system

Abstract

The system according to this embodiment aims to make it easier for elderly people to use apps and to manage their health and safety. [Solution] The system according to the embodiment comprises an installation unit, a support unit, and a management unit. The installation unit installs applications and registers users. The support unit supports the use of applications installed by the installation unit. The management unit manages the health and safety of users supported by the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for the elderly to install an application and perform user registration, and the elimination of the digital divide has not been sufficiently achieved.

[0005] The system according to the embodiment aims to make it easier for the elderly to use an application and manage their health and safety.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an installation unit, a support unit, and a management unit. The installation unit installs applications and registers users. The support unit supports the use of applications installed by the installation unit. The management unit manages the health and safety of users supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment makes it easier for elderly people to use the app and manage their health and safety. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smartphone agent system according to an embodiment of the present invention is a smartphone agent for the elderly. This smartphone agent system helps the elderly overcome the digital divide by installing apps and registering for services on their behalf, and by providing advice and support for service use. The smartphone agent system also periodically contacts the user to manage their health and safety, and sends alerts to government agencies as needed. For example, the smartphone agent system installs apps and registers for services on behalf of the elderly. In this case, the smartphone agent system recommends apps and services according to the elderly person's purpose of use based on their instructions, and then installs and registers them on their behalf. For example, if an elderly person requests by voice, "I want to book a flight," the smartphone agent system will recommend an appropriate app, install it, and register the user. Next, the smartphone agent system will provide advice and support for service use. For example, if an elderly person requests by voice, "I want to register for Enemall," the smartphone agent system will automatically log in and display the information, and provide support for the necessary operations. The smartphone agent system also manages the user's ID, password, and security conditions to prevent risks. Furthermore, the smartphone agent system regularly contacts users to manage their health and safety. For example, the smartphone agent system periodically calls users to check on their health status. If necessary, it can send alerts to government agencies and relevant organizations, enabling a swift response. This system allows elderly people to use smartphones with peace of mind, bridging the digital divide. In addition, the smartphone agent system's health and safety management improves the quality of life for the elderly. For example, elderly people living alone can use the smartphone agent system to live with peace of mind even if they do not have family or caregivers.This allows the smartphone agent system to enable elderly people to install apps and use services smoothly, and also to manage their health and safety.

[0029] The smartphone agent system according to this embodiment comprises an installation unit, a support unit, and a management unit. The installation unit performs application installation and user registration. The installation unit can, for example, manually install applications. The installation unit also has an automatic installation function and can automatically install applications based on user instructions. Furthermore, the installation unit can set installation conditions and perform installations based on specific conditions. For example, the installation unit can install applications during a time period specified by the user. The installation unit can also perform installations only under a Wi-Fi environment specified by the user. User registration is performed by entering information such as name, email address, and password. The installation unit has a function to automatically enter the information required during user registration, saving the user time. The support unit supports the use of applications installed by the installation unit. The support unit can, for example, provide technical support. Furthermore, the support unit has a function to guide users on how to use applications, helping them to use applications smoothly. Furthermore, the support unit has a troubleshooting function that can resolve problems that occur while using applications. For example, the support unit can explain how to configure applications to users. The support department can also provide users with solutions to app error messages. The administration department manages the health and safety of users supported by the support department. For example, the administration department can collect health data. The administration department can also set up safety check procedures and take measures to ensure user safety. Furthermore, the administration department can analyze the collected health data and monitor the user's health status. For example, the administration department can periodically measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. The administration department can also record the user's activity level and provide advice for health management.As a result, the smartphone agent system according to this embodiment allows elderly people to smoothly install apps and use services, and also enables health and safety management.

[0030] The installation unit handles app installation and user registration. For example, the installation unit allows for manual app installation. It also features an automatic installation function, enabling automatic app installation based on user instructions. Specifically, when a user requests app installation, the installation unit automatically selects the appropriate version for the user's device and begins the installation process. Furthermore, the installation unit allows users to set installation conditions and perform installations based on specific criteria. For example, the installation unit can install apps during a time period specified by the user. It can also perform installations only under a user-specified Wi-Fi environment. This allows users to save data usage and reduce waiting times during installation. User registration involves entering information such as name, email address, and password. The installation unit has a function to automatically fill in the necessary information during user registration, saving users time and effort. For example, the installation unit can automatically fill in the required information using contact information and past input history stored on the user's device. This allows users to complete app installation and registration quickly and easily. Furthermore, the installation unit includes a function to encrypt and store entered information to protect user privacy and prevent unauthorized access by third parties. This allows the installation unit to achieve both user convenience and security.

[0031] The support department assists users with the use of apps installed by the installation department. For example, the support department can provide technical support. Specifically, if a user has questions about how to operate or configure the app, the support department can provide real-time answers. The support department also has features to guide users through app usage, helping them to use the app smoothly. For example, the support department can display a tutorial when the app is first launched, explaining basic operation methods to the user. Furthermore, the support department has troubleshooting capabilities, resolving problems that arise during app use. For example, the support department can explain how to configure the app to the user. The support department can also provide users with solutions to app error messages. This allows users to quickly resolve problems encountered during app use and use the app smoothly. Additionally, the support department can collect user feedback to improve the app. For example, the support department can collect requests and opinions from users and provide feedback to the development team to improve the app's functionality and usability. This allows the support department to increase user satisfaction and promote app usage.

[0032] The Management Department manages the health and safety of users supported by the Support Department. For example, the Management Department can collect health data. Specifically, it can collect health data such as heart rate, blood pressure, body temperature, and activity levels from users' smartphones and wearable devices. The Management Department can also set up safety check procedures and take measures to ensure user safety. For example, the Management Department can issue alerts to users if they have not used the app for a certain period or if abnormal health data is detected. Furthermore, the Management Department can analyze the collected health data and monitor the user's health status. For example, it can periodically measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. The Management Department can also record the user's activity level and provide health management advice. For example, it can send notifications encouraging exercise if the user's activity level is insufficient. In this way, the Management Department can continuously monitor the user's health status and support their health by providing appropriate advice and warnings. Furthermore, the management department can create and provide individual health reports to users based on the collected data. This allows users to understand their own health status and take necessary measures. In addition, the management department has a function to encrypt and store collected data to protect user privacy and prevent unauthorized access by third parties. This enables the management department to comprehensively manage users' health and safety and provide an environment in which they can use the app with peace of mind.

[0033] The recommendation unit can recommend apps based on user instructions. For example, if a user requests by voice, "I want to book a flight," the recommendation unit will recommend an appropriate app. The recommendation unit can analyze the content of the user's instructions and select the optimal app using a recommendation algorithm. For example, the recommendation unit can recommend apps based on the user's past usage history and current needs. The recommendation unit can also recommend multiple apps based on user instructions and allow the user to choose. For example, the recommendation unit can recommend multiple travel booking apps and allow the user to select the most suitable app. This enables the recommendation of appropriate apps based on user instructions. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input user instructions into generative AI, which can then select the optimal app using a recommendation algorithm.

[0034] The security management department can manage IDs, passwords, and security conditions. For example, the security management department has the function to securely store user IDs and passwords. The security management department can evaluate password strength and encourage users to set strong passwords. The security management department also has the function to set up two-factor authentication, which can further protect user accounts. For example, the security management department can require users to enter a one-time password in addition to their password when logging in. Furthermore, the security management department can detect security incidents and take measures to prevent risks from occurring. For example, the security management department can detect fraudulent login attempts and issue alerts to users. This strengthens security management and prevents risks from occurring. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the security management department can input user IDs and passwords into a generative AI, which can evaluate security conditions and propose optimal security measures.

[0035] The contact unit can contact users on a regular basis. For example, the contact unit can periodically call users to check on their health. The contact unit can monitor users' health and issue alerts if any abnormalities are detected. The contact unit can also periodically send users emails or messages to provide information on health and safety. For example, the contact unit can periodically send users health management advice and safety information. Furthermore, the contact unit can adjust the frequency and method of contact according to the user's lifestyle. For example, the contact unit can send concise messages when users are busy and provide detailed information when users are relaxed. This allows for regular checks on the user's health and safety. Some or all of the above processes in the contact unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the contact unit can input the user's health status into a generative AI, and the generative AI can issue an alert if it detects an abnormality.

[0036] The alert unit can send alerts to government agencies and other relevant organizations as needed. For example, the alert unit can send an alert to government agencies and related organizations if an abnormality occurs in the user's health condition. The alert unit has a function to register contact information for quick response in emergencies, enabling it to provide necessary information quickly. For example, the alert unit can register the user's emergency contact information and automatically contact them in an emergency. The alert unit also has a function to customize the content of alerts, enabling it to send alerts tailored to the user's situation. For example, the alert unit can send appropriate alerts based on the user's health and safety status. This allows for a quick response in emergencies. Some or all of the above-described processes in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's health condition into a generation AI, and send an alert if the generation AI detects an abnormality.

[0037] The installation unit can analyze the user's past app usage history and select the optimal installation method. For example, the installation unit can analyze the trends of apps the user has installed in the past and prioritize the installation of similar apps. The installation unit can also suggest the optimal installation procedure by referring to the installation methods of apps the user has installed in the past. Furthermore, the installation unit can prioritize the installation of important apps by considering the frequency of use of apps the user has installed in the past. This allows the system to select the optimal installation method based on past usage history. Some or all of the above processes in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's past app usage history into a generative AI, which can then select the optimal installation method.

[0038] The installation unit can filter apps during installation based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the installation unit can prioritize installing health-related apps. If the user is interested in travel, the installation unit can also prioritize installing travel-related apps. Furthermore, if the user is busy with work, the installation unit can prioritize installing apps that help improve work efficiency. This allows for the installation of apps tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's lifestyle and areas of interest into a generative AI, which can then perform the filtering.

[0039] The installation unit can prioritize the installation of highly relevant apps by considering the user's geographical location information during app installation. For example, if the user is traveling, the installation unit will prioritize the installation of travel-related apps. If the user lives in a specific region, the installation unit can also prioritize the installation of apps related to that region. Furthermore, if the user is in a specific location, the installation unit can prioritize the installation of apps related to that location. This allows for the installation of highly relevant apps based on the user's geographical location information. Some or all of the above processing in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's geographical location information into a generative AI, which can then select highly relevant apps.

[0040] The installation unit can analyze the user's social media activity during app installation and install relevant apps. For example, the installation unit can prioritize installing apps that the user frequently mentions on social media. The installation unit can also install apps recommended by accounts that the user follows on social media. Furthermore, the installation unit can install apps related to groups that the user participates in on social media. This allows for the installation of relevant apps based on the user's social media activity. Some or all of the above processing in the installation unit may be performed using, for example, generative AI, or without generative AI. For example, the installation unit can input the user's social media activity into a generative AI, which can then select relevant apps.

[0041] The support unit can adjust the level of detail of support based on the importance of the app. For example, the support unit will provide detailed support for important apps. For less important apps, the support unit may provide concise support. The support unit may also provide detailed support for apps that users frequently use. This allows the level of detail of support to be adjusted according to the importance of the app. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the importance of the app into a generative AI, and the generative AI can adjust the level of detail of support.

[0042] The support unit can apply different support algorithms depending on the app's category when providing support. For example, in the case of a health-related app, the support unit can provide health-related support. In the case of a travel-related app, the support unit can also provide travel-related support. Furthermore, in the case of a productivity-related app, the support unit can also provide productivity-related support. This allows for the provision of support tailored to the app's category. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the app's category into a generative AI, which can then apply different support algorithms.

[0043] The support unit can determine the priority of support based on the frequency of app usage. For example, the support unit will prioritize support for apps that users use frequently. The support unit may also postpone support for apps that users use infrequently. Furthermore, the support unit may prioritize support for newly installed apps. This allows the support unit to determine the priority of support according to the frequency of app usage. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the frequency of app usage into a generative AI, and the generative AI can determine the priority of support.

[0044] The support unit can adjust the order of support based on the relevance of the apps during support. For example, if the user is using a health-related app, the support unit will prioritize health-related support. If the user is using a travel-related app, the support unit can also prioritize travel-related support. Furthermore, if the user is using a productivity-related app, the support unit can also prioritize productivity-related support. This allows the order of support to be adjusted according to the relevance of the apps. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the relevance of the apps into a generative AI, and the generative AI can adjust the order of support.

[0045] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can propose the optimal health management method based on the user's past health data. The management department can also predict risks and propose preventive measures based on the user's past health data. Furthermore, the management department can analyze the user's past health data and propose methods to improve their health status. This allows for the selection of the optimal management method based on past health data. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the user's past health data into a generative AI, which can then select the optimal management method.

[0046] The management unit can customize the means of management based on the user's current lifestyle during management. For example, if the user is busy, the management unit can suggest easy-to-implement health management methods. If the user is relaxed, the management unit can also suggest detailed health management methods. Furthermore, if the user has a specific health problem, the management unit can suggest management methods tailored to that problem. This allows the management unit to provide management methods that are appropriate to the user's lifestyle. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the user's lifestyle into a generative AI, which can then customize the means of management.

[0047] The management unit can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user lives in a specific region, the management unit can suggest health management methods relevant to that region. If the user is traveling, the management unit can also suggest health management methods that can be performed at the travel destination. Furthermore, if the user is in a specific location, the management unit can suggest health management methods relevant to that location. This allows the management unit to select the optimal management method based on the user's geographical location information. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the user's geographical location information into a generative AI, which can then select the optimal management method.

[0048] The management department can analyze a user's social media activity and propose management strategies during the management process. For example, the management department can propose health management methods that the user frequently mentions on social media. The management department can also propose health management methods recommended by accounts that the user follows on social media. Furthermore, the management department can propose health management methods related to groups that the user participates in on social media. This allows the management department to propose the most suitable management strategies based on the user's social media activity. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the user's social media activity into a generative AI, which can then propose the most suitable management strategies.

[0049] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the app. For example, it can provide detailed recommendations for important apps, and concise recommendations for less important apps. It can also provide detailed recommendations for apps that users frequently use. This allows the recommendation unit to adjust the level of detail in its recommendations according to the importance of the app. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the importance of the app into a generative AI, which can then adjust the level of detail in its recommendations.

[0050] The recommendation unit can apply different recommendation algorithms depending on the app category when making recommendations. For example, in the case of a health-related app, the recommendation unit can provide health-related recommendations. In the case of a travel-related app, the recommendation unit can also provide travel-related recommendations. Furthermore, in the case of a productivity-related app, the recommendation unit can also provide productivity-related recommendations. This allows for the provision of recommendations tailored to the app category. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the app category into a generative AI, and the generative AI can apply different recommendation algorithms.

[0051] The recommendation unit can determine the recommendation priority based on the frequency of app usage. For example, the recommendation unit may prioritize recommendations for apps that the user uses frequently. It may also postpone recommendations for apps that the user doesn't use often. Furthermore, the recommendation unit may prioritize recommendations for apps that the user has recently installed. This allows the recommendation priority to be determined according to the frequency of app usage. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the frequency of app usage into a generative AI, which can then determine the recommendation priority.

[0052] The recommendation unit can adjust the order of recommendations based on the relevance of the apps. For example, if a user is using a health-related app, the recommendation unit will prioritize health-related recommendations. If a user is using a travel-related app, the recommendation unit can also prioritize travel-related recommendations. Furthermore, if a user is using a productivity-related app, the recommendation unit can prioritize productivity-related recommendations. This allows the order of recommendations to be adjusted according to the relevance of the apps. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the relevance of the apps into a generative AI, which can then adjust the order of recommendations.

[0053] The security management department can analyze a user's past security history to select the optimal management method during security management. For example, the security management department can propose the optimal security management method based on the user's past security history. The security management department can also predict risks and propose preventive measures based on the user's past security history. Furthermore, the security management department can analyze the user's past security history and propose ways to improve the security status. This allows for the selection of the optimal management method based on past security history. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or without a generative AI. For example, the security management department can input the user's past security history into a generative AI, which can then select the optimal management method.

[0054] The security management department can customize security management methods based on the user's current lifestyle. For example, if the user is busy, the security management department can suggest a simple security management method. If the user is relaxed, the security management department can also suggest a more detailed security management method. Furthermore, if the user has a specific security issue, the security management department can suggest a management method tailored to that issue. This allows for the provision of security management methods that are appropriate to the user's lifestyle. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not. For example, the security management department can input the user's lifestyle into a generative AI, which can then customize the management methods.

[0055] The security management department can select the optimal security management method when managing security, taking into account the user's geographical location information. For example, if the user lives in a specific region, the security management department can propose security management methods relevant to that region. If the user is traveling, the security management department can also propose security management methods that can be implemented at the travel destination. Furthermore, if the user is in a specific location, the security management department can propose security management methods relevant to that location. This allows the selection of the optimal security management method based on the user's geographical location information. Some or all of the above processing in the security management department may be performed using, for example, a generative AI, or without a generative AI. For example, the security management department can input the user's geographical location information into a generative AI, which can then select the optimal security management method.

[0056] The security management department can analyze a user's social media activity and propose management measures during security management. For example, the security management department can propose security management methods that the user frequently mentions on social media. The security management department can also propose security management methods recommended by accounts that the user follows on social media. Furthermore, the security management department can propose security management methods related to groups that the user participates in on social media. This allows the department to propose the most suitable security management measures based on the user's social media activity. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the security management department can input the user's social media activity into a generative AI, which can then propose the most suitable management measures.

[0057] The contact unit can analyze the user's past contact history and select the optimal method at the time of contact. For example, the contact unit can propose the optimal contact method based on the user's past contact history. The contact unit can also predict risks and propose preventive measures based on the user's past contact history. Furthermore, the contact unit can analyze the user's past contact history and propose ways to improve the contact method. This allows for the selection of the optimal contact method based on past contact history. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's past contact history into a generative AI, which can then select the optimal contact method.

[0058] The contact unit can customize the means of contact based on the user's current life situation at the time of contact. For example, if the user is busy, the contact unit can suggest a simple contact method. If the user is relaxed, the contact unit can also suggest a more detailed contact method. Furthermore, if the user has a specific problem, the contact unit can suggest a contact method tailored to that problem. This allows the system to provide contact methods that are appropriate to the user's life situation. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's life situation into a generative AI, which can then customize the means of contact.

[0059] The contact unit can select the optimal method of contact, taking into account the user's geographical location information. For example, if the user lives in a specific region, the contact unit can suggest a contact method relevant to that region. If the user is traveling, the contact unit can also suggest a contact method that can be performed at the travel destination. Furthermore, if the user is in a specific location, the contact unit can suggest a contact method relevant to that location. This allows the optimal contact method to be selected based on the user's geographical location information. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's geographical location information into a generative AI, which can then select the optimal contact method.

[0060] The contact unit can analyze a user's social media activity and select the optimal method of contact at the time of contact. For example, the contact unit can suggest contact methods that the user frequently mentions on social media. The contact unit can also suggest contact methods recommended by accounts that the user follows on social media. Furthermore, the contact unit can suggest contact methods related to groups that the user participates in on social media. This allows for the selection of the optimal contact method based on the user's social media activity. Some or all of the above processing in the contact unit may be performed using, for example, generative AI, or without generative AI. For example, the contact unit can input the user's social media activity into a generative AI, which can then select the optimal contact method.

[0061] The alert unit can analyze the user's past alert history to select the optimal alerting method when an alert is issued. For example, the alert unit can propose the optimal alerting method based on the user's past alert history. The alert unit can also predict risks and propose preventive measures based on the user's past alert history. Furthermore, the alert unit can analyze the user's past alert history and propose ways to improve the alerting method. This allows the system to select the optimal alerting method based on past alert history. Some or all of the above processing in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's past alert history into a generation AI, which can then select the optimal alerting method.

[0062] The alert unit can customize the method of alerting based on the user's current living situation when an alert is issued. For example, if the user is busy, the alert unit may suggest an easily understandable method of alerting. If the user is relaxed, the alert unit may also suggest a more detailed method of alerting. Furthermore, if the user is experiencing a specific problem, the alert unit may suggest an alert method tailored to that problem. This allows for the provision of alert methods that are appropriate to the user's living situation. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's living situation into a generative AI, which can then customize the method of alerting.

[0063] The alert unit can select the optimal alerting method when issuing an alert, taking into account the user's geographical location information. For example, if the user lives in a specific region, the alert unit can suggest an alerting method relevant to that region. If the user is traveling, the alert unit can also suggest an alerting method that can be performed at the travel destination. Furthermore, if the user is in a specific location, the alert unit can suggest an alerting method relevant to that location. This allows the system to select the optimal alerting method based on the user's geographical location information. Some or all of the above processing in the alert unit may be performed using, for example, a generating AI, or without a generating AI. For example, the alert unit can input the user's geographical location information into a generating AI, which can then select the optimal alerting method.

[0064] The alert unit can analyze the user's social media activity and select the optimal method of sending an alert when an alert is issued. For example, the alert unit can suggest an alert method that the user frequently mentions on social media. The alert unit can also suggest an alert method recommended by accounts that the user follows on social media. Furthermore, the alert unit can suggest an alert method related to groups that the user participates in on social media. This allows the system to select the optimal alert method based on the user's social media activity. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's social media activity into a generative AI, which can then select the optimal method of sending an alert.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The smartphone agent system can also include a history analysis unit that analyzes the user's past app usage history and selects the optimal installation method. For example, it can analyze the trends of apps the user has installed in the past and prioritize the installation of similar apps. It can also suggest the optimal installation procedure by referring to the installation methods of apps the user has installed in the past. Furthermore, it can prioritize the installation of important apps by considering the frequency of use of apps the user has installed in the past. This allows for the selection of the optimal installation method based on past usage history.

[0067] The smartphone agent system can also include a filtering unit that filters apps based on the user's current lifestyle and areas of interest. For example, if a user is interested in health, health-related apps can be prioritized for installation. Similarly, if a user is interested in travel, travel-related apps can be prioritized. Furthermore, if a user is busy with work, apps that help improve work efficiency can be prioritized for installation. This allows for the installation of apps tailored to the user's lifestyle and areas of interest.

[0068] The smartphone agent system can also include a location information unit that prioritizes the installation of highly relevant apps, taking into account the user's geographical location. For example, if the user is traveling, travel-related apps can be prioritized for installation. Similarly, if the user lives in a specific region, apps related to that region can be prioritized. Furthermore, if the user is in a specific location, apps related to that location can be prioritized for installation. This allows for the installation of highly relevant apps based on the user's geographical location.

[0069] The smartphone agent system can also include a social media analysis unit that analyzes the user's social media activity and installs relevant apps. For example, it can prioritize the installation of apps that the user frequently mentions on social media. It can also install apps recommended by accounts that the user follows on social media. Furthermore, it can install apps related to groups that the user participates in on social media. This allows for the installation of relevant apps based on the user's social media activity.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The installation section handles app installation and user registration. The installation section allows for manual app installation, and also features an automatic installation function that allows for automatic installation based on user instructions. Furthermore, the installation section allows for setting installation conditions, enabling installation based on specific criteria. For example, installation can be restricted to times specified by the user or under specific Wi-Fi conditions. User registration involves entering information such as name, email address, and password, and the installation section has a function to automatically fill in the necessary information during user registration. Step 2: The support unit assists with the use of the app installed by the installation unit. The support unit provides technical support and has the ability to guide users on how to use the app. This helps users to use the app smoothly. In addition, the support unit has the ability to troubleshoot and resolve problems that occur while using the app. For example, it can provide users with instructions on how to configure the app and how to deal with error messages. Step 3: The management department manages the health and safety of users supported by the support department. The management department can collect health data and set up safety check procedures. This allows them to take measures to ensure user safety. Furthermore, the management department can analyze the collected health data and monitor the users' health status. For example, they can regularly measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. They can also record the user's activity level and provide advice for health management.

[0072] (Example of form 2) The smartphone agent system according to an embodiment of the present invention is a smartphone agent for the elderly. This smartphone agent system helps the elderly overcome the digital divide by installing apps and registering for services on their behalf, and by providing advice and support for service use. The smartphone agent system also periodically contacts the user to manage their health and safety, and sends alerts to government agencies as needed. For example, the smartphone agent system installs apps and registers for services on behalf of the elderly. In this case, the smartphone agent system recommends apps and services according to the elderly person's purpose of use based on their instructions, and then installs and registers them on their behalf. For example, if an elderly person requests by voice, "I want to book a flight," the smartphone agent system will recommend an appropriate app, install it, and register the user. Next, the smartphone agent system will provide advice and support for service use. For example, if an elderly person requests by voice, "I want to register for Enemall," the smartphone agent system will automatically log in and display the information, and provide support for the necessary operations. The smartphone agent system also manages the user's ID, password, and security conditions to prevent risks. Furthermore, the smartphone agent system regularly contacts users to manage their health and safety. For example, the smartphone agent system periodically calls users to check on their health status. If necessary, it can send alerts to government agencies and relevant organizations, enabling a swift response. This system allows elderly people to use smartphones with peace of mind, bridging the digital divide. In addition, the smartphone agent system's health and safety management improves the quality of life for the elderly. For example, elderly people living alone can use the smartphone agent system to live with peace of mind even if they do not have family or caregivers.This allows the smartphone agent system to enable elderly people to install apps and use services smoothly, and also to manage their health and safety.

[0073] The smartphone agent system according to this embodiment comprises an installation unit, a support unit, and a management unit. The installation unit performs application installation and user registration. The installation unit can, for example, manually install applications. The installation unit also has an automatic installation function and can automatically install applications based on user instructions. Furthermore, the installation unit can set installation conditions and perform installations based on specific conditions. For example, the installation unit can install applications during a time period specified by the user. The installation unit can also perform installations only under a Wi-Fi environment specified by the user. User registration is performed by entering information such as name, email address, and password. The installation unit has a function to automatically enter the information required during user registration, saving the user time. The support unit supports the use of applications installed by the installation unit. The support unit can, for example, provide technical support. Furthermore, the support unit has a function to guide users on how to use applications, helping them to use applications smoothly. Furthermore, the support unit has a troubleshooting function that can resolve problems that occur while using applications. For example, the support unit can explain how to configure applications to users. The support department can also provide users with solutions to app error messages. The administration department manages the health and safety of users supported by the support department. For example, the administration department can collect health data. The administration department can also set up safety check procedures and take measures to ensure user safety. Furthermore, the administration department can analyze the collected health data and monitor the user's health status. For example, the administration department can periodically measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. The administration department can also record the user's activity level and provide advice for health management.As a result, the smartphone agent system according to this embodiment allows elderly people to smoothly install apps and use services, and also enables health and safety management.

[0074] The installation unit handles app installation and user registration. For example, the installation unit allows for manual app installation. It also features an automatic installation function, enabling automatic app installation based on user instructions. Specifically, when a user requests app installation, the installation unit automatically selects the appropriate version for the user's device and begins the installation process. Furthermore, the installation unit allows users to set installation conditions and perform installations based on specific criteria. For example, the installation unit can install apps during a time period specified by the user. It can also perform installations only under a user-specified Wi-Fi environment. This allows users to save data usage and reduce waiting times during installation. User registration involves entering information such as name, email address, and password. The installation unit has a function to automatically fill in the necessary information during user registration, saving users time and effort. For example, the installation unit can automatically fill in the required information using contact information and past input history stored on the user's device. This allows users to complete app installation and registration quickly and easily. Furthermore, the installation unit includes a function to encrypt and store entered information to protect user privacy and prevent unauthorized access by third parties. This allows the installation unit to achieve both user convenience and security.

[0075] The support department assists users with the use of apps installed by the installation department. For example, the support department can provide technical support. Specifically, if a user has questions about how to operate or configure the app, the support department can provide real-time answers. The support department also has features to guide users through app usage, helping them to use the app smoothly. For example, the support department can display a tutorial when the app is first launched, explaining basic operation methods to the user. Furthermore, the support department has troubleshooting capabilities, resolving problems that arise during app use. For example, the support department can explain how to configure the app to the user. The support department can also provide users with solutions to app error messages. This allows users to quickly resolve problems encountered during app use and use the app smoothly. Additionally, the support department can collect user feedback to improve the app. For example, the support department can collect requests and opinions from users and provide feedback to the development team to improve the app's functionality and usability. This allows the support department to increase user satisfaction and promote app usage.

[0076] The Management Department manages the health and safety of users supported by the Support Department. For example, the Management Department can collect health data. Specifically, it can collect health data such as heart rate, blood pressure, body temperature, and activity levels from users' smartphones and wearable devices. The Management Department can also set up safety check procedures and take measures to ensure user safety. For example, the Management Department can issue alerts to users if they have not used the app for a certain period or if abnormal health data is detected. Furthermore, the Management Department can analyze the collected health data and monitor the user's health status. For example, it can periodically measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. The Management Department can also record the user's activity level and provide health management advice. For example, it can send notifications encouraging exercise if the user's activity level is insufficient. In this way, the Management Department can continuously monitor the user's health status and support their health by providing appropriate advice and warnings. Furthermore, the management department can create and provide individual health reports to users based on the collected data. This allows users to understand their own health status and take necessary measures. In addition, the management department has a function to encrypt and store collected data to protect user privacy and prevent unauthorized access by third parties. This enables the management department to comprehensively manage users' health and safety and provide an environment in which they can use the app with peace of mind.

[0077] The recommendation unit can recommend apps based on user instructions. For example, if a user requests by voice, "I want to book a flight," the recommendation unit will recommend an appropriate app. The recommendation unit can analyze the content of the user's instructions and select the optimal app using a recommendation algorithm. For example, the recommendation unit can recommend apps based on the user's past usage history and current needs. The recommendation unit can also recommend multiple apps based on user instructions and allow the user to choose. For example, the recommendation unit can recommend multiple travel booking apps and allow the user to select the most suitable app. This enables the recommendation of appropriate apps based on user instructions. Some or all of the above processing in the recommendation unit may be performed using, for example, generative AI, or without generative AI. For example, the recommendation unit can input user instructions into generative AI, which can then select the optimal app using a recommendation algorithm.

[0078] The security management department can manage IDs, passwords, and security conditions. For example, the security management department has the function to securely store user IDs and passwords. The security management department can evaluate password strength and encourage users to set strong passwords. The security management department also has the function to set up two-factor authentication, which can further protect user accounts. For example, the security management department can require users to enter a one-time password in addition to their password when logging in. Furthermore, the security management department can detect security incidents and take measures to prevent risks from occurring. For example, the security management department can detect fraudulent login attempts and issue alerts to users. This strengthens security management and prevents risks from occurring. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the security management department can input user IDs and passwords into a generative AI, which can evaluate security conditions and propose optimal security measures.

[0079] The contact unit can contact users on a regular basis. For example, the contact unit can periodically call users to check on their health. The contact unit can monitor users' health and issue alerts if any abnormalities are detected. The contact unit can also periodically send users emails or messages to provide information on health and safety. For example, the contact unit can periodically send users health management advice and safety information. Furthermore, the contact unit can adjust the frequency and method of contact according to the user's lifestyle. For example, the contact unit can send concise messages when users are busy and provide detailed information when users are relaxed. This allows for regular checks on the user's health and safety. Some or all of the above processes in the contact unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the contact unit can input the user's health status into a generative AI, and the generative AI can issue an alert if it detects an abnormality.

[0080] The alert unit can send alerts to government agencies and other relevant organizations as needed. For example, the alert unit can send an alert to government agencies and related organizations if an abnormality occurs in the user's health condition. The alert unit has a function to register contact information for quick response in emergencies, enabling it to provide necessary information quickly. For example, the alert unit can register the user's emergency contact information and automatically contact them in an emergency. The alert unit also has a function to customize the content of alerts, enabling it to send alerts tailored to the user's situation. For example, the alert unit can send appropriate alerts based on the user's health and safety status. This allows for a quick response in emergencies. Some or all of the above-described processes in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's health condition into a generation AI, and send an alert if the generation AI detects an abnormality.

[0081] The installation unit can estimate the user's emotions and adjust the timing of app installation based on the estimated emotions. For example, if the user is feeling stressed, the installation unit will install the app during a time when the user can relax. If the user is relaxed, the installation unit can also start the app installation immediately. Furthermore, if the user is busy, the installation unit can set a reminder to install it later. This allows the app to be installed at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the installation unit may be performed using AI, or not using AI. For example, the installation unit can input user emotion data into a generative AI, which can then adjust the installation timing.

[0082] The installation unit can analyze the user's past app usage history and select the optimal installation method. For example, the installation unit can analyze the trends of apps the user has installed in the past and prioritize the installation of similar apps. The installation unit can also suggest the optimal installation procedure by referring to the installation methods of apps the user has installed in the past. Furthermore, the installation unit can prioritize the installation of important apps by considering the frequency of use of apps the user has installed in the past. This allows the system to select the optimal installation method based on past usage history. Some or all of the above processes in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's past app usage history into a generative AI, which can then select the optimal installation method.

[0083] The installation unit can filter apps during installation based on the user's current lifestyle and areas of interest. For example, if the user is interested in health, the installation unit can prioritize installing health-related apps. If the user is interested in travel, the installation unit can also prioritize installing travel-related apps. Furthermore, if the user is busy with work, the installation unit can prioritize installing apps that help improve work efficiency. This allows for the installation of apps tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's lifestyle and areas of interest into a generative AI, which can then perform the filtering.

[0084] The installation unit can estimate the user's emotions and determine the priority of apps to install based on the estimated emotions. For example, if the user is stressed, the installation unit will prioritize installing relaxing apps. If the user is relaxed, the installation unit can also prioritize installing important apps. Furthermore, if the user is busy, the installation unit can set reminders to install apps later. This allows the priority of apps to be installed to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the installation unit may be performed using AI or not. For example, the installation unit can input user emotion data into a generative AI, which can then determine the priority of apps to install.

[0085] The installation unit can prioritize the installation of highly relevant apps by considering the user's geographical location information during app installation. For example, if the user is traveling, the installation unit will prioritize the installation of travel-related apps. If the user lives in a specific region, the installation unit can also prioritize the installation of apps related to that region. Furthermore, if the user is in a specific location, the installation unit can prioritize the installation of apps related to that location. This allows for the installation of highly relevant apps based on the user's geographical location information. Some or all of the above processing in the installation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the installation unit can input the user's geographical location information into a generative AI, which can then select highly relevant apps.

[0086] The installation unit can analyze the user's social media activity during app installation and install relevant apps. For example, the installation unit can prioritize installing apps that the user frequently mentions on social media. The installation unit can also install apps recommended by accounts that the user follows on social media. Furthermore, the installation unit can install apps related to groups that the user participates in on social media. This allows for the installation of relevant apps based on the user's social media activity. Some or all of the above processing in the installation unit may be performed using, for example, generative AI, or without generative AI. For example, the installation unit can input the user's social media activity into a generative AI, which can then select relevant apps.

[0087] The support unit can estimate the user's emotions and adjust the way it expresses support based on the estimated emotions. For example, if the user is stressed, the support unit can provide simple and easy-to-understand support. If the user is relaxed, the support unit can also provide detailed support. Furthermore, if the user is in a hurry, the support unit can provide rapid support. This allows for the provision of optimal support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input user emotion data into a generative AI, which can then adjust the way it expresses support.

[0088] The support unit can adjust the level of detail of support based on the importance of the app. For example, the support unit will provide detailed support for important apps. For less important apps, the support unit may provide concise support. The support unit may also provide detailed support for apps that users frequently use. This allows the level of detail of support to be adjusted according to the importance of the app. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the importance of the app into a generative AI, and the generative AI can adjust the level of detail of support.

[0089] The support unit can apply different support algorithms depending on the app's category when providing support. For example, in the case of a health-related app, the support unit can provide health-related support. In the case of a travel-related app, the support unit can also provide travel-related support. Furthermore, in the case of a productivity-related app, the support unit can also provide productivity-related support. This allows for the provision of support tailored to the app's category. Some or all of the above-described processes in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the app's category into a generative AI, which can then apply different support algorithms.

[0090] The support unit can estimate the user's emotions and adjust the length of support based on the estimated emotions. For example, if the user is stressed, the support unit can provide short, concise support. If the user is relaxed, the support unit can also provide detailed support. Furthermore, if the user is in a hurry, the support unit can provide rapid support. This allows the length of support to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI, which can then adjust the length of support.

[0091] The support unit can determine the priority of support based on the frequency of app usage. For example, the support unit will prioritize support for apps that users use frequently. The support unit may also postpone support for apps that users use infrequently. Furthermore, the support unit may prioritize support for newly installed apps. This allows the support unit to determine the priority of support according to the frequency of app usage. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the frequency of app usage into a generative AI, and the generative AI can determine the priority of support.

[0092] The support unit can adjust the order of support based on the relevance of the apps during support. For example, if the user is using a health-related app, the support unit will prioritize health-related support. If the user is using a travel-related app, the support unit can also prioritize travel-related support. Furthermore, if the user is using a productivity-related app, the support unit can also prioritize productivity-related support. This allows the order of support to be adjusted according to the relevance of the apps. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the support unit can input the relevance of the apps into a generative AI, and the generative AI can adjust the order of support.

[0093] The management department can estimate the user's emotions and adjust health and safety management methods based on the estimated emotions. For example, if the user is stressed, the management department can suggest ways to relax. If the user is relaxed, the management department can also suggest detailed health management methods. Furthermore, if the user is in a hurry, the management department can suggest quick health management methods. This allows for the adjustment of health and safety management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into a generative AI, which can then adjust health and safety management methods.

[0094] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can propose the optimal health management method based on the user's past health data. The management department can also predict risks and propose preventive measures based on the user's past health data. Furthermore, the management department can analyze the user's past health data and propose methods to improve their health status. This allows for the selection of the optimal management method based on past health data. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or without a generative AI. For example, the management department can input the user's past health data into a generative AI, which can then select the optimal management method.

[0095] The management unit can customize the means of management based on the user's current lifestyle during management. For example, if the user is busy, the management unit can suggest easy-to-implement health management methods. If the user is relaxed, the management unit can also suggest detailed health management methods. Furthermore, if the user has a specific health problem, the management unit can suggest management methods tailored to that problem. This allows the management unit to provide management methods that are appropriate to the user's lifestyle. Some or all of the above processes in the management unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the management unit can input the user's lifestyle into a generative AI, which can then customize the means of management.

[0096] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize ways to help them relax. If the user is relaxed, the management unit may also prioritize detailed health management methods. Furthermore, if the user is in a hurry, the management unit may prioritize quick health management methods. This allows management priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI, which can then determine management priorities.

[0097] The management unit can select the optimal management method during management, taking into account the user's geographical location information. For example, if the user lives in a specific region, the management unit can suggest health management methods relevant to that region. If the user is traveling, the management unit can also suggest health management methods that can be performed at the travel destination. Furthermore, if the user is in a specific location, the management unit can suggest health management methods relevant to that location. This allows the management unit to select the optimal management method based on the user's geographical location information. Some or all of the above processing in the management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the management unit can input the user's geographical location information into a generative AI, which can then select the optimal management method.

[0098] The management department can analyze a user's social media activity and propose management strategies during the management process. For example, the management department can propose health management methods that the user frequently mentions on social media. The management department can also propose health management methods recommended by accounts that the user follows on social media. Furthermore, the management department can propose health management methods related to groups that the user participates in on social media. This allows the management department to propose the most suitable management strategies based on the user's social media activity. Some or all of the above processes in the management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the management department can input the user's social media activity into a generative AI, which can then propose the most suitable management strategies.

[0099] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated emotions. For example, if the user is stressed, the recommendation unit provides simple and easy-to-understand recommendations. If the user is relaxed, the recommendation unit can also provide detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide quick recommendations. This allows the system to provide optimal recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, or not using AI. For example, the recommendation unit can input user emotion data into a generative AI, which can then adjust the way recommendations are presented.

[0100] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the app. For example, it can provide detailed recommendations for important apps, and concise recommendations for less important apps. It can also provide detailed recommendations for apps that users frequently use. This allows the recommendation unit to adjust the level of detail in its recommendations according to the importance of the app. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the importance of the app into a generative AI, which can then adjust the level of detail in its recommendations.

[0101] The recommendation unit can apply different recommendation algorithms depending on the app category when making recommendations. For example, in the case of a health-related app, the recommendation unit can provide health-related recommendations. In the case of a travel-related app, the recommendation unit can also provide travel-related recommendations. Furthermore, in the case of a productivity-related app, the recommendation unit can also provide productivity-related recommendations. This allows for the provision of recommendations tailored to the app category. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the app category into a generative AI, and the generative AI can apply different recommendation algorithms.

[0102] The recommendation unit can estimate the user's emotions and adjust the length of the recommendations based on the estimated emotions. For example, if the user is stressed, the recommendation unit can provide short, concise recommendations. If the user is relaxed, the recommendation unit can also provide detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide quick recommendations. This allows the length of the recommendations to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input user emotion data into the generative AI, which can then adjust the length of the recommendations.

[0103] The recommendation unit can determine the recommendation priority based on the frequency of app usage. For example, the recommendation unit may prioritize recommendations for apps that the user uses frequently. It may also postpone recommendations for apps that the user doesn't use often. Furthermore, the recommendation unit may prioritize recommendations for apps that the user has recently installed. This allows the recommendation priority to be determined according to the frequency of app usage. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the frequency of app usage into a generative AI, which can then determine the recommendation priority.

[0104] The recommendation unit can adjust the order of recommendations based on the relevance of the apps. For example, if a user is using a health-related app, the recommendation unit will prioritize health-related recommendations. If a user is using a travel-related app, the recommendation unit can also prioritize travel-related recommendations. Furthermore, if a user is using a productivity-related app, the recommendation unit can prioritize productivity-related recommendations. This allows the order of recommendations to be adjusted according to the relevance of the apps. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the relevance of the apps into a generative AI, which can then adjust the order of recommendations.

[0105] The security management unit can estimate the user's emotions and adjust security management methods based on the estimated emotions. For example, if the user is stressed, the security management unit can provide a simple and easy-to-understand security management method. If the user is relaxed, the security management unit can also provide a detailed security management method. Furthermore, if the user is in a hurry, the security management unit can provide a rapid security management method. This allows for the provision of the optimal security management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security management unit may be performed using AI, for example, or not using AI. For example, the security management unit can input user emotion data into a generative AI, and the generative AI can adjust security management methods.

[0106] The security management department can analyze a user's past security history to select the optimal management method during security management. For example, the security management department can propose the optimal security management method based on the user's past security history. The security management department can also predict risks and propose preventive measures based on the user's past security history. Furthermore, the security management department can analyze the user's past security history and propose ways to improve the security status. This allows for the selection of the optimal management method based on past security history. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or without a generative AI. For example, the security management department can input the user's past security history into a generative AI, which can then select the optimal management method.

[0107] The security management department can customize security management methods based on the user's current lifestyle. For example, if the user is busy, the security management department can suggest a simple security management method. If the user is relaxed, the security management department can also suggest a more detailed security management method. Furthermore, if the user has a specific security issue, the security management department can suggest a management method tailored to that issue. This allows for the provision of security management methods that are appropriate to the user's lifestyle. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not. For example, the security management department can input the user's lifestyle into a generative AI, which can then customize the management methods.

[0108] The security management department can estimate the user's emotions and determine security management priorities based on those estimated emotions. For example, if the user is stressed, the security management department will prioritize simple and easy-to-understand security management methods. If the user is relaxed, the security management department may also prioritize detailed security management methods. Furthermore, if the user is in a hurry, the security management department may prioritize rapid security management methods. This allows for the determination of security management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security management department may be performed using AI, or not using AI. For example, the security management department can input user emotion data into a generative AI, which can then determine security management priorities.

[0109] The security management department can select the optimal security management method when managing security, taking into account the user's geographical location information. For example, if the user lives in a specific region, the security management department can propose security management methods relevant to that region. If the user is traveling, the security management department can also propose security management methods that can be implemented at the travel destination. Furthermore, if the user is in a specific location, the security management department can propose security management methods relevant to that location. This allows the selection of the optimal security management method based on the user's geographical location information. Some or all of the above processing in the security management department may be performed using, for example, a generative AI, or without a generative AI. For example, the security management department can input the user's geographical location information into a generative AI, which can then select the optimal security management method.

[0110] The security management department can analyze a user's social media activity and propose management measures during security management. For example, the security management department can propose security management methods that the user frequently mentions on social media. The security management department can also propose security management methods recommended by accounts that the user follows on social media. Furthermore, the security management department can propose security management methods related to groups that the user participates in on social media. This allows the department to propose the most suitable security management measures based on the user's social media activity. Some or all of the above processes in the security management department may be performed using, for example, a generative AI, or not using a generative AI. For example, the security management department can input the user's social media activity into a generative AI, which can then propose the most suitable management measures.

[0111] The contact unit can estimate the user's emotions and adjust its contact method based on the estimated emotions. For example, if the user is stressed, the contact unit can contact them in a way that helps them relax. If the user is relaxed, the contact unit can also contact them in a way that provides detailed information. Furthermore, if the user is in a hurry, the contact unit can contact them in a quick manner. This allows the system to provide the optimal contact method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contact unit may be performed using AI, or not using AI. For example, the contact unit can input user emotion data into a generative AI, which can then adjust its contact method.

[0112] The contact unit can analyze the user's past contact history and select the optimal method at the time of contact. For example, the contact unit can propose the optimal contact method based on the user's past contact history. The contact unit can also predict risks and propose preventive measures based on the user's past contact history. Furthermore, the contact unit can analyze the user's past contact history and propose ways to improve the contact method. This allows for the selection of the optimal contact method based on past contact history. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's past contact history into a generative AI, which can then select the optimal contact method.

[0113] The contact unit can customize the means of contact based on the user's current life situation at the time of contact. For example, if the user is busy, the contact unit can suggest a simple contact method. If the user is relaxed, the contact unit can also suggest a more detailed contact method. Furthermore, if the user has a specific problem, the contact unit can suggest a contact method tailored to that problem. This allows the system to provide contact methods that are appropriate to the user's life situation. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's life situation into a generative AI, which can then customize the means of contact.

[0114] The contact unit can estimate the user's emotions and determine the priority of contacts based on the estimated emotions. For example, if the user is stressed, the contact unit will prioritize contacts in a relaxing manner. If the user is relaxed, the contact unit may also prioritize contacts that provide detailed information. Furthermore, if the user is in a hurry, the contact unit may prioritize contacts in a quick manner. This allows for the prioritization of contacts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the contact unit may be performed using AI or not using AI. For example, the contact unit can input user emotion data into a generative AI, which can then determine the priority of contacts.

[0115] The contact unit can select the optimal method of contact, taking into account the user's geographical location information. For example, if the user lives in a specific region, the contact unit can suggest a contact method relevant to that region. If the user is traveling, the contact unit can also suggest a contact method that can be performed at the travel destination. Furthermore, if the user is in a specific location, the contact unit can suggest a contact method relevant to that location. This allows the optimal contact method to be selected based on the user's geographical location information. Some or all of the above processing in the contact unit may be performed using, for example, a generative AI, or without a generative AI. For example, the contact unit can input the user's geographical location information into a generative AI, which can then select the optimal contact method.

[0116] The contact unit can analyze a user's social media activity and select the optimal method of contact at the time of contact. For example, the contact unit can suggest contact methods that the user frequently mentions on social media. The contact unit can also suggest contact methods recommended by accounts that the user follows on social media. Furthermore, the contact unit can suggest contact methods related to groups that the user participates in on social media. This allows for the selection of the optimal contact method based on the user's social media activity. Some or all of the above processing in the contact unit may be performed using, for example, generative AI, or without generative AI. For example, the contact unit can input the user's social media activity into a generative AI, which can then select the optimal contact method.

[0117] The alert unit can estimate the user's emotions and adjust the way it delivers alerts based on those emotions. For example, if the user is stressed, the alert unit will deliver an alert in a calm tone. If the user is relaxed, the alert unit can deliver an alert containing detailed information. If the user is in a hurry, the alert unit can deliver a quick and concise alert. This allows the system to provide the most appropriate alert delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI, which can then adjust the way it delivers alerts.

[0118] The alert unit can analyze the user's past alert history to select the optimal alerting method when an alert is issued. For example, the alert unit can propose the optimal alerting method based on the user's past alert history. The alert unit can also predict risks and propose preventive measures based on the user's past alert history. Furthermore, the alert unit can analyze the user's past alert history and propose ways to improve the alerting method. This allows the system to select the optimal alerting method based on past alert history. Some or all of the above processing in the alert unit may be performed using, for example, a generation AI, or without a generation AI. For example, the alert unit can input the user's past alert history into a generation AI, which can then select the optimal alerting method.

[0119] The alert unit can customize the method of alerting based on the user's current living situation when an alert is issued. For example, if the user is busy, the alert unit may suggest an easily understandable method of alerting. If the user is relaxed, the alert unit may also suggest a more detailed method of alerting. Furthermore, if the user is experiencing a specific problem, the alert unit may suggest an alert method tailored to that problem. This allows for the provision of alert methods that are appropriate to the user's living situation. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's living situation into a generative AI, which can then customize the method of alerting.

[0120] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit may prioritize sending alerts in a calm tone. If the user is relaxed, the alert unit may also prioritize sending alerts containing detailed information. Furthermore, if the user is in a hurry, the alert unit may prioritize sending quick and concise alerts. This allows for the prioritization of alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not using AI. For example, the alert unit can input user emotion data into a generative AI, which can then determine the priority of alerts.

[0121] The alert unit can select the optimal alerting method when issuing an alert, taking into account the user's geographical location information. For example, if the user lives in a specific region, the alert unit can suggest an alerting method relevant to that region. If the user is traveling, the alert unit can also suggest an alerting method that can be performed at the travel destination. Furthermore, if the user is in a specific location, the alert unit can suggest an alerting method relevant to that location. This allows the system to select the optimal alerting method based on the user's geographical location information. Some or all of the above processing in the alert unit may be performed using, for example, a generating AI, or without a generating AI. For example, the alert unit can input the user's geographical location information into a generating AI, which can then select the optimal alerting method.

[0122] The alert unit can analyze the user's social media activity and select the optimal method of sending an alert when an alert is issued. For example, the alert unit can suggest an alert method that the user frequently mentions on social media. The alert unit can also suggest an alert method recommended by accounts that the user follows on social media. Furthermore, the alert unit can suggest an alert method related to groups that the user participates in on social media. This allows the system to select the optimal alert method based on the user's social media activity. Some or all of the above processing in the alert unit may be performed using, for example, a generative AI, or without a generative AI. For example, the alert unit can input the user's social media activity into a generative AI, which can then select the optimal method of sending an alert.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The smartphone agent system can further include an emotion estimation unit that estimates the user's emotions and optimizes app installation and service usage based on those emotions. For example, if the user is stressed, it can prioritize recommending and installing relaxing apps. Conversely, if the user is relaxed, it can prioritize recommending and installing important apps and services. Furthermore, if the user is busy, it can set a reminder to install them later. This allows the system to provide the most suitable apps and services according to the user's emotions.

[0125] The smartphone agent system can also include a history analysis unit that analyzes the user's past app usage history and selects the optimal installation method. For example, it can analyze the trends of apps the user has installed in the past and prioritize the installation of similar apps. It can also suggest the optimal installation procedure by referring to the installation methods of apps the user has installed in the past. Furthermore, it can prioritize the installation of important apps by considering the frequency of use of apps the user has installed in the past. This allows for the selection of the optimal installation method based on past usage history.

[0126] The smartphone agent system can also include a filtering unit that filters apps based on the user's current lifestyle and areas of interest. For example, if a user is interested in health, health-related apps can be prioritized for installation. Similarly, if a user is interested in travel, travel-related apps can be prioritized. Furthermore, if a user is busy with work, apps that help improve work efficiency can be prioritized for installation. This allows for the installation of apps tailored to the user's lifestyle and areas of interest.

[0127] The smartphone agent system can also include a location information unit that prioritizes the installation of highly relevant apps, taking into account the user's geographical location. For example, if the user is traveling, travel-related apps can be prioritized for installation. Similarly, if the user lives in a specific region, apps related to that region can be prioritized. Furthermore, if the user is in a specific location, apps related to that location can be prioritized for installation. This allows for the installation of highly relevant apps based on the user's geographical location.

[0128] The smartphone agent system can also include a social media analysis unit that analyzes the user's social media activity and installs relevant apps. For example, it can prioritize the installation of apps that the user frequently mentions on social media. It can also install apps recommended by accounts that the user follows on social media. Furthermore, it can install apps related to groups that the user participates in on social media. This allows for the installation of relevant apps based on the user's social media activity.

[0129] The smartphone agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the way support is expressed based on the estimated emotions. For example, if the user is stressed, it can provide simple and easy-to-understand support. If the user is relaxed, it can provide detailed support. Furthermore, if the user is in a hurry, it can provide quick support. This allows for the provision of optimal support according to the user's emotions.

[0130] The smartphone agent system can also include an emotion estimation unit that estimates the user's emotions and adjusts the length of support based on those emotions. For example, if the user is stressed, it can provide short, concise support. If the user is relaxed, it can provide detailed support. Furthermore, if the user is in a hurry, it can provide rapid support. This allows the length of support to be adjusted according to the user's emotions.

[0131] The smartphone agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts health and safety management methods based on those emotions. For example, if the user is feeling stressed, it can suggest ways to relax. If the user is relaxed, it can also suggest detailed methods for health management. Furthermore, if the user is in a hurry, it can suggest quick health management methods. This allows for the adjustment of health and safety management methods according to the user's emotions.

[0132] The smartphone agent system can also include an emotion estimation unit that estimates the user's emotions and adjusts the alert delivery method based on the estimated emotions. For example, if the user is stressed, an alert can be delivered in a calm tone. If the user is relaxed, an alert containing detailed information can be delivered. Furthermore, if the user is in a hurry, a quick and concise alert can be delivered. This allows the system to provide the most appropriate alert delivery method according to the user's emotions.

[0133] The smartphone agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the contact method based on the estimated emotions. For example, if the user is stressed, the system can contact them in a relaxing way. If the user is relaxed, the system can contact them in a way that provides detailed information. Furthermore, if the user is in a hurry, the system can contact them in a quick manner. This allows the system to provide the optimal contact method according to the user's emotions.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The installation section handles app installation and user registration. The installation section allows for manual app installation, and also features an automatic installation function that allows for automatic installation based on user instructions. Furthermore, the installation section allows for setting installation conditions, enabling installation based on specific criteria. For example, installation can be restricted to times specified by the user or under specific Wi-Fi conditions. User registration involves entering information such as name, email address, and password, and the installation section has a function to automatically fill in the necessary information during user registration. Step 2: The support unit assists with the use of the app installed by the installation unit. The support unit provides technical support and has the ability to guide users on how to use the app. This helps users to use the app smoothly. In addition, the support unit has the ability to troubleshoot and resolve problems that occur while using the app. For example, it can provide users with instructions on how to configure the app and how to deal with error messages. Step 3: The management department manages the health and safety of users supported by the support department. The management department can collect health data and set up safety check procedures. This allows them to take measures to ensure user safety. Furthermore, the management department can analyze the collected health data and monitor the users' health status. For example, they can regularly measure the user's heart rate and blood pressure and issue alerts if abnormalities are detected. They can also record the user's activity level and provide advice for health management.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0138] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the installation unit, support unit, management unit, recommendation unit, security management unit, contact unit, and alert unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the smart device 14 and performs app installation and user registration. The support unit is implemented by the control unit 46A of the smart device 14 and supports the use of the installed app. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's health and safety. The recommendation unit is implemented by the control unit 46A of the smart device 14 and recommends apps based on user instructions. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages IDs, passwords, and security conditions. The contact unit is implemented by the control unit 46A of the smart device 14 and periodically contacts the user. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends alerts to government agencies, etc., as needed. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0150] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the installation unit, support unit, management unit, recommendation unit, security management unit, contact unit, and alert unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the smart glasses 214 and performs app installation and user registration. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports the use of installed apps. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's health and safety. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and recommends apps based on user instructions. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages IDs, passwords, and security conditions. The contact unit is implemented by the control unit 46A of the smart glasses 214 and periodically contacts the user. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends alerts to government agencies, etc., as needed. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the installation unit, support unit, management unit, recommendation unit, security management unit, contact unit, and alert unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the headset terminal 314 and performs application installation and user registration. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports the use of installed applications. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's health and safety. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and recommends applications based on user instructions. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages IDs, passwords, and security conditions. The contact unit is implemented by the control unit 46A of the headset terminal 314 and periodically contacts the user. The alert function is implemented by the specific processing unit 290 of the data processing device 12, and sends alerts to government agencies, etc., as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0176] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0178] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0179] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0180] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the installation unit, support unit, management unit, recommendation unit, security management unit, contact unit, and alert unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the installation unit is implemented by the control unit 46A of the robot 414 and performs application installation and user registration. The support unit is implemented by the control unit 46A of the robot 414 and supports the use of the installed application. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the user's health and safety. The recommendation unit is implemented by the control unit 46A of the robot 414 and recommends applications based on user instructions. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages IDs, passwords, and security conditions. The contact unit is implemented by the control unit 46A of the robot 414 and periodically contacts the user. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends alerts to government agencies, etc., as needed. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0191] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0192] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0193] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0197] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0198] 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.

[0199] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0200] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0201] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0202] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0204] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0205] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0206] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0207] (Note 1) The installation section is where the app is installed and user registration takes place, A support unit that supports the use of the application installed by the aforementioned installation unit, The system includes a management unit that manages the health and safety of users supported by the aforementioned support unit. A system characterized by the following features. (Note 2) It includes a recommendation section that recommends apps based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a security management department that manages IDs, passwords, and security conditions. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a contact unit that makes regular contact with users. The system described in Appendix 1, characterized by the features described herein. (Note 5) It is equipped with an alert unit that sends alerts to government agencies and other relevant organizations as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned installation unit is The system estimates the user's emotions and adjusts the timing of app installation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned installation unit is Analyze the user's past app usage history to select the optimal installation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned installation unit is When installing an app, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned installation unit is It estimates the user's emotions and prioritizes which apps to install based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned installation unit is When installing an app, the system prioritizes installing the most relevant apps by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned installation unit is When installing an app, the system analyzes the user's social media activity and installs relevant apps. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned support unit is It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned support unit is When providing support, we adjust the level of detail based on the importance of the app. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is When providing support, different support algorithms are applied depending on the app category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is It estimates the user's emotions and adjusts the length of support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is When providing support, we prioritize support based on how often the app is used. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is When providing support, we adjust the order of support based on the relevance of the apps. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, It estimates the user's emotions and adjusts health and safety management methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, During management, the system analyzes the user's past health data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, During management, the management methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recommendation unit is, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the app. The system described in Appendix 2, characterized by the features described herein. (Note 26) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the app category. The system described in Appendix 2, characterized by the features described herein. (Note 27) The recommendation unit is, It estimates the user's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on how often the app is used. The system described in Appendix 2, characterized by the features described herein. (Note 29) The recommendation unit is, When making recommendations, adjust the order of recommendations based on the relevance of the apps. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned security management department, It estimates user sentiment and adjusts security management methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned security management department, During security management, the system analyzes the user's past security history to select the optimal management method. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned security management department, During security management, customize the management methods based on the user's current lifestyle. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned security management department, It estimates user sentiment and prioritizes security management based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned security management department, When managing security, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned security management department, During security management, we analyze users' social media activity and propose management strategies. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned contact portion is It estimates the user's emotions and adjusts the contact method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned contact portion is When making contact, the system analyzes the user's past contact history to select the most suitable method. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned contact portion is When making contact, the method of contact is customized based on the user's current life circumstances. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned contact portion is It estimates the user's emotions and determines the priority of contacts based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned contact portion is When making contact, the system selects the most suitable method, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned contact portion is When making contact, we analyze the user's social media activity to select the most suitable method. The system described in Appendix 4, characterized by the features described herein. (Note 42) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 43) The alert unit is, When an alert is issued, the system analyzes the user's past alert history to select the most suitable method of notification. The system described in Appendix 5, characterized by the features described herein. (Note 44) The alert unit is, When an alert is issued, the method of notification is customized based on the user's current living situation. The system described in Appendix 5, characterized by the features described herein. (Note 45) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 46) The alert unit is, When issuing an alert, the system selects the optimal method of notification by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 47) The alert unit is, When an alert is issued, the system analyzes the user's social media activity to select the most appropriate communication method. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The installation section is where the app is installed and user registration takes place, A support unit that supports the use of the application installed by the aforementioned installation unit, The system includes a management unit that manages the health and safety of users supported by the aforementioned support unit. A system characterized by the following features.

2. It includes a recommendation section that recommends apps based on user instructions. The system according to feature 1.

3. It includes a security management department that manages IDs, passwords, and security conditions. The system according to feature 1.

4. It includes a contact unit that makes regular contact with users. The system according to feature 1.

5. It is equipped with an alert unit that sends alerts to government agencies and other relevant organizations as needed. The system according to feature 1.

6. The aforementioned installation unit is The system estimates the user's emotions and adjusts the timing of app installation based on those estimated emotions. The system according to feature 1.

7. The aforementioned installation unit is Analyze the user's past app usage history to select the optimal installation method. The system according to feature 1.

8. The aforementioned installation unit is When installing an app, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned installation unit is It estimates the user's emotions and prioritizes which apps to install based on those estimated emotions. The system according to feature 1.

10. The aforementioned installation unit is When installing an app, the system prioritizes installing the most relevant apps by considering the user's geographical location. The system according to feature 1.

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