System

Abstract

An object of a system according to an embodiment is to propose training, meals, and a sleep environment that are optimal for an individual user.SOLUTION: A system includes an image analysis part, a body composition meter cooperation part, a meal proposal part, and a sleep proposal part. The image analysis unit analyzes a body shape intended by the user and a photograph of the user. The body composition meter interlocking unit measures a muscle mass and a fat mass based on the data analyzed by the image analysis unit. The meal suggestion unit suggests a meal menu and time based on the data measured by the body composition meter link unit. The sleep suggestion unit suggests a sleep environment based on the data measured by the body composition meter link unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to propose optimal training, diet, and sleeping environments for individual users all at once.

[0005] The system according to the embodiment aims to propose optimal training, diet, and sleep environments for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a body composition meter interlocking unit, a meal suggestion unit, and a sleep suggestion unit. The image analysis unit analyzes the user's desired body shape and a photo of the user. The body composition meter interlocking unit measures muscle mass and fat mass based on the data analyzed by the image analysis unit. The meal suggestion unit suggests meal menus and times based on the data measured by the body composition meter interlocking unit. The sleep suggestion unit suggests a sleeping environment based on the data measured by the body composition meter interlocking unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal training, diet, and sleeping environments for each individual user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The application according to an embodiment of the present invention is a system that combines a user's desired body shape with their own photo to indicate which parts of the body need targeted training, measures muscle mass and fat mass in conjunction with a body composition scale, and proposes optimal meal menus, times, and sleep durations to the user. This allows the application to comprehensively propose training, meals, and sleep based on the user's desired body shape.

[0029] The application according to the embodiment includes an image analysis unit, a body composition meter interlocking unit, a meal suggestion unit, and a sleep suggestion unit. The image analysis unit analyzes a user's desired body shape and a photo of the user. For example, the user uploads an image of the desired body shape and a photo of the user, and the generation AI analyzes these images to suggest which body parts should be focused on training. The body composition meter interlocking unit measures muscle mass and fat mass based on data analyzed by the image analysis unit. For example, the application interlocking unit interlocks with a body composition meter to measure the user's muscle mass and fat mass in real time, and the generation AI analyzes this data to suggest optimal training and meal menus for the user. The meal suggestion unit suggests meal menus and times based on data measured by the body composition meter interlocking unit. For example, the generation AI suggests optimal meal menus and intake times based on the user's body composition data. The sleep suggestion unit suggests sleep times based on data measured by the body composition meter interlocking unit. For example, the generation AI suggests optimal sleep times based on the user's training data and body composition data. As a result, the application according to the embodiment can comprehensively suggest training, diet, and sleep based on the user's desired body shape.

[0030] The image analysis unit analyzes the user's past training history and changes in body shape, and can propose more accurate training plans. For example, the image analysis unit uses a generation AI to analyze the user's past training history and identify patterns of body shape changes. For example, it analyzes which training was effective based on past training data and proposes the optimal training plan. This makes it possible to propose highly accurate training plans based on past data.

[0031] The image analysis unit analyzes the user's posture and movements in real time and can suggest form corrections during training. For example, the image analysis unit uses a generation AI to analyze the user's posture during training using a camera and suggests form corrections in real time. For example, it analyzes the position of the knees and the angle of the back during squats and instructs the correct form. This allows form corrections to be suggested in real time.

[0032] The body composition meter linkage unit can analyze the user's metabolic rate and energy consumption and optimize the training plan. For example, the body composition meter linkage unit uses a generative AI to analyze the user's metabolic rate based on body composition data and propose an optimal training plan. For example, it can propose training to increase metabolism for a user with a low metabolic rate. This makes it possible to optimize the training plan based on metabolic rate and energy consumption.

[0033] The body composition meter linkage unit can predict the user's health risks and suggest preventative measures. For example, the body composition meter linkage unit uses a generation AI to predict the user's health risks based on body composition data and suggest preventative measures. For example, it suggests dietary restrictions and exercise plans for users who are at high risk of obesity. This makes it possible to predict health risks and suggest preventative measures.

[0034] The meal suggestion unit can analyze the user's meal history and suggest meal menus that optimize nutritional balance. For example, the generation AI analyzes the user's meal history and suggests meal menus that optimize nutritional balance. For example, it suggests menus that supplement missing nutrients based on past meal data. This makes it possible to suggest meal menus that optimize nutritional balance based on the meal history.

[0035] The meal suggestion unit can propose individually customized meal menus taking into consideration the user's allergy information and food preferences. For example, the generation AI of the meal suggestion unit proposes meal menus that avoid allergies based on the user's allergy information. For example, if a user is allergic to a specific ingredient, it will propose a menu that does not include that ingredient. This makes it possible to propose meal menus that are customized based on allergy information and food preferences.

[0036] The sleep suggestion unit can analyze the user's sleep data and suggest the optimal sleeping environment. For example, the generation AI of the sleep suggestion unit can suggest the optimal sleeping environment based on the user's sleep data. For example, it can analyze the user's sleep patterns and suggest the optimal temperature and humidity. This makes it possible to suggest the optimal sleeping environment based on the sleep data.

[0037] The sleep suggestion unit can analyze the user's sleep patterns and suggest optimal wake-up times and nap timings. For example, the sleep suggestion unit uses a generation AI to suggest optimal wake-up times based on the user's sleep patterns. For example, it analyzes the user's sleep cycle and suggests the time when it is easiest to wake up. This makes it possible to suggest optimal wake-up times and nap timings based on the sleep patterns.

[0038] The sleep suggestion unit can suggest relaxation music or guided meditation based on the user's sleep data. For example, the sleep suggestion unit uses a generation AI to suggest optimal relaxation music based on the user's sleep data. For example, the unit analyzes the user's sleep patterns and suggests relaxing music. This makes it possible to suggest relaxation music or guided meditation based on the sleep data.

[0039] The sleep suggestion unit can predict sleep-related health risks based on the user's sleep data and suggest preventive measures. For example, the generation AI predicts sleep-related health risks based on the user's sleep data and suggests preventive measures. For example, it predicts health risks due to lack of sleep and suggests appropriate sleep hours. This makes it possible to predict health risks based on sleep data and suggest preventive measures.

[0040] A subscription-based application can analyze a user's usage history and suggest the most suitable plan or option. For example, a generation AI can analyze a user's usage history and suggest the most suitable plan or option. For example, a subscription-based application can suggest the most suitable training plan for a user based on past usage data. This makes it possible to suggest the most suitable plan or option based on usage history.

[0041] The subscription application can analyze user feedback and continuously improve the application's features and content. For example, a generative AI can analyze user feedback and continuously improve the application's features and content. For example, a new training plan can be added based on user feedback. This allows the application's features and content to be continuously improved based on feedback.

[0042] A subscription-based application can propose personalized training plans and meal menus based on the user's usage data. For example, a generation AI can propose personalized training plans based on the user's usage data. For example, it can propose the optimal plan based on the user's past training data. This makes it possible to propose personalized training plans and meal menus based on usage data.

[0043] The subscription-based application can link with other health-related services based on the user's usage data. For example, the generative AI in the subscription-based application can link with other health-related services based on the user's usage data. For example, the application can suggest a training plan based on fitness equipment data. This allows the application to link with other health-related services based on usage data.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The application can also analyze the user's training history and visualize their training progress. For example, it can display past training data in graphs and charts, allowing users to visually see their progress. It can also provide badges and rewards to enhance a sense of accomplishment according to training progress. It can also display progress against user-set goals in real time, providing feedback to maintain motivation.

[0046] The application can also analyze the user's training history and suggest rest days to maximize the effectiveness of training. For example, it can calculate appropriate rest days based on past training data and notify the user. It can also provide advice on how to spend rest days, suggesting stretches and light exercises to promote recovery. It can also suggest meal menus for rest days and provide advice on maintaining a balanced diet.

[0047] The application can also analyze a user's posture and movements during training to predict injury risk. For example, it can use a camera to analyze a user's form during training and identify movements that increase the risk of injury. It can also suggest form corrections and changes to training menus to prevent injury. Furthermore, it can provide rehabilitation and preventive training menus for users at high risk of injury.

[0048] The application can analyze a user's metabolic rate and energy consumption and recommend supplements to maximize the effectiveness of their training. For example, it can suggest supplements to boost metabolism for a user with a low metabolic rate, and supplements to replenish energy for a user with a high energy consumption. It can also suggest supplements to maintain nutritional balance based on the user's meal plan. It can also provide advice on when to take supplements.

[0049] The application can also predict users' health risks and suggest regular health checks. For example, users at high risk of obesity are suggested to regularly measure their weight and blood pressure, and users at high risk of heart disease are suggested to regularly undergo electrocardiograms. Depending on the health risk, the application can also recommend consultations with specialists. Furthermore, it can suggest lifestyle improvements to reduce health risks.

[0050] The application can also analyze the user's meal history and automatically generate a shopping list for ingredients. For example, it can create a list of ingredients based on past meal data, allowing the user to easily purchase them. It can also provide information on where to buy ingredients and their prices to help the user make the best choices. It can also provide advice on how to store and cook ingredients.

[0051] The application can also provide recipe customization features that take into account the user's allergy information and food preferences. For example, if a user is allergic to a particular ingredient, the application can suggest alternative ingredients to replace that ingredient. The application can also customize the seasoning and cooking method of the recipe to suit the user's preferences. The application can also suggest recipe variations to encourage users to try new ingredients and dishes.

[0052] The application can analyze the user's sleep data and suggest environmental settings to improve the quality of sleep. For example, it can suggest optimal bedding and pillow selection based on the user's sleep patterns. It can also provide advice on temperature, humidity, and lighting settings for the sleeping environment. It can also suggest relaxing scents and music to help create a comfortable sleeping environment.

[0053] The application can also analyze the user's sleep patterns and suggest optimal sleep cycles. For example, it can suggest optimal bedtimes and wake-up times based on the user's sleep data. It can also provide advice on the timing and length of naps to help users rest efficiently. It can also suggest a sleep schedule that suits the user's lifestyle and promote healthy sleep habits.

[0054] The app can also use the user's sleep data to suggest lifestyle improvements to improve sleep quality. For example, it can analyze the user's sleep patterns and suggest appropriate exercise and meal timings. It can also advise limiting caffeine and alcohol intake and suggest lifestyle habits to improve sleep quality. It can also suggest a relaxing nighttime routine to help the user sleep better.

[0055] The application can also predict sleep-related health risks and suggest preventative measures based on the user's sleep data. For example, it can predict health risks due to lack of sleep and suggest appropriate sleep duration. It can also recommend that users at risk of sleep apnea syndrome consult a specialist. It can also suggest lifestyle improvements to improve sleep quality and provide support to reduce health risks.

[0056] The application can analyze the user's usage history and provide personalized advice to maximize the effectiveness of their training. For example, it can suggest the optimal training menu for the user based on past training data. It can also provide advice on adjusting the intensity and frequency of training according to the user's goals. It can also display the user's progress toward their set goals in real time and provide feedback to maintain motivation.

[0057] Applications can also analyze user feedback to continuously improve their features and content. For example, they can add new training plans based on user feedback to meet user needs. They can also incorporate user feedback to improve the UI / UX to improve the application's usability. They can also add new features and content that users request, increasing the value of the application.

[0058] The application can also link with other health-related services based on the user's usage data. For example, it can suggest training plans based on data from fitness equipment and help users maintain consistent training. It can also integrate data from health management apps and wearable devices to provide comprehensive health management. Furthermore, it can recommend collaboration with medical institutions and experts based on the user's health data and provide professional advice.

[0059] The application can also suggest personalized training plans and meal menus based on the user's usage data. For example, it can suggest the optimal training plan based on the user's past training data. It can also analyze the user's diet history and suggest meal menus that optimize nutritional balance. Furthermore, it can adjust training and meal plans according to the user's goals to support effective health management.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The image analysis unit analyzes the user's desired body shape and their own photos. For example, the user uploads an image of their desired body shape and a photo of themselves, and the generative AI analyzes these images and suggests which parts of the body should be trained. Step 2: The body composition analyzer linkage unit measures muscle mass and fat mass based on the data analyzed by the image analysis unit. For example, linking with a body composition analyzer, it measures the user's muscle mass and fat mass in real time, and the generation AI analyzes this data to suggest optimal training and meal menus for the user. Step 3: The meal suggestion unit suggests meal menus and times based on the data measured by the body composition scale linkage unit. For example, the generation AI suggests the optimal meal menu and intake times based on the user's body composition data. Step 4: The sleep suggestion unit proposes a sleep environment based on the data measured by the body composition monitor. For example, the AI ​​generator suggests optimal sleep duration based on the user's training data and body composition data.

[0062] (Example 2) The application according to an embodiment of the present invention is a system that combines a user's desired body shape with their own photo to indicate which parts of the body need targeted training, measures muscle mass and fat mass in conjunction with a body composition scale, and proposes optimal meal menus, times, and sleep durations to the user. This allows the application to comprehensively propose training, meals, and sleep based on the user's desired body shape.

[0063] The application according to the embodiment includes an image analysis unit, a body composition meter interlocking unit, a meal suggestion unit, and a sleep suggestion unit. The image analysis unit analyzes a user's desired body shape and a photo of the user. For example, the user uploads an image of the desired body shape and a photo of the user, and the generation AI analyzes these images to suggest which body parts should be focused on training. The body composition meter interlocking unit measures muscle mass and fat mass based on data analyzed by the image analysis unit. For example, the application interlocking unit interlocks with a body composition meter to measure the user's muscle mass and fat mass in real time, and the generation AI analyzes this data to suggest optimal training and meal menus for the user. The meal suggestion unit suggests meal menus and times based on data measured by the body composition meter interlocking unit. For example, the generation AI suggests optimal meal menus and intake times based on the user's body composition data. The sleep suggestion unit suggests sleep times based on data measured by the body composition meter interlocking unit. For example, the generation AI suggests optimal sleep times based on the user's training data and body composition data. As a result, the application according to the embodiment can comprehensively suggest training, diet, and sleep based on the user's desired body shape.

[0064] The image analysis unit analyzes the user's past training history and changes in body shape, and can propose more accurate training plans. For example, the image analysis unit uses a generation AI to analyze the user's past training history and identify patterns of body shape changes. For example, it analyzes which training was effective based on past training data and proposes the optimal training plan. This makes it possible to propose highly accurate training plans based on past data.

[0065] The image analysis unit analyzes the user's posture and movements in real time and can suggest form corrections during training. For example, the image analysis unit uses a generation AI to analyze the user's posture during training using a camera and suggests form corrections in real time. For example, it analyzes the position of the knees and the angle of the back during squats and instructs the correct form. This allows form corrections to be suggested in real time.

[0066] The image analysis unit can use the emotion estimation function to analyze the user's emotional state and propose a training plan based on the user's motivation and emotional state. For example, the image analysis unit can use the emotion estimation function to analyze the user's emotional state during training and propose a training plan to maintain motivation. For example, if the user is tired, the image analysis unit can propose a lighter training plan. This allows the image analysis unit to propose a training plan based on the user's emotional state.

[0067] The body composition meter linkage unit can analyze the user's metabolic rate and energy consumption and optimize the training plan. For example, the body composition meter linkage unit uses a generative AI to analyze the user's metabolic rate based on body composition data and propose an optimal training plan. For example, it can propose training to increase metabolism for a user with a low metabolic rate. This makes it possible to optimize the training plan based on metabolic rate and energy consumption.

[0068] The body composition meter linkage unit can predict the user's health risks and suggest preventative measures. For example, the body composition meter linkage unit uses a generation AI to predict the user's health risks based on body composition data and suggest preventative measures. For example, it suggests dietary restrictions and exercise plans for users who are at high risk of obesity. This makes it possible to predict health risks and suggest preventative measures.

[0069] The body composition meter interlocking unit can use the emotion estimation function to analyze the user's emotional state and provide advice to maintain motivation. The body composition meter interlocking unit can, for example, use the emotion estimation function to analyze the user's emotional state based on body composition data and provide advice to maintain motivation. For example, positive feedback can be provided when weight is lost. This makes it possible to provide advice to maintain motivation based on the user's emotional state.

[0070] The meal suggestion unit can analyze the user's meal history and suggest meal menus that optimize nutritional balance. For example, the generation AI analyzes the user's meal history and suggests meal menus that optimize nutritional balance. For example, it suggests menus that supplement missing nutrients based on past meal data. This makes it possible to suggest meal menus that optimize nutritional balance based on the meal history.

[0071] The meal suggestion unit can propose individually customized meal menus taking into consideration the user's allergy information and food preferences. For example, the generation AI of the meal suggestion unit proposes meal menus that avoid allergies based on the user's allergy information. For example, if a user is allergic to a specific ingredient, it will propose a menu that does not include that ingredient. This makes it possible to propose meal menus that are customized based on allergy information and food preferences.

[0072] The meal suggestion unit can use the emotion estimation function to analyze the user's emotional response to the meal and make suggestions to improve meal satisfaction. The meal suggestion unit can, for example, use the emotion estimation function to analyze the user's emotional response to the meal and make suggestions to improve meal satisfaction. For example, it can make new suggestions based on meal menus that the user is satisfied with. This makes it possible to make suggestions to improve meal satisfaction based on the emotional response.

[0073] The sleep suggestion unit can analyze the user's sleep data and suggest the optimal sleeping environment. For example, the generation AI of the sleep suggestion unit can suggest the optimal sleeping environment based on the user's sleep data. For example, it can analyze the user's sleep patterns and suggest the optimal temperature and humidity. This makes it possible to suggest the optimal sleeping environment based on the sleep data.

[0074] The sleep suggestion unit can analyze the user's sleep patterns and suggest optimal wake-up times and nap timings. For example, the sleep suggestion unit uses a generation AI to suggest optimal wake-up times based on the user's sleep patterns. For example, it analyzes the user's sleep cycle and suggests the time when it is easiest to wake up. This makes it possible to suggest optimal wake-up times and nap timings based on the sleep patterns.

[0075] The sleep suggestion unit can use the emotion estimation function to analyze the user's emotional response to sleep and provide advice to improve the quality of sleep. For example, the sleep suggestion unit can use the emotion estimation function to analyze the user's emotional response to sleep and provide advice to improve the quality of sleep. For example, the sleep suggestion unit can suggest an environment in which the user can relax. This makes it possible to provide advice to improve the quality of sleep based on the emotional response.

[0076] The sleep suggestion unit can suggest relaxation music or guided meditation based on the user's sleep data. For example, the sleep suggestion unit uses a generation AI to suggest optimal relaxation music based on the user's sleep data. For example, the unit analyzes the user's sleep patterns and suggests relaxing music. This makes it possible to suggest relaxation music or guided meditation based on the sleep data.

[0077] The sleep suggestion unit can predict sleep-related health risks based on the user's sleep data and suggest preventive measures. For example, the generation AI predicts sleep-related health risks based on the user's sleep data and suggests preventive measures. For example, it predicts health risks due to lack of sleep and suggests appropriate sleep hours. This makes it possible to predict health risks based on sleep data and suggest preventive measures.

[0078] The sleep suggestion unit can use the emotion estimation function to analyze the user's emotional response to sleep and provide feedback to provide a positive sleeping experience. The sleep suggestion unit can, for example, use the emotion estimation function to analyze the user's emotional response to sleep and provide feedback to provide a positive sleeping experience. For example, it can suggest an environment in which the user can relax. This makes it possible to provide feedback to provide a positive sleeping experience based on the emotional response.

[0079] A subscription-based application can analyze a user's usage history and suggest the most suitable plan or option. For example, a generation AI can analyze a user's usage history and suggest the most suitable plan or option. For example, a subscription-based application can suggest the most suitable training plan for a user based on past usage data. This makes it possible to suggest the most suitable plan or option based on usage history.

[0080] The subscription application can analyze user feedback and continuously improve the application's features and content. For example, a generative AI can analyze user feedback and continuously improve the application's features and content. For example, a new training plan can be added based on user feedback. This allows the application's features and content to be continuously improved based on feedback.

[0081] A flat-rate application can use an emotion estimation function to analyze a user's emotional response to application use and make suggestions to improve user satisfaction. A flat-rate application can, for example, use an emotion estimation function to analyze a user's emotional response to application use and make suggestions to improve user satisfaction. For example, a new suggestion can be made based on a function that the user is satisfied with. This makes it possible to make suggestions to improve user satisfaction based on the emotional response.

[0082] A subscription-based application can propose personalized training plans and meal menus based on the user's usage data. For example, a generation AI can propose personalized training plans based on the user's usage data. For example, it can propose the optimal plan based on the user's past training data. This makes it possible to propose personalized training plans and meal menus based on usage data.

[0083] The subscription-based application can link with other health-related services based on the user's usage data. For example, the generative AI in the subscription-based application can link with other health-related services based on the user's usage data. For example, the application can suggest a training plan based on fitness equipment data. This allows the application to link with other health-related services based on usage data.

[0084] A flat-rate application can use an emotion estimation function to analyze a user's emotional response to application usage and provide feedback to provide a positive usage experience. For example, a flat-rate application can use an emotion estimation function to analyze a user's emotional response to application usage and provide feedback to provide a positive usage experience. For example, new suggestions can be made based on features that the user is satisfied with. This makes it possible to provide feedback to provide a positive usage experience based on the emotional response.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The application can also analyze the user's training history and visualize their training progress. For example, it can display past training data in graphs and charts, allowing users to visually see their progress. It can also provide badges and rewards to enhance a sense of accomplishment according to training progress. It can also display progress against user-set goals in real time, providing feedback to maintain motivation.

[0087] The application can also analyze the user's training history and suggest rest days to maximize the effectiveness of training. For example, it can calculate appropriate rest days based on past training data and notify the user. It can also provide advice on how to spend rest days, suggesting stretches and light exercises to promote recovery. It can also suggest meal menus for rest days and provide advice on maintaining a balanced diet.

[0088] The application can also analyze a user's posture and movements during training to predict injury risk. For example, it can use a camera to analyze a user's form during training and identify movements that increase the risk of injury. It can also suggest form corrections and changes to training menus to prevent injury. Furthermore, it can provide rehabilitation and preventive training menus for users at high risk of injury.

[0089] The application can also analyze the user's emotional state and suggest music or podcasts to keep them motivated during their workout. For example, if the user is tired, it will suggest relaxing music, and if they are motivated, it will suggest energetic music. It can also suggest motivational podcasts or audiobooks based on the user's emotional state. It can also learn the user's preferred music and content and provide personalized suggestions.

[0090] The application can analyze a user's metabolic rate and energy consumption and recommend supplements to maximize the effectiveness of their training. For example, it can suggest supplements to boost metabolism for a user with a low metabolic rate, and supplements to replenish energy for a user with a high energy consumption. It can also suggest supplements to maintain nutritional balance based on the user's meal plan. It can also provide advice on when to take supplements.

[0091] The application can also predict users' health risks and suggest regular health checks. For example, users at high risk of obesity are suggested to regularly measure their weight and blood pressure, and users at high risk of heart disease are suggested to regularly undergo electrocardiograms. Depending on the health risk, the application can also recommend consultations with specialists. Furthermore, it can suggest lifestyle improvements to reduce health risks.

[0092] The application can also analyze the user's emotional state and provide mental health support to help maintain motivation during training. For example, it can suggest relaxation techniques if the user is feeling stressed and provide positive feedback if motivation is declining. It can also recommend consulting a mental health professional based on the user's emotional state. It can also suggest a relaxing environment to support mental health.

[0093] The application can also analyze the user's meal history and automatically generate a shopping list for ingredients. For example, it can create a list of ingredients based on past meal data, allowing the user to easily purchase them. It can also provide information on where to buy ingredients and their prices to help the user make the best choices. It can also provide advice on how to store and cook ingredients.

[0094] The application can also provide recipe customization features that take into account the user's allergy information and food preferences. For example, if a user is allergic to a particular ingredient, the application can suggest alternative ingredients to replace that ingredient. The application can also customize the seasoning and cooking method of the recipe to suit the user's preferences. The application can also suggest recipe variations to encourage users to try new ingredients and dishes.

[0095] The application can also analyze the user's emotional response to meals and provide feedback to improve meal satisfaction. For example, it can suggest new recipes and ingredients based on the meal menu that the user is satisfied with. It can also provide advice on adjusting meal timing and environment according to the user's emotional state. It can also suggest ideas and techniques to help the user enjoy meals, improving meal satisfaction.

[0096] The application can analyze the user's sleep data and suggest environmental settings to improve the quality of sleep. For example, it can suggest optimal bedding and pillow selection based on the user's sleep patterns. It can also provide advice on temperature, humidity, and lighting settings for the sleeping environment. It can also suggest relaxing scents and music to help create a comfortable sleeping environment.

[0097] The application can also analyze the user's sleep patterns and suggest optimal sleep cycles. For example, it can suggest optimal bedtimes and wake-up times based on the user's sleep data. It can also provide advice on the timing and length of naps to help users rest efficiently. It can also suggest a sleep schedule that suits the user's lifestyle and promote healthy sleep habits.

[0098] The application can also analyze the user's emotional state and suggest relaxation techniques to improve sleep quality. For example, if the user is feeling stressed, it can suggest deep breathing or meditation to create a relaxing environment. It can also suggest relaxing music or aromatherapy depending on the user's emotional state. It can also provide advice on bedroom layout and decoration to help the user relax.

[0099] The app can also use the user's sleep data to suggest lifestyle improvements to improve sleep quality. For example, it can analyze the user's sleep patterns and suggest appropriate exercise and meal timings. It can also advise limiting caffeine and alcohol intake and suggest lifestyle habits to improve sleep quality. It can also suggest a relaxing nighttime routine to help the user sleep better.

[0100] The application can also predict sleep-related health risks and suggest preventative measures based on the user's sleep data. For example, it can predict health risks due to lack of sleep and suggest appropriate sleep duration. It can also recommend that users at risk of sleep apnea syndrome consult a specialist. It can also suggest lifestyle improvements to improve sleep quality and provide support to reduce health risks.

[0101] The application can also analyze the user's emotional state and provide feedback to provide a positive sleep experience. For example, it can suggest a relaxing environment to help the user sleep comfortably. It can also suggest relaxation music or guided meditations based on the user's emotional state. It can also suggest gratitude journaling and positive affirmations to help the user fall asleep with positive emotions.

[0102] The application can analyze the user's usage history and provide personalized advice to maximize the effectiveness of their training. For example, it can suggest the optimal training menu for the user based on past training data. It can also provide advice on adjusting the intensity and frequency of training according to the user's goals. It can also display the user's progress toward their set goals in real time and provide feedback to maintain motivation.

[0103] Applications can also analyze user feedback to continuously improve their features and content. For example, they can add new training plans based on user feedback to meet user needs. They can also incorporate user feedback to improve the UI / UX to improve the application's usability. They can also add new features and content that users request, increasing the value of the application.

[0104] Applications can also analyze a user's emotional state and provide personalized feedback to improve user satisfaction. For example, new suggestions can be made based on the user's satisfaction with certain features. Applications can also provide advice on how to use the application and its features based on the user's emotional state. Furthermore, applications can suggest gratitude journaling and positive affirmations to help users use the application with positive emotions.

[0105] The application can also link with other health-related services based on the user's usage data. For example, it can suggest training plans based on data from fitness equipment and help users maintain consistent training. It can also integrate data from health management apps and wearable devices to provide comprehensive health management. Furthermore, it can recommend collaboration with medical institutions and experts based on the user's health data and provide professional advice.

[0106] The application can also suggest personalized training plans and meal menus based on the user's usage data. For example, it can suggest the optimal training plan based on the user's past training data. It can also analyze the user's diet history and suggest meal menus that optimize nutritional balance. Furthermore, it can adjust training and meal plans according to the user's goals to support effective health management.

[0107] The application can use emotion estimation to analyze the user's emotional response to the application and provide feedback to ensure a positive user experience. For example, it can make new suggestions based on the user's satisfaction with certain features. It can also provide advice on how to use the application and how to make the most of its features based on the user's emotional state. Furthermore, it can suggest gratitude journaling and positive affirmations to help users use the application with positive emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The image analysis unit analyzes the user's desired body shape and their own photos. For example, the user uploads an image of their desired body shape and a photo of themselves, and the generative AI analyzes these images and suggests which parts of the body should be trained. Step 2: The body composition analyzer linkage unit measures muscle mass and fat mass based on the data analyzed by the image analysis unit. For example, linking with a body composition analyzer, it measures the user's muscle mass and fat mass in real time, and the generation AI analyzes this data to suggest optimal training and meal menus for the user. Step 3: The meal suggestion unit suggests meal menus and times based on the data measured by the body composition scale linkage unit. For example, the generation AI suggests the optimal meal menu and intake times based on the user's body composition data. Step 4: The sleep suggestion unit proposes a sleep environment based on the data measured by the body composition monitor. For example, the AI ​​generator suggests optimal sleep duration based on the user's training data and body composition data.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image analysis unit that analyzes the user's desired body shape and their own photos; a body composition analyzer linkage unit that measures muscle mass and fat mass based on the data analyzed by the image analysis unit; a meal suggestion unit that suggests meal menus and meal times based on the data measured by the body composition meter interlocking unit; a sleep suggestion unit that suggests a sleep environment based on the data measured by the body composition meter interlocking unit. A system characterized by:

2. The body composition meter linkage unit is Analyzes the user's metabolic rate and energy consumption to optimize training plans 2. The system of claim 1.

3. The meal suggestion unit Analyzes the user's dietary history and suggests meal menus to optimize nutritional balance 2. The system of claim 1.

4. The sleep suggestion unit Analyzes user sleep data and suggests optimal sleeping environment 2. The system of claim 1.

5. Flat-rate applications are Analyze users' usage history and propose optimal plans and options 2. The system of claim 1.

6. The image analysis unit Analyzes the user's emotional state and suggests training plans based on their motivation and emotional state 2. The system of claim 1.

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