system

Abstract

The system according to the embodiment aims to generate and provide an optimal training menu based on an individual's body type. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a body shape status. The generating unit generates a training menu based on the body shape status received by the receiving unit. The providing unit provides the training menu generated by the generating unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not adequately automated the creation and provision of training menus that are optimal for individual body types, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide an optimal training menu based on an individual's body type. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives a body type status. The generating unit generates a training menu based on the body type status received by the receiving unit. The providing unit provides the training menu generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide an optimal training menu based on an individual's body type. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training suggestion system according to an embodiment of the present invention proposes an optimal training menu based on a user's body type status. In this training suggestion system, a user inputs their own body type status, and an AI analyzes the data to generate an optimal training menu for the user. The generated training menu is customized to match the user's body type and goals. Companies can also utilize this data to enhance training menus and as reference data for the apparel industry. For example, when a user inputs their body type status, they enter detailed data such as height, weight, body fat percentage, muscle mass, age, and gender. For example, a user may enter data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. This information is then input into an AI. The AI ​​then analyzes the input data and generates an optimal training menu for the user. The AI ​​customizes a training menu, including strength training, aerobic exercise, and stretching, based on the user's body type status. For example, strength training includes bench presses and squats, aerobic exercise includes running and cycling, and stretching includes yoga and Pilates. The generated training menu is customized to match the user's body type and goals. For example, a user who wants to build muscle will be suggested a menu that emphasizes strength training, while a user who wants to lose body fat will be suggested a menu that emphasizes aerobic exercise. Furthermore, companies can use this data to enhance their training menus and as reference data for the apparel industry. For example, the effectiveness of training menus can be analyzed to develop new training programs. In addition, the apparel industry can use user body shape data to develop sportswear that fits well. This allows the training suggestion system to suggest optimal training menus based on the user's body shape status.

[0029] A training suggestion system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit allows a user to input their own body type status. The body type status input by the user includes, for example, height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows a user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. The generation unit uses AI to generate a training menu based on the body type status received by the reception unit. The generation unit customizes a training menu, for example, strength training, aerobic exercise, and stretching. For example, the generation unit generates a training menu that includes bench press and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. The provision unit provides the user with the training menu generated by the generation unit. For example, the provision unit displays the generated training menu to the user. The provision unit can also provide companies with data analyzing the effectiveness of the training menu. For example, the provision unit analyzes the effectiveness of the training menu and provides companies with data for developing new training programs. The providing unit can also provide the apparel industry with data useful for developing well-fitting sportswear. For example, the providing unit provides the apparel industry with data useful for developing well-fitting sportswear based on the user's body shape data. This allows the training suggestion system according to the embodiment to suggest an optimal training menu based on the user's body shape status. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0030] The reception unit can receive data such as height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows a user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. This allows the user's detailed body shape status to be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data entered by the user to a generation AI and have the generation AI analyze the data.

[0031] The generation unit can generate training menus for strength training, aerobic exercise, and stretching. The generation unit generates a training menu that includes, for example, bench presses and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. This makes it possible to generate a variety of training menus based on the user's body shape status. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's body shape status into the generation AI and cause the generation AI to generate a training menu.

[0032] The providing unit can provide the generated training menu to the user. The providing unit, for example, displays the generated training menu to the user. This makes it possible to provide an optimal training menu for the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0033] The providing unit can provide the company with data for analyzing the effectiveness of a training menu. For example, the providing unit analyzes the effectiveness of a training menu and provides the company with data for developing a new training program. This allows the company to be provided with data for analyzing the effectiveness of a training menu. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data for analyzing the effectiveness of a training menu into a generating AI and have the generating AI analyze the data.

[0034] The providing unit can provide the apparel industry with data useful for developing well-fitting sportswear. The providing unit provides the apparel industry with data useful for developing well-fitting sportswear, for example, based on the user's body type data. This allows the apparel industry to be provided with data for developing well-fitting sportswear. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's body type data into a generating AI and have the generating AI analyze the data.

[0035] The reception unit can analyze the user's past body type data and select the optimal input method. For example, the reception unit provides a simple input form based on data previously input by the user. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest an input method for a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past body type data. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past body type data into a generation AI and have the generation AI analyze the data.

[0036] The reception unit can filter the body shape status based on the user's current health condition and lifestyle habits when the user inputs the status. For example, when the user inputs their current health condition, the reception unit filters and displays related questions. The reception unit can also determine the priority of the data to be input based on the user's lifestyle habits. The reception unit can also adjust the level of detail of the data to be input depending on the user's health condition. This makes it possible to filter the input data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's health condition and lifestyle habit data to the generation AI and have the generation AI perform data filtering.

[0037] When inputting the body shape status, the reception unit can prioritize input of highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize input of data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize input of data related to the climate and environment of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize input of data related to daily life. This makes it possible to prioritize input of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a process of determining the priority of the data.

[0038] The reception unit can analyze the user's social media activity and input relevant data when inputting the body shape status. The reception unit can suggest data to input based on, for example, health information shared by the user on social media. The reception unit can also extract and input data related to the current body shape status from the user's social media activity. The reception unit can also suggest data to input based on information about health-related accounts the user follows on social media. This makes it possible to input relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and have the generation AI analyze the data.

[0039] When generating a training menu, the generation unit can adjust the level of detail of the menu based on the user's goal. For example, if the user wants to increase muscle mass, the generation unit can generate a detailed strength training menu. Furthermore, if the user wants to reduce body fat, the generation unit can generate a detailed aerobic exercise menu. Furthermore, if the user wants to improve flexibility, the generation unit can generate a detailed stretching menu. This allows the level of detail of the training menu to be adjusted based on the user's goal. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's goal data into the generation AI and cause the generation AI to perform processing to adjust the level of detail of the training menu.

[0040] When generating a training menu, the generation unit can apply a generation algorithm according to the user's body type category. For example, if the user has an ectomorph body type, the generation unit generates a menu that emphasizes muscle training. Furthermore, if the user has a mesomorph body type, the generation unit can generate a balanced training menu. Furthermore, if the user has an endomorph body type, the generation unit can generate a menu that emphasizes aerobic exercise. This makes it possible to generate an optimal training menu according to the user's body type category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's body type category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0041] When generating a training menu, the generation unit can determine the priority of the menu based on the time when the user's body type data was submitted. For example, if the user has recently submitted body type data, the generation unit generates the latest training menu based on that data. The generation unit can also determine the priority of the training menu based on data submitted by the user in the past. The generation unit can also determine the priority of the training menu based on data submitted by the user within a specific period. This makes it possible to determine the priority of the training menu based on the time when the user's body type data was submitted. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the time when the user's body type data was submitted into the generation AI and cause the generation AI to execute a process of determining the priority of the menu.

[0042] When generating a training menu, the generation unit can adjust the order of the menu based on the user's related data. The generation unit can adjust the order of the menu based on, for example, the user's past training history. The generation unit can also adjust the order of the menu based on the user's current body shape data. The generation unit can also adjust the order of the menu based on the user's goals. This makes it possible to adjust the order of the training menu based on the user's related data. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and cause the generation AI to perform processing to adjust the order of the menu.

[0043] When providing a training menu, the providing unit can select the optimal provision method by referring to the user's past training history. The providing unit selects the optimal provision method, for example, based on training menus used by the user in the past. The providing unit can also select an effective provision method from the user's past training history. The providing unit can also analyze the user's past training history and select the most efficient provision method. This makes it possible to select the optimal provision method based on the user's past training history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history data into the generation AI and have the generation AI analyze the data.

[0044] When providing a training menu, the providing unit can customize the providing means based on the user's current living situation. For example, if the user is busy, the providing unit can provide an effective training menu in a short amount of time. Furthermore, if the user is relaxed, the providing unit can provide a training menu at a leisurely pace. Furthermore, if the user has a specific living habit, the providing unit can provide a training menu tailored to that habit. This makes it possible to customize the providing means for the training menu according to the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation data into the generating AI and have the generating AI analyze the data.

[0045] When providing a training menu, the providing unit can select a provision method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a training menu related to that area. Furthermore, if the user is traveling, the providing unit can also provide a training menu related to the climate and environment of the travel destination. Furthermore, if the user is at home, the providing unit can also provide a training menu related to daily life. This makes it possible to provide an optimal training menu based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to execute processing to select a provision method.

[0046] When providing a training menu, the providing unit can analyze the user's social media activity and suggest a means of providing the menu. The providing unit can provide the training menu based on, for example, health information shared by the user on social media. The providing unit can also provide a training menu related to the user's current body shape status from the user's social media activity. The providing unit can also provide a training menu based on information about health-related accounts the user follows on social media. This makes it possible to provide an optimal training menu based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI analyze the data.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can receive information on the user's eating habits and allergies in addition to the user's body shape status. For example, if the user is allergic to a specific food ingredient, the generation unit can generate a training menu that takes the allergy into consideration by inputting that information. Also, if the user is on a specific diet, the training menu can be adjusted based on that information. Furthermore, it is possible to optimize the timing and content of training based on the user's eating habits. This makes it possible to provide a training menu that suits the user's health condition and eating habits.

[0049] The generation unit can adjust the difficulty of the training menu based on the user's body type status. For example, it can generate a menu centered on basic exercises for a beginner user and provide a menu including advanced training for an advanced user. The generation unit can also monitor the user's progress and gradually increase the difficulty of the training menu. Furthermore, it can adjust the training menu in real time based on user feedback. This makes it possible to provide an optimal training menu according to the user's skill level.

[0050] The providing unit can collect user feedback on the generated training menu and improve the training menu based on that data. For example, if the user feels that a particular exercise is difficult, the next training menu can be adjusted based on that feedback. Also, if the user prefers a particular exercise, a menu that includes many of that exercise can be provided. Furthermore, it is possible to analyze the user's feedback, identify common problems, and improve the quality of the overall training menu. This makes it possible to provide a flexible training menu that meets the user's needs.

[0051] The providing unit can visualize the progress of a training menu based on the user's training history. For example, it can display data on past training sessions performed by the user in graphs and charts, allowing the user to check their progress at a glance. The providing unit can also display the user's progress toward their goals in real time and provide feedback to maintain motivation. It can also evaluate the user's progress toward the goals they set and suggest next steps. This allows the user to understand their own progress and continue training effectively toward their goals.

[0052] The provider can introduce gamification elements to increase the user's motivation for the training menu. For example, the provider can allow users to earn points each time they complete a training menu and use those points to purchase virtual items. It can also introduce a ranking system in which users compete with each other, allowing users to receive rewards by ranking highly. It can also introduce a system in which users can earn badges and titles according to their level of achievement in the training menu. This allows users to continue training while having fun, and helps maintain their motivation.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit is a section where the user inputs their own body type status. The body type status input by the user includes, for example, height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows the user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. Step 2: The generation unit uses AI to generate a training menu based on the body type status received by the reception unit. The generation unit customizes the training menu, for example, including strength training, aerobic exercise, stretching, etc. For example, the generation unit generates a training menu that includes bench press and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. Step 3: The providing unit is a component that provides the user with the training menu generated by the generating unit. The providing unit, for example, displays the generated training menu to the user. The providing unit can also provide companies with data analyzing the effectiveness of the training menu. For example, the providing unit analyzes the effectiveness of the training menu and provides companies with data for developing new training programs. The providing unit can also provide the apparel industry with data useful for developing well-fitting sportswear. For example, the providing unit provides the apparel industry with data useful for developing well-fitting sportswear based on the user's body shape data. This allows the training suggestion system according to the embodiment to suggest an optimal training menu based on the user's body shape status. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0055] (Example 2) A training suggestion system according to an embodiment of the present invention proposes an optimal training menu based on a user's body type status. In this training suggestion system, a user inputs their own body type status, and an AI analyzes the data to generate an optimal training menu for the user. The generated training menu is customized to match the user's body type and goals. Companies can also utilize this data to enhance training menus and as reference data for the apparel industry. For example, when a user inputs their body type status, they enter detailed data such as height, weight, body fat percentage, muscle mass, age, and gender. For example, a user may enter data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. This information is then input into an AI. The AI ​​then analyzes the input data and generates an optimal training menu for the user. The AI ​​customizes a training menu, including strength training, aerobic exercise, and stretching, based on the user's body type status. For example, strength training includes bench presses and squats, aerobic exercise includes running and cycling, and stretching includes yoga and Pilates. The generated training menu is customized to match the user's body type and goals. For example, a user who wants to build muscle will be suggested a menu that emphasizes strength training, while a user who wants to lose body fat will be suggested a menu that emphasizes aerobic exercise. Furthermore, companies can use this data to enhance their training menus and as reference data for the apparel industry. For example, the effectiveness of training menus can be analyzed to develop new training programs. In addition, the apparel industry can use user body shape data to develop sportswear that fits well. This allows the training suggestion system to suggest optimal training menus based on the user's body shape status.

[0056] A training suggestion system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit allows a user to input their own body type status. The body type status input by the user includes, for example, height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows a user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. The generation unit uses AI to generate a training menu based on the body type status received by the reception unit. The generation unit customizes a training menu, for example, strength training, aerobic exercise, and stretching. For example, the generation unit generates a training menu that includes bench press and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. The provision unit provides the user with the training menu generated by the generation unit. For example, the provision unit displays the generated training menu to the user. The provision unit can also provide companies with data analyzing the effectiveness of the training menu. For example, the provision unit analyzes the effectiveness of the training menu and provides companies with data for developing new training programs. The providing unit can also provide the apparel industry with data useful for developing well-fitting sportswear. For example, the providing unit provides the apparel industry with data useful for developing well-fitting sportswear based on the user's body shape data. This allows the training suggestion system according to the embodiment to suggest an optimal training menu based on the user's body shape status. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0057] The reception unit can receive data such as height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows a user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. This allows the user's detailed body shape status to be received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the data entered by the user to a generation AI and have the generation AI analyze the data.

[0058] The generation unit can generate training menus for strength training, aerobic exercise, and stretching. The generation unit generates a training menu that includes, for example, bench presses and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. This makes it possible to generate a variety of training menus based on the user's body shape status. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's body shape status into the generation AI and cause the generation AI to generate a training menu.

[0059] The providing unit can provide the generated training menu to the user. The providing unit, for example, displays the generated training menu to the user. This makes it possible to provide an optimal training menu for the user. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0060] The providing unit can provide the company with data for analyzing the effectiveness of a training menu. For example, the providing unit analyzes the effectiveness of a training menu and provides the company with data for developing a new training program. This allows the company to be provided with data for analyzing the effectiveness of a training menu. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data for analyzing the effectiveness of a training menu into a generating AI and have the generating AI analyze the data.

[0061] The providing unit can provide the apparel industry with data useful for developing well-fitting sportswear. The providing unit provides the apparel industry with data useful for developing well-fitting sportswear, for example, based on the user's body type data. This allows the apparel industry to be provided with data for developing well-fitting sportswear. Some or all of the above-described processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's body type data into a generating AI and have the generating AI analyze the data.

[0062] The reception unit can estimate the user's emotions and adjust the timing of inputting the body shape status based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input the body shape status during a time when the user can relax. Furthermore, if the user is relaxed, the reception unit can also prompt the user to input the body shape status immediately. Furthermore, if the user is in a hurry, the reception unit can also set a reminder to input the status later. This allows the timing of inputting the body shape status to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0063] The reception unit can analyze the user's past body type data and select the optimal input method. For example, the reception unit provides a simple input form based on data previously input by the user. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest an input method for a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past body type data. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past body type data into a generation AI and have the generation AI analyze the data.

[0064] The reception unit can filter the body shape status based on the user's current health condition and lifestyle habits when the user inputs the status. For example, when the user inputs their current health condition, the reception unit filters and displays related questions. The reception unit can also determine the priority of the data to be input based on the user's lifestyle habits. The reception unit can also adjust the level of detail of the data to be input depending on the user's health condition. This makes it possible to filter the input data based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's health condition and lifestyle habit data to the generation AI and have the generation AI perform data filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of the body type data to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize input of only important data. Furthermore, when the user is relaxed, the reception unit can also prompt the user to input detailed data. Furthermore, when the user is in a hurry, the reception unit can also prompt the user to input only the minimum amount of data. This makes it possible to determine the priority of the body type data to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0066] When inputting the body shape status, the reception unit can prioritize input of highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize input of data related to that area. Furthermore, when the user is traveling, the reception unit can prioritize input of data related to the climate and environment of the travel destination. Furthermore, when the user is at home, the reception unit can prioritize input of data related to daily life. This makes it possible to prioritize input of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a process of determining the priority of the data.

[0067] The reception unit can analyze the user's social media activity and input relevant data when inputting the body shape status. The reception unit can suggest data to input based on, for example, health information shared by the user on social media. The reception unit can also extract and input data related to the current body shape status from the user's social media activity. The reception unit can also suggest data to input based on information about health-related accounts the user follows on social media. This makes it possible to input relevant data based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and have the generation AI analyze the data.

[0068] The generation unit can estimate the user's emotions and adjust the presentation method of the training menu based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a training menu that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can also generate a training menu that is effective in a short amount of time. Furthermore, if the user is excited, the generation unit can also generate a training menu that adds visually stimulating effects. This allows the presentation method of the training menu to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When generating a training menu, the generation unit can adjust the level of detail of the menu based on the user's goal. For example, if the user wants to increase muscle mass, the generation unit can generate a detailed strength training menu. Furthermore, if the user wants to reduce body fat, the generation unit can generate a detailed aerobic exercise menu. Furthermore, if the user wants to improve flexibility, the generation unit can generate a detailed stretching menu. This allows the level of detail of the training menu to be adjusted based on the user's goal. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's goal data into the generation AI and cause the generation AI to perform processing to adjust the level of detail of the training menu.

[0070] When generating a training menu, the generation unit can apply a generation algorithm according to the user's body type category. For example, if the user has an ectomorph body type, the generation unit generates a menu that emphasizes muscle training. Furthermore, if the user has a mesomorph body type, the generation unit can generate a balanced training menu. Furthermore, if the user has an endomorph body type, the generation unit can generate a menu that emphasizes aerobic exercise. This makes it possible to generate an optimal training menu according to the user's body type category. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's body type category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0071] The generation unit can estimate the user's emotions and adjust the length of the training menu based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a longer training menu. Furthermore, if the user is in a hurry, the generation unit can also generate a short, effective training menu. Furthermore, if the user is excited, the generation unit can also generate a training menu with visually stimulating effects. This allows the length of the training menu to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] When generating a training menu, the generation unit can determine the priority of the menu based on the time when the user's body type data was submitted. For example, if the user has recently submitted body type data, the generation unit generates the latest training menu based on that data. The generation unit can also determine the priority of the training menu based on data submitted by the user in the past. The generation unit can also determine the priority of the training menu based on data submitted by the user within a specific period. This makes it possible to determine the priority of the training menu based on the time when the user's body type data was submitted. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the time when the user's body type data was submitted into the generation AI and cause the generation AI to execute a process of determining the priority of the menu.

[0073] When generating a training menu, the generation unit can adjust the order of the menu based on the user's related data. The generation unit can adjust the order of the menu based on, for example, the user's past training history. The generation unit can also adjust the order of the menu based on the user's current body shape data. The generation unit can also adjust the order of the menu based on the user's goals. This makes it possible to adjust the order of the training menu based on the user's related data. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the user's related data into the generation AI and cause the generation AI to perform processing to adjust the order of the menu.

[0074] The providing unit can estimate the user's emotions and adjust the method of providing a training menu based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a training menu at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can also provide a short, effective training menu. Furthermore, if the user is excited, the providing unit can also provide a training menu with visually stimulating effects. This allows the method of providing a training menu to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] When providing a training menu, the providing unit can select the optimal provision method by referring to the user's past training history. The providing unit selects the optimal provision method, for example, based on training menus used by the user in the past. The providing unit can also select an effective provision method from the user's past training history. The providing unit can also analyze the user's past training history and select the most efficient provision method. This makes it possible to select the optimal provision method based on the user's past training history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past training history data into the generation AI and have the generation AI analyze the data.

[0076] When providing a training menu, the providing unit can customize the providing means based on the user's current living situation. For example, if the user is busy, the providing unit can provide an effective training menu in a short amount of time. Furthermore, if the user is relaxed, the providing unit can provide a training menu at a leisurely pace. Furthermore, if the user has a specific living habit, the providing unit can provide a training menu tailored to that habit. This makes it possible to customize the providing means for the training menu according to the user's current living situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's living situation data into the generating AI and have the generating AI analyze the data.

[0077] The providing unit can estimate the user's emotions and determine the priority of training menus based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing a detailed training menu. Furthermore, if the user is in a hurry, the providing unit can prioritize providing a short, effective training menu. Furthermore, if the user is excited, the providing unit can prioritize providing a training menu with visually stimulating effects. This makes it possible to determine the priority of training menus according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] When providing a training menu, the providing unit can select a provision method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide a training menu related to that area. Furthermore, if the user is traveling, the providing unit can also provide a training menu related to the climate and environment of the travel destination. Furthermore, if the user is at home, the providing unit can also provide a training menu related to daily life. This makes it possible to provide an optimal training menu based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to execute processing to select a provision method.

[0079] When providing a training menu, the providing unit can analyze the user's social media activity and suggest a means of providing the menu. The providing unit can provide the training menu based on, for example, health information shared by the user on social media. The providing unit can also provide a training menu related to the user's current body shape status from the user's social media activity. The providing unit can also provide a training menu based on information about health-related accounts the user follows on social media. This makes it possible to provide an optimal training menu based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and have the generation AI analyze the data. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input the user's body shape status using the touch panel 38A or microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a training menu using AI. The provision unit provides the generated training menu to the user using the display 40A or speaker 40B of the smart device 14. The provision unit can also provide data to companies via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's body shape status using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a training menu using AI. The provision unit provides the generated training menu to the user using the speaker 240 of the smart glasses 214. The provision unit can also provide data to companies via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can input the user's body shape status using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a training menu using AI. The provision unit provides the generated training menu to the user using the display 343 and speaker 240 of the headset type terminal 314. The provision unit can also provide data to companies via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's body shape status using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a training menu using AI. The provision unit provides the generated training menu to the user using the speaker 240 of the robot 414. The provision unit can also provide data to companies via the specific processing unit 290 of the data processing device 12.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can receive information on the user's eating habits and allergies in addition to the user's body shape status. For example, if the user is allergic to a specific food ingredient, the generation unit can generate a training menu that takes the allergy into consideration by inputting that information. Also, if the user is on a specific diet, the training menu can be adjusted based on that information. Furthermore, it is possible to optimize the timing and content of training based on the user's eating habits. This makes it possible to provide a training menu that suits the user's health condition and eating habits.

[0082] The generation unit can adjust the difficulty of the training menu based on the user's body type status. For example, it can generate a menu centered on basic exercises for a beginner user and provide a menu including advanced training for an advanced user. The generation unit can also monitor the user's progress and gradually increase the difficulty of the training menu. Furthermore, it can adjust the training menu in real time based on user feedback. This makes it possible to provide an optimal training menu according to the user's skill level.

[0083] The providing unit can collect user feedback on the generated training menu and improve the training menu based on that data. For example, if the user feels that a particular exercise is difficult, the next training menu can be adjusted based on that feedback. Also, if the user prefers a particular exercise, a menu that includes many of that exercise can be provided. Furthermore, it is possible to analyze the user's feedback, identify common problems, and improve the quality of the overall training menu. This makes it possible to provide a flexible training menu that meets the user's needs.

[0084] The providing unit can visualize the progress of a training menu based on the user's training history. For example, it can display data on past training sessions performed by the user in graphs and charts, allowing the user to check their progress at a glance. The providing unit can also display the user's progress toward their goals in real time and provide feedback to maintain motivation. It can also evaluate the user's progress toward the goals they set and suggest next steps. This allows the user to understand their own progress and continue training effectively toward their goals.

[0085] The provider can introduce gamification elements to increase the user's motivation for the training menu. For example, the provider can allow users to earn points each time they complete a training menu and use those points to purchase virtual items. It can also introduce a ranking system in which users compete with each other, allowing users to receive rewards by ranking highly. It can also introduce a system in which users can earn badges and titles according to their level of achievement in the training menu. This allows users to continue training while having fun, and helps maintain their motivation.

[0086] The reception unit can estimate the user's emotions and adjust the method of suggesting a training menu based on the estimated user's emotions. For example, if the user is feeling stressed, a light training menu that will help them relax can be suggested. Also, if the user wants to increase their motivation, a more challenging training menu can be suggested. Furthermore, if the user is tired, it is possible to suggest a stretching or yoga menu that will promote recovery. In this way, it is possible to provide the optimal training menu according to the user's emotions.

[0087] The generation unit can estimate the user's emotions and adjust the content of the training menu based on the estimated user's emotions. For example, if the user is relaxed, a training menu that progresses at a leisurely pace can be generated. If the user is in a hurry, a training menu that is effective in a short amount of time can be generated. Furthermore, if the user is excited, a training menu that includes visually stimulating effects can be generated. This makes it possible to provide a training menu that matches the user's emotions.

[0088] The provision unit can estimate the user's emotions and adjust the timing of providing a training menu based on the estimated user's emotions. For example, if the user is relaxed, the training menu can be provided immediately. Also, if the user is feeling stressed, the training menu can be provided at a time when the user is able to relax. Furthermore, if the user is in a hurry, it is possible to set a reminder to provide the training menu later. This makes it possible to provide a training menu at the optimal timing according to the user's emotions.

[0089] The providing unit can estimate the user's emotions and adjust the method of providing feedback on the training menu based on the estimated user's emotions. For example, if the user is relaxed, a message containing a lot of positive feedback can be provided. Also, if the user is feeling stressed, an encouraging message can be provided. Furthermore, if the user is excited, it is also possible to provide feedback that enhances the user's sense of accomplishment. In this way, it is possible to provide optimal feedback according to the user's emotions.

[0090] The providing unit can estimate the user's emotions and suggest customization options for the training menu based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can suggest an option to add exercises that have a relaxing effect. Also, if the user wants to increase motivation, the providing unit can suggest an option to add challenging exercises. Furthermore, if the user is tired, the providing unit can suggest an option to add exercises that promote recovery. In this way, it is possible to provide optimal customization options according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit is a section where the user inputs their own body type status. The body type status input by the user includes, for example, height, weight, body fat percentage, muscle mass, age, and gender. For example, the reception unit allows the user to input data such as height 170 cm, weight 70 kg, body fat percentage 20%, and muscle mass 30 kg. Step 2: The generation unit uses AI to generate a training menu based on the body type status received by the reception unit. The generation unit customizes the training menu, for example, including strength training, aerobic exercise, stretching, etc. For example, the generation unit generates a training menu that includes bench press and squats as strength training, running and cycling as aerobic exercise, and yoga and Pilates as stretching. Step 3: The providing unit is a component that provides the user with the training menu generated by the generating unit. The providing unit, for example, displays the generated training menu to the user. The providing unit can also provide companies with data analyzing the effectiveness of the training menu. For example, the providing unit analyzes the effectiveness of the training menu and provides companies with data for developing new training programs. The providing unit can also provide the apparel industry with data useful for developing well-fitting sportswear. For example, the providing unit provides the apparel industry with data useful for developing well-fitting sportswear based on the user's body shape data. This allows the training suggestion system according to the embodiment to suggest an optimal training menu based on the user's body shape status. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide the training menu using an AI model that receives the training menu generated by the generating unit as input and outputs a training menu.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives body shape status; a generation unit that generates a training menu based on the body type status received by the reception unit; a providing unit that provides the training menu generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Accepts data on height, weight, body fat percentage, muscle mass, age, and gender The system of claim 1 .

3. The generation unit Generate workouts for strength training, cardio, and stretching The system of claim 1 .

4. The providing unit Provide the generated training menu to the user The system of claim 1 .

5. The providing unit Providing data to companies to analyze the effectiveness of their training programs The system of claim 1 .

6. The providing unit Providing data to the apparel industry that will help develop sportswear that fits well The system of claim 1 .

7. The reception unit The system estimates the user's emotions and adjusts the timing of inputting the body shape status based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past body shape data and select the input method The system of claim 1 .

Images