system

Abstract

The system according to this embodiment aims to improve the IT literacy of elderly people. [Solution] The system according to the embodiment comprises a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, and a community management unit. The learning plan generation unit generates a learning plan. The dialogue unit engages in dialogue based on the plan generated by the learning plan generation unit. The guide unit provides practical guidance based on the information obtained by the dialogue unit. The security education unit provides security education based on the guidance provided by the guide unit. The collaboration unit collaborates with families based on the education provided by the security education unit. The community management unit manages the community based on the information obtained by the collaboration unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 prior art, there was a problem that there was a lack of a comprehensive system for effectively supporting the improvement of IT literacy of the elderly.

[0005] The system according to the embodiment aims to improve the IT literacy of the elderly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, and a community management unit. The learning plan generation unit generates a learning plan. The dialogue unit engages in dialogue based on the plan generated by the learning plan generation unit. The guide unit provides practical guidance based on the information obtained by the dialogue unit. The security education unit provides security education based on the guidance provided by the guide unit. The collaboration unit collaborates with families based on the education provided by the security education unit. The community management unit manages the community based on the information obtained by the collaboration unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the IT literacy of elderly people. [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, etc. The communication I / F manages 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 system according to an embodiment of the present invention is an innovative system that supports the improvement of IT literacy among the elderly. This system provides personalized learning plans, interactive learning, practical guidance, emphasis on safety and privacy, family collaboration, and community features. For example, the system uses a generating AI to create a learning plan customized to each elderly person's skill level and learning pace. Next, the system incorporates interactive learning, with the generating AI providing real-time answers to questions and doubts from the elderly person, facilitating learning through dialogue. Furthermore, the system provides practical guidance, with the generating AI guiding the elderly person step-by-step as they actually use digital devices. Emphasis on safety and privacy is also a key feature; the system uses the generating AI to provide security education and explain the importance of privacy protection. The system also includes family collaboration features, allowing the elderly person to receive support from family members as they learn digital technology. Finally, the system provides community features, with the generating AI operating an online community where the elderly person can interact with other users and share information. In this way, the system can provide a comprehensive system to support the improvement of IT literacy among the elderly.

[0029] The system according to this embodiment comprises a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, and a community management unit. The learning plan generation unit generates a customized learning plan tailored to each elderly person's skill level and learning pace. For example, the learning plan generation unit can use a generating AI to evaluate the elderly person's skill level and create an individual learning plan based on that. The learning plan generation unit can also use a generating AI to analyze the elderly person's learning pace and set a progress schedule accordingly. The dialogue unit provides real-time answers to questions and doubts from elderly people, facilitating learning through dialogue. For example, the dialogue unit can use a generating AI to provide immediate answers to questions from elderly people. The dialogue unit can also use a generating AI to provide detailed explanations to questions from elderly people. The guide unit provides step-by-step guidance to elderly people when they actually use digital devices. For example, the guide unit can use a generating AI to explain the operating procedures to elderly people sequentially. The guide unit can also use a generating AI to monitor the elderly person's operations in real time and provide advice as needed. The security education unit provides security education and explains the importance of privacy protection. For example, the Security Education Department can use generative AI to explain security risks to the elderly and teach them countermeasures. The Security Education Department can also use generative AI to emphasize the importance of privacy protection to the elderly. The Collaboration Department works with families to support the elderly as they learn digital technologies. For example, the Collaboration Department can use generative AI to facilitate communication between the elderly and their families. The Collaboration Department can also use generative AI to report the elderly's learning progress to their families. The Community Management Department operates online communities where the elderly can interact with other users and share information. For example, the Community Management Department can use generative AI to facilitate interaction among the elderly. The Community Management Department can also use generative AI to provide the elderly with useful information.As a result, the system according to this embodiment can provide a comprehensive system for supporting the improvement of IT literacy among the elderly.

[0030] The learning plan generation unit generates customized learning plans tailored to each elderly person's skill level and learning pace. Specifically, the learning plan generation unit uses a generation AI to evaluate the skill level of each elderly person and creates an individualized learning plan based on that evaluation. The generation AI analyzes, for example, past learning history and current skill set to suggest the most suitable learning content for each elderly person. Furthermore, the generation AI regularly monitors the elderly person's progress and updates the learning plan as needed. This allows elderly people to learn at their own pace and acquire skills without undue pressure. The learning plan generation unit also uses the generation AI to analyze the elderly person's learning pace and sets a corresponding progress schedule. For example, the generation AI evaluates the elderly person's learning speed and comprehension in real time and suggests new tasks or reviews at appropriate times. This allows elderly people to learn efficiently and acquire skills smoothly. In addition, the learning plan generation unit can also use the generation AI to provide learning content based on the elderly person's interests and concerns. For example, the generation AI analyzes the fields and themes that elderly people are interested in and suggests related learning content. This allows elderly people to learn while maintaining interest and motivation.

[0031] The dialogue unit provides real-time answers to questions and doubts from elderly individuals, facilitating learning through dialogue. Specifically, the dialogue unit uses generative AI to instantly respond to elderly individuals' questions. The generative AI uses natural language processing technology to understand the elderly individual's questions and generate appropriate answers. For example, if an elderly individual asks about a specific operation method, the generative AI will explain the procedure in detail. The dialogue unit can also use the generative AI to provide detailed explanations to elderly individuals' questions. For example, if an elderly individual does not understand a specific technical term, the generative AI will explain its meaning and background in an easy-to-understand manner. Furthermore, the dialogue unit can use the generative AI to monitor the elderly individual's learning progress and provide feedback at the appropriate time. For example, when an elderly individual completes a specific task, the generative AI will evaluate their achievement and provide advice for moving on to the next step. This allows elderly individuals to learn at their own pace, acquiring skills while resolving doubts and anxieties. In addition, the dialogue unit can use the generative AI to analyze the elderly individual's learning history and provide personalized support based on past questions and answers. This allows elderly individuals to learn with consistent support, facilitating smooth skill acquisition.

[0032] The guidance unit provides step-by-step instructions to elderly users as they operate digital devices. Specifically, the guidance unit uses generative AI to explain the operating procedures to the elderly user sequentially. The generative AI supports the elderly user in proceeding with the operation without getting lost, for example, through on-screen instructions or voice guidance. For example, when changing smartphone settings, the generative AI explains each step clearly, ensuring that the elderly user can perform the operation accurately. The guidance unit can also use the generative AI to monitor the elderly user's operation in real time and provide advice as needed. For example, if the elderly user makes an incorrect operation, the generative AI will immediately point out the error and teach the correct method. This allows the elderly user to operate digital devices with confidence and acquire skills smoothly. Furthermore, the guidance unit can use the generative AI to analyze the elderly user's operation history and evaluate their understanding of specific operations. For example, if the generative AI repeatedly performs a particular operation, it will determine that the elderly user's understanding of that operation is low and suggest additional guidance or practice. This allows the elderly user to learn at their own pace and ensures that they acquire the skills.

[0033] The Security Education Department will conduct security education and explain the importance of privacy protection. Specifically, the Security Education Department will use generating AI to explain security risks to the elderly and teach them countermeasures. For example, the generating AI will explain risks such as phishing scams and malware using specific examples to make it easy for the elderly to understand. The generating AI will also teach the elderly specific security measures they should take on a daily basis. For example, it will explain how to create strong passwords and the importance of regular software updates. Furthermore, the Security Education Department will use the generating AI to emphasize the importance of privacy protection to the elderly. For example, the generating AI will provide specific guidelines on handling personal information and support the elderly in managing their information securely. This will allow the elderly to deepen their understanding of security risks and take appropriate measures. The Security Education Department can also use the generating AI to provide educational programs to continuously improve the security awareness of the elderly. For example, through regular security checklists and quiz-style learning content, the elderly can acquire security knowledge in an enjoyable way. This will allow the elderly to stay up-to-date with the latest security information and use digital technology with peace of mind.

[0034] The Collaboration Department will work with families to provide support to seniors as they learn digital technologies. Specifically, the Collaboration Department will use generative AI to facilitate communication between seniors and their families. For example, the generative AI will report the senior's learning progress and current challenges to the family, enabling them to provide appropriate support. The generative AI can also suggest activities and tasks for seniors and families to learn together. This allows seniors to learn with peace of mind while receiving support from their families. Furthermore, the Collaboration Department will use the generative AI to report the senior's learning progress to the family. For example, the generative AI will periodically send reports summarizing the senior's learning status to the family, allowing them to understand the senior's progress. This enables the family to provide specific advice to support the senior's learning. The Collaboration Department can also use the generative AI to provide tools and functions to facilitate communication between seniors and their families. For example, the generative AI will provide a shared calendar and task management tool for seniors and their families, enabling them to create learning plans together. This allows seniors to learn efficiently in collaboration with their families, facilitating smooth technology acquisition.

[0035] The Community Management Department operates an online community where seniors can interact with other users and share information. Specifically, the Community Management Department uses generative AI to facilitate interaction among seniors. For example, the generative AI matches seniors with common interests and supports online discussions and information exchange. The generative AI can also plan online events and workshops for seniors to participate in, revitalizing interaction within the community. This allows seniors to learn while sharing information with other users, making skill acquisition enjoyable. Furthermore, the Community Management Department uses generative AI to provide seniors with useful information. For example, the generative AI regularly delivers the latest technology trends, security information, and resources useful for learning, ensuring seniors stay up-to-date. The generative AI also provides quick answers to questions and inquiries posted by seniors, promoting knowledge sharing within the community. This allows seniors to learn collaboratively with other users, facilitating smooth skill acquisition. In addition, the Community Management Department can use generative AI to monitor community activities and provide appropriate support. For example, the generative AI can detect troubles and problems within the community early and take appropriate action. This allows seniors to participate in the community and pursue their studies with peace of mind.

[0036] The learning plan generation unit can generate customized learning plans tailored to each elderly person's skill level and learning pace. For example, the learning plan generation unit can use a generation AI to evaluate the elderly person's skill level and create an individualized learning plan based on that evaluation. It can also use the generation AI to analyze the elderly person's learning pace and set a corresponding progress schedule. This allows for the provision of an optimal learning plan for each elderly person, thereby enhancing learning effectiveness. Some or all of the above-described processes in the learning plan generation unit may be performed using the generation AI, or they may be performed without it. For example, the learning plan generation unit can input data regarding the elderly person's skill level and learning pace into the generation AI, which can then generate a learning plan based on that data.

[0037] The dialogue unit can answer questions and doubts from elderly people in real time, allowing them to learn through dialogue. For example, the dialogue unit can use generative AI to provide immediate answers to questions from elderly people. It can also use generative AI to provide detailed explanations to questions from elderly people. This allows elderly people to resolve their doubts in real time as they learn. Some or all of the above-described processes in the dialogue unit may be performed using generative AI, or they may not. For example, the dialogue unit can input a question from an elderly person into the generative AI, which can then generate an answer to that question.

[0038] The guide unit can provide step-by-step guidance to elderly individuals as they actually use and operate digital devices. For example, the guide unit can use generative AI to sequentially explain the operating procedures to the elderly. Furthermore, the guide unit can use generative AI to monitor the elderly's operations in real time and provide advice as needed. This allows elderly individuals to operate digital devices with confidence. Some or all of the above-described processes in the guide unit may be performed using generative AI or not. For example, the guide unit can input the elderly's operating procedures into the generative AI, which can then generate a guide based on those procedures.

[0039] The Security Education Department can conduct security education and explain the importance of privacy protection. For example, the Security Education Department can use generative AI to explain security risks to the elderly and teach them countermeasures. The Security Education Department can also use generative AI to emphasize the importance of privacy protection to the elderly. This will enable the elderly to understand the importance of security and privacy protection. Some or all of the above processes in the Security Education Department may be performed using generative AI or not. For example, the Security Education Department can input information about security risks into a generative AI, and the AI ​​can generate educational content based on that information.

[0040] The collaboration unit allows elderly individuals to receive support from their families in the process of learning digital technologies. For example, the collaboration unit can use generative AI to facilitate communication between elderly individuals and their families. It can also use generative AI to report the elderly individual's learning progress to their families. This allows elderly individuals to learn digital technologies with the support of their families. Some or all of the above-described processes in the collaboration unit may be performed using generative AI, or they may not. For example, the collaboration unit can input data on the elderly individual's learning progress into the generative AI, which can then generate a report for the family based on that data.

[0041] The Community Management Department can operate online communities where elderly people can interact with other users and share information. For example, the Community Management Department can use generative AI to facilitate interaction among elderly people. Furthermore, the Community Management Department can use generative AI to provide elderly people with useful information. This allows elderly people to increase their motivation to learn by interacting with other users and sharing information. Some or all of the above-described processes in the Community Management Department may be performed using generative AI, or they may not. For example, the Community Management Department can input data related to the operation of the online community into a generative AI, and the generative AI can generate operational content based on that data.

[0042] The learning plan generation unit can analyze the past learning history of elderly individuals and select the optimal learning plan. For example, the learning plan generation unit uses its AI to suggest what the elderly individual should learn next, based on what they have learned in the past. The learning plan generation unit can also use its AI to create a plan that focuses on areas where the elderly individual has struggled in the past. Furthermore, the learning plan generation unit can use its AI to create a learning plan that leverages areas where the elderly individual has excelled in the past. This allows for improved learning effectiveness by providing an optimal learning plan based on past learning history. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may not. For example, the learning plan generation unit can input the elderly individual's past learning history data into the AI, which can then select the optimal learning plan based on that data.

[0043] The learning plan generation unit can provide customized content based on the interests and concerns of elderly individuals when generating a learning plan. For example, the AI ​​can generate learning content related to the elderly person's hobbies. The AI ​​can also provide learning content related to the latest technologies that the elderly person is interested in. Furthermore, the AI ​​can create a learning plan based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing learning content tailored to the elderly person's interests. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may not. For example, the learning plan generation unit can input data on the elderly person's interests into the AI, which can then provide customized content based on that data.

[0044] The learning plan generation unit can prioritize providing highly relevant learning content by considering the geographical location information of elderly individuals when generating learning plans. For example, the learning plan generation unit can generate learning content related to the area where the elderly person lives using its AI. It can also generate learning content related to places the elderly person frequently visits using its AI. Furthermore, it can generate content that the elderly person wants to learn while traveling. This enhances learning effectiveness by providing learning content based on the elderly person's geographical location information. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may be performed without the AI. For example, the learning plan generation unit can input the elderly person's geographical location information into the AI, which can then provide highly relevant learning content based on that data.

[0045] The learning plan generation unit can analyze the social media activities of elderly individuals and provide relevant learning content when generating a learning plan. For example, the learning plan generation unit can use an AI to create a learning plan based on topics that elderly individuals have shown interest in on social media. The learning plan generation unit can also use an AI to provide learning content related to accounts that elderly individuals follow on social media. Furthermore, the learning plan generation unit can use an AI to create a learning plan based on articles that elderly individuals have shared on social media. This enhances learning effectiveness by providing learning content based on the elderly individuals' social media activities. Some or all of the above-described processes in the learning plan generation unit may be performed using an AI, or they may be performed without an AI. For example, the learning plan generation unit can input social media activity data of elderly individuals into an AI, and the AI ​​can provide relevant learning content based on that data.

[0046] The dialogue unit can provide optimal answers by referring to the elderly person's past dialogue history during a conversation. For example, the dialogue unit can use the generative AI to provide relevant answers based on questions the elderly person has asked in the past. The dialogue unit can also use the generative AI to provide answers based on explanation methods that the elderly person found easy to understand in the past. Furthermore, the dialogue unit can use the generative AI to provide answers based on topics the elderly person has shown interest in in the past. This enhances the learning effect by providing optimal answers based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the elderly person's past dialogue history data into the generative AI, and the generative AI can provide optimal answers based on that data.

[0047] The dialogue unit can customize the dialogue content based on the elderly person's interests and concerns during the conversation. For example, the dialogue unit can generate dialogue content related to the elderly person's hobbies using AI. The dialogue unit can also generate dialogue content related to the latest technologies that the elderly person is interested in using AI. Furthermore, the dialogue unit can generate dialogue content based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing dialogue content tailored to the elderly person's interests and concerns. Some or all of the above processing in the dialogue unit may be performed using or without AI. For example, the dialogue unit can input data about the elderly person's interests and concerns into the AI, which can then customize the dialogue content based on that data.

[0048] The dialogue unit can provide highly relevant information during conversations, taking into account the geographical location of the elderly person. For example, the dialogue unit can generate AI to provide information related to the area where the elderly person lives. It can also generate AI to provide information related to places the elderly person frequently visits. Furthermore, the dialogue unit can generate AI to provide information the elderly person wants to know about while traveling. This enhances the learning effect by providing information based on the elderly person's geographical location. Some or all of the above processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant information based on that data.

[0049] The dialogue unit can analyze the social media activities of elderly individuals during a conversation and provide relevant information. For example, the dialogue unit can generate conversational content based on topics that the elderly individual has shown interest in on social media. The dialogue unit can also generate conversational content based on information related to accounts that the elderly individual follows on social media. Furthermore, the dialogue unit can generate conversational content based on articles that the elderly individual has shared on social media. This enhances the learning effect by providing information based on the elderly individual's social media activities. Some or all of the above processing in the dialogue unit may be performed using the generational AI, or it may be performed without the generational AI. For example, the dialogue unit can input social media activity data of the elderly individual into the generational AI, and the generational AI can provide relevant information based on that data.

[0050] The guiding unit can provide the optimal guiding method by referring to the elderly person's past operation history during guiding. For example, the guiding unit can use a generating AI to provide relevant guidance based on operations the elderly person has performed in the past. The guiding unit can also use a generating AI to provide guidance based on guiding methods that the elderly person found easy to understand in the past. Furthermore, the guiding unit can use a generating AI to provide guidance based on operations the elderly person has shown interest in in the past. This enhances the learning effect by providing the optimal guiding method based on past operation history. Some or all of the above processing in the guiding unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the guiding unit can input data on the elderly person's past operation history into a generating AI, and the generating AI can provide the optimal guiding method based on that data.

[0051] The guide unit can customize the guide content based on the interests and concerns of the elderly person during the guiding process. For example, the guide unit can generate AI to provide guide content related to the elderly person's hobbies. The guide unit can also generate AI to provide guide content related to the latest technologies that the elderly person is interested in. Furthermore, the guide unit can generate AI to provide guide content based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing guide content tailored to the elderly person's interests. Some or all of the above-described processes in the guide unit may be performed using or without the generation AI. For example, the guide unit can input data on the elderly person's interests into the generation AI, which can then customize the guide content based on that data.

[0052] The guide unit can provide highly relevant guide content by considering the geographical location information of elderly individuals during the guiding process. For example, the guide unit can generate AI to provide guide content related to the area where the elderly person lives. The guide unit can also generate AI to provide guide content related to places frequently visited by the elderly person. Furthermore, the guide unit can generate AI to provide information that the elderly person wants to know at their travel destination. This enhances the learning effect by providing guide content based on the elderly person's geographical location information. Some or all of the above processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant guide content based on that data.

[0053] The guiding unit can analyze the social media activities of elderly individuals and provide relevant guidance content during the guiding process. For example, the guiding unit can use a generating AI to provide guidance content based on topics that elderly individuals have shown interest in on social media. The guiding unit can also use a generating AI to provide guidance content related to accounts that elderly individuals follow on social media. Furthermore, the guiding unit can use a generating AI to provide guidance content based on articles that elderly individuals have shared on social media. This enhances the learning effect by providing guidance content based on the elderly individuals' social media activities. Some or all of the above processing in the guiding unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the guiding unit can input social media activity data of elderly individuals into a generating AI, which can then use that data to provide relevant guidance content.

[0054] The Security Education Department can provide optimal educational methods during security education by referencing the elderly's past security knowledge. For example, the Security Education Department can use a generative AI to provide relevant educational content based on the security knowledge the elderly have learned in the past. Furthermore, the Security Education Department can use a generative AI to provide education based on educational methods that the elderly found easy to understand in the past. In addition, the Security Education Department can use a generative AI to provide education based on security topics that the elderly have shown interest in in the past. This allows for improved learning effectiveness by providing optimal educational methods based on past security knowledge. Some or all of the above processes in the Security Education Department may be performed using a generative AI, or they may not. For example, the Security Education Department can input data on the elderly's past security knowledge into a generative AI, which can then use that data to provide optimal educational methods.

[0055] The Security Education Department can customize security education content based on the interests and concerns of elderly individuals. For example, the Security Education Department can use a generating AI to provide security education content related to the elderly person's hobbies. The Security Education Department can also use the generating AI to provide education content on the latest security technologies that the elderly person is interested in. Furthermore, the Security Education Department can use the generating AI to provide education content based on security topics that the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing education content tailored to the elderly person's interests. Some or all of the above processes in the Security Education Department may be performed using a generating AI, or not. For example, the Security Education Department can input data on the elderly person's interests into a generating AI, which can then customize the education content based on that data.

[0056] The Security Education Department can provide highly relevant educational content during security education by taking into account the geographical location information of elderly individuals. For example, the Security Education Department can generate security education content related to the area where the elderly person lives using an AI. It can also generate security education content related to places the elderly person frequently visits using an AI. Furthermore, it can generate security information that the elderly person wants to know while traveling using an AI. This allows for improved learning effectiveness by providing educational content based on the elderly person's geographical location information. Some or all of the above processing by the Security Education Department may be performed using an AI, or without one. For example, the Security Education Department can input the elderly person's geographical location information into an AI, which can then use that data to provide highly relevant educational content.

[0057] The Security Education Department can analyze the social media activities of elderly individuals during security education and provide relevant educational content. For example, the Security Education Department can use a generating AI to provide educational content based on security topics that elderly individuals have shown interest in on social media. The Security Education Department can also use a generating AI to provide security educational content related to accounts that elderly individuals follow on social media. Furthermore, the Security Education Department can use a generating AI to provide educational content based on security articles that elderly individuals have shared on social media. This allows for enhanced learning effectiveness by providing educational content based on the social media activities of elderly individuals. Some or all of the above processes by the Security Education Department may be performed using a generating AI, or they may not. For example, the Security Education Department can input social media activity data of elderly individuals into a generating AI, which can then use that data to provide relevant educational content.

[0058] The collaboration unit can provide the optimal collaboration method by referring to the elderly person's past collaboration history during collaboration. For example, the collaboration unit can use the generating AI to provide relevant collaboration methods based on how the elderly person has collaborated with their family in the past. The collaboration unit can also use the generating AI to provide collaborations based on collaboration methods that the elderly person found easy to understand in the past. Furthermore, the collaboration unit can use the generating AI to provide collaborations based on collaboration methods that the elderly person has shown interest in in the past. This enhances the learning effect by providing the optimal collaboration method based on past collaboration history. Some or all of the above processing in the collaboration unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the collaboration unit can input the elderly person's past collaboration history data into the generating AI, and the generating AI can provide the optimal collaboration method based on that data.

[0059] The collaboration unit can customize the content of the collaboration based on the interests and concerns of the elderly person during the collaboration process. For example, the collaboration unit can generate AI to provide collaboration content related to the elderly person's hobbies. The collaboration unit can also generate AI to provide collaboration content related to the latest technologies that the elderly person is interested in. Furthermore, the collaboration unit can generate AI to provide collaboration content based on topics that the elderly person has shown interest in in the past. By providing collaboration content based on the elderly person's interests and concerns, it is possible to increase their motivation to learn. Some or all of the above processing in the collaboration unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the collaboration unit can input data on the elderly person's interests and concerns into the generation AI, and the generation AI can customize the collaboration content based on that data.

[0060] The collaboration unit can provide highly relevant collaboration content by considering the geographical location information of elderly individuals during the collaboration process. For example, the collaboration unit can generate AI to provide collaboration content related to the area where the elderly person lives. It can also generate AI to provide collaboration content related to places the elderly person frequently visits. Furthermore, the collaboration unit can generate AI to provide information that the elderly person wants to know while traveling. This enhances the learning effect by providing collaboration content based on the elderly person's geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using or without the generation AI. For example, the collaboration unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant collaboration content based on that data.

[0061] The collaboration unit can analyze the social media activities of elderly individuals during the collaboration process and provide relevant collaboration content. For example, the collaboration unit can generate AI content based on topics that elderly individuals have shown interest in on social media. The collaboration unit can also generate AI content related to accounts that elderly individuals follow on social media. Furthermore, the collaboration unit can generate AI content based on articles that elderly individuals have shared on social media. This enhances the learning effect by providing collaboration content based on the social media activities of elderly individuals. Some or all of the above processing in the collaboration unit may be performed using or without the generation AI. For example, the collaboration unit can input social media activity data of elderly individuals into the generation AI, and the generation AI can provide relevant collaboration content based on that data.

[0062] The community management department can provide optimal management methods by referring to the past community activity history of elderly individuals during community management. For example, the community management department can use a generative AI to provide relevant management methods based on the community activities that elderly individuals have participated in in the past. The community management department can also use a generative AI to provide management methods based on management methods that elderly individuals found easy to understand in the past. Furthermore, the community management department can use a generative AI to provide management methods based on community activities that elderly individuals have shown interest in in the past. This enhances the learning effect by providing optimal management methods based on past community activity history. Some or all of the above processing in the community management department may be performed using a generative AI, or it may be performed without a generative AI. For example, the community management department can input data on the elderly individuals' past community activity history into a generative AI, and the generative AI can provide optimal management methods based on that data.

[0063] The community management department can customize the content of community activities based on the interests and concerns of elderly individuals. For example, the community management department can use a generating AI to provide community activities related to the hobbies of elderly individuals. The AI ​​can also provide community activities related to the latest technologies that elderly individuals are interested in. Furthermore, the AI ​​can provide community activities based on topics that elderly individuals have shown interest in in the past. This allows for increased motivation to learn by providing activities tailored to the interests and concerns of elderly individuals. Some or all of the above-described processes in the community management department may be performed using a generating AI, or they may not. For example, the community management department can input data on the interests and concerns of elderly individuals into the generating AI, which can then customize the content based on that data.

[0064] The community management department can provide highly relevant community management content by considering the geographical location information of elderly individuals during community management. For example, the community management department can use a generating AI to provide community management content related to the area where the elderly person lives. Furthermore, the community management department can use a generating AI to provide community management content related to places the elderly person frequently visits. In addition, the community management department can use a generating AI to provide information that the elderly person wants to know while traveling. This enhances the learning effect by providing community management content based on the geographical location information of the elderly person. Some or all of the above processing in the community management department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the community management department can input the geographical location information of elderly individuals into a generating AI, which can then use to provide highly relevant community management content based on that data.

[0065] The community management department can analyze the social media activities of elderly people and provide relevant management content when managing the community. For example, the community management department can use a generating AI to provide community management content based on topics that elderly people have shown interest in on social media. The community management department can also use a generating AI to provide community management content related to accounts that elderly people follow on social media. Furthermore, the community management department can use a generating AI to provide community management content based on articles that elderly people have shared on social media. This enhances the learning effect by providing management content based on the social media activities of elderly people. Some or all of the above processing in the community management department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the community management department can input social media activity data of elderly people into a generating AI, and the generating AI can provide relevant management content based on that data.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a feedback collection unit. The feedback collection unit can collect feedback from elderly users and use it to improve the system. For example, the feedback collection unit can evaluate the level of satisfaction elderly users felt with the learning plan and provide the results to the learning plan generation unit. The feedback collection unit can also evaluate the level of understanding elderly users felt regarding the dialogue content in the dialogue unit and provide the results to the dialogue unit. Furthermore, the feedback collection unit can evaluate the usefulness elderly users felt regarding the guide content in the guide unit and provide the results to the guide unit. In this way, the system can improve the functions of each unit based on feedback from elderly users and provide more effective learning support.

[0068] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and a progress management unit. The progress management unit manages the learning progress of elderly individuals and provides appropriate feedback. For example, the progress management unit monitors whether elderly individuals are progressing according to their learning plan, and the generating AI reports on their progress. The progress management unit can also have the generating AI suggest areas for improvement if the elderly individual has not reached their learning goals. Furthermore, if the elderly individual has achieved their learning goals, the generating AI can also suggest the next learning steps. In this way, the system can effectively manage the learning progress of elderly individuals and provide appropriate support.

[0069] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a reminder unit. The reminder unit provides reminders to prevent elderly people from forgetting what they are learning. For example, the reminder unit's generating AI periodically sends reminders to elderly people to help them progress according to their learning plan. The reminder unit can also provide reminders based on the elderly person's progress to help them achieve their learning goals. Furthermore, if an elderly person interrupts their learning, the reminder unit can provide a reminder from the generating AI to encourage them to resume. In this way, the system can provide support to help elderly people continue their learning.

[0070] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a customization unit. The customization unit customizes the system settings according to the individual needs of the elderly. For example, the customization unit allows the generating AI to adjust the display font and audio guide settings according to the elderly person's visual and hearing condition. The customization unit can also allow the generating AI to change the format of the learning content according to the elderly person's learning style. Furthermore, the customization unit can allow the generating AI to adjust the learning schedule according to the elderly person's daily rhythm. In this way, the system can provide learning support tailored to the individual needs of the elderly person.

[0071] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and a health management unit. The health management unit monitors the health status of elderly individuals and manages factors that affect their learning. For example, the health management unit monitors the heart rate and blood pressure of elderly individuals, and the generating AI suggests a learning pace appropriate to their health status. The health management unit can also prompt elderly individuals to take breaks when they feel fatigued. Furthermore, the health management unit can provide advice on diet and exercise tailored to the elderly individual's health condition. In this way, the system can provide learning support that takes into account the health status of elderly individuals.

[0072] The system can include a learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, community management unit, and an environment adaptation unit. The environment adaptation unit adjusts the system settings according to the elderly person's learning environment. For example, the environment adaptation unit can use the generating AI to adjust the screen brightness and audio guide volume according to the brightness and volume of the place where the elderly person is learning. The environment adaptation unit can also use the generating AI to adjust the difficulty level of the learning content according to the time of day the elderly person is learning. Furthermore, the environment adaptation unit can use the generating AI to adjust the display format and operation method according to the device the elderly person is using for learning. As a result, the system can provide optimal learning support tailored to the elderly person's learning environment.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The learning plan generation unit generates a customized learning plan tailored to each elderly person's skill level and learning pace. For example, it uses a generation AI to evaluate the elderly person's skill level and creates an individual learning plan based on that evaluation. It also uses a generation AI to analyze the elderly person's learning pace and sets a progress schedule accordingly. Step 2: The dialogue unit provides real-time answers to questions and doubts from elderly individuals, facilitating learning through dialogue. For example, it uses generative AI to instantly provide answers to elderly individuals' questions and offer detailed explanations to address their doubts. Step 3: The guide unit provides step-by-step instructions as the elderly person actually uses the digital device. For example, it uses generative AI to explain the operating procedure sequentially, monitors the operation in real time, and provides advice as needed. Step 4: The Security Education Department conducts security education and explains the importance of privacy protection. For example, it uses generated AI to explain security risks, teaches countermeasures, and emphasizes the importance of privacy protection. Step 5: The collaboration department works with families to provide support to seniors as they learn digital technologies. For example, it uses generative AI to facilitate communication between seniors and their families and reports on the seniors' learning progress to the families. Step 6: The community management team operates an online community where seniors can interact with other users and share information. For example, they might use generative AI to facilitate interaction among seniors and provide useful information.

[0075] (Example of form 2) The system according to an embodiment of the present invention is an innovative system that supports the improvement of IT literacy among the elderly. This system provides personalized learning plans, interactive learning, practical guidance, emphasis on safety and privacy, family collaboration, and community features. For example, the system uses a generating AI to create a learning plan customized to each elderly person's skill level and learning pace. Next, the system incorporates interactive learning, with the generating AI providing real-time answers to questions and doubts from the elderly person, facilitating learning through dialogue. Furthermore, the system provides practical guidance, with the generating AI guiding the elderly person step-by-step as they actually use digital devices. Emphasis on safety and privacy is also a key feature; the system uses the generating AI to provide security education and explain the importance of privacy protection. The system also includes family collaboration features, allowing the elderly person to receive support from family members as they learn digital technology. Finally, the system provides community features, with the generating AI operating an online community where the elderly person can interact with other users and share information. In this way, the system can provide a comprehensive system to support the improvement of IT literacy among the elderly.

[0076] The system according to this embodiment comprises a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, and a community management unit. The learning plan generation unit generates a customized learning plan tailored to each elderly person's skill level and learning pace. For example, the learning plan generation unit can use a generating AI to evaluate the elderly person's skill level and create an individual learning plan based on that. The learning plan generation unit can also use a generating AI to analyze the elderly person's learning pace and set a progress schedule accordingly. The dialogue unit provides real-time answers to questions and doubts from elderly people, facilitating learning through dialogue. For example, the dialogue unit can use a generating AI to provide immediate answers to questions from elderly people. The dialogue unit can also use a generating AI to provide detailed explanations to questions from elderly people. The guide unit provides step-by-step guidance to elderly people when they actually use digital devices. For example, the guide unit can use a generating AI to explain the operating procedures to elderly people sequentially. The guide unit can also use a generating AI to monitor the elderly person's operations in real time and provide advice as needed. The security education unit provides security education and explains the importance of privacy protection. For example, the Security Education Department can use generative AI to explain security risks to the elderly and teach them countermeasures. The Security Education Department can also use generative AI to emphasize the importance of privacy protection to the elderly. The Collaboration Department works with families to support the elderly as they learn digital technologies. For example, the Collaboration Department can use generative AI to facilitate communication between the elderly and their families. The Collaboration Department can also use generative AI to report the elderly's learning progress to their families. The Community Management Department operates online communities where the elderly can interact with other users and share information. For example, the Community Management Department can use generative AI to facilitate interaction among the elderly. The Community Management Department can also use generative AI to provide the elderly with useful information.As a result, the system according to this embodiment can provide a comprehensive system for supporting the improvement of IT literacy among the elderly.

[0077] The learning plan generation unit generates customized learning plans tailored to each elderly person's skill level and learning pace. Specifically, the learning plan generation unit uses a generation AI to evaluate the skill level of each elderly person and creates an individualized learning plan based on that evaluation. The generation AI analyzes, for example, past learning history and current skill set to suggest the most suitable learning content for each elderly person. Furthermore, the generation AI regularly monitors the elderly person's progress and updates the learning plan as needed. This allows elderly people to learn at their own pace and acquire skills without undue pressure. The learning plan generation unit also uses the generation AI to analyze the elderly person's learning pace and sets a corresponding progress schedule. For example, the generation AI evaluates the elderly person's learning speed and comprehension in real time and suggests new tasks or reviews at appropriate times. This allows elderly people to learn efficiently and acquire skills smoothly. In addition, the learning plan generation unit can also use the generation AI to provide learning content based on the elderly person's interests and concerns. For example, the generation AI analyzes the fields and themes that elderly people are interested in and suggests related learning content. This allows elderly people to learn while maintaining interest and motivation.

[0078] The dialogue unit provides real-time answers to questions and doubts from elderly individuals, facilitating learning through dialogue. Specifically, the dialogue unit uses generative AI to instantly respond to elderly individuals' questions. The generative AI uses natural language processing technology to understand the elderly individual's questions and generate appropriate answers. For example, if an elderly individual asks about a specific operation method, the generative AI will explain the procedure in detail. The dialogue unit can also use the generative AI to provide detailed explanations to elderly individuals' questions. For example, if an elderly individual does not understand a specific technical term, the generative AI will explain its meaning and background in an easy-to-understand manner. Furthermore, the dialogue unit can use the generative AI to monitor the elderly individual's learning progress and provide feedback at the appropriate time. For example, when an elderly individual completes a specific task, the generative AI will evaluate their achievement and provide advice for moving on to the next step. This allows elderly individuals to learn at their own pace, acquiring skills while resolving doubts and anxieties. In addition, the dialogue unit can use the generative AI to analyze the elderly individual's learning history and provide personalized support based on past questions and answers. This allows elderly individuals to learn with consistent support, facilitating smooth skill acquisition.

[0079] The guidance unit provides step-by-step instructions to elderly users as they operate digital devices. Specifically, the guidance unit uses generative AI to explain the operating procedures to the elderly user sequentially. The generative AI supports the elderly user in proceeding with the operation without getting lost, for example, through on-screen instructions or voice guidance. For example, when changing smartphone settings, the generative AI explains each step clearly, ensuring that the elderly user can perform the operation accurately. The guidance unit can also use the generative AI to monitor the elderly user's operation in real time and provide advice as needed. For example, if the elderly user makes an incorrect operation, the generative AI will immediately point out the error and teach the correct method. This allows the elderly user to operate digital devices with confidence and acquire skills smoothly. Furthermore, the guidance unit can use the generative AI to analyze the elderly user's operation history and evaluate their understanding of specific operations. For example, if the generative AI repeatedly performs a particular operation, it will determine that the elderly user's understanding of that operation is low and suggest additional guidance or practice. This allows the elderly user to learn at their own pace and ensures that they acquire the skills.

[0080] The Security Education Department will conduct security education and explain the importance of privacy protection. Specifically, the Security Education Department will use generating AI to explain security risks to the elderly and teach them countermeasures. For example, the generating AI will explain risks such as phishing scams and malware using specific examples to make it easy for the elderly to understand. The generating AI will also teach the elderly specific security measures they should take on a daily basis. For example, it will explain how to create strong passwords and the importance of regular software updates. Furthermore, the Security Education Department will use the generating AI to emphasize the importance of privacy protection to the elderly. For example, the generating AI will provide specific guidelines on handling personal information and support the elderly in managing their information securely. This will allow the elderly to deepen their understanding of security risks and take appropriate measures. The Security Education Department can also use the generating AI to provide educational programs to continuously improve the security awareness of the elderly. For example, through regular security checklists and quiz-style learning content, the elderly can acquire security knowledge in an enjoyable way. This will allow the elderly to stay up-to-date with the latest security information and use digital technology with peace of mind.

[0081] The Collaboration Department will work with families to provide support to seniors as they learn digital technologies. Specifically, the Collaboration Department will use generative AI to facilitate communication between seniors and their families. For example, the generative AI will report the senior's learning progress and current challenges to the family, enabling them to provide appropriate support. The generative AI can also suggest activities and tasks for seniors and families to learn together. This allows seniors to learn with peace of mind while receiving support from their families. Furthermore, the Collaboration Department will use the generative AI to report the senior's learning progress to the family. For example, the generative AI will periodically send reports summarizing the senior's learning status to the family, allowing them to understand the senior's progress. This enables the family to provide specific advice to support the senior's learning. The Collaboration Department can also use the generative AI to provide tools and functions to facilitate communication between seniors and their families. For example, the generative AI will provide a shared calendar and task management tool for seniors and their families, enabling them to create learning plans together. This allows seniors to learn efficiently in collaboration with their families, facilitating smooth technology acquisition.

[0082] The Community Management Department operates an online community where seniors can interact with other users and share information. Specifically, the Community Management Department uses generative AI to facilitate interaction among seniors. For example, the generative AI matches seniors with common interests and supports online discussions and information exchange. The generative AI can also plan online events and workshops for seniors to participate in, revitalizing interaction within the community. This allows seniors to learn while sharing information with other users, making skill acquisition enjoyable. Furthermore, the Community Management Department uses generative AI to provide seniors with useful information. For example, the generative AI regularly delivers the latest technology trends, security information, and resources useful for learning, ensuring seniors stay up-to-date. The generative AI also provides quick answers to questions and inquiries posted by seniors, promoting knowledge sharing within the community. This allows seniors to learn collaboratively with other users, facilitating smooth skill acquisition. In addition, the Community Management Department can use generative AI to monitor community activities and provide appropriate support. For example, the generative AI can detect troubles and problems within the community early and take appropriate action. This allows seniors to participate in the community and pursue their studies with peace of mind.

[0083] The learning plan generation unit can generate customized learning plans tailored to each elderly person's skill level and learning pace. For example, the learning plan generation unit can use a generation AI to evaluate the elderly person's skill level and create an individualized learning plan based on that evaluation. It can also use the generation AI to analyze the elderly person's learning pace and set a corresponding progress schedule. This allows for the provision of an optimal learning plan for each elderly person, thereby enhancing learning effectiveness. Some or all of the above-described processes in the learning plan generation unit may be performed using the generation AI, or they may be performed without it. For example, the learning plan generation unit can input data regarding the elderly person's skill level and learning pace into the generation AI, which can then generate a learning plan based on that data.

[0084] The dialogue unit can answer questions and doubts from elderly people in real time, allowing them to learn through dialogue. For example, the dialogue unit can use generative AI to provide immediate answers to questions from elderly people. It can also use generative AI to provide detailed explanations to questions from elderly people. This allows elderly people to resolve their doubts in real time as they learn. Some or all of the above-described processes in the dialogue unit may be performed using generative AI, or they may not. For example, the dialogue unit can input a question from an elderly person into the generative AI, which can then generate an answer to that question.

[0085] The guide unit can provide step-by-step guidance to elderly individuals as they actually use and operate digital devices. For example, the guide unit can use generative AI to sequentially explain the operating procedures to the elderly. Furthermore, the guide unit can use generative AI to monitor the elderly's operations in real time and provide advice as needed. This allows elderly individuals to operate digital devices with confidence. Some or all of the above-described processes in the guide unit may be performed using generative AI or not. For example, the guide unit can input the elderly's operating procedures into the generative AI, which can then generate a guide based on those procedures.

[0086] The Security Education Department can conduct security education and explain the importance of privacy protection. For example, the Security Education Department can use generative AI to explain security risks to the elderly and teach them countermeasures. The Security Education Department can also use generative AI to emphasize the importance of privacy protection to the elderly. This will enable the elderly to understand the importance of security and privacy protection. Some or all of the above processes in the Security Education Department may be performed using generative AI or not. For example, the Security Education Department can input information about security risks into a generative AI, and the AI ​​can generate educational content based on that information.

[0087] The collaboration unit allows elderly individuals to receive support from their families in the process of learning digital technologies. For example, the collaboration unit can use generative AI to facilitate communication between elderly individuals and their families. It can also use generative AI to report the elderly individual's learning progress to their families. This allows elderly individuals to learn digital technologies with the support of their families. Some or all of the above-described processes in the collaboration unit may be performed using generative AI, or they may not. For example, the collaboration unit can input data on the elderly individual's learning progress into the generative AI, which can then generate a report for the family based on that data.

[0088] The Community Management Department can operate online communities where elderly people can interact with other users and share information. For example, the Community Management Department can use generative AI to facilitate interaction among elderly people. Furthermore, the Community Management Department can use generative AI to provide elderly people with useful information. This allows elderly people to increase their motivation to learn by interacting with other users and sharing information. Some or all of the above-described processes in the Community Management Department may be performed using generative AI, or they may not. For example, the Community Management Department can input data related to the operation of the online community into a generative AI, and the generative AI can generate operational content based on that data.

[0089] The learning plan generation unit can estimate the emotions of elderly individuals and adjust the difficulty level of the learning plan based on the estimated emotions. For example, if an elderly individual is feeling stressed, the generating AI in the learning plan generation unit will prioritize providing easier tasks. Conversely, if an elderly individual is relaxed, the generating AI can provide more difficult tasks. Furthermore, if an elderly individual is agitated, the generating AI can provide tasks containing interesting content. This allows for improved learning effectiveness by providing learning plans with difficulty levels tailored to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning plan generation unit may be performed using or without a generating AI. For example, the learning plan generation unit can input emotional data of elderly individuals into a generating AI, which can then adjust the difficulty level of the learning plan based on that data.

[0090] The learning plan generation unit can analyze the past learning history of elderly individuals and select the optimal learning plan. For example, the learning plan generation unit uses its AI to suggest what the elderly individual should learn next, based on what they have learned in the past. The learning plan generation unit can also use its AI to create a plan that focuses on areas where the elderly individual has struggled in the past. Furthermore, the learning plan generation unit can use its AI to create a learning plan that leverages areas where the elderly individual has excelled in the past. This allows for improved learning effectiveness by providing an optimal learning plan based on past learning history. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may not. For example, the learning plan generation unit can input the elderly individual's past learning history data into the AI, which can then select the optimal learning plan based on that data.

[0091] The learning plan generation unit can provide customized content based on the interests and concerns of elderly individuals when generating a learning plan. For example, the AI ​​can generate learning content related to the elderly person's hobbies. The AI ​​can also provide learning content related to the latest technologies that the elderly person is interested in. Furthermore, the AI ​​can create a learning plan based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing learning content tailored to the elderly person's interests. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may not. For example, the learning plan generation unit can input data on the elderly person's interests into the AI, which can then provide customized content based on that data.

[0092] The learning plan generation unit can estimate the emotions of elderly individuals and adjust the pace of the learning plan based on the estimated emotions. For example, if an elderly individual is anxious, the generating AI can provide a learning plan at a slow pace. If the elderly individual is relaxed, the generating AI can provide a learning plan at a normal pace. Furthermore, if the elderly individual is agitated, the generating AI can provide a learning plan at a fast pace. This allows for improved learning effectiveness by providing a learning plan with a pace appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the learning plan generation unit may be performed using or without a generating AI. For example, the learning plan generation unit can input the elderly individual's emotion data into a generating AI, which can then adjust the pace of the learning plan based on that data.

[0093] The learning plan generation unit can prioritize providing highly relevant learning content by considering the geographical location information of elderly individuals when generating learning plans. For example, the learning plan generation unit can generate learning content related to the area where the elderly person lives using its AI. It can also generate learning content related to places the elderly person frequently visits using its AI. Furthermore, it can generate content that the elderly person wants to learn while traveling. This enhances learning effectiveness by providing learning content based on the elderly person's geographical location information. Some or all of the above-described processes in the learning plan generation unit may be performed using the AI, or they may be performed without the AI. For example, the learning plan generation unit can input the elderly person's geographical location information into the AI, which can then provide highly relevant learning content based on that data.

[0094] The learning plan generation unit can analyze the social media activities of elderly individuals and provide relevant learning content when generating a learning plan. For example, the learning plan generation unit can use an AI to create a learning plan based on topics that elderly individuals have shown interest in on social media. The learning plan generation unit can also use an AI to provide learning content related to accounts that elderly individuals follow on social media. Furthermore, the learning plan generation unit can use an AI to create a learning plan based on articles that elderly individuals have shared on social media. This enhances learning effectiveness by providing learning content based on the elderly individuals' social media activities. Some or all of the above-described processes in the learning plan generation unit may be performed using an AI, or they may be performed without an AI. For example, the learning plan generation unit can input social media activity data of elderly individuals into an AI, and the AI ​​can provide relevant learning content based on that data.

[0095] The dialogue unit can estimate the emotions of elderly individuals and adjust the tone and expression of the dialogue based on the estimated emotions. For example, if the elderly individual is tense, the dialogue unit's generating AI can engage in dialogue in a gentle tone. Similarly, if the elderly individual is relaxed, the generating AI can engage in dialogue in a friendly tone. Furthermore, if the elderly individual is excited, the generating AI can engage in dialogue in a lively tone. This enhances the learning effect by engaging in dialogue with a tone and expression appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the dialogue unit may be performed using or without a generating AI. For example, the dialogue unit can input the elderly individual's emotion data into the generating AI, which can then adjust the tone and expression of the dialogue based on that data.

[0096] The dialogue unit can provide optimal answers by referring to the elderly person's past dialogue history during a conversation. For example, the dialogue unit can use the generative AI to provide relevant answers based on questions the elderly person has asked in the past. The dialogue unit can also use the generative AI to provide answers based on explanation methods that the elderly person found easy to understand in the past. Furthermore, the dialogue unit can use the generative AI to provide answers based on topics the elderly person has shown interest in in the past. This enhances the learning effect by providing optimal answers based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using the generative AI, or it may be performed without the generative AI. For example, the dialogue unit can input the elderly person's past dialogue history data into the generative AI, and the generative AI can provide optimal answers based on that data.

[0097] The dialogue unit can customize the dialogue content based on the elderly person's interests and concerns during the conversation. For example, the dialogue unit can generate dialogue content related to the elderly person's hobbies using AI. The dialogue unit can also generate dialogue content related to the latest technologies that the elderly person is interested in using AI. Furthermore, the dialogue unit can generate dialogue content based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing dialogue content tailored to the elderly person's interests and concerns. Some or all of the above processing in the dialogue unit may be performed using or without AI. For example, the dialogue unit can input data about the elderly person's interests and concerns into the AI, which can then customize the dialogue content based on that data.

[0098] The dialogue unit can estimate the emotions of elderly individuals and adjust the length of the dialogue based on the estimated emotions. For example, if the elderly individual is anxious, the dialogue unit can use a generative AI to provide a short dialogue. The dialogue unit can also use a generative AI to provide a dialogue of normal length if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the dialogue unit can use a generative AI to provide a longer dialogue. This enhances the learning effect by providing dialogue lengths appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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-described processes in the dialogue unit may be performed using or without a generative AI. For example, the dialogue unit can input the elderly individual's emotion data into a generative AI, which can then adjust the length of the dialogue based on that data.

[0099] The dialogue unit can provide highly relevant information during conversations, taking into account the geographical location of the elderly person. For example, the dialogue unit can generate AI to provide information related to the area where the elderly person lives. It can also generate AI to provide information related to places the elderly person frequently visits. Furthermore, the dialogue unit can generate AI to provide information the elderly person wants to know about while traveling. This enhances the learning effect by providing information based on the elderly person's geographical location. Some or all of the above processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant information based on that data.

[0100] The dialogue unit can analyze the social media activities of elderly individuals during a conversation and provide relevant information. For example, the dialogue unit can generate conversational content based on topics that the elderly individual has shown interest in on social media. The dialogue unit can also generate conversational content based on information related to accounts that the elderly individual follows on social media. Furthermore, the dialogue unit can generate conversational content based on articles that the elderly individual has shared on social media. This enhances the learning effect by providing information based on the elderly individual's social media activities. Some or all of the above processing in the dialogue unit may be performed using the generational AI, or it may be performed without the generational AI. For example, the dialogue unit can input social media activity data of the elderly individual into the generational AI, and the generational AI can provide relevant information based on that data.

[0101] The guide unit can estimate the emotions of elderly individuals and adjust the pace of the guide based on the estimated emotions. For example, if the elderly individual is anxious, the guide unit can use a generating AI to provide a guide at a slow pace. The guide unit can also use a generating AI to provide a guide at a normal pace if the elderly individual is relaxed. Furthermore, if the elderly individual is agitated, the guide unit can use a generating AI to provide a guide at a fast pace. This enhances the learning effect by providing a guide at a pace appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the guide unit may be performed using or without a generating AI. For example, the guide unit can input the elderly individual's emotion data into a generating AI, which can then adjust the pace of the guide based on that data.

[0102] The guiding unit can provide the optimal guiding method by referring to the elderly person's past operation history during guiding. For example, the guiding unit can use a generating AI to provide relevant guidance based on operations the elderly person has performed in the past. The guiding unit can also use a generating AI to provide guidance based on guiding methods that the elderly person found easy to understand in the past. Furthermore, the guiding unit can use a generating AI to provide guidance based on operations the elderly person has shown interest in in the past. This enhances the learning effect by providing the optimal guiding method based on past operation history. Some or all of the above processing in the guiding unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the guiding unit can input data on the elderly person's past operation history into a generating AI, and the generating AI can provide the optimal guiding method based on that data.

[0103] The guide unit can customize the guide content based on the interests and concerns of the elderly person during the guiding process. For example, the guide unit can generate AI to provide guide content related to the elderly person's hobbies. The guide unit can also generate AI to provide guide content related to the latest technologies that the elderly person is interested in. Furthermore, the guide unit can generate AI to provide guide content based on topics the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing guide content tailored to the elderly person's interests. Some or all of the above-described processes in the guide unit may be performed using or without the generation AI. For example, the guide unit can input data on the elderly person's interests into the generation AI, which can then customize the guide content based on that data.

[0104] The guide unit can estimate the emotions of elderly individuals and adjust the level of detail in the guide based on the estimated emotions. For example, if an elderly individual is anxious, the guide unit can use a generative AI to provide a concise guide. If the elderly individual is relaxed, the guide unit can also use a generative AI to provide a detailed guide. Furthermore, if the elderly individual is agitated, the guide unit can use a generative AI to provide a detailed and visually stimulating guide. This enhances the learning effect by providing a guide with a level of detail appropriate to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the guide unit may be performed using or without a generative AI. For example, the guide unit can input emotional data of elderly individuals into a generative AI, which can then adjust the level of detail in the guide based on that data.

[0105] The guide unit can provide highly relevant guide content by considering the geographical location information of elderly individuals during the guiding process. For example, the guide unit can generate AI to provide guide content related to the area where the elderly person lives. The guide unit can also generate AI to provide guide content related to places frequently visited by the elderly person. Furthermore, the guide unit can generate AI to provide information that the elderly person wants to know at their travel destination. This enhances the learning effect by providing guide content based on the elderly person's geographical location information. Some or all of the above processing in the guide unit may be performed using or without the generation AI. For example, the guide unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant guide content based on that data.

[0106] The guiding unit can analyze the social media activities of elderly individuals and provide relevant guidance content during the guiding process. For example, the guiding unit can use a generating AI to provide guidance content based on topics that elderly individuals have shown interest in on social media. The guiding unit can also use a generating AI to provide guidance content related to accounts that elderly individuals follow on social media. Furthermore, the guiding unit can use a generating AI to provide guidance content based on articles that elderly individuals have shared on social media. This enhances the learning effect by providing guidance content based on the elderly individuals' social media activities. Some or all of the above processing in the guiding unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the guiding unit can input social media activity data of elderly individuals into a generating AI, which can then use that data to provide relevant guidance content.

[0107] The Security Education Department can estimate the emotions of elderly individuals and adjust the pace of security education based on the estimated emotions. For example, if an elderly person is anxious, the Generative AI can provide security education at a slow pace. If the elderly person is relaxed, the Generative AI can provide security education at a normal pace. Furthermore, if the elderly person is agitated, the Generative AI can provide security education at a fast pace. This allows for enhanced learning effectiveness by providing security education at a pace appropriate to the elderly person'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-described processes in the Security Education Department may be performed using or without a Generative AI. For example, the Security Education Department can input elderly individuals' emotion data into a Generative AI, which can then adjust the pace of security education based on that data.

[0108] The Security Education Department can provide optimal educational methods during security education by referencing the elderly's past security knowledge. For example, the Security Education Department can use a generative AI to provide relevant educational content based on the security knowledge the elderly have learned in the past. Furthermore, the Security Education Department can use a generative AI to provide education based on educational methods that the elderly found easy to understand in the past. In addition, the Security Education Department can use a generative AI to provide education based on security topics that the elderly have shown interest in in the past. This allows for improved learning effectiveness by providing optimal educational methods based on past security knowledge. Some or all of the above processes in the Security Education Department may be performed using a generative AI, or they may not. For example, the Security Education Department can input data on the elderly's past security knowledge into a generative AI, which can then use that data to provide optimal educational methods.

[0109] The Security Education Department can customize security education content based on the interests and concerns of elderly individuals. For example, the Security Education Department can use a generating AI to provide security education content related to the elderly person's hobbies. The Security Education Department can also use the generating AI to provide education content on the latest security technologies that the elderly person is interested in. Furthermore, the Security Education Department can use the generating AI to provide education content based on security topics that the elderly person has shown interest in in the past. This allows for increased motivation to learn by providing education content tailored to the elderly person's interests. Some or all of the above processes in the Security Education Department may be performed using a generating AI, or not. For example, the Security Education Department can input data on the elderly person's interests into a generating AI, which can then customize the education content based on that data.

[0110] The Security Education Department can estimate the emotions of elderly individuals and adjust the level of detail in security education based on the estimated emotions. For example, if an elderly person is anxious, the Generative AI can provide concise security education. If the elderly person is relaxed, the Generative AI can provide detailed security education. Furthermore, if the elderly person is agitated, the Generative AI can provide detailed and visually stimulating security education. This enhances learning effectiveness by providing security education with a level of detail appropriate to the elderly person'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-described processes in the Security Education Department may be performed using or without a Generative AI. For example, the Security Education Department can input elderly individuals' emotion data into a Generative AI, which can then adjust the level of detail in security education based on that data.

[0111] The Security Education Department can provide highly relevant educational content during security education by taking into account the geographical location information of elderly individuals. For example, the Security Education Department can generate security education content related to the area where the elderly person lives using an AI. It can also generate security education content related to places the elderly person frequently visits using an AI. Furthermore, it can generate security information that the elderly person wants to know while traveling using an AI. This allows for improved learning effectiveness by providing educational content based on the elderly person's geographical location information. Some or all of the above processing by the Security Education Department may be performed using an AI, or without one. For example, the Security Education Department can input the elderly person's geographical location information into an AI, which can then use that data to provide highly relevant educational content.

[0112] The Security Education Department can analyze the social media activities of elderly individuals during security education and provide relevant educational content. For example, the Security Education Department can use a generating AI to provide educational content based on security topics that elderly individuals have shown interest in on social media. The Security Education Department can also use a generating AI to provide security educational content related to accounts that elderly individuals follow on social media. Furthermore, the Security Education Department can use a generating AI to provide educational content based on security articles that elderly individuals have shared on social media. This allows for enhanced learning effectiveness by providing educational content based on the social media activities of elderly individuals. Some or all of the above processes by the Security Education Department may be performed using a generating AI, or they may not. For example, the Security Education Department can input social media activity data of elderly individuals into a generating AI, which can then use that data to provide relevant educational content.

[0113] The collaboration unit can estimate the emotions of elderly individuals and adjust the method of collaboration with family members based on the estimated emotions. For example, if the elderly individual is feeling anxious, the collaboration unit's generating AI can facilitate a gentler approach to family collaboration. If the elderly individual is relaxed, the collaboration unit's generating AI can also facilitate a more proactive approach to family collaboration. Furthermore, if the elderly individual is agitated, the collaboration unit's generating AI can facilitate a more rapid approach to family collaboration. This enhances the learning effect by providing a collaboration method tailored to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using or without a generating AI. For example, the collaboration unit can input the elderly individual's emotional data into a generating AI, which can then adjust the method of collaboration with family members based on that data.

[0114] The collaboration unit can provide the optimal collaboration method by referring to the elderly person's past collaboration history during collaboration. For example, the collaboration unit can use the generating AI to provide relevant collaboration methods based on how the elderly person has collaborated with their family in the past. The collaboration unit can also use the generating AI to provide collaborations based on collaboration methods that the elderly person found easy to understand in the past. Furthermore, the collaboration unit can use the generating AI to provide collaborations based on collaboration methods that the elderly person has shown interest in in the past. This enhances the learning effect by providing the optimal collaboration method based on past collaboration history. Some or all of the above processing in the collaboration unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the collaboration unit can input the elderly person's past collaboration history data into the generating AI, and the generating AI can provide the optimal collaboration method based on that data.

[0115] The collaboration unit can customize the content of the collaboration based on the interests and concerns of the elderly person during the collaboration process. For example, the collaboration unit can generate AI to provide collaboration content related to the elderly person's hobbies. The collaboration unit can also generate AI to provide collaboration content related to the latest technologies that the elderly person is interested in. Furthermore, the collaboration unit can generate AI to provide collaboration content based on topics that the elderly person has shown interest in in the past. By providing collaboration content based on the elderly person's interests and concerns, it is possible to increase their motivation to learn. Some or all of the above processing in the collaboration unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the collaboration unit can input data on the elderly person's interests and concerns into the generation AI, and the generation AI can customize the collaboration content based on that data.

[0116] The collaboration unit can estimate the emotions of elderly individuals and adjust the frequency of collaboration based on the estimated emotions. For example, if the elderly individual is tense, the collaboration unit can have the generating AI reduce the frequency of collaboration. The collaboration unit can also have the generating AI maintain a normal frequency of collaboration if the elderly individual is relaxed. Furthermore, if the elderly individual is excited, the collaboration unit can have the generating AI increase the frequency of collaboration. This enhances the learning effect by providing a collaboration frequency that corresponds to the elderly individual's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the collaboration unit may be performed using or without a generating AI. For example, the collaboration unit can input the elderly individual's emotion data into a generating AI, which can then adjust the frequency of collaboration based on that data.

[0117] The collaboration unit can provide highly relevant collaboration content by considering the geographical location information of elderly individuals during the collaboration process. For example, the collaboration unit can generate AI to provide collaboration content related to the area where the elderly person lives. It can also generate AI to provide collaboration content related to places the elderly person frequently visits. Furthermore, the collaboration unit can generate AI to provide information that the elderly person wants to know while traveling. This enhances the learning effect by providing collaboration content based on the elderly person's geographical location information. Some or all of the above-described processes in the collaboration unit may be performed using or without the generation AI. For example, the collaboration unit can input the elderly person's geographical location information into the generation AI, which can then provide highly relevant collaboration content based on that data.

[0118] The collaboration unit can analyze the social media activities of elderly individuals during the collaboration process and provide relevant collaboration content. For example, the collaboration unit can generate AI content based on topics that elderly individuals have shown interest in on social media. The collaboration unit can also generate AI content related to accounts that elderly individuals follow on social media. Furthermore, the collaboration unit can generate AI content based on articles that elderly individuals have shared on social media. This enhances the learning effect by providing collaboration content based on the social media activities of elderly individuals. Some or all of the above processing in the collaboration unit may be performed using or without the generation AI. For example, the collaboration unit can input social media activity data of elderly individuals into the generation AI, and the generation AI can provide relevant collaboration content based on that data.

[0119] The community management department can estimate the emotions of elderly individuals and adjust the community management methods based on the estimated emotions. For example, if an elderly individual is feeling tense, the community management department can use a generative AI to manage the community gently. Conversely, if an elderly individual is relaxed, the community management department can use the generative AI to manage the community more actively. Furthermore, if an elderly individual is agitated, the community management department can use the generative AI to manage the community more quickly. This enhances the learning effect by providing management methods that are appropriate to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the community management department may be performed using a generative AI or not. For example, the community management department can input elderly individuals' emotional data into a generative AI, which can then adjust the community management methods based on that data.

[0120] The community management department can provide optimal management methods by referring to the past community activity history of elderly individuals during community management. For example, the community management department can use a generative AI to provide relevant management methods based on the community activities that elderly individuals have participated in in the past. The community management department can also use a generative AI to provide management methods based on management methods that elderly individuals found easy to understand in the past. Furthermore, the community management department can use a generative AI to provide management methods based on community activities that elderly individuals have shown interest in in the past. This enhances the learning effect by providing optimal management methods based on past community activity history. Some or all of the above processing in the community management department may be performed using a generative AI, or it may be performed without a generative AI. For example, the community management department can input data on the elderly individuals' past community activity history into a generative AI, and the generative AI can provide optimal management methods based on that data.

[0121] The community management department can customize the content of community activities based on the interests and concerns of elderly individuals. For example, the community management department can use a generating AI to provide community activities related to the hobbies of elderly individuals. The AI ​​can also provide community activities related to the latest technologies that elderly individuals are interested in. Furthermore, the AI ​​can provide community activities based on topics that elderly individuals have shown interest in in the past. This allows for increased motivation to learn by providing activities tailored to the interests and concerns of elderly individuals. Some or all of the above-described processes in the community management department may be performed using a generating AI, or they may not. For example, the community management department can input data on the interests and concerns of elderly individuals into the generating AI, which can then customize the content based on that data.

[0122] The community management department can estimate the emotions of elderly individuals and adjust the frequency of community activities based on these estimates. For example, if an elderly individual is feeling anxious, the community management department can use a generative AI to reduce the frequency of community activities. Conversely, if an elderly individual is relaxed, the community management department can use the generative AI to maintain a normal frequency of community activities. Furthermore, if an elderly individual is agitated, the community management department can use the generative AI to increase the frequency of community activities. This enhances the learning effect by providing activity frequencies that correspond to the emotions of elderly individuals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The 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-described processes in the community management department may be performed using or without a generative AI. For example, the community management department can input elderly individuals' emotion data into a generative AI, which can then adjust the frequency of community activities based on that data.

[0123] The community management department can provide highly relevant community management content by considering the geographical location information of elderly individuals during community management. For example, the community management department can use a generating AI to provide community management content related to the area where the elderly person lives. Furthermore, the community management department can use a generating AI to provide community management content related to places the elderly person frequently visits. In addition, the community management department can use a generating AI to provide information that the elderly person wants to know while traveling. This enhances the learning effect by providing community management content based on the geographical location information of the elderly person. Some or all of the above processing in the community management department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the community management department can input the geographical location information of elderly individuals into a generating AI, which can then use to provide highly relevant community management content based on that data.

[0124] The community management department can analyze the social media activities of elderly people and provide relevant management content when managing the community. For example, the community management department can use a generating AI to provide community management content based on topics that elderly people have shown interest in on social media. The community management department can also use a generating AI to provide community management content related to accounts that elderly people follow on social media. Furthermore, the community management department can use a generating AI to provide community management content based on articles that elderly people have shared on social media. This enhances the learning effect by providing management content based on the social media activities of elderly people. Some or all of the above processing in the community management department may be performed using a generating AI, or it may be performed without using a generating AI. For example, the community management department can input social media activity data of elderly people into a generating AI, and the generating AI can provide relevant management content based on that data.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a feedback collection unit. The feedback collection unit can collect feedback from elderly users and use it to improve the system. For example, the feedback collection unit can evaluate the level of satisfaction elderly users felt with the learning plan and provide the results to the learning plan generation unit. The feedback collection unit can also evaluate the level of understanding elderly users felt regarding the dialogue content in the dialogue unit and provide the results to the dialogue unit. Furthermore, the feedback collection unit can evaluate the usefulness elderly users felt regarding the guide content in the guide unit and provide the results to the guide unit. In this way, the system can improve the functions of each unit based on feedback from elderly users and provide more effective learning support.

[0127] The system can include a learning plan generation unit, a dialogue unit, a guidance unit, a security education unit, a collaboration unit, a community management unit, and a motivation maintenance unit. The motivation maintenance unit provides functions to maintain the learning motivation of older adults. For example, when an older adult achieves a learning goal, the motivation maintenance unit's generating AI provides messages of praise and encouragement. The motivation maintenance unit can also provide messages encouraging older adults to take a break when they feel tired of learning. Furthermore, when an older adult loses interest in learning, the generating AI in the motivation maintenance unit can suggest new learning topics. In this way, the system can maintain the learning motivation of older adults and support continuous learning.

[0128] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and a progress management unit. The progress management unit manages the learning progress of elderly individuals and provides appropriate feedback. For example, the progress management unit monitors whether elderly individuals are progressing according to their learning plan, and the generating AI reports on their progress. The progress management unit can also have the generating AI suggest areas for improvement if the elderly individual has not reached their learning goals. Furthermore, if the elderly individual has achieved their learning goals, the generating AI can also suggest the next learning steps. In this way, the system can effectively manage the learning progress of elderly individuals and provide appropriate support.

[0129] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a reminder unit. The reminder unit provides reminders to prevent elderly people from forgetting what they are learning. For example, the reminder unit's generating AI periodically sends reminders to elderly people to help them progress according to their learning plan. The reminder unit can also provide reminders based on the elderly person's progress to help them achieve their learning goals. Furthermore, if an elderly person interrupts their learning, the reminder unit can provide a reminder from the generating AI to encourage them to resume. In this way, the system can provide support to help elderly people continue their learning.

[0130] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and an emotion analysis unit. The emotion analysis unit analyzes the emotions of elderly individuals in real time and provides appropriate responses. For example, if an elderly person is feeling stressed during learning, the emotion analysis unit can have the generating AI provide advice to help them relax. Furthermore, if an elderly person is excited about learning, the emotion analysis unit can have the generating AI suggest learning content that leverages that excitement. Additionally, if an elderly person is indifferent to learning, the emotion analysis unit can have the generating AI suggest new topics to pique their interest. This allows the system to provide learning support tailored to the emotions of elderly individuals.

[0131] The system can include a learning plan generation unit, a dialogue unit, a guide unit, a security education unit, a collaboration unit, a community management unit, and a customization unit. The customization unit customizes the system settings according to the individual needs of the elderly. For example, the customization unit allows the generating AI to adjust the display font and audio guide settings according to the elderly person's visual and hearing condition. The customization unit can also allow the generating AI to change the format of the learning content according to the elderly person's learning style. Furthermore, the customization unit can allow the generating AI to adjust the learning schedule according to the elderly person's daily rhythm. In this way, the system can provide learning support tailored to the individual needs of the elderly person.

[0132] The system can include a learning plan generation unit, a dialogue unit, a guidance unit, a security education unit, a collaboration unit, a community management unit, and a reward system unit. The reward system unit provides rewards when seniors achieve their learning goals. For example, the reward system unit can award badges or points to seniors when they complete their learning plans. The reward system unit can also provide digital gifts to seniors when they achieve specific learning goals. Furthermore, the reward system unit can provide special rewards to seniors when they continue learning consistently. This allows the system to increase seniors' motivation to learn and promote continuous learning.

[0133] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and a health management unit. The health management unit monitors the health status of elderly individuals and manages factors that affect their learning. For example, the health management unit monitors the heart rate and blood pressure of elderly individuals, and the generating AI suggests a learning pace appropriate to their health status. The health management unit can also prompt elderly individuals to take breaks when they feel fatigued. Furthermore, the health management unit can provide advice on diet and exercise tailored to the elderly individual's health condition. In this way, the system can provide learning support that takes into account the health status of elderly individuals.

[0134] The system can include a learning plan generation unit, dialogue unit, guidance unit, security education unit, collaboration unit, community management unit, and an emotion sharing unit. The emotion sharing unit shares the emotions that elderly people feel while learning with their families and community. For example, the emotion sharing unit has the generating AI report to the family the joy and sense of accomplishment that the elderly person felt while learning. The emotion sharing unit can also have the generating AI share the difficulties and anxieties that the elderly person felt while learning with the community. Furthermore, based on the emotions the elderly person felt while learning, the generating AI can also encourage support from family and community. In this way, the system can share the emotions of elderly people and receive support from family and community, thereby enhancing the learning effect.

[0135] The system can include a learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, community management unit, and an environment adaptation unit. The environment adaptation unit adjusts the system settings according to the elderly person's learning environment. For example, the environment adaptation unit can use the generating AI to adjust the screen brightness and audio guide volume according to the brightness and volume of the place where the elderly person is learning. The environment adaptation unit can also use the generating AI to adjust the difficulty level of the learning content according to the time of day the elderly person is learning. Furthermore, the environment adaptation unit can use the generating AI to adjust the display format and operation method according to the device the elderly person is using for learning. As a result, the system can provide optimal learning support tailored to the elderly person's learning environment.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The learning plan generation unit generates a customized learning plan tailored to each elderly person's skill level and learning pace. For example, it uses a generation AI to evaluate the elderly person's skill level and creates an individual learning plan based on that evaluation. It also uses a generation AI to analyze the elderly person's learning pace and sets a progress schedule accordingly. Step 2: The dialogue unit provides real-time answers to questions and doubts from elderly individuals, facilitating learning through dialogue. For example, it uses generative AI to instantly provide answers to elderly individuals' questions and offer detailed explanations to address their doubts. Step 3: The guide unit provides step-by-step instructions as the elderly person actually uses the digital device. For example, it uses generative AI to explain the operating procedure sequentially, monitors the operation in real time, and provides advice as needed. Step 4: The Security Education Department conducts security education and explains the importance of privacy protection. For example, it uses generated AI to explain security risks, teaches countermeasures, and emphasizes the importance of privacy protection. Step 5: The collaboration department works with families to provide support to seniors as they learn digital technologies. For example, it uses generative AI to facilitate communication between seniors and their families and reports on the seniors' learning progress to the families. Step 6: The community management team operates an online community where seniors can interact with other users and share information. For example, they might use generative AI to facilitate interaction among seniors and provide useful information.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, and community management unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the learning plan generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The guide unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The security education unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The collaboration unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The community management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, and community management unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the learning plan generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The guide unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The security education unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The collaboration unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The community management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, and community management unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning plan generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The guide unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The security education unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The community management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the learning plan generation unit, dialogue unit, guide unit, security education unit, collaboration unit, and community management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the learning plan generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The guide unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The security education unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The collaboration unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The community management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A learning plan generation unit that generates a learning plan, A dialogue unit that engages in dialogue based on the plan generated by the learning plan generation unit, A guide unit provides practical guidance based on the information obtained by the aforementioned dialogue unit, The Security Education Department conducts security education based on the guidance provided by the aforementioned Guide Department, The Liaison Department, which collaborates with families based on the education provided by the aforementioned Security Education Department, The system includes a community management unit that operates the community based on the information obtained by the aforementioned collaboration unit. A system characterized by the following features. (Note 2) The aforementioned learning plan generation unit, Generates customized learning plans tailored to each senior citizen's skill level and learning pace. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, When elderly people have questions or concerns, the system provides real-time answers and facilitates learning through dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned guide section is To guide elderly people step-by-step when they actually use and operate digital devices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Security Education Department, We provide security education and explain the importance of protecting privacy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, Seniors receive support from their families in the process of learning digital technology. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned community management department, We operate an online community where elderly people can interact with other users and share information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning plan generation unit, The system estimates the emotions of elderly individuals and adjusts the difficulty level of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning plan generation unit, Analyze the past learning history of elderly individuals to select the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning plan generation unit, When generating a learning plan, provide customized content based on the interests and concerns of older adults. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning plan generation unit, The system estimates the emotions of elderly individuals and adjusts the pace of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning plan generation unit, When generating a learning plan, the system prioritizes providing highly relevant learning content by taking into account the geographical location of the elderly user. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning plan generation unit, When generating learning plans, the social media activity of older adults is analyzed, and relevant learning content is provided. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, The system estimates the emotions of elderly individuals and adjusts the tone and expression of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, During conversations, the system provides the most appropriate response by referring to the elderly person's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, During conversations, customize the content of the dialogue based on the interests and concerns of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, The system estimates the emotions of elderly individuals and adjusts the length of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dialogue unit, During conversations, provide highly relevant information while considering the geographical location of the elderly person. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, During the conversation, we analyze the social media activity of older adults and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is The system estimates the emotions of elderly individuals and adjusts the pace of the guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is During guidance, the system provides the optimal guidance method by referring to the elderly person's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is During the guided tour, the content will be customized based on the interests and concerns of the elderly participants. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is The system estimates the emotions of older adults and adjusts the level of detail in the guide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is When providing guidance, we take into account the geographical location of elderly individuals to provide highly relevant guide content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is During the guidance session, we analyze the social media activity of older adults and provide relevant guidance content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Security Education Department, The system estimates the emotions of elderly individuals and adjusts the pace of security education based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Security Education Department, During security training, we refer to the past security knowledge of older individuals to provide the most effective training methods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Security Education Department, During security training, customize the content based on the interests and concerns of the elderly participants. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Security Education Department, The system estimates the emotions of older adults and adjusts the level of detail in security education based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Security Education Department, When providing security training, consider the geographical location of elderly individuals to ensure the content is highly relevant to their needs. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Security Education Department, During security training, we analyze the social media activities of older adults and provide relevant educational content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, The system estimates the emotions of elderly individuals and adjusts the method of communication with their families based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When linking, the system provides the optimal linking method by referring to the elderly person's past linking history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, When collaborating, the content of the collaboration will be customized based on the interests and concerns of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, The system estimates the emotions of elderly individuals and adjusts the frequency of interaction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, When collaborating, we will provide highly relevant collaboration content by taking into account the geographical location information of elderly individuals. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, During the collaboration process, we will analyze the social media activities of elderly individuals and provide relevant collaboration content. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned community management department, Estimate the emotions of the elderly and adjust community management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned community management department, When managing a community, we provide optimal management methods by referring to the past community activity history of elderly residents. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned community management department, When managing a community, customize the activities based on the interests and concerns of the elderly. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned community management department, The system estimates the emotions of older adults and adjusts the frequency of community activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned community management department, When managing a community, consider the geographical location of elderly residents to provide highly relevant services. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned community management department, When managing a community, we analyze the social media activities of senior citizens and provide relevant operational guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning plan generation unit that generates a learning plan, A dialogue unit that engages in dialogue based on the plan generated by the learning plan generation unit, A guide unit provides practical guidance based on the information obtained by the aforementioned dialogue unit, The Security Education Department conducts security education based on the guidance provided by the aforementioned Guide Department, The Liaison Department, which collaborates with families based on the education provided by the aforementioned Security Education Department, The system includes a community management unit that operates the community based on the information obtained by the aforementioned collaboration unit. A system characterized by the following features.

2. The aforementioned learning plan generation unit, Generates customized learning plans tailored to each senior citizen's skill level and learning pace. The system according to feature 1.

3. The aforementioned dialogue unit, When elderly people have questions or concerns, the system provides real-time answers and facilitates learning through dialogue. The system according to feature 1.

4. The aforementioned guide section is To guide elderly people step-by-step when they actually use and operate digital devices. The system according to feature 1.

5. The aforementioned Security Education Department, We provide security education and explain the importance of protecting privacy. The system according to feature 1.

6. The aforementioned linkage unit is, Seniors receive support from their families in the process of learning digital technology. The system according to feature 1.

7. The aforementioned community management department, We operate an online community where elderly people can interact with other users and share information. The system according to feature 1.

8. The aforementioned learning plan generation unit, The system estimates the emotions of elderly individuals and adjusts the difficulty level of the learning plan based on those estimated emotions. The system according to feature 1.

9. The aforementioned learning plan generation unit, Analyze the past learning history of elderly individuals to select the optimal learning plan. The system according to feature 1.

10. The aforementioned learning plan generation unit, When generating a learning plan, provide customized content based on the interests and concerns of older adults. The system according to feature 1.

Images