system

Abstract

The system according to this embodiment aims to provide training and meal plans tailored to the individual user's physical characteristics and health condition. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user information. The analysis unit analyzes the information received by the reception unit. The generation unit generates a plan based on the analysis results obtained by the analysis unit. The provision unit provides the plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, training and diet plans according to the constitution and health condition of each individual user have not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide training and diet plans according to the constitution and health condition of each individual user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives user information. The analysis unit analyzes the information received by the reception unit. The generation unit generates a plan based on the analysis results obtained by the analysis unit. The provision unit provides the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide training and meal plans tailored to the individual user's physical characteristics and health condition. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The training and meal suggestion system according to an embodiment of the present invention is a system that suggests training and meals that take into account an individual's physical constitution, health condition, and preferences. This system begins with the user inputting information such as their physical constitution, health condition, desired physique, and preferences into an application. Next, the system uses a generation AI to analyze the user's input information. Based on the analysis results, it generates an optimal training plan and meal plan for the individual. The generated plan is provided to the user, and their implementation is supported. For example, if the user inputs "I want to increase muscle mass," the system will take into account the user's physical constitution and health condition and suggest a training plan and meal plan that are effective for muscle building. Also, if the user inputs "I want to lose weight," the system will suggest a diet plan that takes into account the user's preferences. Through this system, an optimal training and meal plan is provided for each individual, enabling them to effectively overcome a lack of exercise. In this way, the training and meal suggestion system can provide an optimal plan based on the user's physical constitution, health condition, and preferences.

[0029] The training and meal suggestion system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their physical constitution, health condition, desired physique, and preferences. For example, the user can input their height, weight, allergy information, and exercise preferences through an application. The analysis unit analyzes the user's input information and generates an optimal training plan and meal plan for each individual. For example, the analysis unit considers the user's physical constitution and health condition to determine appropriate exercise intensity and meal content. The generation unit uses a generation AI to analyze the user's input information and generate an optimal plan. For example, the generation AI generates a plan effective for muscle building or weight loss based on the user's desired physique. The provision unit provides the generated plan to the user. For example, the provision unit displays the training plan and meal plan to the user through an application. The provision unit can also receive user feedback and adjust the plan. As a result, the training and meal suggestion system according to this embodiment can efficiently input, analyze, generate, and provide user information.

[0030] The reception area allows users to input information such as their physical characteristics, health status, desired appearance, and preferences. For example, users can input their height, weight, allergy information, and exercise preferences through the application. When users input their physical characteristics, they can include information such as genetic factors and body type. Health information can include blood pressure, weight, and medical history. Desired appearance information can include ideal body type and health goals. Preferences can include dietary and exercise preferences. This allows for the provision of more personalized plans by inputting detailed user information.

[0031] The analysis unit can analyze user input information and generate personalized training and meal plans. For example, the analysis unit considers the user's physical constitution and health condition to determine appropriate exercise intensity and dietary content. For instance, based on the user's physical constitution information, the analysis unit generates a training plan tailored to genetic factors and body type. Furthermore, based on the user's health condition, the analysis unit generates a meal plan tailored to blood pressure, weight, and medical history. In addition, based on the user's desired physique, the analysis unit generates a plan tailored to their ideal body type and health goals. For example, if the user aims to increase muscle mass, the analysis unit generates a training and meal plan effective for muscle building. Similarly, if the user aims to lose weight, the analysis unit generates a training and meal plan effective for weight loss. In this way, by analyzing user input information, the system can generate personalized plans.

[0032] The generation unit can analyze user input information using generation AI and generate an optimal plan. For example, the generation unit uses generation AI to analyze information such as the user's physical characteristics, health status, desired physique, and preferences, and generates an optimal training plan and meal plan. For example, based on the user's physical characteristics information, the generation AI generates a training plan tailored to genetic factors and body type. The generation AI also generates a meal plan based on the user's health status, including blood pressure, weight, and medical history. Furthermore, based on the user's desired physique, the generation AI generates a plan tailored to their ideal body type and health goals. For example, if the user aims to increase muscle mass, the generation AI generates a training plan and meal plan effective for muscle growth. Similarly, if the user aims to lose weight, the generation AI generates a training plan and meal plan effective for weight loss. As a result, using generation AI improves the accuracy of plan generation.

[0033] The service provider can provide the generated plans to users. For example, the service provider can display training plans and meal plans to users through an application. For instance, when a user opens the application, the service provider can display the generated plan on a dashboard. The service provider can also receive user feedback and adjust the plans accordingly. For example, when a user provides feedback on a plan, they can use a feedback form within the application. Based on user feedback, the service provider adjusts the plan content to provide a more effective plan. This makes it easier for users to follow the generated plans provided to them.

[0034] The service provider can receive user feedback and adjust the plan accordingly. For example, when a user provides feedback on a plan, they can use a feedback form within the application. If a user provides feedback that the exercise intensity of a training plan is too high, the service provider will generate a new plan with adjusted exercise intensity. Similarly, if a user provides feedback that they dislike a particular food item in a meal plan, the service provider will generate a new plan that excludes that food item. Furthermore, the service provider can periodically review and optimize the plan content based on user feedback. This allows for the provision of more effective plans by adjusting them based on user feedback.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk automatically displays as suggestions information about the user's physical characteristics and health status that the user has frequently entered in the past. For instance, it automatically displays as suggestions information such as the user's height, weight, and allergy information that the user has entered in the past, saving the user the trouble of re-entering the information. It also prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). For example, if the user has used voice input in the past, it will prioritize suggesting voice input. Furthermore, it predicts and suggests information that the user will enter at specific times of day based on their past input history. For example, if the user has entered health information at night in the past, it will suggest entering health information at night. In this way, the user's input efficiency is improved by suggesting the optimal input method based on past input history.

[0036] The input field can customize input fields based on the user's current lifestyle and health status during data entry. For example, when a user enters their current health status, the input field suggests the most suitable input fields based on past health data. For instance, if a user has been diagnosed with hypertension in the past, input fields related to blood pressure will be displayed preferentially. The input fields are also customized considering the user's lifestyle (work schedule, family situation, etc.). For example, if a user is busy with work, fields that can be completed quickly will be displayed preferentially. Furthermore, if a user has a specific health condition (e.g., diabetes), input fields related to that condition will be displayed preferentially. For example, if a user has diabetes, input fields related to blood glucose levels will be displayed preferentially. This allows for more accurate information to be entered by providing input fields tailored to the user's lifestyle and health status.

[0037] The input system can prioritize displaying highly relevant input fields by considering the user's geographical location during input. For example, if a user lives in a specific region, the system will prioritize displaying health information relevant to that region. For instance, if a user lives in a hot and humid region, it will prioritize displaying input fields related to heatstroke prevention. If a user is traveling, it will display input fields related to the climate and food culture of their destination. For example, if a user is traveling in a cold region, it will display input fields related to cold weather countermeasures. Furthermore, if a user wishes to train in a specific region, it will display information about facilities in that region as input fields. For example, if a user wishes to train in a mountainous area, it will display input fields related to mountain climbing. In this way, by considering the user's geographical location, the system can provide more relevant input fields.

[0038] The input system can analyze a user's social media activity during input and suggest relevant input fields. For example, it can suggest relevant input fields based on health information shared by the user on social media. For instance, if a user posts on social media that they recently started jogging, it will suggest input fields related to jogging. It can also customize input fields based on information from training and nutrition experts the user follows. For example, if a user follows a specific fitness instructor, it will suggest input fields related to the training recommended by that instructor. Furthermore, it can suggest input fields based on information from health communities the user participates in on social media. For example, if a user participates in a diet community, it will suggest input fields related to dieting. This improves user input efficiency by suggesting relevant input fields based on social media activity.

[0039] The analysis unit can optimize its analysis algorithm by referring to the user's past health data during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to the user's past health checkup results. For instance, if the user has been diagnosed with hypertension in the past, the analysis unit will optimize the analysis algorithm based on data related to hypertension. It can also optimize the analysis algorithm based on the user's past training history. For example, it will adjust the analysis algorithm based on the effectiveness of the training the user has done in the past. Furthermore, it can adjust the analysis algorithm by referring to the user's past dietary records. For example, it will optimize the analysis algorithm based on data on nutrients the user has consumed in the past. By optimizing the analysis algorithm based on past health data, the accuracy of the analysis is improved.

[0040] The analysis unit can perform analyses while considering the user's lifestyle and dietary history. For example, the analysis unit considers the user's lifestyle (sleep patterns, exercise frequency, etc.) when performing analyses. For instance, if the user has a nocturnal lifestyle, it will generate a training plan suitable for nighttime activities. It also performs analyses that consider nutritional balance based on the user's dietary history. For example, it will generate a nutritionally balanced meal plan based on data of nutrients the user has consumed in the past. Furthermore, it performs analyses while considering the user's lifestyle (work hours, frequency of vacations, etc.). For example, if the user works shifts, it will generate a training plan that matches their shifts. By considering the user's lifestyle and dietary history, it is possible to provide more accurate analysis results.

[0041] The analysis unit can perform analyses while considering the user's geographical location. For example, the analysis unit considers the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, it will perform analyses related to heatstroke prevention. If the user is traveling, it will consider the climate and food culture of the travel destination. For example, if the user is traveling to a cold region, it will perform analyses related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, it will consider information about facilities in that region. For example, if the user wishes to train in a mountainous area, it will perform analyses related to mountain climbing. By considering the user's geographical location, the analysis unit can provide more relevant results.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data related to the user. For example, the analysis unit performs analysis by referring to the latest research data related to the user's health status. For example, if the user has been diagnosed with hypertension, the analysis will be based on the latest research data related to hypertension. It also improves the accuracy of the analysis based on literature related to the user's physical characteristics. For example, if the user has a specific genetic factor, the analysis will be based on literature related to that factor. Furthermore, the analysis will be based on training methods related to the user's desired physique. For example, if the user aims to increase muscle mass, the analysis will be based on training methods related to muscle growth. By improving the accuracy of the analysis based on relevant literature and research data, it is possible to provide more accurate analysis results.

[0043] The generation unit can generate an optimal plan by referring to the user's past training history during the generation process. For example, the generation unit generates an optimal plan based on the effectiveness of the user's past training. For example, it analyzes data from the user's past training, selects effective exercises, and generates a plan. It also generates plans that are tailored to the user's progress based on their past training history. For example, it provides a plan that includes appropriate exercises as the next step based on goals the user has achieved in the past. Furthermore, it analyzes the user's past training history and evaluates the effectiveness of specific exercises. For example, it evaluates the effectiveness of exercises the user has performed in the past and generates a plan that prioritizes the inclusion of effective exercises. In this way, by generating an optimal plan based on past training history, it can provide plans that are tailored to the user's progress.

[0044] The generation unit can customize the plan during generation based on the user's current health condition and desired outcome. For example, the generation unit generates a manageable plan considering the user's current health condition. For instance, if the user has been diagnosed with high blood pressure, it will provide a training and meal plan to manage blood pressure. It also generates a plan tailored to the user's desired outcome (e.g., muscle building or weight loss). For example, if the user aims to build muscle, it will provide exercises effective for muscle building and a high-protein meal plan. Furthermore, it generates a plan that includes specific exercises based on the user's health condition. For example, if the user has joint problems, it will provide a plan that includes exercises that do not put stress on the joints. By providing a plan tailored to the user's health condition and desired outcome, it can provide a more effective plan.

[0045] The generation unit can create an optimal plan by considering the user's geographical location information during the generation process. For example, the generation unit generates a plan considering the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, it will provide training and meal plans related to heatstroke prevention. If the user is traveling, it will generate a plan considering the climate and food culture of the travel destination. For example, if the user is traveling in a cold region, it will provide training and meal plans related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, it will generate a plan considering the facility information of that region. For example, if the user wishes to train in a mountainous area, it will provide a training plan related to mountain climbing. In this way, by considering the user's geographical location information, it is possible to provide a more relevant plan.

[0046] The generation unit can analyze the user's social media activity during generation and propose relevant plans. For example, it can propose relevant plans based on health information shared by the user on social media. For instance, if a user posts on social media that they recently started jogging, it will propose a training plan related to jogging. It can also customize plans based on information from training and nutrition experts the user follows. For example, if a user follows a specific fitness instructor, it will propose a plan related to the training recommended by that instructor. Furthermore, it can propose plans based on information from health communities the user participates in on social media. For example, if a user participates in a diet community, it will propose a diet-related plan. In this way, by proposing relevant plans based on social media activity, it can provide plans tailored to the user's interests.

[0047] The service provider can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider can adjust the delivery method based on the user's feedback on plans previously provided. For example, if the user previously provided feedback that "video-format plans are easy to understand," the service provider will prioritize providing video-format plans. The service provider will also prioritize using delivery methods that the user has preferred in the past (e.g., video, text). For example, if the user previously preferred text-format plans, the service provider will provide text-format plans. Furthermore, the service provider will analyze the user's past feedback and select the most effective delivery method. For example, based on the user's feedback on plans previously provided, the service provider will select and provide the most effective delivery method. By selecting the optimal delivery method based on past feedback, user satisfaction will improve.

[0048] The service provider can customize the content offered based on the user's current lifestyle and health condition. For example, the service provider will select content that is manageable considering the user's current health condition. For instance, if the user has been diagnosed with hypertension, they will provide a training plan and meal plan to manage their blood pressure. They will also customize the content considering the user's lifestyle (work schedule, family situation, etc.). For example, if the user is busy with work, they will provide a training plan that can be completed in a short time and a meal plan that is easy to prepare. Furthermore, if the user has a specific health condition (e.g., diabetes), they will prioritize displaying content related to that condition. For example, if the user has diabetes, they will provide a meal plan to manage their blood sugar levels. This allows for more effective support by providing content tailored to the user's lifestyle and health condition.

[0049] The service provider can select the most appropriate delivery method at the time of delivery, taking into account the user's geographical location. For example, the service provider can select a delivery method considering the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, they will provide training and meal plans related to heatstroke prevention. If the user is traveling, they will select a delivery method considering the climate and food culture of their destination. For example, if the user is traveling in a cold region, they will provide training and meal plans related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, they will select a delivery method considering the facility information of that region. For example, if the user wishes to train in a mountainous area, they will provide training plans related to mountain climbing. In this way, by considering the user's geographical location, a more relevant delivery method can be provided.

[0050] The service provider can analyze a user's social media activity and provide relevant plans at the time of delivery. For example, the service provider can provide relevant plans based on health information shared by the user on social media. For instance, if a user posts on social media that they "recently started jogging," the service provider will provide a training plan related to jogging. The service provider can also customize plans based on information from training and nutrition experts that the user follows. For example, if a user follows a specific fitness instructor, the service provider will provide a plan related to the training recommended by that instructor. Furthermore, the service provider can provide plans based on information from health communities that the user participates in on social media. For example, if a user participates in a diet community, the service provider will provide a diet-related plan. In this way, by providing relevant plans based on social media activity, the service provider can provide plans that match the user's interests.

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

[0052] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display information about the user's physical characteristics and health status that they have frequently entered in the past as suggestions. It also prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will enter at specific times based on their past input history. In this way, by suggesting the most suitable input method based on past input history, the user's input efficiency is improved.

[0053] The analysis unit can perform analyses while considering the user's lifestyle and dietary history. For example, it can analyze data while considering the user's lifestyle (sleep patterns, exercise frequency, etc.). It can also perform analyses that consider nutritional balance based on the user's dietary history. Furthermore, it can analyze data while considering the user's lifestyle (work hours, frequency of vacations, etc.). By considering the user's lifestyle and dietary history, it can provide more accurate analysis results.

[0054] The generation unit can generate an optimal plan by referencing the user's past training history during the generation process. For example, it can generate an optimal plan based on the effectiveness of the user's past training. It can also generate a plan that matches the user's progress based on their past training history. Furthermore, it analyzes the user's past training history and evaluates the effectiveness of specific exercises. By generating an optimal plan based on past training history, it can provide a plan that matches the user's progress.

[0055] The service provider can customize the content offered based on the user's current lifestyle and health condition. For example, it can select content that is manageable considering the user's current health condition. It can also customize the content considering the user's lifestyle (work schedule, family situation, etc.). Furthermore, if the user has a specific health condition (e.g., diabetes), it can prioritize displaying content related to that condition. This allows for more effective support by providing content tailored to the user's lifestyle and health condition.

[0056] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if a user lives in a specific region, it will prioritize displaying health information related to that region. If a user is traveling, it will display input fields related to the climate and food culture of their destination. Furthermore, if a user wishes to train in a specific region, it will display facility information for that region as an input field. In this way, by considering the user's geographical location, it can provide more relevant input fields.

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

[0058] Step 1: The reception desk allows users to input information such as their physical characteristics, health status, desired appearance, and preferences. For example, users can input their height, weight, allergy information, and exercise preferences through the application. Step 2: The analysis unit analyzes the user's input information and generates an optimal training plan and meal plan for each individual. For example, the analysis unit considers the user's physical characteristics and health condition to determine the appropriate exercise intensity and dietary content. Step 3: The generation unit uses a generation AI to analyze the user's input information and generate an optimal plan. For example, the generation AI generates a plan that is effective for muscle building or weight loss based on the user's desired physique. Step 4: The service provider delivers the generated plan to the user. For example, the service provider displays training plans and meal plans to the user through the application. The service provider can also receive user feedback and adjust the plan accordingly.

[0059] (Example of form 2) The training and meal suggestion system according to an embodiment of the present invention is a system that suggests training and meals that take into account an individual's physical constitution, health condition, and preferences. This system begins with the user inputting information such as their physical constitution, health condition, desired physique, and preferences into an application. Next, the system uses a generation AI to analyze the user's input information. Based on the analysis results, it generates an optimal training plan and meal plan for the individual. The generated plan is provided to the user, and their implementation is supported. For example, if the user inputs "I want to increase muscle mass," the system will take into account the user's physical constitution and health condition and suggest a training plan and meal plan that are effective for muscle building. Also, if the user inputs "I want to lose weight," the system will suggest a diet plan that takes into account the user's preferences. Through this system, an optimal training and meal plan is provided for each individual, enabling them to effectively overcome a lack of exercise. In this way, the training and meal suggestion system can provide an optimal plan based on the user's physical constitution, health condition, and preferences.

[0060] The training and meal suggestion system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from the user, such as their physical constitution, health condition, desired physique, and preferences. For example, the user can input their height, weight, allergy information, and exercise preferences through an application. The analysis unit analyzes the user's input information and generates an optimal training plan and meal plan for each individual. For example, the analysis unit considers the user's physical constitution and health condition to determine appropriate exercise intensity and meal content. The generation unit uses a generation AI to analyze the user's input information and generate an optimal plan. For example, the generation AI generates a plan effective for muscle building or weight loss based on the user's desired physique. The provision unit provides the generated plan to the user. For example, the provision unit displays the training plan and meal plan to the user through an application. The provision unit can also receive user feedback and adjust the plan. As a result, the training and meal suggestion system according to this embodiment can efficiently input, analyze, generate, and provide user information.

[0061] The reception area allows users to input information such as their physical characteristics, health status, desired appearance, and preferences. For example, users can input their height, weight, allergy information, and exercise preferences through the application. When users input their physical characteristics, they can include information such as genetic factors and body type. Health information can include blood pressure, weight, and medical history. Desired appearance information can include ideal body type and health goals. Preferences can include dietary and exercise preferences. This allows for the provision of more personalized plans by inputting detailed user information.

[0062] The analysis unit can analyze user input information and generate personalized training and meal plans. For example, the analysis unit considers the user's physical constitution and health condition to determine appropriate exercise intensity and dietary content. For instance, based on the user's physical constitution information, the analysis unit generates a training plan tailored to genetic factors and body type. Furthermore, based on the user's health condition, the analysis unit generates a meal plan tailored to blood pressure, weight, and medical history. In addition, based on the user's desired physique, the analysis unit generates a plan tailored to their ideal body type and health goals. For example, if the user aims to increase muscle mass, the analysis unit generates a training and meal plan effective for muscle building. Similarly, if the user aims to lose weight, the analysis unit generates a training and meal plan effective for weight loss. In this way, by analyzing user input information, the system can generate personalized plans.

[0063] The generation unit can analyze user input information using generation AI and generate an optimal plan. For example, the generation unit uses generation AI to analyze information such as the user's physical characteristics, health status, desired physique, and preferences, and generates an optimal training plan and meal plan. For example, based on the user's physical characteristics information, the generation AI generates a training plan tailored to genetic factors and body type. The generation AI also generates a meal plan based on the user's health status, including blood pressure, weight, and medical history. Furthermore, based on the user's desired physique, the generation AI generates a plan tailored to their ideal body type and health goals. For example, if the user aims to increase muscle mass, the generation AI generates a training plan and meal plan effective for muscle growth. Similarly, if the user aims to lose weight, the generation AI generates a training plan and meal plan effective for weight loss. As a result, using generation AI improves the accuracy of plan generation.

[0064] The service provider can provide the generated plans to users. For example, the service provider can display training plans and meal plans to users through an application. For instance, when a user opens the application, the service provider can display the generated plan on a dashboard. The service provider can also receive user feedback and adjust the plans accordingly. For example, when a user provides feedback on a plan, they can use a feedback form within the application. Based on user feedback, the service provider adjusts the plan content to provide a more effective plan. This makes it easier for users to follow the generated plans provided to them.

[0065] The service provider can receive user feedback and adjust the plan accordingly. For example, when a user provides feedback on a plan, they can use a feedback form within the application. If a user provides feedback that the exercise intensity of a training plan is too high, the service provider will generate a new plan with adjusted exercise intensity. Similarly, if a user provides feedback that they dislike a particular food item in a meal plan, the service provider will generate a new plan that excludes that food item. Furthermore, the service provider can periodically review and optimize the plan content based on user feedback. This allows for the provision of more effective plans by adjusting them based on user feedback.

[0066] The reception desk can estimate the user's emotions and adjust the level of detail in the input information based on that estimation. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For instance, when a user is stressed, it displays an interface that only requires the minimum necessary information. Conversely, if the user is relaxed, it provides detailed input options and suggests customizable input methods. For example, when a user is relaxed, it displays an interface that allows for detailed health and preference information. Furthermore, if the user is in a hurry, it prioritizes voice input to allow for quick information entry. For example, when a user is in a hurry, it displays an interface that allows for quick information entry using voice input. This reduces the user's burden by adjusting the level of detail in the input information according to the user's emotions.

[0067] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk automatically displays as suggestions information about the user's physical characteristics and health status that the user has frequently entered in the past. For instance, it automatically displays as suggestions information such as the user's height, weight, and allergy information that the user has entered in the past, saving the user the trouble of re-entering the information. It also prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). For example, if the user has used voice input in the past, it will prioritize suggesting voice input. Furthermore, it predicts and suggests information that the user will enter at specific times of day based on their past input history. For example, if the user has entered health information at night in the past, it will suggest entering health information at night. In this way, the user's input efficiency is improved by suggesting the optimal input method based on past input history.

[0068] The input field can customize input fields based on the user's current lifestyle and health status during data entry. For example, when a user enters their current health status, the input field suggests the most suitable input fields based on past health data. For instance, if a user has been diagnosed with hypertension in the past, input fields related to blood pressure will be displayed preferentially. The input fields are also customized considering the user's lifestyle (work schedule, family situation, etc.). For example, if a user is busy with work, fields that can be completed quickly will be displayed preferentially. Furthermore, if a user has a specific health condition (e.g., diabetes), input fields related to that condition will be displayed preferentially. For example, if a user has diabetes, input fields related to blood glucose levels will be displayed preferentially. This allows for more accurate information to be entered by providing input fields tailored to the user's lifestyle and health status.

[0069] The reception system can estimate the user's emotions and prioritize inputs based on those emotions. For example, if the user is stressed, the reception system will prioritize displaying important input items and postpone other items. For instance, when a user is stressed, it will prioritize displaying important items related to their health and postpone items related to their preferences. If the user is relaxed, it will display all input items equally. For example, when a user is relaxed, it will display all items such as health status, constitution, and preferences equally. Furthermore, if the user is in a hurry, it will display only the most important input items to allow for quick completion. For example, when a user is in a hurry, it will display only important items related to their health to allow for quick completion. In this way, the system reduces the user's burden by prioritizing inputs according to their emotions.

[0070] The input system can prioritize displaying highly relevant input fields by considering the user's geographical location during input. For example, if a user lives in a specific region, the system will prioritize displaying health information relevant to that region. For instance, if a user lives in a hot and humid region, it will prioritize displaying input fields related to heatstroke prevention. If a user is traveling, it will display input fields related to the climate and food culture of their destination. For example, if a user is traveling in a cold region, it will display input fields related to cold weather countermeasures. Furthermore, if a user wishes to train in a specific region, it will display information about facilities in that region as input fields. For example, if a user wishes to train in a mountainous area, it will display input fields related to mountain climbing. In this way, by considering the user's geographical location, the system can provide more relevant input fields.

[0071] The input system can analyze a user's social media activity during input and suggest relevant input fields. For example, it can suggest relevant input fields based on health information shared by the user on social media. For instance, if a user posts on social media that they recently started jogging, it will suggest input fields related to jogging. It can also customize input fields based on information from training and nutrition experts the user follows. For example, if a user follows a specific fitness instructor, it will suggest input fields related to the training recommended by that instructor. Furthermore, it can suggest input fields based on information from health communities the user participates in on social media. For example, if a user participates in a diet community, it will suggest input fields related to dieting. This improves user input efficiency by suggesting relevant input fields based on social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will increase the accuracy of the analysis and provide more detailed results. For instance, when a user is stressed, the analysis unit will perform the analysis based on detailed data about the user's health and physical condition to provide highly accurate results. Conversely, if the user is relaxed, the analysis accuracy will be maintained at normal levels. For example, when a user is relaxed, the analysis will be performed using the standard analysis algorithm. Furthermore, if the user is in a hurry, the analysis accuracy will be slightly reduced to provide results quickly. For example, when a user is in a hurry, a simplified analysis algorithm will be used to provide results in a short time. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0073] The analysis unit can optimize its analysis algorithm by referring to the user's past health data during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to the user's past health checkup results. For instance, if the user has been diagnosed with hypertension in the past, the analysis unit will optimize the analysis algorithm based on data related to hypertension. It can also optimize the analysis algorithm based on the user's past training history. For example, it will adjust the analysis algorithm based on the effectiveness of the training the user has done in the past. Furthermore, it can adjust the analysis algorithm by referring to the user's past dietary records. For example, it will optimize the analysis algorithm based on data on nutrients the user has consumed in the past. By optimizing the analysis algorithm based on past health data, the accuracy of the analysis is improved.

[0074] The analysis unit can perform analyses while considering the user's lifestyle and dietary history. For example, the analysis unit considers the user's lifestyle (sleep patterns, exercise frequency, etc.) when performing analyses. For instance, if the user has a nocturnal lifestyle, it will generate a training plan suitable for nighttime activities. It also performs analyses that consider nutritional balance based on the user's dietary history. For example, it will generate a nutritionally balanced meal plan based on data of nutrients the user has consumed in the past. Furthermore, it performs analyses while considering the user's lifestyle (work hours, frequency of vacations, etc.). For example, if the user works shifts, it will generate a training plan that matches their shifts. By considering the user's lifestyle and dietary history, it is possible to provide more accurate analysis results.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For instance, when the user is stressed, the analysis results are displayed concisely, highlighting only the important information. If the user is relaxed, the analysis results are displayed in detail, including explanations for each item. Furthermore, if the user is in a hurry, the analysis results are displayed concisely, allowing for quick understanding. In this way, by adjusting the display method of the analysis results according to the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand.

[0076] The analysis unit can perform analyses while considering the user's geographical location. For example, the analysis unit considers the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, it will perform analyses related to heatstroke prevention. If the user is traveling, it will consider the climate and food culture of the travel destination. For example, if the user is traveling to a cold region, it will perform analyses related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, it will consider information about facilities in that region. For example, if the user wishes to train in a mountainous area, it will perform analyses related to mountain climbing. By considering the user's geographical location, the analysis unit can provide more relevant results.

[0077] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and research data related to the user. For example, the analysis unit performs analysis by referring to the latest research data related to the user's health status. For example, if the user has been diagnosed with hypertension, the analysis will be based on the latest research data related to hypertension. It also improves the accuracy of the analysis based on literature related to the user's physical characteristics. For example, if the user has a specific genetic factor, the analysis will be based on literature related to that factor. Furthermore, the analysis will be based on training methods related to the user's desired physique. For example, if the user aims to increase muscle mass, the analysis will be based on training methods related to muscle growth. By improving the accuracy of the analysis based on relevant literature and research data, it is possible to provide more accurate analysis results.

[0078] The generation unit can estimate the user's emotions and adjust the level of detail in the generated plan based on those emotions. For example, if the user is stressed, the generation unit will generate a simple and easy-to-follow plan. For instance, when the user is stressed, it will provide a simple exercise or a quick meal plan. If the user is relaxed, it will generate a plan with detailed explanations. For example, when the user is relaxed, it will provide a plan with detailed exercise instructions or a meal recipe. Furthermore, if the user is in a hurry, it will generate a quick and effective plan. For example, when the user is in a hurry, it will provide a quick exercise or an easy-to-prepare meal plan. In this way, by adjusting the level of detail in the plan according to the user's emotions, it is possible to provide a plan that is easy for the user to follow.

[0079] The generation unit can generate an optimal plan by referring to the user's past training history during the generation process. For example, the generation unit generates an optimal plan based on the effectiveness of the user's past training. For example, it analyzes data from the user's past training, selects effective exercises, and generates a plan. It also generates plans that are tailored to the user's progress based on their past training history. For example, it provides a plan that includes appropriate exercises as the next step based on goals the user has achieved in the past. Furthermore, it analyzes the user's past training history and evaluates the effectiveness of specific exercises. For example, it evaluates the effectiveness of exercises the user has performed in the past and generates a plan that prioritizes the inclusion of effective exercises. In this way, by generating an optimal plan based on past training history, it can provide plans that are tailored to the user's progress.

[0080] The generation unit can customize the plan during generation based on the user's current health condition and desired outcome. For example, the generation unit generates a manageable plan considering the user's current health condition. For instance, if the user has been diagnosed with high blood pressure, it will provide a training and meal plan to manage blood pressure. It also generates a plan tailored to the user's desired outcome (e.g., muscle building or weight loss). For example, if the user aims to build muscle, it will provide exercises effective for muscle building and a high-protein meal plan. Furthermore, it generates a plan that includes specific exercises based on the user's health condition. For example, if the user has joint problems, it will provide a plan that includes exercises that do not put stress on the joints. By providing a plan tailored to the user's health condition and desired outcome, it can provide a more effective plan.

[0081] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on those emotions. For example, if the user is stressed, the generation unit will prioritize generating plans with a relaxing effect. For instance, when the user is stressed, it will provide plans that include relaxing exercises such as yoga or meditation. If the user is relaxed, it will generate a normal plan. For example, when the user is relaxed, it will provide a normal training plan or meal plan. Furthermore, if the user is in a hurry, it will prioritize generating quick and effective plans. For example, when the user is in a hurry, it will provide exercises that can be completed in a short time or meal plans that are easy to prepare. In this way, by determining the priority of plans according to the user's emotions, the system can provide the user with the most suitable plan.

[0082] The generation unit can create an optimal plan by considering the user's geographical location information during the generation process. For example, the generation unit generates a plan considering the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, it will provide training and meal plans related to heatstroke prevention. If the user is traveling, it will generate a plan considering the climate and food culture of the travel destination. For example, if the user is traveling in a cold region, it will provide training and meal plans related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, it will generate a plan considering the facility information of that region. For example, if the user wishes to train in a mountainous area, it will provide a training plan related to mountain climbing. In this way, by considering the user's geographical location information, it is possible to provide a more relevant plan.

[0083] The generation unit can analyze the user's social media activity during generation and propose relevant plans. For example, it can propose relevant plans based on health information shared by the user on social media. For instance, if a user posts on social media that they recently started jogging, it will propose a training plan related to jogging. It can also customize plans based on information from training and nutrition experts the user follows. For example, if a user follows a specific fitness instructor, it will propose a plan related to the training recommended by that instructor. Furthermore, it can propose plans based on information from health communities the user participates in on social media. For example, if a user participates in a diet community, it will propose a diet-related plan. In this way, by proposing relevant plans based on social media activity, it can provide plans tailored to the user's interests.

[0084] The delivery unit can estimate the user's emotions and adjust the way the plan is delivered based on those emotions. For example, if the user is stressed, the delivery unit will provide a simple and highly visible delivery method. For instance, when the user is stressed, it will highlight only the key points of the plan and display them concisely. If the user is relaxed, it will provide a delivery method that includes detailed information. For example, when the user is relaxed, it will provide a display method that includes a detailed explanation of the plan and the steps to be taken. Furthermore, if the user is in a hurry, it will provide a delivery method that gets straight to the point. For example, when the user is in a hurry, it will display only the key points of the plan so that it can be understood in a short time. In this way, by adjusting the way the plan is delivered according to the user's emotions, it becomes possible to deliver the plan in a way that is easy for the user to understand.

[0085] The service provider can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider can adjust the delivery method based on the user's feedback on plans previously provided. For example, if the user previously provided feedback that "video-format plans are easy to understand," the service provider will prioritize providing video-format plans. The service provider will also prioritize using delivery methods that the user has preferred in the past (e.g., video, text). For example, if the user previously preferred text-format plans, the service provider will provide text-format plans. Furthermore, the service provider will analyze the user's past feedback and select the most effective delivery method. For example, based on the user's feedback on plans previously provided, the service provider will select and provide the most effective delivery method. By selecting the optimal delivery method based on past feedback, user satisfaction will improve.

[0086] The service provider can customize the content offered based on the user's current lifestyle and health condition. For example, the service provider will select content that is manageable considering the user's current health condition. For instance, if the user has been diagnosed with hypertension, they will provide a training plan and meal plan to manage their blood pressure. They will also customize the content considering the user's lifestyle (work schedule, family situation, etc.). For example, if the user is busy with work, they will provide a training plan that can be completed in a short time and a meal plan that is easy to prepare. Furthermore, if the user has a specific health condition (e.g., diabetes), they will prioritize displaying content related to that condition. For example, if the user has diabetes, they will provide a meal plan to manage their blood sugar levels. This allows for more effective support by providing content tailored to the user's lifestyle and health condition.

[0087] The service provider can estimate the user's emotions and prioritize the plans offered based on those emotions. For example, if the user is stressed, the service provider will prioritize plans with a relaxing effect. For instance, when the user is stressed, it will offer plans that include relaxing exercises such as yoga or meditation. If the user is relaxed, it will offer standard plans. For example, when the user is relaxed, it will offer standard training or meal plans. Furthermore, if the user is in a hurry, it will prioritize plans that are quick and effective. For example, when the user is in a hurry, it will offer exercises that can be completed in a short time or meal plans that are easy to prepare. In this way, by prioritizing plans according to the user's emotions, the service provider can offer the optimal plan for the user.

[0088] The service provider can select the most appropriate delivery method at the time of delivery, taking into account the user's geographical location. For example, the service provider can select a delivery method considering the climate and environment of the area where the user lives. For instance, if the user lives in a hot and humid region, they will provide training and meal plans related to heatstroke prevention. If the user is traveling, they will select a delivery method considering the climate and food culture of their destination. For example, if the user is traveling in a cold region, they will provide training and meal plans related to cold weather countermeasures. Furthermore, if the user wishes to train in a specific region, they will select a delivery method considering the facility information of that region. For example, if the user wishes to train in a mountainous area, they will provide training plans related to mountain climbing. In this way, by considering the user's geographical location, a more relevant delivery method can be provided.

[0089] The service provider can analyze a user's social media activity and provide relevant plans at the time of delivery. For example, the service provider can provide relevant plans based on health information shared by the user on social media. For instance, if a user posts on social media that they "recently started jogging," the service provider will provide a training plan related to jogging. The service provider can also customize plans based on information from training and nutrition experts that the user follows. For example, if a user follows a specific fitness instructor, the service provider will provide a plan related to the training recommended by that instructor. Furthermore, the service provider can provide plans based on information from health communities that the user participates in on social media. For example, if a user participates in a diet community, the service provider will provide a diet-related plan. In this way, by providing relevant plans based on social media activity, the service provider can provide plans that match the user's interests. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs information such as physical characteristics, health status, and preferences through an application. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the user's input information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an optimal training plan and meal plan using generation AI. The provision unit is implemented, for example, by the output device 40 of the smart device 14, and provides the generated plan to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs information such as physical characteristics, health status, and preferences by voice. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the user's input information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates an optimal training plan and meal plan using generation AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214, which provides the generated plan to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs information such as physical characteristics, health status, and preferences by voice. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the user's input information. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which generates an optimal training plan and meal plan using generation AI. The provision unit is implemented by, for example, the display 343 of the headset terminal 314, which provides the generated plan to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs information such as physical characteristics, health status, and preferences by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's input information. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal training plan and meal plan using a generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414, which provides the generated plan to the user.

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

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, it will prioritize analyzing training and meal plans that are effective in reducing stress. If the user is relaxed, it will analyze normal training and meal plans. Furthermore, if the user is in a hurry, it will prioritize analyzing plans that are effective in a short amount of time. In this way, by determining the priority of analysis according to the user's emotions, the system can provide the user with the most optimal plan.

[0092] The service provider can estimate the user's emotions and adjust the timing of plan delivery based on those emotions. For example, if the user is feeling stressed, the plan can be delivered during a time when they can relax. If the user is relaxed, the plan can be delivered at any time. Furthermore, if the user is in a hurry, a plan that can be completed in a short time can be delivered immediately. In this way, by adjusting the timing of plan delivery according to the user's emotions, the user can receive the plan at the optimal time.

[0093] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display information about the user's physical characteristics and health status that they have frequently entered in the past as suggestions. It also prioritizes suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will enter at specific times based on their past input history. In this way, by suggesting the most suitable input method based on past input history, the user's input efficiency is improved.

[0094] The analysis unit can perform analyses while considering the user's lifestyle and dietary history. For example, it can analyze data while considering the user's lifestyle (sleep patterns, exercise frequency, etc.). It can also perform analyses that consider nutritional balance based on the user's dietary history. Furthermore, it can analyze data while considering the user's lifestyle (work hours, frequency of vacations, etc.). By considering the user's lifestyle and dietary history, it can provide more accurate analysis results.

[0095] The generation unit can generate an optimal plan by referencing the user's past training history during the generation process. For example, it can generate an optimal plan based on the effectiveness of the user's past training. It can also generate a plan that matches the user's progress based on their past training history. Furthermore, it analyzes the user's past training history and evaluates the effectiveness of specific exercises. By generating an optimal plan based on past training history, it can provide a plan that matches the user's progress.

[0096] The service provider can customize the content offered based on the user's current lifestyle and health condition. For example, it can select content that is manageable considering the user's current health condition. It can also customize the content considering the user's lifestyle (work schedule, family situation, etc.). Furthermore, if the user has a specific health condition (e.g., diabetes), it can prioritize displaying content related to that condition. This allows for more effective support by providing content tailored to the user's lifestyle and health condition.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. Furthermore, if the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to display information in a way that is easy for the user to understand.

[0098] The generation unit can estimate the user's emotions and adjust the level of detail in the generated plan based on those emotions. For example, if the user is stressed, it will generate a simple and easy-to-follow plan. If the user is relaxed, it will generate a plan with detailed explanations. Furthermore, if the user is in a hurry, it will generate a quick and effective plan. In this way, by adjusting the level of detail in the plan according to the user's emotions, it can provide a plan that is easy for the user to follow.

[0099] The delivery unit can estimate the user's emotions and adjust the way the plan is delivered based on those emotions. For example, if the user is stressed, a simple and highly visual delivery method is provided. If the user is relaxed, a delivery method including detailed information is provided. Furthermore, if the user is in a hurry, a delivery method that gets straight to the point is provided. By adjusting the delivery method of the plan according to the user's emotions, it becomes possible to deliver the plan in a way that is easy for the user to understand.

[0100] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location during input. For example, if a user lives in a specific region, it will prioritize displaying health information related to that region. If a user is traveling, it will display input fields related to the climate and food culture of their destination. Furthermore, if a user wishes to train in a specific region, it will display facility information for that region as an input field. In this way, by considering the user's geographical location, it can provide more relevant input fields.

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

[0102] Step 1: The reception desk allows users to input information such as their physical characteristics, health status, desired appearance, and preferences. For example, users can input their height, weight, allergy information, and exercise preferences through the application. Step 2: The analysis unit analyzes the user's input information and generates an optimal training plan and meal plan for each individual. For example, the analysis unit considers the user's physical characteristics and health condition to determine the appropriate exercise intensity and dietary content. Step 3: The generation unit uses a generation AI to analyze the user's input information and generate an optimal plan. For example, the generation AI generates a plan that is effective for muscle building or weight loss based on the user's desired physique. Step 4: The service provider delivers the generated plan to the user. For example, the service provider displays training plans and meal plans to the user through the application. The service provider can also receive user feedback and adjust the plan accordingly.

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

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

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

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

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

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0174] [Explanation of symbols]

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

Claims

1. A reception area where user information is entered, An analysis unit analyzes the information input by the reception unit, A generation unit that generates a plan based on the analysis results obtained by the analysis unit, A providing unit that provides the plan generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is You will enter information such as the user's physical characteristics, health status, desired lifestyle, and preferences. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes user input information to generate personalized training and meal plans. The system according to feature 1.

4. The generating unit is Using generative AI, the system analyzes user input information and generates the optimal plan. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated plan to the user. The system according to feature 1.

6. The aforementioned supply unit is, We will gather user feedback and adjust the plan accordingly. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the level of detail in the input information based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

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