system

Abstract

The system according to the embodiment aims to provide a personalized vision training program according to the age and lifestyle of each individual. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, an execution unit, and a monitoring unit. The reception unit receives input of age or lifestyle. The generation unit generates a vision training program based on the information received by the reception unit. The provision unit provides the program generated by the generation unit. The execution unit executes the program provided by the provision unit. The monitoring unit monitors the progress of the exercise performed by the execution unit and adjusts the program as necessary.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to provide personalized vision training programs tailored to individual ages and lifestyles.

[0005] The system according to the embodiment aims to provide a personalized vision training program according to the age and lifestyle of each individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, an execution unit, and a monitoring unit. The reception unit receives input of age or lifestyle. The generation unit generates a vision training program based on the information received by the reception unit. The provision unit provides the program generated by the generation unit. The execution unit executes the program provided by the provision unit. The monitoring unit monitors the progress of the exercise performed by the execution unit and adjusts the program as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized vision training program according to the age and lifestyle of each individual. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A vision training system according to an embodiment of the present invention provides personalized vision training to improve eye mobility. The vision training system allows users to input their age and lifestyle information and generates an optimal vision training program based on that information. The program can be administered using a smartphone or tablet and aims to improve eye mobility through visual exercises. The system is also effective for people with disabilities (such as those with attention deficit hyperactivity disorder or Down's syndrome) and can be used by adults who have graduated from school and are lacking in mental and physical care. Eye training is expected to contribute to mental and physical care. For example, when a user inputs their age and lifestyle information, the system generates a personalized vision training program based on that information. The program includes visual exercises that the user can administer using a smartphone or tablet. The exercises are designed to improve eye mobility and aim to improve brain and physical performance through visual stimulation. Furthermore, the system is integrated into a messaging service, allowing users to easily access and use the system. For example, users can receive the vision training program through a chatbot in the messaging service and practice the training routine. Thus, the present invention aims to provide personalized vision training to improve eye mobility and brain and physical performance. This is particularly effective for children who spend a lot of time looking at smartphones and tablets, as well as people with disabilities and adults who lack mental and physical care. This allows the vision training system to provide an individual vision training program based on the user's age and lifestyle, and to improve the range of eye movement.

[0029] A vision training system according to an embodiment includes a reception unit, a generation unit, a provision unit, an execution unit, and a monitoring unit. The reception unit receives input from a user about their age and lifestyle. For example, the user can enter their age and daily activities. The generation unit generates a vision training program based on the information received by the reception unit. For example, the generation unit generates a program including exercises optimal for the user's age and lifestyle. The provision unit provides the program generated by the generation unit. For example, the provision unit sends the program to the user via a messaging service. The execution unit executes the program provided by the provision unit. For example, the execution unit executes visual exercises using a smartphone or tablet. The monitoring unit monitors the progress of the exercises executed by the execution unit and adjusts the program as necessary. For example, the monitoring unit evaluates the degree of completion of the exercises and changes in the user's eye range of motion and adjusts the program accordingly. This allows the vision training system according to an embodiment to provide a personalized vision training program based on the user's age and lifestyle, thereby improving the user's eye range of motion.

[0030] The generation unit can generate an individual vision training program based on the user's age or lifestyle. The individual vision training program is generated based on, for example, a customization method for each age or lifestyle. For example, the generation unit can generate a program for children, a program for the elderly, a program for athletes, a program for desk workers, etc. This makes it possible to provide an optimal vision training program according to the user's age and lifestyle.

[0031] The providing unit may provide the vision training program through a messaging service. Examples of messaging services include LINE (registered trademark), WhatsApp (registered trademark), and Facebook (registered trademark) Messenger. The providing unit transmits the vision training program to the user through these messaging services. For example, the providing unit may provide the program to the user through a LINE chatbot. This allows the user to easily receive the vision training program through the messaging service.

[0032] The execution unit can execute the visual exercise using a smartphone or a tablet. Examples of smartphones or tablets include iOS devices, Android devices, and the like. The execution unit executes the visual exercise using these devices. For example, the execution unit can execute the exercise using an app on the smartphone. Alternatively, the execution unit can execute the exercise using an app on the tablet. This allows the user to execute the visual exercise using the smartphone or tablet.

[0033] The monitoring unit can monitor the progress of the exercises and adjust the program as necessary. Criteria for evaluating the progress include, for example, the degree of completion of the exercises, changes in the range of eye movement, and the degree of improvement in vision. The monitoring unit monitors the progress of the exercises based on these criteria. For example, the monitoring unit can evaluate the degree of completion of the exercises and measure changes in the range of eye movement. The monitoring unit can also evaluate the degree of improvement in vision and adjust the program as necessary. This allows for effective training by monitoring the progress of the exercises and adjusting the program as necessary.

[0034] The generation unit can also generate a vision training program for a person with a disability or an adult who lacks physical or mental care. Persons with disabilities or adults who lack physical or mental care include, for example, visually impaired people, motor impairments, and people who lack mental care. The generation unit generates a vision training program that is effective for these users as well. For example, the generation unit can generate a program for a person with a visual impairment, a program for a person with a motor impairment, and a program for adults who require mental care. This makes it possible to provide an effective vision training program for a person with a disability or an adult who lacks physical or mental care.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. Methods for collecting past input history include, for example, log data storage methods and data analysis algorithms. The reception unit uses these methods to collect and analyze the user's past input history. For example, the reception unit can automatically display information about the user's age and lifestyle that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest age and lifestyle information that will be used during a specific time period based on the user's past input history. This makes user input more efficient by providing an optimal input interface based on the user's past input history.

[0036] The reception unit can filter the input of age and lifestyle information based on the user's current health condition and lifestyle habits. Examples of health conditions and lifestyle habits include exercise habits, dietary habits, and sleep patterns. The reception unit filters input options for age and lifestyle based on this information. For example, the reception unit can provide appropriate input options for age and lifestyle based on the user's health condition. The reception unit can also prioritize displaying highly relevant age and lifestyle information based on the user's lifestyle habits. Furthermore, the reception unit can automatically adjust the input content based on the user's health condition and lifestyle habits. This allows more accurate information to be input by providing appropriate input options based on the user's health condition and lifestyle habits.

[0037] When inputting age and lifestyle information, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The reception unit filters input options for age and lifestyle based on this information. For example, if the user lives in a specific region, the reception unit can cause the user to prioritize input of lifestyle information related to that region. Furthermore, if the user is traveling, the reception unit can also cause the user to prioritize input of relevant information based on the user's current location. Furthermore, if the user lives in a specific city, the reception unit can also cause the user to prioritize input of age and lifestyle information related to that city. In this way, more relevant information is input by taking into account the user's geographical location information.

[0038] When inputting age and lifestyle information, the reception unit can analyze the user's social media activity and input relevant information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit filters input options for age and lifestyle based on this information. For example, the reception unit can analyze the content of the user's social media posts and prompt the user to input relevant age and lifestyle information. The reception unit can also analyze the user's social media friendships and prompt the user to input relevant information. Furthermore, the reception unit can analyze the user's social media activity history and prompt the user to input relevant age and lifestyle information. In this way, more relevant information can be input by analyzing the user's social media activity.

[0039] When generating a vision training program, the generation unit can adjust the intensity of the program based on the user's health condition. Health conditions include, for example, heart rate, blood pressure, and weight. The generation unit adjusts the intensity of the vision training program based on this information. For example, if the user is in good health, the generation unit can generate a program including high-intensity exercises. Furthermore, if the user is tired, the generation unit can generate a program including lighter exercises. Furthermore, if the user is not feeling well, the generation unit can generate a program including exercises that have a relaxing effect. In this way, by adjusting the intensity of the program according to the user's health condition, more appropriate training can be provided.

[0040] When generating a vision training program, the generation unit can apply different training algorithms depending on the user's lifestyle. Lifestyle habits include, for example, exercise habits, dietary habits, and sleep patterns. The generation unit adjusts the algorithm of the vision training program based on this information. For example, if the user lives a desk-based lifestyle, the generation unit can generate a program including exercises to reduce eye fatigue. Also, if the user prefers outdoor activities, the generation unit can generate a program including exercises to improve visual reaction speed. Furthermore, if the user lives a nocturnal lifestyle, the generation unit can generate a program including exercises to improve nighttime vision. In this way, by applying a training algorithm according to the user's lifestyle, more effective training can be provided.

[0041] When generating a vision training program, the generation unit can determine the priority of the program based on the user's activity history. The activity history includes, for example, past training history, exercise records, etc. The generation unit determines the priority of the vision training program based on this information. For example, the generation unit can generate a program that prioritizes the most effective exercises based on the history of exercises the user has performed in the past. The generation unit can also generate a program that prioritizes specific exercises from the user's activity history. Furthermore, the generation unit can analyze the user's activity history and generate a program that prioritizes the most effective exercises. In this way, by determining the priority of the program based on the user's activity history, more effective training can be provided.

[0042] When generating a vision training program, the generation unit can adjust the order of the program based on the user's related data. The related data includes, for example, health data, exercise data, and dietary data. The generation unit adjusts the order of the vision training program based on this information. For example, the generation unit can generate a program including exercises in an optimal order based on the user's health data. The generation unit can also generate a program including exercises in an optimal order based on the user's lifestyle habit data. Furthermore, the generation unit can generate a program including exercises in an optimal order based on the user's past training data. In this way, adjusting the order of the program based on the user's related data provides more effective training.

[0043] When providing a program, the providing unit can select the optimal delivery method by referring to the user's past usage history. The past usage history includes, for example, past training history, device usage history, etc. The providing unit selects the program delivery method based on this information. For example, the providing unit can provide the program in the optimal method based on the delivery method used by the user in the past. The providing unit can also select the most effective delivery method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most effective delivery method. In this way, more effective training can be provided by selecting the optimal delivery method based on the user's past usage history.

[0044] When providing a program, the providing unit can customize the method of providing the program based on the user's device information. The device information includes, for example, the device type, the OS version, and the apps used. The providing unit customizes the method of providing the program based on this information. For example, if the user is using a smartphone, the providing unit can provide the program in a manner optimized for the smartphone. Also, if the user is using a tablet, the providing unit can provide the program in a manner optimized for the tablet. Furthermore, if the user is using a desktop, the providing unit can provide the program in a manner optimized for the desktop. In this way, by customizing the method of providing the program based on the user's device information, more effective training can be provided.

[0045] When providing a program, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The providing unit selects the program delivery method based on this information. For example, if the user lives in a specific region, the providing unit can provide the program in a method optimal for that region. Furthermore, if the user is traveling, the providing unit can also provide the program in an optimal method based on the user's current location. Furthermore, if the user lives in a specific city, the providing unit can also provide the program in a method optimal for that city. In this way, more effective training can be provided by taking into account the user's geographical location information.

[0046] When providing a program, the provision unit can analyze the user's social media activity and suggest a provision method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The provision unit suggests a program provision method based on this information. For example, the provision unit can analyze the content of the user's social media posts and provide the program in an optimal manner. The provision unit can also analyze the user's social media friendships and provide the program in an optimal manner. Furthermore, the provision unit can analyze the user's social media activity history and provide the program in an optimal manner. In this way, more effective training can be provided by analyzing the user's social media activity.

[0047] When performing an exercise, the execution unit can select the optimal execution method by referring to the user's past training history. The past training history includes, for example, past exercise content, training frequency, etc. The execution unit selects the exercise execution method based on this information. For example, the execution unit can select the most effective execution method based on the history of exercises the user has performed in the past. The execution unit can also prioritize the execution of specific exercises from the user's past training history. Furthermore, the execution unit can analyze the user's past training history and select the most effective execution method. In this way, more effective training can be provided by selecting the optimal execution method based on the user's past training history.

[0048] The execution unit can customize the execution method based on the user's current health condition when performing an exercise. Health conditions include, for example, heart rate, blood pressure, and weight. The execution unit customizes the exercise execution method based on this information. For example, if the user is in good health, the execution unit can execute a high-intensity exercise. If the user is tired, the execution unit can also execute a lighter exercise. Furthermore, if the user is not feeling well, the execution unit can execute an exercise that has a relaxing effect. In this way, by customizing the execution method based on the user's current health condition, more effective training can be provided.

[0049] When performing an exercise, the execution unit can select the optimal execution method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The execution unit selects the exercise execution method based on this information. For example, if the user lives in a specific region, the execution unit can execute the exercise in a method optimal for that region. Furthermore, if the user is traveling, the execution unit can execute the exercise in a method optimal for the user's current location. Furthermore, if the user lives in a specific city, the execution unit can execute the exercise in a method optimal for that city. In this way, by taking into account the user's geographical location information, more effective training can be provided.

[0050] When performing an exercise, the execution unit can analyze the user's social media activity and suggest an exercise method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The execution unit suggests an exercise method based on this information. For example, the execution unit can analyze the content of the user's social media posts and perform the exercise in the optimal way. The execution unit can also analyze the user's social media friendships and perform the exercise in the optimal way. Furthermore, the execution unit can analyze the user's social media activity history and perform the exercise in the optimal way. In this way, more effective training can be provided by analyzing the user's social media activity.

[0051] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past training history. The past training history includes, for example, past exercise content, training frequency, etc. The monitoring unit selects the monitoring method based on this information. For example, the monitoring unit can select the most effective monitoring method based on the user's past exercise history. The monitoring unit can also prioritize monitoring of specific exercises from the user's past training history. Furthermore, the monitoring unit can analyze the user's past training history and select the most effective monitoring method. In this way, more effective training can be provided by selecting the optimal monitoring method based on the user's past training history.

[0052] During monitoring, the monitoring unit can customize the monitoring method based on the user's current health condition. Health conditions include, for example, heart rate, blood pressure, and weight. The monitoring unit customizes the monitoring method based on this information. For example, the monitoring unit can perform high-intensity monitoring when the user is in good health. The monitoring unit can also perform lighter monitoring when the user is tired. Furthermore, the monitoring unit can also perform monitoring that has a relaxing effect when the user is not feeling well. In this way, customizing the monitoring method based on the user's current health condition provides more effective training.

[0053] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The monitoring unit selects the monitoring method based on this information. For example, if the user lives in a specific region, the monitoring unit can perform monitoring using a method optimal for that region. Furthermore, if the user is traveling, the monitoring unit can perform monitoring using a method optimal for the user's current location. Furthermore, if the user lives in a specific city, the monitoring unit can perform monitoring using a method optimal for that city. In this way, more effective training can be provided by taking into account the user's geographical location information.

[0054] During monitoring, the monitoring unit can analyze the user's social media activity and suggest a monitoring method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The monitoring unit suggests a monitoring method based on this information. For example, the monitoring unit can analyze the content of the user's social media posts and perform monitoring in an optimal manner. The monitoring unit can also analyze the user's social media friendships and perform monitoring in an optimal manner. Furthermore, the monitoring unit can analyze the user's social media activity history and perform monitoring in an optimal manner. In this way, more effective training can be provided by analyzing the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can refer to the user's past training history and suggest an optimal vision training program. For example, a program including the most effective exercises can be generated based on the user's past exercise history. The reception unit can also suggest a program that prioritizes specific exercises based on the user's past training history. Furthermore, the reception unit can analyze the user's past training history and suggest a program that prioritizes the most effective exercises. This allows for more effective training by suggesting an optimal program based on the user's past training history.

[0057] The generation unit can adjust the content of the vision training program based on the user's health data. For example, a program including exercises suited to the user's health condition can be generated based on data such as heart rate, blood pressure, and weight. The generation unit can also generate a program including light exercises if the user is tired. Furthermore, the generation unit can generate a program including exercises that have a relaxing effect if the user is not feeling well. In this way, by adjusting the content of the program according to the user's health condition, more appropriate training can be provided.

[0058] The providing unit can provide a vision training program based on the user's geographic location information. For example, if the user lives in a specific area, the providing unit can provide a program based on lifestyle information related to that area. In addition, if the user is traveling, the providing unit can provide relevant information based on the user's current location. Furthermore, if the user lives in a specific city, the providing unit can provide a program based on age and lifestyle information related to that city. In this way, more relevant information can be provided by taking the user's geographic location information into consideration.

[0059] The execution unit can analyze the user's social media activity and suggest optimal exercises. For example, it can analyze the content of the user's social media posts and suggest related exercises. The execution unit can also analyze the user's social media friendships and suggest related exercises. Furthermore, the execution unit can analyze the user's social media activity history and suggest related exercises. In this way, more effective training can be provided by analyzing the user's social media activity.

[0060] The monitoring unit can customize the monitoring method based on the user's current health condition. For example, monitoring can be performed according to the user's health condition based on data such as heart rate, blood pressure, and weight. The monitoring unit can also perform lighter monitoring if the user is tired. Furthermore, the monitoring unit can also perform monitoring that has a relaxing effect if the user is not feeling well. In this way, customizing the monitoring method based on the user's current health condition provides more effective training.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives input of the user's age and lifestyle. For example, the user can input their age and daily activities. Step 2: The generator generates a vision training program based on the information received by the receiver. For example, the generator generates a program including optimal exercises according to the user's age and lifestyle. Step 3: The providing unit provides the program generated by the generating unit. For example, the providing unit transmits the program to the user through a messaging service. Step 4: The execution unit executes the program provided by the provider. For example, the execution unit executes a visual exercise using a smartphone or tablet. Step 5: The monitoring unit monitors the progress of the exercises performed by the execution unit and adjusts the program as necessary. For example, the monitoring unit evaluates the degree of completion of the exercises and changes in the range of eye movement, and adjusts the program accordingly.

[0063] (Example 2) A vision training system according to an embodiment of the present invention provides personalized vision training to improve eye mobility. The vision training system allows users to input their age and lifestyle information and generates an optimal vision training program based on that information. The program can be administered using a smartphone or tablet and aims to improve eye mobility through visual exercises. The system is also effective for people with disabilities (such as those with attention deficit hyperactivity disorder or Down's syndrome) and can be used by adults who have graduated from school and are lacking in mental and physical care. Eye training is expected to contribute to mental and physical care. For example, when a user inputs their age and lifestyle information, the system generates a personalized vision training program based on that information. The program includes visual exercises that the user can administer using a smartphone or tablet. The exercises are designed to improve eye mobility and aim to improve brain and physical performance through visual stimulation. Furthermore, the system is integrated into a messaging service, allowing users to easily access and use the system. For example, users can receive the vision training program through a chatbot in the messaging service and practice the training routine. Thus, the present invention aims to provide personalized vision training to improve eye mobility and brain and physical performance. This is particularly effective for children who spend a lot of time looking at smartphones and tablets, as well as people with disabilities and adults who lack mental and physical care. This allows the vision training system to provide an individual vision training program based on the user's age and lifestyle, and to improve the range of eye movement.

[0064] A vision training system according to an embodiment includes a reception unit, a generation unit, a provision unit, an execution unit, and a monitoring unit. The reception unit receives input from a user about their age and lifestyle. For example, the user can enter their age and daily activities. The generation unit generates a vision training program based on the information received by the reception unit. For example, the generation unit generates a program including exercises optimal for the user's age and lifestyle. The provision unit provides the program generated by the generation unit. For example, the provision unit sends the program to the user via a messaging service. The execution unit executes the program provided by the provision unit. For example, the execution unit executes visual exercises using a smartphone or tablet. The monitoring unit monitors the progress of the exercises executed by the execution unit and adjusts the program as necessary. For example, the monitoring unit evaluates the degree of completion of the exercises and changes in the user's eye range of motion and adjusts the program accordingly. This allows the vision training system according to an embodiment to provide a personalized vision training program based on the user's age and lifestyle, thereby improving the user's eye range of motion.

[0065] The generation unit can generate an individual vision training program based on the user's age or lifestyle. The individual vision training program is generated based on, for example, a customization method for each age or lifestyle. For example, the generation unit can generate a program for children, a program for the elderly, a program for athletes, a program for desk workers, etc. This makes it possible to provide an optimal vision training program according to the user's age and lifestyle.

[0066] The providing unit may provide the vision training program through a messaging service. Examples of messaging services include LINE, WhatsApp, and Facebook Messenger. The providing unit transmits the vision training program to the user through these messaging services. For example, the providing unit may provide the program to the user through a LINE chatbot. This allows the user to easily receive the vision training program through the messaging service.

[0067] The execution unit can execute the visual exercise using a smartphone or a tablet. Examples of smartphones or tablets include iOS devices, Android devices, and the like. The execution unit executes the visual exercise using these devices. For example, the execution unit can execute the exercise using an app on the smartphone. Alternatively, the execution unit can execute the exercise using an app on the tablet. This allows the user to execute the visual exercise using the smartphone or tablet.

[0068] The monitoring unit can monitor the progress of the exercises and adjust the program as necessary. Criteria for evaluating the progress include, for example, the degree of completion of the exercises, changes in the range of eye movement, and the degree of improvement in vision. The monitoring unit monitors the progress of the exercises based on these criteria. For example, the monitoring unit can evaluate the degree of completion of the exercises and measure changes in the range of eye movement. The monitoring unit can also evaluate the degree of improvement in vision and adjust the program as necessary. This allows for effective training by monitoring the progress of the exercises and adjusting the program as necessary.

[0069] The generation unit can also generate a vision training program for a person with a disability or an adult who lacks physical or mental care. Persons with disabilities or adults who lack physical or mental care include, for example, visually impaired people, motor impairments, and people who lack mental care. The generation unit generates a vision training program that is effective for these users as well. For example, the generation unit can generate a program for a person with a visual impairment, a program for a person with a motor impairment, and a program for adults who require mental care. This makes it possible to provide an effective vision training program for a person with a disability or an adult who lacks physical or mental care.

[0070] The reception unit can estimate the user's emotions and adjust the input method for age and lifestyle information based on the estimated user emotions. Emotion estimation methods include, for example, facial expression recognition, voice analysis, and self-reporting. The reception unit estimates the user's emotions using these methods. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the reception unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the reception unit can adjust the input method for age and lifestyle information. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly input their age and lifestyle information. This allows the input method to be adjusted according to the user's emotions, enabling more appropriate input.

[0071] The reception unit can analyze the user's past input history and provide an optimal input interface. Methods for collecting past input history include, for example, log data storage methods and data analysis algorithms. The reception unit uses these methods to collect and analyze the user's past input history. For example, the reception unit can automatically display information about the user's age and lifestyle that the user has frequently entered in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest age and lifestyle information that will be used during a specific time period based on the user's past input history. This makes user input more efficient by providing an optimal input interface based on the user's past input history.

[0072] The reception unit can filter the input of age and lifestyle information based on the user's current health condition and lifestyle habits. Examples of health conditions and lifestyle habits include exercise habits, dietary habits, and sleep patterns. The reception unit filters input options for age and lifestyle based on this information. For example, the reception unit can provide appropriate input options for age and lifestyle based on the user's health condition. The reception unit can also prioritize displaying highly relevant age and lifestyle information based on the user's lifestyle habits. Furthermore, the reception unit can automatically adjust the input content based on the user's health condition and lifestyle habits. This allows more accurate information to be input by providing appropriate input options based on the user's health condition and lifestyle habits.

[0073] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. Emotion estimation methods include, for example, facial expression recognition, voice analysis, and self-reporting. The reception unit estimates the user's emotions using these methods. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the reception unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the reception unit determines the priority of information to be input. For example, if the user is feeling stressed, the reception unit can prioritize input of the most important information. If the user is relaxed, the reception unit can prioritize input of detailed information. If the user is in a hurry, the reception unit can prioritize input of the minimum necessary information. This allows efficient input by determining the priority of information to be input based on the user's emotions.

[0074] When inputting age and lifestyle information, the reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The reception unit filters input options for age and lifestyle based on this information. For example, if the user lives in a specific region, the reception unit can cause the user to prioritize input of lifestyle information related to that region. Furthermore, if the user is traveling, the reception unit can also cause the user to prioritize input of relevant information based on the user's current location. Furthermore, if the user lives in a specific city, the reception unit can also cause the user to prioritize input of age and lifestyle information related to that city. In this way, more relevant information is input by taking into account the user's geographical location information.

[0075] When inputting age and lifestyle information, the reception unit can analyze the user's social media activity and input relevant information. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The reception unit filters input options for age and lifestyle based on this information. For example, the reception unit can analyze the content of the user's social media posts and prompt the user to input relevant age and lifestyle information. The reception unit can also analyze the user's social media friendships and prompt the user to input relevant information. Furthermore, the reception unit can analyze the user's social media activity history and prompt the user to input relevant age and lifestyle information. In this way, more relevant information can be input by analyzing the user's social media activity.

[0076] The generation unit can estimate the user's emotions and adjust the content of the vision training program based on the estimated user's emotions. Emotion estimation includes, for example, methods such as facial expression recognition, voice analysis, and self-reporting. The generation unit estimates the user's emotions using these methods. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the generation unit adjusts the content of the vision training program. For example, if the user is relaxed, the generation unit generates a vision training program that progresses at a leisurely pace. If the user is in a hurry, the generation unit generates a program that includes short, effective exercises. Furthermore, if the user is excited, the generation unit generates a program that includes visually stimulating exercises. This allows for more effective training by adjusting the content of the program according to the user's emotions.

[0077] When generating a vision training program, the generation unit can adjust the intensity of the program based on the user's health condition. Health conditions include, for example, heart rate, blood pressure, and weight. The generation unit adjusts the intensity of the vision training program based on this information. For example, if the user is in good health, the generation unit can generate a program including high-intensity exercises. Furthermore, if the user is tired, the generation unit can generate a program including lighter exercises. Furthermore, if the user is not feeling well, the generation unit can generate a program including exercises that have a relaxing effect. In this way, by adjusting the intensity of the program according to the user's health condition, more appropriate training can be provided.

[0078] When generating a vision training program, the generation unit can apply different training algorithms depending on the user's lifestyle. Lifestyle habits include, for example, exercise habits, dietary habits, and sleep patterns. The generation unit adjusts the algorithm of the vision training program based on this information. For example, if the user lives a desk-based lifestyle, the generation unit can generate a program including exercises to reduce eye fatigue. Also, if the user prefers outdoor activities, the generation unit can generate a program including exercises to improve visual reaction speed. Furthermore, if the user lives a nocturnal lifestyle, the generation unit can generate a program including exercises to improve nighttime vision. In this way, by applying a training algorithm according to the user's lifestyle, more effective training can be provided.

[0079] The generation unit can estimate the user's emotions and adjust the length of the program based on the estimated user emotions. Emotion estimation includes, for example, methods such as facial expression recognition, voice analysis, and self-reporting. The generation unit estimates the user's emotions using these methods. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can estimate the emotions based on the user's self-reported emotions. The generation unit adjusts the length of the program based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a program including short, effective exercises. If the user is relaxed, the generation unit generates a program including long exercises. Furthermore, if the user is excited, the generation unit generates a program including visually stimulating exercises. In this way, by adjusting the length of the program according to the user's emotions, more effective training can be provided.

[0080] When generating a vision training program, the generation unit can determine the priority of the program based on the user's activity history. The activity history includes, for example, past training history, exercise records, etc. The generation unit determines the priority of the vision training program based on this information. For example, the generation unit can generate a program that prioritizes the most effective exercises based on the history of exercises the user has performed in the past. The generation unit can also generate a program that prioritizes specific exercises from the user's activity history. Furthermore, the generation unit can analyze the user's activity history and generate a program that prioritizes the most effective exercises. In this way, by determining the priority of the program based on the user's activity history, more effective training can be provided.

[0081] When generating a vision training program, the generation unit can adjust the order of the program based on the user's related data. The related data includes, for example, health data, exercise data, and dietary data. The generation unit adjusts the order of the vision training program based on this information. For example, the generation unit can generate a program including exercises in an optimal order based on the user's health data. The generation unit can also generate a program including exercises in an optimal order based on the user's lifestyle habit data. Furthermore, the generation unit can generate a program including exercises in an optimal order based on the user's past training data. In this way, adjusting the order of the program based on the user's related data provides more effective training.

[0082] The providing unit can estimate the user's emotions and adjust the program delivery method based on the estimated user's emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, and self-reporting. The providing unit estimates the user's emotions using these methods. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the providing unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the providing unit adjusts the program delivery method. For example, if the user is relaxed, the program is delivered at a leisurely pace. If the user is in a hurry, the program is delivered quickly. Furthermore, if the user is excited, the program is delivered in a visually stimulating manner. By adjusting the program delivery method according to the user's emotions, more effective training can be provided.

[0083] When providing a program, the providing unit can select the optimal delivery method by referring to the user's past usage history. The past usage history includes, for example, past training history, device usage history, etc. The providing unit selects the program delivery method based on this information. For example, the providing unit can provide the program in the optimal method based on the delivery method used by the user in the past. The providing unit can also select the most effective delivery method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most effective delivery method. In this way, more effective training can be provided by selecting the optimal delivery method based on the user's past usage history.

[0084] When providing a program, the providing unit can customize the method of providing the program based on the user's device information. The device information includes, for example, the device type, the OS version, and the apps used. The providing unit customizes the method of providing the program based on this information. For example, if the user is using a smartphone, the providing unit can provide the program in a manner optimized for the smartphone. Also, if the user is using a tablet, the providing unit can provide the program in a manner optimized for the tablet. Furthermore, if the user is using a desktop, the providing unit can provide the program in a manner optimized for the desktop. In this way, by customizing the method of providing the program based on the user's device information, more effective training can be provided.

[0085] The providing unit can estimate the user's emotions and adjust the frequency of program provision based on the estimated user's emotions. Emotion estimation includes, for example, methods such as facial expression recognition, voice analysis, and self-reporting. The providing unit estimates the user's emotions using these methods. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the providing unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the providing unit adjusts the frequency of program provision. For example, if the user is relaxed, the program is provided more frequently. If the user is in a hurry, the program is provided less frequently. Furthermore, if the user is excited, the program is provided more frequently in a visually stimulating manner. This allows for more effective training by adjusting the frequency of program provision according to the user's emotions.

[0086] When providing a program, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The providing unit selects the program delivery method based on this information. For example, if the user lives in a specific region, the providing unit can provide the program in a method optimal for that region. Furthermore, if the user is traveling, the providing unit can also provide the program in an optimal method based on the user's current location. Furthermore, if the user lives in a specific city, the providing unit can also provide the program in a method optimal for that city. In this way, more effective training can be provided by taking into account the user's geographical location information.

[0087] When providing a program, the provision unit can analyze the user's social media activity and suggest a provision method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The provision unit suggests a program provision method based on this information. For example, the provision unit can analyze the content of the user's social media posts and provide the program in an optimal manner. The provision unit can also analyze the user's social media friendships and provide the program in an optimal manner. Furthermore, the provision unit can analyze the user's social media activity history and provide the program in an optimal manner. In this way, more effective training can be provided by analyzing the user's social media activity.

[0088] The execution unit can estimate the user's emotions and adjust the exercise execution method based on the estimated user's emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, and self-reporting. The execution unit estimates the user's emotions using these methods. For example, the execution unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The execution unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the execution unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the execution unit adjusts the exercise execution method. For example, if the user is relaxed, the exercises can be performed at a leisurely pace. If the user is in a hurry, the exercises can be performed in a short, effective manner. If the user is excited, the exercises can be performed in a visually stimulating manner. By adjusting the exercise execution method according to the user's emotions, more effective training can be provided.

[0089] When performing an exercise, the execution unit can select the optimal execution method by referring to the user's past training history. The past training history includes, for example, past exercise content, training frequency, etc. The execution unit selects the exercise execution method based on this information. For example, the execution unit can select the most effective execution method based on the history of exercises the user has performed in the past. The execution unit can also prioritize the execution of specific exercises from the user's past training history. Furthermore, the execution unit can analyze the user's past training history and select the most effective execution method. In this way, more effective training can be provided by selecting the optimal execution method based on the user's past training history.

[0090] The execution unit can customize the execution method based on the user's current health condition when performing an exercise. Health conditions include, for example, heart rate, blood pressure, and weight. The execution unit customizes the exercise execution method based on this information. For example, if the user is in good health, the execution unit can execute a high-intensity exercise. If the user is tired, the execution unit can also execute a lighter exercise. Furthermore, if the user is not feeling well, the execution unit can execute an exercise that has a relaxing effect. In this way, by customizing the execution method based on the user's current health condition, more effective training can be provided.

[0091] The execution unit can estimate the user's emotions and prioritize exercises based on the estimated user's emotions. Emotion estimation can be achieved using methods such as facial expression recognition, voice analysis, and self-reporting. The execution unit estimates the user's emotions using these methods. For example, the execution unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The execution unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the execution unit can estimate the emotions based on the user's self-reported emotions. The execution unit prioritizes exercises based on the estimated emotions. For example, if the user is relaxed, exercises that have a relaxing effect can be prioritized. If the user is in a hurry, exercises that are effective and can be completed in a short amount of time can be prioritized. If the user is excited, exercises that are visually stimulating can be prioritized. Thus, by prioritizing exercises according to the user's emotions, more effective training can be provided.

[0092] When performing an exercise, the execution unit can select the optimal execution method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The execution unit selects the exercise execution method based on this information. For example, if the user lives in a specific region, the execution unit can execute the exercise in a method optimal for that region. Furthermore, if the user is traveling, the execution unit can execute the exercise in a method optimal for the user's current location. Furthermore, if the user lives in a specific city, the execution unit can execute the exercise in a method optimal for that city. In this way, by taking into account the user's geographical location information, more effective training can be provided.

[0093] When performing an exercise, the execution unit can analyze the user's social media activity and suggest an exercise method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The execution unit suggests an exercise method based on this information. For example, the execution unit can analyze the content of the user's social media posts and perform the exercise in the optimal way. The execution unit can also analyze the user's social media friendships and perform the exercise in the optimal way. Furthermore, the execution unit can analyze the user's social media activity history and perform the exercise in the optimal way. In this way, more effective training can be provided by analyzing the user's social media activity.

[0094] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. Emotion estimation includes, for example, methods such as facial expression recognition, voice analysis, and self-reporting. The monitoring unit estimates the user's emotions using these methods. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the monitoring unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the monitoring unit adjusts the monitoring method. For example, if the user is relaxed, monitoring is performed at a leisurely pace. If the user is in a hurry, monitoring is performed quickly. Furthermore, if the user is excited, monitoring is performed in a visually stimulating manner. In this way, by adjusting the monitoring method according to the user's emotions, more effective training can be provided.

[0095] During monitoring, the monitoring unit can select the optimal monitoring method by referring to the user's past training history. The past training history includes, for example, past exercise content, training frequency, etc. The monitoring unit selects the monitoring method based on this information. For example, the monitoring unit can select the most effective monitoring method based on the user's past exercise history. The monitoring unit can also prioritize monitoring of specific exercises from the user's past training history. Furthermore, the monitoring unit can analyze the user's past training history and select the most effective monitoring method. In this way, more effective training can be provided by selecting the optimal monitoring method based on the user's past training history.

[0096] During monitoring, the monitoring unit can customize the monitoring method based on the user's current health condition. Health conditions include, for example, heart rate, blood pressure, and weight. The monitoring unit customizes the monitoring method based on this information. For example, the monitoring unit can perform high-intensity monitoring when the user is in good health. The monitoring unit can also perform lighter monitoring when the user is tired. Furthermore, the monitoring unit can also perform monitoring that has a relaxing effect when the user is not feeling well. In this way, customizing the monitoring method based on the user's current health condition provides more effective training.

[0097] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. Emotion estimation includes, for example, methods such as facial expression recognition, voice analysis, and self-reporting. The monitoring unit estimates the user's emotions using these methods. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the monitoring unit can estimate the emotions based on the user's self-reported emotions. Based on the estimated emotions, the monitoring unit adjusts the monitoring frequency. For example, if the user is relaxed, the monitoring unit performs monitoring more frequently. If the user is in a hurry, the monitoring unit reduces the monitoring frequency. Furthermore, if the user is excited, the monitoring unit performs monitoring more frequently in a visually stimulating manner. This allows for more effective training by adjusting the monitoring frequency according to the user's emotions.

[0098] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. Geographical location information includes, for example, city, region, country, etc. The monitoring unit selects the monitoring method based on this information. For example, if the user lives in a specific region, the monitoring unit can perform monitoring using a method optimal for that region. Furthermore, if the user is traveling, the monitoring unit can perform monitoring using a method optimal for the user's current location. Furthermore, if the user lives in a specific city, the monitoring unit can perform monitoring using a method optimal for that city. In this way, more effective training can be provided by taking into account the user's geographical location information.

[0099] During monitoring, the monitoring unit can analyze the user's social media activity and suggest a monitoring method. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. The monitoring unit suggests a monitoring method based on this information. For example, the monitoring unit can analyze the content of the user's social media posts and perform monitoring in an optimal manner. The monitoring unit can also analyze the user's social media friendships and perform monitoring in an optimal manner. Furthermore, the monitoring unit can analyze the user's social media activity history and perform monitoring in an optimal manner. In this way, more effective training can be provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, provision unit, execution unit, and monitoring unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, and the user inputs their age and lifestyle information. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates a vision training program based on the information received by the reception unit. The provision unit is implemented, for example, by the output device 40 of the smart device 14, and provides the generated program to the user. The execution unit is implemented by the control unit 46A of the smart device 14, and executes the visual exercise. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the exercise and adjusts the program as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, execution unit, and monitoring unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs their age and lifestyle. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a vision training program based on the information received by the reception unit. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the generated program to the user. The execution unit is realized by the control unit 46A of the smart glasses 214, and executes visual exercises. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the exercise and adjusts the program as necessary. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, execution unit, and monitoring unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs their age and lifestyle. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a vision training program based on the information received by the reception unit. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated program to the user. The execution unit is realized by the control unit 46A of the headset-type terminal 314, and executes visual exercises. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the exercise and adjusts the program as necessary. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, execution unit, and monitoring unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs their age and lifestyle. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a vision training program based on the information received by the reception unit. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated program to the user. The execution unit is realized by the control unit 46A of the robot 414, and executes the visual exercise. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the progress of the exercise and adjusts the program as necessary.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can refer to the user's past training history and suggest an optimal vision training program. For example, a program including the most effective exercises can be generated based on the user's past exercise history. The reception unit can also suggest a program that prioritizes specific exercises based on the user's past training history. Furthermore, the reception unit can analyze the user's past training history and suggest a program that prioritizes the most effective exercises. This allows for more effective training by suggesting an optimal program based on the user's past training history.

[0102] The generation unit can adjust the content of the vision training program based on the user's health data. For example, a program including exercises suited to the user's health condition can be generated based on data such as heart rate, blood pressure, and weight. The generation unit can also generate a program including light exercises if the user is tired. Furthermore, the generation unit can generate a program including exercises that have a relaxing effect if the user is not feeling well. In this way, by adjusting the content of the program according to the user's health condition, more appropriate training can be provided.

[0103] The providing unit can provide a vision training program based on the user's geographic location information. For example, if the user lives in a specific area, the providing unit can provide a program based on lifestyle information related to that area. In addition, if the user is traveling, the providing unit can provide relevant information based on the user's current location. Furthermore, if the user lives in a specific city, the providing unit can provide a program based on age and lifestyle information related to that city. In this way, more relevant information can be provided by taking the user's geographic location information into consideration.

[0104] The execution unit can analyze the user's social media activity and suggest optimal exercises. For example, it can analyze the content of the user's social media posts and suggest related exercises. The execution unit can also analyze the user's social media friendships and suggest related exercises. Furthermore, the execution unit can analyze the user's social media activity history and suggest related exercises. In this way, more effective training can be provided by analyzing the user's social media activity.

[0105] The monitoring unit can customize the monitoring method based on the user's current health condition. For example, monitoring can be performed according to the user's health condition based on data such as heart rate, blood pressure, and weight. The monitoring unit can also perform lighter monitoring if the user is tired. Furthermore, the monitoring unit can also perform monitoring that has a relaxing effect if the user is not feeling well. In this way, customizing the monitoring method based on the user's current health condition provides more effective training.

[0106] The reception unit can estimate the user's emotions and adjust the input method for age and lifestyle based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface is provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options are provided and a customizable input method is suggested. Furthermore, if the user is in a hurry, voice input is prioritized to enable the user to quickly input age and lifestyle. This allows for more appropriate input by adjusting the input method according to the user's emotions.

[0107] The generation unit can estimate the user's emotions and adjust the content of the vision training program based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a vision training program that proceeds at a leisurely pace. If the user is in a hurry, the generation unit generates a program that includes short, effective exercises. Furthermore, if the user is excited, the generation unit generates a program that includes visually stimulating exercises. In this way, more effective training can be achieved by adjusting the content of the program according to the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the program provision method based on the estimated user's emotions. For example, if the user is relaxed, the program is provided at a leisurely pace. If the user is in a hurry, the program is provided quickly. Furthermore, if the user is excited, the program is provided in a visually stimulating manner. In this way, by adjusting the program provision method according to the user's emotions, more effective training can be provided.

[0109] The exercise unit can estimate the user's emotions and adjust the exercise execution method based on the estimated user emotions. For example, if the user is relaxed, the exercise is executed at a leisurely pace. If the user is in a hurry, an effective exercise is executed in a short time. Furthermore, if the user is excited, an exercise that is visually stimulating is executed. In this way, by adjusting the exercise execution method according to the user's emotions, more effective training can be provided.

[0110] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. For example, if the user is relaxed, monitoring is performed at a leisurely pace. If the user is in a hurry, monitoring is performed quickly. Furthermore, if the user is excited, monitoring is performed in a visually stimulating manner. In this way, by adjusting the monitoring method according to the user's emotions, more effective training can be provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives input of the user's age and lifestyle. For example, the user can input their age and daily activities. Step 2: The generator generates a vision training program based on the information received by the receiver. For example, the generator generates a program including optimal exercises according to the user's age and lifestyle. Step 3: The providing unit provides the program generated by the generating unit. For example, the providing unit transmits the program to the user through a messaging service. Step 4: The execution unit executes the program provided by the provider. For example, the execution unit executes a visual exercise using a smartphone or tablet. Step 5: The monitoring unit monitors the progress of the exercises performed by the execution unit and adjusts the program as necessary. For example, the monitoring unit evaluates the degree of completion of the exercises and changes in the range of eye movement, and adjusts the program accordingly.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of age or lifestyle; a generation unit that generates a vision training program based on the information received by the reception unit; a providing unit that provides the program generated by the generating unit; an execution unit that executes the program provided by the provision unit; a monitoring unit that monitors the progress of the exercise performed by the execution unit and adjusts the program as necessary. A system characterized by:

2. The generation unit Generate a personalized vision training program based on the user's age or lifestyle 2. The system of claim 1.

3. The providing unit Offering vision training programs through messaging services 2. The system of claim 1.

4. The execution unit: Perform visual exercises using your smartphone or tablet 2. The system of claim 1.

5. The monitoring unit Monitor your exercise progress and adjust your program as needed 2. The system of claim 1.

6. The generation unit Create vision training programs for adults with disabilities or those in need of physical or mental health care 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input method for age and lifestyle based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's input history and provides the optimal input interface 2. The system of claim 1.

9. The reception unit When entering age and lifestyle information, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1.

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