system

Abstract

The system according to this embodiment aims to provide personalized diet and training advice based on the user's individual data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a recommendation unit. The data collection unit collects data such as the user's height, weight, and body fat. The analysis unit analyzes the data collected by the data collection unit and provides advice on diet and training to balance the PFC ratio. The data provision unit provides advice based on the analysis results obtained by the analysis unit. The recommendation unit recommends calorie management and exercise menus based on the advice provided by the data provision unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide personalized diet and training advice based on individual data of users.

[0005] The system according to the embodiment aims to provide personalized diet and training advice based on individual data of users.

Means for Solving the Problems

[0007] The system according to this embodiment can provide personalized diet and training advice based on the user's individual data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​personal trainer system according to an embodiment of the present invention is a system that provides advice on diet and training to balance the PFC ratio through dialogue, based on the user's data such as height, weight, and body fat, and their goals. In this system, the user manages calories by uploading or searching for photos, and the AI ​​recommends exercise menus. Furthermore, it adopts a freemium model, generating revenue from advertising for free users and also offering a monthly paid version. Additional charges provide features such as advice from famous AI personal trainers and collaboration with food manufacturers and restaurants. This comprehensively supports the user's diet. For example, a data collection unit is required to collect user data. The data collection unit collects data such as the user's height, weight, and body fat. Next, an analysis unit is required to analyze the collected data. The analysis unit analyzes the collected data and provides advice on diet and training to balance the PFC ratio. Furthermore, a provision unit is required to provide advice based on the analysis results. The provision unit provides advice based on the analysis results. A recommendation unit is also required to perform calorie management and recommend exercise menus. The recommendation unit recommends calorie management and exercise menus based on the advice provided by the service unit. These elements are interconnected. The flow is clear: the data collection unit collects data, the analysis unit analyzes it, and the service unit provides advice. The recommendation unit is related to the service unit. This allows the AI ​​personal trainer system to comprehensively support the user's diet.

[0029] The AI ​​personal trainer system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a recommendation unit. The data collection unit collects data such as the user's height, weight, and body fat. For example, the data collection unit collects data entered by the user. The data collection unit can also collect data when the user uploads a photo. For example, the data collection unit estimates the user's height and weight from a photo uploaded by the user with a smartphone. Furthermore, the data collection unit can also collect data searched by the user. For example, the data collection unit collects information about diet and exercise searched by the user and stores it as user data. The analysis unit analyzes the data collected by the data collection unit and provides advice on diet and training to improve the PFC balance. For example, the analysis unit calculates the user's PFC balance based on the collected data. Furthermore, the analysis unit can also provide optimal diet and training advice based on the user's goals. For example, the analysis unit suggests meal menus and training content based on the user's target weight and body fat percentage. Furthermore, the analysis unit can also analyze the user's data and evaluate their health status. For example, the analysis unit analyzes changes in the user's weight and body fat percentage and evaluates the improvement in their health. The service unit provides advice based on the analysis results obtained by the analysis unit. The service unit notifies the user of the analysis results, for example. The service unit can also provide specific advice to the user. For example, the service unit suggests meal menus and training programs to the user. Furthermore, the service unit can customize the advice according to the user's goals. For example, the service unit adjusts the advice based on the user's target weight and body fat percentage. The recommendation unit recommends calorie management and exercise programs based on the advice provided by the service unit. The recommendation unit, for example, calculates the calories in the user's meals and supports calorie management. The recommendation unit can also suggest exercise programs for the user. For example, the recommendation unit suggests an optimal exercise program based on the user's goals. Furthermore, the recommendation unit can also recommend calorie management and exercise programs based on the user's data.For example, the recommendation section analyzes the user's eating and exercise history and proposes optimal calorie management and exercise menus. This allows the AI ​​personal trainer system according to this embodiment to comprehensively support the user's weight loss efforts.

[0030] The data collection unit collects data such as the user's height, weight, and body fat percentage. For example, the unit collects data entered by the user. Specifically, users can access a dedicated application or website and manually enter information such as their height, weight, and body fat percentage. The unit can also collect data when users upload photos. For example, the unit estimates the user's height and weight from photos uploaded by the user with their smartphone. This involves an algorithm that uses image analysis technology to analyze the user's body shape and posture from the photo and estimate their height and weight. Furthermore, the unit can collect data searched by the user. For example, it collects information about diet and exercise searched by the user and stores it as user data. This allows the unit to understand the user's interests and provide more personalized advice. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit and provides diet and training advice to improve the PFC balance. For example, the analysis unit calculates the user's PFC balance based on the collected data. PFC balance refers to the balance of protein, fat, and carbohydrates, and is an important element for maintaining a healthy diet. The analysis unit calculates the optimal PFC balance for each individual user based on data such as the user's height, weight, body fat percentage, age, and gender. The analysis unit can also provide optimal diet and training advice based on the user's goals. For example, the analysis unit suggests meal menus and training content based on the user's target weight and body fat percentage. Furthermore, the analysis unit can analyze the user's data and evaluate their health status. For example, the analysis unit analyzes changes in the user's weight and body fat percentage and evaluates the improvement in their health status. This allows the analysis unit to understand the user's health status in real time and provide appropriate advice. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past data, it can predict the impact of specific diets or training on users and plan future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The service provider provides advice based on the analysis results obtained by the analysis unit. For example, the service provider notifies users of the analysis results. Specifically, it can notify users of the analysis results in real time through a dedicated application or website. The service provider can also provide specific advice to users. For example, it can suggest meal plans and training programs. This includes specific meal plans to improve the user's PFC balance and training plans based on target weight and body fat percentage. Furthermore, the service provider can customize the advice according to the user's goals. For example, it adjusts the advice based on the user's target weight and body fat percentage. This allows the service provider to provide personalized advice tailored to the user's individual needs. Additionally, the service provider can collect user feedback to continuously improve the accuracy and effectiveness of the advice. For example, it can receive feedback on the results of users following the provided advice regarding diet and training, and revise the advice based on that data. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with prompt and reliable advice and support their health management.

[0033] The recommendation department recommends calorie management and exercise menus based on advice provided by the service provider. For example, the recommendation department calculates the calories in a user's meals and supports calorie management. Specifically, it automatically calculates the calories in meals based on the meal details entered by the user and photos uploaded, and notifies the user. The recommendation department can also suggest exercise menus for users. For example, it suggests an optimal exercise menu based on the user's goals. This includes training plans tailored to the user's fitness level and objectives. Furthermore, the recommendation department can also recommend calorie management and exercise menus based on user data. For example, it analyzes the user's meal and exercise history to suggest optimal calorie management and exercise menus. This allows the recommendation department to comprehensively support the user's diet. Additionally, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, the system can provide feedback on the results of users performing suggested exercise routines and revise its recommendations based on that data. Furthermore, the recommendation system can provide personalized recommendations tailored to the user's lifestyle and preferences. This allows the recommendation system to offer users optimal calorie management and exercise routines, supporting their health management.

[0034] The service provider can offer an AI-powered personal trainer advice feature for an additional fee. For example, users can receive advice from a renowned personal trainer by paying an additional fee. The service provider can also provide advice from a renowned personal trainer using AI. For example, the service provider can have the AI ​​learn advice from a renowned personal trainer and then provide that advice to the user. Furthermore, the service provider can customize the advice from a renowned personal trainer based on the user's data. For example, the service provider can adjust the advice from a renowned personal trainer based on the user's goals and health condition. This allows the service provider to improve user satisfaction by offering advice from a renowned personal trainer for an additional fee.

[0035] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history and select the most effective collection method. The data collection unit can also adjust the frequency of data collection according to the user's health status. Furthermore, the data collection unit can optimize the timing of data collection based on the user's lifestyle patterns. This allows the optimal data collection method to be selected by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current lifestyle and health status during data collection. For example, if the user is busy, the collection unit will collect only important data. Furthermore, if the user is in poor health, the collection unit can limit the types of data collected. The collection unit can also adjust the scope of data collection according to the user's lifestyle. This allows for efficient collection of important data by filtering it according to the user's lifestyle and health status. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input user lifestyle data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data. Similarly, if the user is at a restaurant, the data collection unit can prioritize the collection of meal data. Furthermore, if the user is at home, the data collection unit can collect overall health data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit can collect exercise data. It can also collect diet data if the user posts about food. Furthermore, if the user posts about health, the data collection unit can collect overall health data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data directly related to the user's goals. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. It can also apply an exercise analysis algorithm to exercise data. Furthermore, it can apply a weight fluctuation analysis algorithm to weight data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform analysis while referring to past data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically change the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, the service provider can refer to the user's past dietary data to advise on areas for improvement in diet. It can also refer to the user's past exercise data to advise on areas for improvement in exercise. Furthermore, the service provider can refer to the user's past weight data to provide advice on weight management. In this way, optimal advice can be provided by referring to the user's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0044] The service provider can customize the content of advice based on the user's current living situation when providing advice. For example, if the user is busy, the service provider can provide easy-to-implement advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user has a specific goal, the service provider can provide advice tailored to that goal. By customizing the content of advice based on the user's current living situation, the service provider can provide advice that is easy for the user to implement. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI customize the content of the advice.

[0045] The service provider can provide optimal advice by considering the user's geographical location. For example, if the user is at a gym, the service provider can provide exercise advice that can be done at the gym. It can also provide advice on healthy food choices if the user is at a restaurant. Furthermore, if the user is at home, the service provider can provide exercise advice that can be done at home. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide optimal advice.

[0046] The service provider can analyze the user's social media activity and adjust the content of the advice when providing it. For example, if the user posts about exercise on social media, the service provider can provide exercise advice. It can also provide dietary advice if the user posts about food. Furthermore, if the user posts about health, the service provider can provide general health advice. This allows the service provider to provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0047] The recommendation unit can provide the optimal exercise menu by referring to the user's past exercise history when making recommendations. For example, the recommendation unit can refer to the user's past exercise history to provide the optimal exercise menu. The recommendation unit can also suggest effective exercise menus based on the user's past exercise history. Furthermore, the recommendation unit can analyze the user's past exercise history and provide menus that increase the variety of exercises. In this way, the recommendation unit can provide the optimal exercise menu by referring to the user's past exercise history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past exercise history data into a generating AI and have the generating AI perform the task of providing the optimal exercise menu.

[0048] The recommendation unit can customize exercise menus based on the user's current health status when making recommendations. For example, if the user is tired, the recommendation unit can provide a light exercise menu. It can also provide a regular exercise menu if the user is healthy. Furthermore, if the user is unwell, the recommendation unit can provide an exercise menu that includes rest. This allows the recommendation unit to provide the optimal exercise menu for the user by customizing it based on their current health status. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's health status data into a generating AI and have the generating AI perform the exercise menu customization.

[0049] The recommendation unit can provide the most suitable exercise menu by considering the user's geographical location information during the recommendation process. For example, if the user is at a gym, the recommendation unit can provide exercise menus that can be done at the gym. It can also provide exercise menus that can be done in a park if the user is in a park. Furthermore, if the user is at home, the recommendation unit can provide exercise menus that can be done at home. This allows the recommendation unit to provide the most suitable exercise menu by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the most suitable exercise menu.

[0050] The recommendation unit can analyze the user's social media activity and suggest exercise routines when making recommendations. For example, if the user has posted about exercise on social media, the recommendation unit will suggest an exercise routine. Furthermore, if the user has posted about food, the recommendation unit can suggest an exercise routine linked to their diet. Additionally, if the user has posted about health, the recommendation unit can suggest an overall health routine. This allows the recommendation unit to suggest relevant exercise routines by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI suggest exercise routines.

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

[0052] The data collection unit can analyze the user's past exercise data and select the optimal data collection method. For example, the data collection unit can analyze the user's past exercise data and select the most effective collection method. The data collection unit can also adjust the frequency of data collection based on the user's exercise patterns. Furthermore, the data collection unit can optimize the timing of data collection based on the user's exercise history. This allows the optimal data collection method to be selected by analyzing past exercise data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past exercise data into a generating AI and have the generating AI select the optimal data collection method.

[0053] The service provider can analyze the user's past meal data and provide optimal dietary advice. For example, the service provider can analyze the user's past meal data and advise on areas for improvement in their diet. Furthermore, the service provider can adjust the content of meals based on the user's eating patterns. In addition, the service provider can optimize meal timing based on the user's meal history. This allows the service provider to provide optimal dietary advice by analyzing past meal data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past meal data into a generating AI and have the generating AI provide optimal dietary advice.

[0054] The recommendation unit can analyze the user's past exercise data and provide an optimal exercise menu. For example, the recommendation unit can analyze the user's past exercise data and suggest an effective exercise menu. Furthermore, the recommendation unit can adjust the content of the exercise menu based on the user's exercise patterns. In addition, the recommendation unit can optimize the timing of the exercise menu based on the user's exercise history. This allows the recommendation unit to provide an optimal exercise menu by analyzing past exercise data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past exercise data into a generating AI and have the generating AI provide an optimal exercise menu.

[0055] The data collection unit can adjust the timing of data collection, taking into account the user's geographical location. For example, if the user is at a gym, the data collection unit can prioritize collecting exercise data. It can also prioritize collecting meal data if the user is at a restaurant. Furthermore, if the user is at home, the data collection unit can collect overall health data. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0056] The service provider can analyze a user's social media activity and provide optimal advice. For example, if a user posts about exercise on social media, the service provider can provide exercise advice. It can also provide dietary advice if a user posts about food. Furthermore, if a user posts about health, the service provider can provide overall health advice. This allows the service provider to provide relevant advice by analyzing a user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects data such as the user's height, weight, and body fat. The data collection unit can collect data entered by the user or uploaded photos. For example, the user can upload a photo taken with their smartphone, and the unit will estimate their height and weight from that photo. It can also collect information about diet and exercise that the user has searched for. Step 2: The analysis unit analyzes the data collected by the data collection unit and provides diet and training advice to improve the PFC balance. Based on the collected data, the analysis unit calculates the user's PFC balance and provides optimal diet and training advice based on the user's goals. It can also assess the user's health status. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider notifies the user of the analysis results and provides specific advice. For example, it suggests meal menus and training content, and customizes the advice according to the user's goals. Step 4: The recommendation unit recommends calorie management and exercise menus based on the advice provided by the service provider. The recommendation unit calculates the calories in the user's meals and supports calorie management. It also proposes an optimal exercise menu based on the user's goals and recommends calorie management and exercise menus based on the user's data.

[0059] (Example of form 2) The AI ​​personal trainer system according to an embodiment of the present invention is a system that provides advice on diet and training to balance the PFC ratio through dialogue, based on the user's data such as height, weight, and body fat, and their goals. In this system, the user manages calories by uploading or searching for photos, and the AI ​​recommends exercise menus. Furthermore, it adopts a freemium model, generating revenue from advertising for free users and also offering a monthly paid version. Additional charges provide features such as advice from famous AI personal trainers and collaboration with food manufacturers and restaurants. This comprehensively supports the user's diet. For example, a data collection unit is required to collect user data. The data collection unit collects data such as the user's height, weight, and body fat. Next, an analysis unit is required to analyze the collected data. The analysis unit analyzes the collected data and provides advice on diet and training to balance the PFC ratio. Furthermore, a provision unit is required to provide advice based on the analysis results. The provision unit provides advice based on the analysis results. A recommendation unit is also required to perform calorie management and recommend exercise menus. The recommendation unit recommends calorie management and exercise menus based on the advice provided by the service unit. These elements are interconnected. The flow is clear: the data collection unit collects data, the analysis unit analyzes it, and the service unit provides advice. The recommendation unit is related to the service unit. This allows the AI ​​personal trainer system to comprehensively support the user's diet.

[0060] The AI ​​personal trainer system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and a recommendation unit. The data collection unit collects data such as the user's height, weight, and body fat. For example, the data collection unit collects data entered by the user. The data collection unit can also collect data when the user uploads a photo. For example, the data collection unit estimates the user's height and weight from a photo uploaded by the user with a smartphone. Furthermore, the data collection unit can also collect data searched by the user. For example, the data collection unit collects information about diet and exercise searched by the user and stores it as user data. The analysis unit analyzes the data collected by the data collection unit and provides advice on diet and training to improve the PFC balance. For example, the analysis unit calculates the user's PFC balance based on the collected data. Furthermore, the analysis unit can also provide optimal diet and training advice based on the user's goals. For example, the analysis unit suggests meal menus and training content based on the user's target weight and body fat percentage. Furthermore, the analysis unit can also analyze the user's data and evaluate their health status. For example, the analysis unit analyzes changes in the user's weight and body fat percentage and evaluates the improvement in their health. The service unit provides advice based on the analysis results obtained by the analysis unit. The service unit notifies the user of the analysis results, for example. The service unit can also provide specific advice to the user. For example, the service unit suggests meal menus and training programs to the user. Furthermore, the service unit can customize the advice according to the user's goals. For example, the service unit adjusts the advice based on the user's target weight and body fat percentage. The recommendation unit recommends calorie management and exercise programs based on the advice provided by the service unit. The recommendation unit, for example, calculates the calories in the user's meals and supports calorie management. The recommendation unit can also suggest exercise programs for the user. For example, the recommendation unit suggests an optimal exercise program based on the user's goals. Furthermore, the recommendation unit can also recommend calorie management and exercise programs based on the user's data.For example, the recommendation section analyzes the user's eating and exercise history and proposes optimal calorie management and exercise menus. This allows the AI ​​personal trainer system according to this embodiment to comprehensively support the user's weight loss efforts.

[0061] The data collection unit collects data such as the user's height, weight, and body fat percentage. For example, the unit collects data entered by the user. Specifically, users can access a dedicated application or website and manually enter information such as their height, weight, and body fat percentage. The unit can also collect data when users upload photos. For example, the unit estimates the user's height and weight from photos uploaded by the user with their smartphone. This involves an algorithm that uses image analysis technology to analyze the user's body shape and posture from the photo and estimate their height and weight. Furthermore, the unit can collect data searched by the user. For example, it collects information about diet and exercise searched by the user and stores it as user data. This allows the unit to understand the user's interests and provide more personalized advice. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provision departments. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the data collected by the data collection unit and provides diet and training advice to improve the PFC balance. For example, the analysis unit calculates the user's PFC balance based on the collected data. PFC balance refers to the balance of protein, fat, and carbohydrates, and is an important element for maintaining a healthy diet. The analysis unit calculates the optimal PFC balance for each individual user based on data such as the user's height, weight, body fat percentage, age, and gender. The analysis unit can also provide optimal diet and training advice based on the user's goals. For example, the analysis unit suggests meal menus and training content based on the user's target weight and body fat percentage. Furthermore, the analysis unit can analyze the user's data and evaluate their health status. For example, the analysis unit analyzes changes in the user's weight and body fat percentage and evaluates the improvement in their health status. This allows the analysis unit to understand the user's health status in real time and provide appropriate advice. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past data, it can predict the impact of specific diets or training on users and plan future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0063] The service provider provides advice based on the analysis results obtained by the analysis unit. For example, the service provider notifies users of the analysis results. Specifically, it can notify users of the analysis results in real time through a dedicated application or website. The service provider can also provide specific advice to users. For example, it can suggest meal plans and training programs. This includes specific meal plans to improve the user's PFC balance and training plans based on target weight and body fat percentage. Furthermore, the service provider can customize the advice according to the user's goals. For example, it adjusts the advice based on the user's target weight and body fat percentage. This allows the service provider to provide personalized advice tailored to the user's individual needs. Additionally, the service provider can collect user feedback to continuously improve the accuracy and effectiveness of the advice. For example, it can receive feedback on the results of users following the provided advice regarding diet and training, and revise the advice based on that data. The service provider can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to provide users with prompt and reliable advice and support their health management.

[0064] The recommendation department recommends calorie management and exercise menus based on advice provided by the service provider. For example, the recommendation department calculates the calories in a user's meals and supports calorie management. Specifically, it automatically calculates the calories in meals based on the meal details entered by the user and photos uploaded, and notifies the user. The recommendation department can also suggest exercise menus for users. For example, it suggests an optimal exercise menu based on the user's goals. This includes training plans tailored to the user's fitness level and objectives. Furthermore, the recommendation department can also recommend calorie management and exercise menus based on user data. For example, it analyzes the user's meal and exercise history to suggest optimal calorie management and exercise menus. This allows the recommendation department to comprehensively support the user's diet. Additionally, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, the system can provide feedback on the results of users performing suggested exercise routines and revise its recommendations based on that data. Furthermore, the recommendation system can provide personalized recommendations tailored to the user's lifestyle and preferences. This allows the recommendation system to offer users optimal calorie management and exercise routines, supporting their health management.

[0065] The service provider can offer an AI-powered personal trainer advice feature for an additional fee. For example, users can receive advice from a renowned personal trainer by paying an additional fee. The service provider can also provide advice from a renowned personal trainer using AI. For example, the service provider can have the AI ​​learn advice from a renowned personal trainer and then provide that advice to the user. Furthermore, the service provider can customize the advice from a renowned personal trainer based on the user's data. For example, the service provider can adjust the advice from a renowned personal trainer based on the user's goals and health condition. This allows the service provider to improve user satisfaction by offering advice from a renowned personal trainer for an additional fee.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data when the user is relaxed. The data collection unit can also proactively collect data when the user is highly motivated. Furthermore, if the user is tired, the data collection unit can collect data after the user has rested. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past data collection history and select the most effective collection method. The data collection unit can also adjust the frequency of data collection according to the user's health status. Furthermore, the data collection unit can optimize the timing of data collection based on the user's lifestyle patterns. This allows the optimal data collection method to be selected by analyzing past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0068] The data collection unit can filter data based on the user's current lifestyle and health status during data collection. For example, if the user is busy, the collection unit will collect only important data. Furthermore, if the user is in poor health, the collection unit can limit the types of data collected. The collection unit can also adjust the scope of data collection according to the user's lifestyle. This allows for efficient collection of important data by filtering it according to the user's lifestyle and health status. Some or all of the above processing in the collection unit may be performed using AI, or without AI. For example, the collection unit can input user lifestyle data into a generating AI and have the generating AI perform data filtering.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. It can also collect overall health data if the user is relaxed. Furthermore, if the user is highly motivated, the data collection unit can prioritize collecting exercise data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data. Similarly, if the user is at a restaurant, the data collection unit can prioritize the collection of meal data. Furthermore, if the user is at home, the data collection unit can collect overall health data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0071] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit can collect exercise data. It can also collect diet data if the user posts about food. Furthermore, if the user posts about health, the data collection unit can collect overall health data. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is highly motivated, the analysis unit can provide an analysis result that includes a concrete action plan. In this way, by adjusting the presentation of the analysis according to the user's emotions, an easy-to-understand analysis result can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. It can also perform a simplified analysis on general data. Furthermore, the analysis unit can perform a particularly detailed analysis on data directly related to the user's goals. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a nutrition analysis algorithm to dietary data. It can also apply an exercise analysis algorithm to exercise data. Furthermore, it can apply a weight fluctuation analysis algorithm to weight data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform analysis while referring to past data. Furthermore, the analysis unit can focus on analyzing data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically change the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0078] The service provider can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is stressed, the service provider will offer advice in gentle language. If the user is relaxed, the service provider can also offer detailed advice. Furthermore, if the user is highly motivated, the service provider can offer advice that includes a concrete action plan. By adjusting the way advice is expressed according to the user's emotions, the service provider can provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0079] The service provider can provide optimal advice by referring to the user's past health data when providing advice. For example, the service provider can refer to the user's past dietary data to advise on areas for improvement in diet. It can also refer to the user's past exercise data to advise on areas for improvement in exercise. Furthermore, the service provider can refer to the user's past weight data to provide advice on weight management. In this way, optimal advice can be provided by referring to the user's past health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past health data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0080] The service provider can customize the content of advice based on the user's current living situation when providing advice. For example, if the user is busy, the service provider can provide easy-to-implement advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user has a specific goal, the service provider can provide advice tailored to that goal. By customizing the content of advice based on the user's current living situation, the service provider can provide advice that is easy for the user to implement. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into a generating AI and have the generating AI customize the content of the advice.

[0081] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is feeling stressed, the service provider will prioritize stress reduction advice. It can also provide general health advice if the user is relaxed. Furthermore, if the user is highly motivated, the service provider can prioritize exercise advice. This allows for the prioritization of important advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of advice.

[0082] The service provider can provide optimal advice by considering the user's geographical location. For example, if the user is at a gym, the service provider can provide exercise advice that can be done at the gym. It can also provide advice on healthy food choices if the user is at a restaurant. Furthermore, if the user is at home, the service provider can provide exercise advice that can be done at home. This allows the service provider to provide optimal advice by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into a generating AI and have the generating AI provide optimal advice.

[0083] The service provider can analyze the user's social media activity and adjust the content of the advice when providing it. For example, if the user posts about exercise on social media, the service provider can provide exercise advice. It can also provide dietary advice if the user posts about food. Furthermore, if the user posts about health, the service provider can provide general health advice. This allows the service provider to provide relevant advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0084] The recommendation unit can estimate the user's emotions and adjust its recommendation method based on the estimated emotions. For example, if the user is feeling stressed, the recommendation unit may recommend a relaxing exercise routine. If the user is relaxed, the recommendation unit may also recommend a general health routine. Furthermore, if the user is highly motivated, the recommendation unit may recommend a challenging exercise routine. By adjusting the recommendation method according to the user's emotions, the system can provide the most suitable recommendations for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI adjust the recommendation method.

[0085] The recommendation unit can provide the optimal exercise menu by referring to the user's past exercise history when making recommendations. For example, the recommendation unit can refer to the user's past exercise history to provide the optimal exercise menu. The recommendation unit can also suggest effective exercise menus based on the user's past exercise history. Furthermore, the recommendation unit can analyze the user's past exercise history and provide menus that increase the variety of exercises. In this way, the recommendation unit can provide the optimal exercise menu by referring to the user's past exercise history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past exercise history data into a generating AI and have the generating AI perform the task of providing the optimal exercise menu.

[0086] The recommendation unit can customize exercise menus based on the user's current health status when making recommendations. For example, if the user is tired, the recommendation unit can provide a light exercise menu. It can also provide a regular exercise menu if the user is healthy. Furthermore, if the user is unwell, the recommendation unit can provide an exercise menu that includes rest. This allows the recommendation unit to provide the optimal exercise menu for the user by customizing it based on their current health status. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's health status data into a generating AI and have the generating AI perform the exercise menu customization.

[0087] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on those emotions. For example, if the user is feeling stressed, the recommendation unit may prioritize stress-reducing exercise programs. It may also provide a general health program if the user is relaxed. Furthermore, if the user is highly motivated, the recommendation unit may prioritize challenging exercise programs. This allows for the prioritization of important recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI determine the priority of recommendations.

[0088] The recommendation unit can provide the most suitable exercise menu by considering the user's geographical location information during the recommendation process. For example, if the user is at a gym, the recommendation unit can provide exercise menus that can be done at the gym. It can also provide exercise menus that can be done in a park if the user is in a park. Furthermore, if the user is at home, the recommendation unit can provide exercise menus that can be done at home. This allows the recommendation unit to provide the most suitable exercise menu by considering the user's geographical location information. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the most suitable exercise menu.

[0089] The recommendation unit can analyze the user's social media activity and suggest exercise routines when making recommendations. For example, if the user has posted about exercise on social media, the recommendation unit will suggest an exercise routine. Furthermore, if the user has posted about food, the recommendation unit can suggest an exercise routine linked to their diet. Additionally, if the user has posted about health, the recommendation unit can suggest an overall health routine. This allows the recommendation unit to suggest relevant exercise routines by analyzing the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI suggest exercise routines.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit will perform the analysis when the user is relaxed. The analysis unit can also proactively perform the analysis when the user is highly motivated. Furthermore, if the user is tired, the analysis unit can perform the analysis after the user has rested. By adjusting the timing of the analysis according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0092] The service provider can estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide advice at a time when the user is relaxed. The service provider can also proactively provide advice if the user is highly motivated. Furthermore, if the user is tired, the service provider can provide advice after the user has rested. This allows for more appropriate advice by adjusting the timing of advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform the user's emotion estimation.

[0093] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit will make recommendations at a time when the user is relaxed. The recommendation unit can also proactively make recommendations if the user is highly motivated. Furthermore, if the user is tired, the recommendation unit can make recommendations after the user has rested. This allows for more appropriate recommendations by adjusting the timing of recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation unit may be performed using AI, or not. For example, the recommendation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The data collection unit can estimate the user's emotions and determine the type of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting stress-related data. It may also collect overall health data if the user is relaxed. Furthermore, if the user is highly motivated, the data collection unit may prioritize collecting exercise data. This allows for the priority collection of important data by determining the type of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the type of data.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing stress-related data. It can also analyze overall health data if the user is relaxed. Furthermore, if the user is highly motivated, the analysis unit can prioritize analyzing exercise data. This allows for the prioritization of important data by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the analysis priority.

[0096] The data collection unit can analyze the user's past exercise data and select the optimal data collection method. For example, the data collection unit can analyze the user's past exercise data and select the most effective collection method. The data collection unit can also adjust the frequency of data collection based on the user's exercise patterns. Furthermore, the data collection unit can optimize the timing of data collection based on the user's exercise history. This allows the optimal data collection method to be selected by analyzing past exercise data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past exercise data into a generating AI and have the generating AI select the optimal data collection method.

[0097] The service provider can analyze the user's past meal data and provide optimal dietary advice. For example, the service provider can analyze the user's past meal data and advise on areas for improvement in their diet. Furthermore, the service provider can adjust the content of meals based on the user's eating patterns. In addition, the service provider can optimize meal timing based on the user's meal history. This allows the service provider to provide optimal dietary advice by analyzing past meal data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past meal data into a generating AI and have the generating AI provide optimal dietary advice.

[0098] The recommendation unit can analyze the user's past exercise data and provide an optimal exercise menu. For example, the recommendation unit can analyze the user's past exercise data and suggest an effective exercise menu. Furthermore, the recommendation unit can adjust the content of the exercise menu based on the user's exercise patterns. In addition, the recommendation unit can optimize the timing of the exercise menu based on the user's exercise history. This allows the recommendation unit to provide an optimal exercise menu by analyzing past exercise data. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's past exercise data into a generating AI and have the generating AI provide an optimal exercise menu.

[0099] The data collection unit can adjust the timing of data collection, taking into account the user's geographical location. For example, if the user is at a gym, the data collection unit can prioritize collecting exercise data. It can also prioritize collecting meal data if the user is at a restaurant. Furthermore, if the user is at home, the data collection unit can collect overall health data. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0100] The service provider can analyze a user's social media activity and provide optimal advice. For example, if a user posts about exercise on social media, the service provider can provide exercise advice. It can also provide dietary advice if a user posts about food. Furthermore, if a user posts about health, the service provider can provide overall health advice. This allows the service provider to provide relevant advice by analyzing a user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user social media activity data into a generating AI and have the generating AI adjust the content of the advice.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects data such as the user's height, weight, and body fat. The data collection unit can collect data entered by the user or uploaded photos. For example, the user can upload a photo taken with their smartphone, and the unit will estimate their height and weight from that photo. It can also collect information about diet and exercise that the user has searched for. Step 2: The analysis unit analyzes the data collected by the data collection unit and provides diet and training advice to improve the PFC balance. Based on the collected data, the analysis unit calculates the user's PFC balance and provides optimal diet and training advice based on the user's goals. It can also assess the user's health status. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. The service provider notifies the user of the analysis results and provides specific advice. For example, it suggests meal menus and training content, and customizes the advice according to the user's goals. Step 4: The recommendation unit recommends calorie management and exercise menus based on the advice provided by the service provider. The recommendation unit calculates the calories in the user's meals and supports calorie management. It also proposes an optimal exercise menu based on the user's goals and recommends calorie management and exercise menus based on the user's data.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as the user's height, weight, and body fat using the camera 42 and reception device 38 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to provide advice on diet and training to improve the PFC balance. The provision unit is implemented in the specific processing unit 46A of the smart device 14, and provides advice based on the analysis results. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends calorie management and exercise menus based on the advice provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as the user's height, weight, and body fat using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to provide advice on diet and training to improve the PFC balance. The provision unit is implemented in the specific processing unit 46A of the smart glasses 214, and provides advice based on the analysis results. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends calorie management and exercise menus based on the advice provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data such as the user's height, weight, and body fat using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to provide advice on diet and training to improve the PFC balance. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314, and provides advice based on the analysis results. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends calorie management and exercise menus based on the advice provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as the user's height, weight, and body fat using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to provide advice on diet and training to balance the PFC ratio. The provision unit is implemented in the control unit 46A of the robot 414, and provides advice based on the analysis results. The recommendation unit is implemented in the specific processing unit 290 of the data processing unit 12, and recommends calorie management and exercise menus based on the advice provided by the provision unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A data collection unit that collects data such as the user's height, weight, and body fat, The data collected by the aforementioned collection unit is analyzed by an analysis unit that provides advice on diet and training to improve the PFC balance, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a recommendation unit that recommends calorie management and exercise menus based on the advice provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, For an additional fee, we offer advice from a renowned AI personal trainer. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and health status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing advice, we refer to the user's past health data to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing advice, the content of the advice is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, the system provides the most suitable exercise program by referencing the user's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, the exercise program is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recommendation unit is, When making recommendations, the system provides the most suitable exercise menu, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity to suggest exercise routines. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as the user's height, weight, and body fat, The data collected by the aforementioned collection unit is analyzed by an analysis unit that provides advice on diet and training to improve the PFC balance, A provision unit that provides advice based on the analysis results obtained by the aforementioned analysis unit, The system includes a recommendation unit that recommends calorie management and exercise menus based on the advice provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned supply unit is, For an additional fee, we offer advice from a renowned AI personal trainer. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system according to feature 1.

5. The aforementioned collection unit is During data collection, filtering is performed based on the user's current living situation and health status. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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