unit can customize the content of the feedback based on the user's current exercise status when providing the feedback. The providing unit uses the generation AI to customize the content of the feedback based on the user's current exercise status when providing the feedback. For example, appropriate feedback is provided according to the type of exercise currently being performed by the user. The content of the feedback can also be adjusted based on the user's current exercise intensity. Furthermore, the user's current exercise status can be analyzed in real time and feedback can be provided immediately. This allows more effective feedback to be provided by customizing the feedback based on the current exercise status. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can provide feedback using a generation AI model that uses the user's current exercise status data as input and customizes the feedback.

[0057] The providing unit can provide appropriate feedback based on the geographical location information of the user when providing feedback. The providing unit uses the generating AI to provide appropriate feedback based on the geographical location information of the user when providing feedback. For example, appropriate feedback is provided taking into consideration the climate and environment of the place where the user is exercising. In addition, feedback can be provided by referring to health data specific to the region based on the geographical location information of the user. Furthermore, feedback suitable for the exercise environment can be provided based on the geographical location information of the user. In this way, feedback suitable for the user can be provided by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the providing unit is performed using the generating AI. For example, the providing unit can provide feedback using a generating AI model that inputs the geographical location information of the user and provides appropriate feedback.

[0058] The providing unit can analyze the user's social media activities and suggest the content of the feedback when providing the feedback. The providing unit uses the generating AI to analyze the user's social media activities and suggest the content of the feedback when providing the feedback. For example, based on the exercise data and results shared by the user on social media, the providing unit can provide relevant feedback. In addition, based on the user's social media activities, feedback related to the exercises and activities in which the user is interested can be provided. Furthermore, based on the exercise data of the user's friends and followers on social media, relevant feedback can be provided. In this way, by analyzing social media activities, feedback based on the user's interests and concerns can be provided. Part or all of the above-mentioned processing in the providing unit is performed using the generating AI. For example, the providing unit can provide feedback using a generating AI model that inputs the user's social media activity data and suggests the content of the feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When inputting the user's fitness goal, the reception unit can improve the accuracy of goal setting by referring to the user's past exercise history. For example, the reception unit can analyze the goals that the user has achieved or failed to achieve in the past and suggest realistic goals. In addition, the reception unit can extract specific exercise patterns and preferences from the user's past exercise history and customize goals based on the extracted patterns and preferences. Furthermore, the reception unit can suggest specific steps for achieving the goal based on the user's past exercise history. This makes it possible to set optimal goals for the user by utilizing the past exercise history.

[0061] The generator can adjust the difficulty of the exercise plan based on the user's fitness goal and body metrics. For example, if the user is a beginner, it can propose an easy exercise plan and gradually increase the difficulty. Also, if the user is an advanced user, it can propose a challenging exercise plan. Furthermore, it can customize the difficulty of the exercise plan according to the user's fitness goal. This makes it possible to provide an optimal exercise plan according to the user's fitness level.

[0062] The providing unit can customize the content of the feedback based on the user's current exercise status when providing the feedback. For example, appropriate feedback is provided depending on the type of exercise the user is currently performing. The content of the feedback can also be adjusted based on the user's current exercise intensity. Furthermore, the user's current exercise status can be analyzed in real time and feedback can be provided immediately. This allows for more effective feedback to be provided by customizing the feedback based on the current exercise status.

[0063] The reception unit can analyze the user's past fitness goal history and suggest an appropriate goal setting method. For example, fitness goals set by the user in the past can be automatically displayed as candidates. Also, it can preferentially suggest goal setting methods used by the user in the past (specific numerical goals, time period goals, etc.). Furthermore, it can predict and suggest specific goal setting methods based on the user's past goal achievement history. In this way, it is possible to support the user in achieving their goals by suggesting the optimal goal setting method based on the past history.

[0064] When providing feedback, the providing unit can select an appropriate feedback providing method by referring to the user's past feedback history. For example, the providing unit can suggest an optimal feedback providing method based on feedback the user has received in the past. Also, the providing unit can predict and suggest a specific feedback providing method from the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and suggest a method for improving the accuracy of feedback. In this way, more appropriate feedback can be provided by referring to the past feedback history.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The reception unit inputs a fitness goal of the user. The fitness goal of the user may include, but is not limited to, weight loss, muscle building, and endurance improvement. Step 2: The collection unit collects data from the body composition scale and the wearable device. The collected data includes, but is not limited to, body metrics such as weight, muscle mass, and body fat percentage. Step 3: The generation unit uses the generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goal accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the user's fitness goal and body metrics. Step 4: The analysis unit analyzes the performance data during exercise in real time, including, but not limited to, heart rate, exercise intensity, and duration. Step 5: The providing unit provides feedback based on the results obtained by the analyzing unit. The feedback is provided, for example, through a voice service.

[0067] (Example 2) The personalized fitness system according to the embodiment of the present invention is a system that uses a generation AI to create a personalized exercise plan based on the user's individual fitness goals, abilities, and specific body metrics (weight, muscle mass, body fat percentage, etc.) obtained from a body composition scale. This system works in conjunction with a body composition scale and a wearable device to grasp the user's current physical condition and provide an optimal exercise plan. For example, the user inputs a fitness goal and collects specific body metrics obtained from a body composition scale and a wearable device. The generation AI creates an exercise plan according to the user's fitness goal based on these data. During exercise, the generation AI analyzes performance data obtained from the wearable device in real time and determines whether the exercise is appropriate. Based on the analysis results, the generation AI provides feedback such as changing the intensity of the exercise or correcting the form through a voice service. For example, it gives instructions such as "Let's pick up the pace a little more" or "Please correct your form" in real time. This mechanism allows the user to execute the exercise plan that is optimal for him / her and effectively achieve his / her fitness goal. In addition, real-time feedback can improve the quality of exercise and train safely. This enables the personalized fitness system to provide an optimal exercise plan based on the user's individual fitness goals and provide feedback in real time.

[0068] The personalized fitness system according to the embodiment includes a reception unit, a collection unit, a generation unit, an analysis unit, and a provision unit. The reception unit inputs a fitness goal of the user. The fitness goal of the user includes, for example, weight loss, muscle strength, and endurance improvement, but is not limited to such examples. The collection unit collects data from a body composition scale and a wearable device. The collected data includes, for example, body metrics such as weight, muscle mass, and body fat percentage, but is not limited to such examples. The generation unit uses a generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goal accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the fitness goal and body metrics of the user. The analysis unit analyzes performance data during exercise in real time. The performance data includes, for example, heart rate, exercise intensity, and duration, but is not limited to such examples. The provision unit provides feedback based on the results obtained by the analysis unit. The feedback is provided, for example, through a voice service. Thereby, the personalized fitness system according to the embodiment can create a personalized exercise plan based on the fitness goal of the user and provide feedback in real time. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can create an exercise plan using a generation AI model that inputs the fitness goal and body metrics of the user and outputs a personalized exercise plan. A part or all of the above-mentioned processing in the analysis unit can be performed using an AI or without an AI. For example, the analysis unit can analyze the performance data using an AI model that inputs performance data obtained from a wearable device and outputs analysis results in real time. A part or all of the above-mentioned processing in the provision unit can be performed using an AI or without an AI. For example, the provision unit can provide feedback using an AI model that inputs the analysis results obtained by the analysis unit and outputs feedback.

[0069] The reception unit inputs the user's fitness goals. The user's fitness goals include, but are not limited to, weight loss, muscle strength, and endurance. Specifically, the reception unit allows the user to set his or her goals in detail through a user interface. For example, a user aiming to lose weight can input a target weight and a period of time to achieve the weight. A user aiming to increase muscle strength can set specific muscle group strengthening and specific weight goals. A user aiming to increase endurance can input distance and time goals for running and cycling. In addition, the reception unit can collect additional information such as the user's lifestyle, exercise history, and dietary habits. This allows for more accurate fitness goal setting according to the user's individual situation. The reception unit centrally manages this information and serves to provide it to the subsequent processing department.

[0070] The collection unit collects data from the body composition scale and the wearable device. The collected data includes, but is not limited to, body metrics such as weight, muscle mass, and body fat percentage. Specifically, the body composition scale measures weight, body fat percentage, muscle mass, water content, and the like by the user simply standing on the scale, and transmits the data to the collection unit via wireless communication. The wearable device continuously collects data such as heart rate, steps, calories burned, and sleep patterns by the user wearing it on a daily basis. These devices transmit data to the collection unit via Bluetooth or Wi-Fi and are updated in real time. The collection unit centrally manages these data and organizes and stores them for each user. In addition, the collection unit also has a function to detect abnormal values ​​and correct data to ensure the accuracy and reliability of the data. For example, if a sudden change in weight or an abnormal value of the heart rate is detected, the collection unit rechecks the data and notifies the user as necessary. In this way, the collection unit provides accurate and reliable data to assist in subsequent analysis and generation processes.

[0071] The generation unit uses the generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goals accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the user's fitness goals and body metrics. Specifically, the generation AI uses a deep learning model to compare past data with the user's current condition and propose an optimal exercise program. For example, a program combining aerobic exercise and strength training is proposed to a user who aims to lose weight. A set and number of repetitions of weight training targeting specific muscle groups are proposed to a user who aims to increase muscle strength. A program that gradually increases the distance and time of running and cycling is proposed to a user who aims to improve endurance. The generation AI can continuously adjust and optimize the program based on the user's feedback and exercise history. For example, if a user finds a certain exercise difficult, the generation AI adjusts the intensity of that exercise so that the user can continue without difficulty. The generation AI also monitors the user's progress and provides advice and motivation to achieve the goal. As a result, the generation unit provides the optimal exercise plan for the user and helps achieve effective fitness.

[0072] The analysis unit analyzes performance data during exercise in real time. Performance data includes, for example, heart rate, exercise intensity, duration, etc., but is not limited to such examples. Specifically, the analysis unit receives data transmitted from the wearable device in real time and performs analysis using an AI algorithm. For example, the analysis unit analyzes heart rate fluctuations and evaluates whether the user is training at an appropriate exercise intensity. Based on the exercise intensity and duration, the analysis unit calculates calories burned and the effect of exercise. Furthermore, the analysis unit can monitor the user's exercise form and posture, and provide correction instructions as necessary. For example, if the user is running with an inappropriate form, the analysis unit detects that information and provides advice to correct to an appropriate form. In this way, the analysis unit optimizes the user's exercise performance and supports effective training. In addition, the analysis unit can accumulate long-term data and evaluate the user's progress and training effect. This allows the user to realize his or her own growth and maintain motivation.

[0073] The providing unit provides feedback based on the results obtained by the analyzing unit. The feedback is provided, for example, through a voice service. Specifically, the providing unit transmits feedback to the user's smartphone or wearable device, and provides exercise advice and motivation in real time. For example, if the user's heart rate exceeds the target range during exercise, the providing unit notifies the user by voice, "Your heart rate is too high. Slow down a little." After exercise, the providing unit provides feedback on calories burned and the effects of exercise, and gives advice for the next training. Furthermore, the providing unit can evaluate the user's progress and goal achievement, and provide rewards and praise for the achieved goals. For example, when the user achieves a set goal, the providing unit notifies the user, "Congratulations! You have achieved your goal," and encourages the user to set the next goal. This allows the providing unit to provide continuous motivation to the user and maximize the effect of fitness. Furthermore, the providing unit can collect user feedback and use it to improve the entire system. For example, the user evaluates the satisfaction level with the feedback provided, and adjusts the feedback content based on the result. This allows the providing unit to provide optimal feedback for the user and improve the performance of the entire system.

[0074] The collection unit can collect specific body metrics from the body composition scale and the wearable device. The body metrics include, for example, body weight, body fat percentage, muscle mass, etc., but are not limited to such examples. For example, the collection unit measures body weight and body fat percentage using a body composition scale and collects data. The collection unit can also measure heart rate and exercise intensity using a wearable device and collect data. For example, the collection unit can input data obtained from the body composition scale to the generation AI, which can analyze the data and extract body metrics. In this way, by collecting specific body metrics, the user's current physical condition can be accurately grasped. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input data obtained from the body composition scale and the wearable device to the generation AI, which can analyze the data and extract body metrics.

[0075] The generation unit can create a personalized exercise plan based on the user's fitness goals and body metrics. The generation unit uses a generation AI to analyze the user's fitness goals and body metrics to create an optimal exercise plan. For example, the generation unit proposes a plan centered on aerobic exercise to a user who wants to lose weight, and proposes a plan centered on strength training to a user who wants to increase muscle mass. The generation AI can create an exercise plan using, for example, a generation AI model that inputs the user's fitness goals and body metrics and outputs a personalized exercise plan. This makes it possible to provide an optimal exercise plan based on the user's individual goals and physical condition. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can create an exercise plan using a generation AI model that inputs the user's fitness goals and body metrics and outputs a personalized exercise plan.

[0076] The analysis unit can analyze the performance data obtained from the wearable device in real time. The analysis unit uses the generation AI to analyze the performance data during exercise in real time. The performance data includes, for example, heart rate, exercise intensity, duration, etc., but is not limited to such examples. The generation AI can analyze the performance data using, for example, a generation AI model that inputs performance data obtained from the wearable device and outputs analysis results in real time. This makes it possible to provide appropriate feedback immediately by analyzing the performance data in real time. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs performance data obtained from the wearable device and outputs analysis results in real time.

[0077] The providing unit can provide feedback based on the analysis result through a voice output device. The providing unit uses a generating AI to provide feedback based on the result obtained by the analyzing unit. The feedback is provided, for example, through a voice service. The generating AI can provide feedback, for example, using a generating AI model that takes the analysis result obtained by the analyzing unit as input and outputs feedback. Thereby, by providing feedback through a voice service, the user can receive instructions in real time during exercise. A part or all of the above-mentioned processing in the providing unit is performed using a generating AI. For example, the providing unit can provide feedback using a generating AI model that takes the analysis result obtained by the analyzing unit as input and outputs feedback.

[0078] The reception unit can estimate the user's emotion and adjust the input method of the fitness goal based on the estimated emotion. The reception unit uses a generation AI to estimate the user's emotion and adjust the input method of the fitness goal based on the estimated emotion. For example, when the user is stressed, a simple interface is provided to minimize the input procedure. Also, when the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to enable the fitness goal to be input quickly. In this way, by adjusting the input method according to the user's emotion, a more appropriate fitness goal can be set. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0079] The reception unit can analyze the user's past fitness goal history and propose an appropriate goal setting method. The reception unit uses a generation AI to analyze the user's past fitness goal history and propose an optimal goal setting method. For example, fitness goals set by the user in the past can be automatically displayed as candidates. In addition, goal setting methods used by the user in the past (specific numerical goals, time goals, etc.) can be preferentially proposed. Furthermore, a specific goal setting method can be predicted and proposed from the user's past goal achievement history. This makes it possible to support the user in achieving their goals by proposing an optimal goal setting method based on the past history. A part or all of the above-mentioned processing in the reception unit is performed using a generation AI. For example, the reception unit can propose a goal setting method using a generation AI model that inputs the user's past fitness goal history and outputs an optimal goal setting method.

[0080] The reception unit can filter the fitness goal based on the user's current health condition and lifestyle habits when inputting the fitness goal using the generation AI. For example, the reception unit proposes a realistic goal in consideration of the user's current health condition (weight, body fat percentage, etc.). Also, it can propose an achievable goal based on the user's lifestyle habits (exercise frequency, dietary habits, etc.). Furthermore, it can set a reasonable goal by referring to the user's past health data. This makes it possible to set a realistic goal based on the user's health condition and lifestyle habits. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can propose a fitness goal using a generation AI model that inputs data on the user's health condition and lifestyle habits and performs filtering.

[0081] The reception unit can estimate the user's emotion and determine the priority of the fitness goals to be input based on the estimated emotion. The reception unit uses the generation AI to estimate the user's emotion and determine the priority of the fitness goals to be input based on the estimated emotion. For example, when the user is highly motivated, a challenging goal is preferentially suggested. In addition, when the user is tired, a realistic and easy-to-achieve goal can be preferentially suggested. Furthermore, when the user is relaxed, a long-term goal can be preferentially suggested. This enables more effective goal setting by determining the priority of goals according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0082] The reception unit can preferentially suggest highly relevant goals based on the user's geographical location information when a fitness goal is input. The reception unit uses the generation AI to preferentially suggest highly relevant goals based on the user's geographical location information when a fitness goal is input. For example, appropriate exercise goals can be suggested taking into account the climate and topography of the area where the user lives. Also, available exercise goals can be suggested based on fitness facilities and parks near the user. Furthermore, area-specific exercise events and activities can be suggested based on the user's geographical location information. As a result, goals suitable for the user can be suggested by taking into account the geographical location information. Part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can suggest goals using a generation AI model that inputs the user's geographical location information and suggests highly relevant goals.

[0083] The reception unit can analyze the user's social media activity when the fitness goal is input, and suggest related goals. The reception unit uses the generation AI to analyze the user's social media activity when the fitness goal is input, and suggest related goals. For example, based on the fitness goals and achievements shared by the user on social media, related goals can be suggested. In addition, from the user's social media activity, exercises and activities in which the user is interested can be analyzed and goals can be suggested. Furthermore, related goals can be suggested with reference to the fitness goals of the user's friends and followers on social media. In this way, by analyzing social media activity, goals based on the user's interests and concerns can be suggested. Part or all of the above-mentioned processing in the reception unit is performed using the generation AI. For example, the reception unit can suggest goals using a generation AI model that inputs the user's social media activity data and suggests related goals.

[0084] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The collection unit uses the generation AI to estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, when the user is relaxed, data collection is performed frequently to collect detailed data. Also, when the user is stressed, the frequency of data collection can be reduced to reduce the burden on the user. Furthermore, when the user is concentrating during exercise, data can be collected in real time and analyzed immediately. This allows more accurate data to be collected by adjusting the timing of data collection according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can estimate emotions using a generation AI model that inputs the user's emotion data and estimates emotions.

[0085] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit uses the generation AI to analyze the user's past data collection history and select the optimal collection method. For example, the optimal data collection method is proposed based on the device and app used by the user in the past. The most effective collection timing can also be predicted and proposed from the user's past data collection history. Furthermore, the user's past data collection history can be analyzed and a method for improving the accuracy of the data can be proposed. As a result, the accuracy of the data can be improved by selecting the optimal collection method based on the past history. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can propose a collection method using a generation AI model that inputs the user's past data collection history and outputs the optimal collection method.

[0086] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. The collection unit uses the generation AI to perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit proposes an appropriate data collection method taking into account the user's current living situation (work, family, etc.). In addition, related data can be preferentially collected based on the user's areas of interest (sports, health, etc.). Furthermore, the timing of data collection can be adjusted to match the user's life rhythm. This makes it possible to collect appropriate data based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can propose a data collection method using a generation AI model that inputs data on the user's living situation and areas of interest and performs filtering.

[0087] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated emotion. The collection unit uses the generation AI to estimate the user's emotion and determine the priority of data to be collected based on the estimated emotion. For example, when the user is highly motivated, detailed data is collected preferentially. Also, when the user is tired, only basic data can be collected preferentially. Furthermore, when the user is relaxed, long-term data collection can be prioritized. In this way, important data can be collected preferentially by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can estimate emotions using a generation AI model that inputs the user's emotion data and estimates emotions.

[0088] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. The collection unit uses the generation AI to prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, appropriate data is collected in consideration of the climate and environment of the place where the user is exercising. In addition, based on the user's geographical location information, it is also possible to preferentially collect region-specific health data. Furthermore, based on the user's geographical location information, it is also possible to collect data suitable for the exercise environment. In this way, by taking the geographical location information into consideration, data suitable for the user can be collected. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can collect data using a generation AI model that inputs the user's geographical location information and collects highly relevant data.

[0089] The collection unit can analyze the user's social media activities at the time of data collection and collect related data. The collection unit uses the generation AI to analyze the user's social media activities at the time of data collection and collect related data. For example, the collection unit collects related data based on exercise data and results shared by the user on social media. In addition, data related to exercises and activities in which the user is interested can be collected from the user's social media activities. Furthermore, related data can be collected with reference to the exercise data of the user's friends and followers on social media. In this way, data based on the user's interests and concerns can be collected by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can collect data using a generation AI model that inputs the user's social media activity data and collects related data.

[0090] The generation unit can estimate the user's emotion and adjust the expression method of the exercise plan based on the estimated emotion. The generation unit uses the generation AI to estimate the user's emotion and adjust the expression method of the exercise plan based on the estimated emotion. For example, when the user is relaxed, an exercise plan that proceeds at a leisurely pace is generated. Also, when the user is in a hurry, an effective exercise plan can be generated in a short time. Furthermore, when the user is excited, an exercise plan with a visually stimulating effect can be generated. In this way, a more effective exercise plan can be provided by adjusting the expression method of the exercise plan according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0091] The generating unit can adjust the specificity of the plan based on the importance of the user's fitness goal when generating the exercise plan. The generating unit adjusts the level of detail of the plan based on the importance of the user's fitness goal when generating the exercise plan using the generating AI. For example, if the user's fitness goal is high, a detailed exercise plan is generated. Also, if the user's fitness goal is low, a basic exercise plan can be generated. Furthermore, the level of detail of the plan can be adjusted based on the deadline for achieving the user's fitness goal. As a result, the level of detail of the plan can be adjusted based on the importance of the fitness goal, thereby providing the user with an optimal exercise plan. A part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the importance of the user's fitness goal and adjusts the level of detail of the plan.

[0092] The generating unit can apply different generation algorithms according to the user's body metric category when generating an exercise plan. The generating unit uses a generating AI to apply different generation algorithms according to the user's body metric category when generating an exercise plan. For example, an appropriate exercise plan is generated according to the user's weight category. In addition, an exercise plan centered on muscle training can be generated according to the user's muscle mass category. Furthermore, an exercise plan centered on aerobic exercise can be generated according to the user's body fat percentage category. This makes it possible to provide an optimal exercise plan according to the body metric category. A part or all of the above-mentioned processing in the generating unit is performed using a generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the user's body metric category and applies different generation algorithms.

[0093] The generation unit can estimate the user's emotion and adjust the length of the exercise plan based on the estimated emotion. The generation unit uses a generation AI to estimate the user's emotion and adjust the length of the exercise plan based on the estimated emotion. For example, when the user is in a hurry, a short and effective exercise plan is generated. Also, when the user is relaxed, a long exercise plan can be generated. Furthermore, when the user is excited, an exercise plan with a visually stimulating effect can be generated. In this way, a more effective exercise plan can be provided by adjusting the length of the exercise plan according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0094] The generating unit can determine the order of the plans based on the submission time of the user's fitness goals when generating the exercise plan. The generating unit uses the generating AI to determine the priority of the plans based on the submission time of the user's fitness goals when generating the exercise plan. For example, an exercise plan that preferentially reflects the fitness goal most recently submitted by the user is generated. In addition, when the user submits a long-term goal, an exercise plan based on the goal can be generated. Furthermore, when the user submits multiple goals, the priority can be determined based on the submission time. As a result, the optimal exercise plan can be provided to the user by determining the priority of the plans based on the submission time of the fitness goals. Part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the submission time of the user's fitness goals and determines the priority of the plans.

[0095] The generating unit can adjust the order of the plan based on the relevance of the user's body metrics when generating the exercise plan. The generating unit adjusts the order of the plan based on the relevance of the user's body metrics when generating the exercise plan using the generating AI. For example, the order of the exercise plan is adjusted taking into account the balance between the user's weight and muscle mass. The order of the exercise plan can also be adjusted based on the relevance between the user's body fat percentage and cardiopulmonary function. Furthermore, the optimal order of the exercise plan can be generated taking into account the overall balance of the user's body metrics. As a result, a more effective exercise plan can be provided by adjusting the order of the plan based on the relevance of the body metrics. A part or all of the above-mentioned processing in the generating unit is performed using the generating AI. For example, the generating unit can generate an exercise plan using a generating AI model that inputs the relevance of the user's body metrics and adjusts the order of the plan.

[0096] The analysis unit can estimate the user's emotion and adjust the analysis criteria of the performance data based on the estimated emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the analysis criteria of the performance data based on the estimated emotion. For example, when the user is relaxed, detailed performance data is analyzed and feedback is provided. Also, when the user is stressed, only basic performance data can be analyzed and brief feedback can be provided. Furthermore, when the user is concentrating, performance data can be analyzed in real time and feedback can be provided instantly. In this way, more appropriate feedback can be provided by adjusting the analysis criteria according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can estimate emotion using a generation AI model that inputs the user's emotion data and estimates emotion.

[0097] The analysis unit can improve the accuracy of the analysis based on the user's exercise history when analyzing the performance data. The analysis unit uses the generation AI to improve the accuracy of the analysis by taking into account the user's exercise history when analyzing the performance data. For example, the current performance data is compared and analyzed based on the user's past exercise history. In addition, a specific pattern can be extracted from the user's exercise history to improve the accuracy of the analysis. Furthermore, individual feedback can be provided by taking into account the user's exercise history. In this way, the accuracy of the analysis can be improved by taking into account the exercise history. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that takes the user's exercise history as input and improves the accuracy of the analysis.

[0098] The analysis unit can perform the analysis taking into account the attribute information of the user when analyzing the performance data. The analysis unit performs the analysis taking into account the attribute information of the user using the generation AI when analyzing the performance data. For example, the performance data is analyzed based on attribute information such as the user's age, sex, and weight. In addition, appropriate analysis criteria can be set taking into account the fitness level of the user. Furthermore, individual feedback can be provided based on the attribute information of the user. In this way, more individualized feedback can be provided by taking the attribute information into account. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs the attribute information of the user and performs analysis.

[0099] The analysis unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the display order of the analysis results based on the estimated emotion. For example, when the user is relaxed, detailed analysis results are preferentially displayed. Also, when the user is in a hurry, analysis results that focus on the main points can be preferentially displayed. Furthermore, when the user is excited, analysis results with visually stimulating effects can be preferentially displayed. In this way, by adjusting the display order according to the user's emotion, more effective feedback can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0100] The analysis unit can perform analysis based on the geographical distribution of the user when analyzing the performance data. The analysis unit uses the generation AI to perform analysis taking into account the geographical distribution of the user when analyzing the performance data. For example, the performance data is analyzed taking into account the climate and environment of the area where the user is exercising. In addition, region-specific health data can be analyzed based on the geographical distribution of the user. Furthermore, analysis suitable for the exercise environment can be performed based on the geographical distribution of the user. As a result, analysis based on region-specific health data can be performed by taking the geographical distribution into account. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that inputs the geographical distribution data of the user and performs analysis.

[0101] The analysis unit can improve the accuracy of the analysis by referring to related literature when analyzing the performance data. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to related literature when analyzing the performance data. For example, the analysis criteria for the performance data can be updated by referring to the latest fitness research. In addition, the user's performance data can be compared and analyzed based on the related literature. Furthermore, individual feedback can be provided by referring to the related literature. This makes it possible to perform analysis based on the latest knowledge by referring to the related literature. Part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can analyze the performance data using a generation AI model that uses the related literature as input and improves the accuracy of the analysis.

[0102] The providing unit can estimate the user's emotion and adjust the feedback providing method based on the estimated emotion. The providing unit uses the generation AI to estimate the user's emotion and adjust the feedback providing method based on the estimated emotion. For example, when the user is relaxed, detailed feedback is provided. Also, when the user is stressed, brief and to the point feedback can be provided. Furthermore, when the user is concentrating, feedback can be provided in real time. In this way, by adjusting the feedback providing method according to the user's emotion, more effective feedback can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0103] The providing unit can select an appropriate providing method by referring to the user's past feedback history when providing feedback. The providing unit uses the generation AI to select the optimal providing method by referring to the user's past feedback history when providing feedback. For example, the optimal providing method is proposed based on the feedback the user has received in the past. In addition, a specific providing method can be predicted and proposed from the user's past feedback history. Furthermore, it is also possible to analyze the user's past feedback history and propose a method for improving the accuracy of the feedback. As a result, more appropriate feedback can be provided by referring to the past feedback history. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can provide feedback using a generation AI model that inputs the user's past feedback history and selects the optimal providing method.

[0104] The providing unit can customize the content of the feedback based on the user's current exercise status when providing the feedback. The providing unit uses the generation AI to customize the content of the feedback based on the user's current exercise status when providing the feedback. For example, appropriate feedback is provided according to the type of exercise currently being performed by the user. The content of the feedback can also be adjusted based on the user's current exercise intensity. Furthermore, the user's current exercise status can be analyzed in real time and feedback can be provided immediately. This allows more effective feedback to be provided by customizing the feedback based on the current exercise status. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can provide feedback using a generation AI model that uses the user's current exercise status data as input and customizes the feedback.

[0105] The providing unit can estimate the user's emotion and determine the priority of the feedback based on the estimated emotion. The providing unit uses the generation AI to estimate the user's emotion and determine the priority of the feedback based on the estimated emotion. For example, when the user is highly motivated, detailed feedback can be provided preferentially. In addition, when the user is tired, basic feedback can be provided preferentially. Furthermore, when the user is relaxed, long-term feedback can be provided preferentially. In this way, by determining the priority of the feedback according to the user's emotion, more effective feedback can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can estimate the emotion using a generation AI model that inputs the user's emotion data and estimates the emotion.

[0106] The providing unit can provide appropriate feedback based on the geographical location information of the user when providing feedback. The providing unit uses the generating AI to provide appropriate feedback based on the geographical location information of the user when providing feedback. For example, appropriate feedback is provided taking into consideration the climate and environment of the place where the user is exercising. In addition, feedback can be provided by referring to health data specific to the region based on the geographical location information of the user. Furthermore, feedback suitable for the exercise environment can be provided based on the geographical location information of the user. In this way, feedback suitable for the user can be provided by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the providing unit is performed using the generating AI. For example, the providing unit can provide feedback using a generating AI model that inputs the geographical location information of the user and provides appropriate feedback.

[0107] The providing unit can analyze the user's social media activities and suggest the content of the feedback when providing the feedback. The providing unit uses the generating AI to analyze the user's social media activities and suggest the content of the feedback when providing the feedback. For example, based on the exercise data and results shared by the user on social media, the providing unit can provide relevant feedback. In addition, based on the user's social media activities, feedback related to the exercises and activities in which the user is interested can be provided. Furthermore, based on the exercise data of the user's friends and followers on social media, relevant feedback can be provided. In this way, by analyzing social media activities, feedback based on the user's interests and concerns can be provided. Part or all of the above-mentioned processing in the providing unit is performed using the generating AI. For example, the providing unit can provide feedback using a generating AI model that inputs the user's social media activity data and suggests the content of the feedback.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When inputting the user's fitness goal, the reception unit can improve the accuracy of goal setting by referring to the user's past exercise history. For example, the reception unit can analyze the goals that the user has achieved or failed to achieve in the past and suggest realistic goals. In addition, the reception unit can extract specific exercise patterns and preferences from the user's past exercise history and customize goals based on the extracted patterns and preferences. Furthermore, the reception unit can suggest specific steps for achieving the goal based on the user's past exercise history. This makes it possible to set optimal goals for the user by utilizing the past exercise history.

[0110] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is concentrating during exercise, data can be collected in real time and analyzed immediately. This allows more accurate data to be collected by adjusting the frequency of data collection according to the user's emotions.

[0111] The generator can adjust the difficulty of the exercise plan based on the user's fitness goal and body metrics. For example, if the user is a beginner, it can propose an easy exercise plan and gradually increase the difficulty. Also, if the user is an advanced user, it can propose a challenging exercise plan. Furthermore, it can customize the difficulty of the exercise plan according to the user's fitness goal. This makes it possible to provide an optimal exercise plan according to the user's fitness level.

[0112] The analysis unit can estimate the user's emotions and adjust the analysis criteria of the performance data based on the estimated emotions. For example, when the user is relaxed, detailed performance data can be analyzed and feedback can be provided. When the user is stressed, only basic performance data can be analyzed and brief feedback can be provided. Furthermore, when the user is concentrating, performance data can be analyzed in real time and feedback can be provided instantly. In this way, more appropriate feedback can be provided by adjusting the analysis criteria according to the user's emotions.

[0113] The providing unit can customize the content of the feedback based on the user's current exercise status when providing the feedback. For example, appropriate feedback is provided depending on the type of exercise the user is currently performing. The content of the feedback can also be adjusted based on the user's current exercise intensity. Furthermore, the user's current exercise status can be analyzed in real time and feedback can be provided immediately. This allows for more effective feedback to be provided by customizing the feedback based on the current exercise status.

[0114] The reception unit can estimate the user's emotion and adjust the input method of the fitness goal based on the estimated emotion. For example, when the user is stressed, a simple interface can be provided to minimize the input procedure. When the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to allow the user to input the fitness goal quickly. In this way, by adjusting the input method according to the user's emotion, a more appropriate fitness goal can be set.

[0115] The reception unit can analyze the user's past fitness goal history and suggest an appropriate goal setting method. For example, fitness goals set by the user in the past can be automatically displayed as candidates. Also, it can preferentially suggest goal setting methods used by the user in the past (specific numerical goals, time period goals, etc.). Furthermore, it can predict and suggest specific goal setting methods based on the user's past goal achievement history. In this way, it is possible to support the user in achieving their goals by suggesting the optimal goal setting method based on the past history.

[0116] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, when the user is relaxed, data collection is performed frequently to collect detailed data. Also, when the user is stressed, the frequency of data collection can be reduced to reduce the burden on the user. Furthermore, when the user is concentrating during exercise, data can be collected in real time and analyzed immediately. This allows more accurate data to be collected by adjusting the timing of data collection according to the user's emotions.

[0117] The generation unit can estimate the user's emotion and adjust the expression method of the exercise plan based on the estimated emotion. For example, when the user is relaxed, an exercise plan that proceeds at a leisurely pace can be generated. When the user is in a hurry, an effective exercise plan can be generated in a short time. Furthermore, when the user is excited, an exercise plan that includes a visually stimulating effect can be generated. In this way, a more effective exercise plan can be provided by adjusting the expression method of the exercise plan according to the user's emotion.

[0118] When providing feedback, the providing unit can select an appropriate feedback providing method by referring to the user's past feedback history. For example, the providing unit can suggest an optimal feedback providing method based on feedback the user has received in the past. Also, the providing unit can predict and suggest a specific feedback providing method from the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and suggest a method for improving the accuracy of feedback. In this way, more appropriate feedback can be provided by referring to the past feedback history.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The reception unit inputs a fitness goal of the user. The fitness goal of the user may include, but is not limited to, weight loss, muscle building, and endurance improvement. Step 2: The collection unit collects data from the body composition scale and the wearable device. The collected data includes, but is not limited to, body metrics such as weight, muscle mass, and body fat percentage. Step 3: The generation unit uses the generation AI to create a personalized exercise plan based on the data collected by the collection unit and the fitness goal accepted by the acceptance unit. The generation AI generates an optimal exercise plan based on, for example, the user's fitness goal and body metrics. Step 4: The analysis unit analyzes the performance data during exercise in real time, including, but not limited to, heart rate, exercise intensity, and duration. Step 5: The providing unit provides feedback based on the results obtained by the analyzing unit. The feedback is provided, for example, through a voice service.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and inputs the user's fitness goal. The collection unit collects data from the body composition scale and the wearable device using the camera 42 and the communication I / F 44 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a personalized exercise plan using a generation AI. The analysis unit is realized by the control unit 46A of the smart device 14, and analyzes performance data during exercise in real time. The provision unit provides feedback using the output device 40 of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0132] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and inputs the user's fitness goal. The collection unit collects data from a body composition scale and a wearable device using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a personalized exercise plan using a generation AI. The analysis unit is realized by the control unit 46A of the smart glasses 214, and analyzes performance data during exercise in real time. The provision unit provides feedback using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0144] 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.

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

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

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

[0148] 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and inputs the user's fitness goal. The collection unit collects data from the body composition scale and the wearable device using the camera 42 and the communication I / F 44 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a personalized exercise plan using a generation AI. The analysis unit is realized by the control unit 46A of the headset type terminal 314, and analyzes performance data during exercise in real time. The provision unit provides feedback using the speaker 240 of the headset type terminal 314. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

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

[0160] 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.

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

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

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

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] Each of the multiple elements including the above-mentioned reception unit, collection unit, generation unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and inputs the user's fitness goal. The collection unit collects data from the body composition scale and the wearable device using the camera 42 and the communication I / F 44 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a personalized exercise plan using a generation AI. The analysis unit is realized by the control unit 46A of the robot 414, and analyzes performance data during exercise in real time. The provision unit provides feedback using the speaker 240 of the robot 414. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) a reception unit for inputting a user's fitness goal; A collection unit that collects data from the body composition meter and the wearable device; A generator that generates a personalized exercise plan based on the data collected by the collector and the fitness goal accepted by the acceptor; An analysis section that instantly analyzes performance data during exercise; A providing unit that provides feedback based on the results obtained by the analysis unit. A system characterized by: (Appendix 2) The collecting unit includes: Collecting specific body metrics from body composition scales and wearable devices 2. The system of claim 1, (Appendix 3) The generation unit is Create a personalized exercise plan based on the user's fitness goals and body metrics 2. The system of claim 1, (Appendix 4) The analysis unit includes: Analyzing performance data from wearable devices in real time 2. The system of claim 1, (Appendix 5) The providing unit is Provide feedback based on the analysis results through a voice output device 2. The system of claim 1, (Appendix 6) The reception unit is Inferring a user's emotions and adjusting a fitness goal input method based on the inferred emotions 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past fitness goal history and suggest appropriate goal setting methods 2. The system of claim 1, (Appendix 8) The reception unit is Filtering fitness goals based on the user's current health and lifestyle habits 2. The system of claim 1, (Appendix 9) The reception unit is Estimating a user's emotions and prioritizing fitness goals to be input based on the estimated emotions 2. The system of claim 1, (Appendix 10) The reception unit is When entering a fitness goal, it will prioritize and suggest relevant goals based on the user's geographic location 2. The system of claim 1, (Appendix 11) The reception unit is Analyzes your social media activity to suggest related goals as you enter your fitness goals 2. The system of claim 1, (Appendix 12) The collecting unit includes: Infer user emotions and adjust the timing of data collection based on the inferred emotions 2. The system of claim 1, (Appendix 13) The collecting unit includes: Analyze the user's past data collection history and select the appropriate collection method 2. The system of claim 1, (Appendix 14) The collecting unit includes: Filter data collection based on the user's current life situation and interests 2. The system of claim 1, (Appendix 15) The collecting unit includes: Infer user emotions and prioritize data collection based on the inferred emotions 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 17) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the user's emotions and adjust the way the motion plan is expressed based on the estimated emotions. 2. The system of claim 1, (Appendix 19) The generation unit is When generating an exercise plan, adjust the specificity of the plan based on the importance of the user's fitness goals 2. The system of claim 1, (Appendix 20) The generation unit is When generating exercise plans, different generation algorithms are applied depending on the user's body metric category. 2. The system of claim 1, (Appendix 21) The generation unit is Estimating a user's emotion and adjusting the length of the motion plan based on the estimated emotion 2. The system of claim 1, (Appendix 22) The generation unit is When generating exercise plans, the order of plans is determined based on when the user's fitness goals are submitted. 2. The system of claim 1, (Appendix 23) The generation unit is When generating a motion plan, adjust the order of the plan based on the relevance of the user's body metrics 2. The system of claim 1, (Appendix 24) The analysis unit includes: Inferring user sentiment and adjusting performance data analysis criteria based on the inferred sentiment 2. The system of claim 1, (Appendix 25) The analysis unit includes: When analyzing performance data, improve the accuracy of the analysis based on the user's exercise history 2. The system of claim 1, (Appendix 26) The analysis unit includes: When analyzing performance data, consider user attribute information. 2. The system of claim 1, (Appendix 27) The analysis unit includes: Infer user sentiment and adjust the display order of analysis results based on the inferred sentiment 2. The system of claim 1, (Appendix 28) The analysis unit includes: When analyzing performance data, analyze it based on the geographic distribution of users. 2. The system of claim 1, (Appendix 29) The analysis unit includes: When analyzing performance data, refer to related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 30) The providing unit is Infer a user's emotions and tailor how you provide feedback based on the inferred emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing feedback, select the appropriate method by referring to the user's past feedback history 2. The system of claim 1, (Appendix 32) The providing unit is When providing feedback, customize the feedback content based on the user's current fitness status 2. The system of claim 1, (Appendix 33) The providing unit is Infer user emotions and prioritize feedback based on the inferred emotions 2. The system of claim 1, (Appendix 34) The providing unit is When providing feedback, provide appropriate feedback based on the user's geographic location 2. The system of claim 1, (Appendix 35) The providing unit is When providing feedback, we analyze users' social media activity to suggest feedback content 2. The system of claim 1, [Explanation of symbols]

[0193] 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 reception unit for inputting a user's fitness goal; A collection unit that collects data from the body composition meter and the wearable device; A generator that generates a personalized exercise plan based on the data collected by the collector and the fitness goal accepted by the acceptor; An analysis section that instantly analyzes performance data during exercise; A providing unit that provides feedback based on the results obtained by the analysis unit. A system characterized by:

2. The collecting unit includes: Collecting specific body metrics from body composition scales and wearable devices 2. The system of claim 1 .

3. The generation unit is Create a personalized exercise plan based on the user's fitness goals and body metrics 2. The system of claim 1 .

4. The analysis unit includes: Analyzing performance data from wearable devices in real time 2. The system of claim 1 .

5. The providing unit is Provide feedback based on the analysis results through a voice output device 2. The system of claim 1 .

6. The reception unit is Inferring a user's emotions and adjusting a fitness goal input method based on the inferred emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past fitness goal history and suggest appropriate goal setting methods 2. The system of claim 1 .

8. The reception unit is Filtering fitness goals based on the user's current health and lifestyle habits 2. The system of claim 1 .

9. The reception unit is Estimating a user's emotions and prioritizing fitness goals to be input based on the estimated emotions 2. The system of claim 1 .

10. The reception unit is When entering a fitness goal, it will prioritize and suggest relevant goals based on the user's geographic location 2. The system of claim 1 .

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