system

Abstract

The system according to this embodiment aims to promote interaction between senior citizens and younger generations and to provide a platform for sharing knowledge and experience. [Solution] The system according to the embodiment comprises an analysis unit, a calculation unit, a matching unit, a communication unit, a guidance unit, and a settlement unit. The analysis unit analyzes the profiles of senior citizens and young people. The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. The communication unit provides a platform for senior citizens and young people matched by the matching unit to communicate online. The guidance unit allows senior citizens to share their knowledge and experience with young people in the platform provided by the communication unit. The settlement unit makes settlements to receive compensation for the guidance and advice provided by the guidance unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] <00OO025>In the conventional technology, there is a problem that the mutual communication between the senior layer and the young layer has not been sufficiently promoted effectively, and a place for sharing knowledge and experience has not been provided.

[0005] The system according to the embodiment aims to promote the mutual communication between the senior layer and the young layer and provide a place for sharing knowledge and experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a calculation unit, a matching unit, a communication unit, a guidance unit, and a settlement unit. The analysis unit analyzes the profiles of senior citizens and young people. The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. The communication unit provides a platform for senior citizens and young people matched by the matching unit to communicate online. The guidance unit allows senior citizens to share their knowledge and experience with young people in the platform provided by the communication unit. The settlement unit makes settlements to receive compensation for the guidance and advice provided by the guidance unit. [Effects of the Invention]

[0007] The system according to this embodiment can promote interaction between senior citizens and younger generations and provide a platform for sharing knowledge and experience. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 platform according to an embodiment of the present invention is a platform that utilizes a sophisticated matching system using generative AI and an electronic payment system to solve the challenges faced by seniors and young people. This platform manages the profiles of seniors and young people, and the generative AI calculates compatibility scores to perform optimal matching. Furthermore, it provides a space where seniors can share their knowledge and experience with young people, and young people can acquire practical knowledge and skills. In addition, a secure tipping system is implemented using the electronic payment system, allowing seniors to receive compensation for the guidance and advice they provide to young people. As a result, seniors can gain a sense of social contribution and self-affirmation, and young people can acquire practical knowledge and skills. For example, the generative AI analyzes the profiles of seniors and young people and calculates compatibility scores. Next, optimal matching is performed based on the compatibility scores, and a space is provided where seniors and young people can communicate online. Furthermore, seniors can share their knowledge and experience with young people, and young people can acquire practical knowledge and skills. Finally, a secure tipping system is implemented using the electronic payment system, allowing seniors to receive compensation for the guidance and advice they provide to young people. This system allows seniors to gain a sense of social contribution and self-esteem, while younger generations can acquire practical knowledge and skills. Furthermore, it promotes community building between seniors and younger generations, alleviating feelings of isolation and career anxiety. In addition, it supports younger generations' adaptation to local environments through the provision of local information and career advice. Thus, the platform solves the challenges faced by both seniors and younger generations, enabling them to gain a sense of social contribution and self-esteem.

[0029] The platform according to this embodiment comprises an analysis unit, a calculation unit, a matching unit, a communication unit, a guidance unit, and a settlement unit. The analysis unit analyzes the profiles of senior citizens and young people. The analysis unit collects information such as age, occupation, hobbies, and skills of senior citizens and young people, and creates profiles. The analysis unit uses a generative AI to analyze the content of the profiles and provides data for calculating a compatibility score. The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. The calculation unit calculates a compatibility score considering, for example, common hobbies and interests of senior citizens and young people. The calculation unit uses a generative AI to improve the accuracy of the compatibility score calculation. The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. The matching unit performs matching based on, for example, common hobbies and interests of senior citizens and young people. The matching unit uses a generative AI to improve the accuracy of the matching. The communication unit provides a platform for senior citizens and young people matched by the matching unit to communicate online. The communication unit provides communication in the form of, for example, chat, video calls, forums, etc. The Communication Department can adjust communication methods using generative AI. The Instruction Department facilitates the sharing of knowledge and experience between seniors and younger generations in the forums provided by the Communication Department. For example, the Instruction Department allows seniors to give online lectures to younger generations. The Instruction Department can adjust instruction methods using generative AI. The Payment Department implements a secure tipping system for receiving compensation for instruction and advice provided by the Instruction Department. For example, the Payment Department allows seniors to receive compensation for instruction and advice provided to younger generations using an electronic payment system. The Payment Department can adjust payment methods using generative AI. As a result, the platform according to this embodiment can solve the challenges faced by seniors and younger generations and enable them to contribute to society and gain a sense of self-affirmation.

[0030] The analysis unit analyzes the profiles of senior and young people. For example, the analysis unit collects information such as age, occupation, hobbies, and skills of senior and young people to create profiles. Specifically, it collects detailed information on senior people's age, work history, hobbies, special skills, and past experiences, and similarly for young people's age, education level, current occupation, interests, and skill sets. This information is obtained through data entered by users when they register on the platform, data collection from social media, and surveys. The analysis unit uses generative AI to analyze the content of the profiles and provides data for calculating compatibility scores. The generative AI uses natural language processing technology to analyze user input data and extract meaning from text data. For example, if a senior person's hobby is "gardening" and a young person's hobby is "plant cultivation," the generative AI will determine that these hobbies are common areas of interest and reflect this in the calculation of the compatibility score. The generative AI also analyzes the user's past behavior history and communication patterns to create more accurate profiles. This allows the analysis unit to create detailed profiles of senior and younger users and provide the data necessary to calculate compatibility scores. Furthermore, the analysis unit can maintain accurate profiles at all times by regularly updating the profile data and reflecting the latest user information.

[0031] The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. For example, the calculation unit considers common hobbies and interests between seniors and younger users when calculating the compatibility score. Specifically, it uses a generative AI to compare the profile data of seniors and younger users and extract commonalities and differences. The generative AI uses a machine learning algorithm to learn from past matching data and improve the accuracy of compatibility score calculation. For example, if seniors and younger users have many common hobbies or their skill sets are complementary, the compatibility score will be higher. The generative AI also considers the user's personality traits and communication style to calculate a more accurate compatibility score. For example, if seniors have a calm and easy-to-talk-to personality and younger users are proactive and have a strong desire to learn, the compatibility score will be higher. The calculation unit comprehensively evaluates these factors and calculates the compatibility score between seniors and younger users. Furthermore, the calculation unit periodically reviews the compatibility score calculation results and incorporates user feedback and new data to always provide compatibility scores based on the latest information. This allows the calculation unit to support the optimal matching of senior citizens and younger generations, maximizing the platform's effectiveness.

[0032] The matching unit performs optimal matching based on compatibility scores calculated by the calculation unit. For example, the matching unit matches users based on shared hobbies and interests between seniors and younger generations. Specifically, it uses a generative AI to analyze profile data of seniors and younger generations and select the most suitable matching candidates. The generative AI considers not only compatibility scores but also the user's past matching history and feedback to achieve more accurate matching. For example, if a senior has had good results matching with a younger generation with a specific hobby in the past, matching with younger generations with similar hobbies will be prioritized. The generative AI also considers the user's geographical location information and prioritizes matching users who live nearby to facilitate actual interaction. The matching unit comprehensively evaluates these factors to perform optimal matching between seniors and younger generations. Furthermore, the matching unit notifies the user of the matching results and supports the user in quickly contacting their matched partner. For example, as soon as the matching results are notified, the chat and video call functions of the communication unit become available. This allows the matching unit to promote effective interaction between seniors and younger generations and enhance the value of using the platform.

[0033] The Communication Department provides a platform for seniors and younger generations, matched by the Matching Department, to communicate online. The Communication Department offers communication in various formats, such as chat, video calls, and forums. Specifically, it uses generative AI to suggest the optimal communication method based on the user's communication style and preferences. For example, if seniors prefer text chat, the chat function is prioritized; if younger generations prefer video calls, the video call function is recommended. The generative AI also analyzes the content of the communication and provides appropriate advice and support. For instance, if the conversation stalls or runs out of topics, the generative AI suggests new topics or provides hints to keep the conversation going. Furthermore, the Communication Department incorporates security features to protect user privacy, ensuring safe and secure communication between users. For example, chat and video call content is encrypted to prevent access by third parties. It also provides a function for users to report inappropriate behavior, and prompt action is taken to maintain a healthy communication environment. This allows the Communication Department to provide a platform for seniors and younger generations to effectively interact and deepen their mutual understanding.

[0034] The leadership team facilitates the sharing of knowledge and experience between senior and younger generations in forums provided by the communications team. For example, senior members can conduct online lectures for younger generations. Specifically, generative AI is used to suggest teaching methods that effectively convey the expertise and experience of senior members. For instance, when senior members teach technical skills, the generative AI assists in creating appropriate teaching materials and presentations, and supports the progress of the lecture. The generative AI also analyzes the understanding and reactions of younger generations in real time and provides appropriate feedback to senior members. For example, if younger generations find a particular topic difficult to understand, the generative AI suggests supplementary explanations or alternative approaches to senior members. Furthermore, the leadership team promotes two-way communication between senior and younger generations, providing an environment where younger generations can actively ask questions and express their opinions. For example, features such as real-time question acceptance during online lectures and discussion forums provide a space where younger generations can freely exchange opinions. This allows the leadership team to effectively convey the knowledge and experience of senior members to younger generations and support their growth.

[0035] The Payment Department will implement a secure tipping system for receiving rewards for guidance and advice provided by the Guidance Department. For example, the Payment Department can receive rewards using an electronic payment system for guidance and advice provided by seniors to younger generations. Specifically, it will use generative AI to analyze users' payment history and behavioral patterns and suggest the optimal payment method. For example, it will select the most suitable payment method from credit cards, debit cards, e-money, bank transfers, etc., based on payment methods used in the past and current usage. The generative AI will also be used to detect and prevent fraudulent activity. For example, if an abnormal payment pattern or unauthorized access is detected, the generative AI will immediately issue a warning and take necessary measures. Furthermore, the Payment Department will employ advanced encryption technology to ensure user privacy and security, preventing payment information from being leaked to third parties. For example, payment information will be protected using the SSL / TLS encryption protocol, and users' personal and payment information will be managed securely. This will enable the Payment Department to provide an environment where seniors and younger generations can conduct transactions and receive rewards with peace of mind.

[0036] The analysis unit can analyze the profiles of senior and young people using generative AI. For example, the analysis unit collects information such as age, occupation, hobbies, and skills of senior and young people and inputs it into the generative AI. The generative AI analyzes the profiles based on the collected information and provides data for calculating compatibility scores. This improves the accuracy of profile analysis by using the generative AI. The generative AI can analyze profiles using, for example, natural language processing technology and machine learning algorithms. The generative AI can analyze the profile content in detail and identify common hobbies and interests between senior and young people. This allows the analysis unit to improve the accuracy of profile analysis using the generative AI.

[0037] The calculation unit can calculate compatibility scores using generative AI. For example, the calculation unit calculates compatibility scores by considering common hobbies and interests between senior citizens and younger people. The generative AI executes an algorithm to calculate compatibility scores based on the profile content. This improves the accuracy of compatibility score calculation by using generative AI. The generative AI can calculate compatibility scores using, for example, machine learning algorithms or data mining techniques. The generative AI identifies common hobbies and interests between senior citizens and younger people and calculates compatibility scores based on them. This allows the calculation unit to improve the accuracy of compatibility score calculation by using generative AI.

[0038] The matching unit can perform optimal matching using generative AI. For example, the matching unit matches based on shared hobbies and interests between seniors and younger generations. The generative AI executes an algorithm to perform optimal matching based on compatibility scores. This improves the accuracy of matching through the use of generative AI. The generative AI can perform optimal matching using, for example, machine learning algorithms and data mining techniques. The generative AI identifies shared hobbies and interests between seniors and younger generations and performs optimal matching based on these. This allows the matching unit to improve the accuracy of matching using generative AI.

[0039] The Communication Department can provide a platform for seniors and young people to communicate online. The Communication Department provides communication in various forms, such as chat, video calls, and forums. The Communication Department can also use generative AI to adjust the communication methods. For example, the generative AI can analyze the emotions of seniors and young people and suggest the most suitable communication method. This facilitates interaction between seniors and young people through online communication. Some or all of the above-described processes in the Communication Department may be performed using generative AI, or they may not.

[0040] The leadership team allows senior members to share their knowledge and experience with younger members. For example, senior members can give online lectures to younger members. The leadership team can also use generative AI to adjust teaching methods. For example, generative AI can analyze the emotions of senior and younger members and suggest the most suitable teaching method. This facilitates learning for younger members by allowing senior members to share their knowledge and experience. Some or all of the above processes in the leadership team may be performed using generative AI or not.

[0041] The payment unit can implement a secure tipping system that allows seniors to receive compensation for the guidance and advice they provide to younger generations. For example, the payment unit can receive compensation from seniors for the guidance and advice they provide to younger generations using an electronic payment system. The payment unit can also use generative AI to adjust the payment method. For example, the generative AI can analyze the emotions of seniors and younger generations and suggest the optimal payment method. This allows seniors to receive compensation by implementing a secure tipping system. Some or all of the above processing in the payment unit may be performed using generative AI or not.

[0042] The analysis unit can analyze the past activity history of senior and younger generations to improve the level of detail in their profiles. For example, the analysis unit can analyze the past volunteer activity history of senior generations and reflect it in their profiles. The analysis unit can also analyze the past learning history of younger generations and reflect it in their profiles. The analysis unit can also analyze the past work experience of senior generations and reflect it in their profiles. In this way, the level of detail in the profiles is improved by analyzing past activity history. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0043] The analysis unit can customize its analysis algorithm based on the interests of senior and younger generations during profile analysis. For example, the analysis unit can analyze the hobbies and interests of senior generations and reflect them in their profiles. The analysis unit can also analyze the learning fields and career goals of younger generations and reflect them in their profiles. The analysis unit can also analyze the past project experience of senior generations and reflect it in their profiles. By customizing the analysis algorithm based on interests, more appropriate profile analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0044] The analysis unit can perform profile analysis while considering the geographical information of senior citizens and young people. For example, the analysis unit can analyze the residential information of senior citizens and reflect it in their profiles. The analysis unit can also analyze the commuting routes of young people and reflect them in their profiles. The analysis unit can also analyze the community activity history of senior citizens and reflect it in their profiles. By considering geographical information during the analysis, more appropriate profile analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0045] The analysis unit can analyze the social media activities of senior citizens and younger generations during profile analysis and reflect them in the profiles. For example, the analysis unit can analyze the social media activity history of senior citizens and reflect it in the profiles. The analysis unit can also analyze the social media interests of younger generations and reflect them in the profiles. The analysis unit can also analyze the number of followers and influence of senior citizens on social media and reflect them in the profiles. This improves the level of detail in the profiles by analyzing social media activities. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0046] The calculation unit can improve the accuracy of the compatibility score calculation by referring to the past matching history of senior and young people. For example, the calculation unit can analyze the past matching history of senior people and reflect it in the compatibility score. The calculation unit can also analyze the past matching history of young people and reflect it in the compatibility score. The calculation unit can also analyze the past matching success rate of senior and young people and reflect it in the compatibility score. In this way, the accuracy of the compatibility score calculation is improved by referring to past matching history. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0047] The calculation unit can apply different calculation algorithms to senior and younger groups when calculating compatibility scores based on their attribute information. For example, the calculation unit can calculate compatibility scores based on senior groups' work history and hobbies. The calculation unit can also calculate compatibility scores based on younger groups' areas of study and career goals. The calculation unit can also calculate compatibility scores based on the common interests of senior and younger groups. By applying different calculation algorithms based on attribute information, a more appropriate compatibility score can be calculated. Some or all of the above-described processes in the calculation unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0048] The calculation unit can perform compatibility score calculations while considering the geographical distribution of senior citizens and young people. For example, the calculation unit can analyze the residential information of senior citizens and reflect it in the compatibility score. The calculation unit can also analyze the commuting routes of young people and reflect them in the compatibility score. The calculation unit can also analyze the community activity history of senior citizens and reflect it in the compatibility score. By performing calculations while considering geographical distribution, a more appropriate compatibility score can be calculated. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0049] The calculation unit can improve the accuracy of the compatibility score calculation by referring to relevant literature for senior and young people. For example, the calculation unit can analyze past research papers of senior people and reflect them in the compatibility score. The calculation unit can also analyze literature related to the learning fields of young people and reflect it in the compatibility score. The calculation unit can also calculate the compatibility score based on common research themes for senior and young people. This improves the accuracy of the compatibility score calculation by referring to relevant literature. Some or all of the above processing in the calculation unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0050] The matching unit can improve the accuracy of matching by considering the interrelationships between senior and younger groups during the matching process. For example, the matching unit can analyze the past matching history of senior and younger groups and perform matching while considering their interrelationships. The matching unit can also analyze the common interests of senior and younger groups and perform matching while considering their interrelationships. The matching unit can also analyze the past communication history of senior and younger groups and perform matching while considering their interrelationships. This improves the accuracy of matching by considering their interrelationships. Some or all of the above-described processes in the matching unit may be performed using generative AI, or they may be performed without using generative AI.

[0051] The matching unit can perform matching while considering the attribute information of senior citizens and young people. For example, the matching unit can perform matching based on the work history and hobbies of senior citizens. The matching unit can also perform matching based on the learning fields and career goals of young people. The matching unit can also perform matching based on the common interests of senior citizens and young people. This allows for more appropriate matching by considering attribute information. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0052] The matching unit can perform matching while considering the geographical distribution of senior citizens and young people. For example, the matching unit can analyze the residential information of senior citizens and match them with young people who are geographically close. The matching unit can also analyze the commuting routes of young people and match them with senior citizens who are geographically close. The matching unit can also analyze the community activity history of senior citizens and match them with young people who are geographically close. This allows for more appropriate matching by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0053] The matching unit can improve the accuracy of matching by referring to relevant literature for senior and younger generations during the matching process. For example, the matching unit can analyze past research papers of senior generations and match them with relevant younger generations. The matching unit can also analyze literature related to the learning fields of younger generations and match them with relevant senior generations. The matching unit can also perform matching based on common research themes for senior and younger generations. This improves the accuracy of matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0054] The communication department can select the optimal method of communication by referring to the past communication history of senior and younger generations. For example, the communication department can analyze the past communication history of senior generations and propose the optimal method. The communication department can also analyze the past communication history of younger generations and propose the optimal method. The communication department can also analyze the past communication success rates of senior and younger generations and propose the optimal method. In this way, the optimal communication method is selected by referring to past communication history. Some or all of the above processing in the communication department may be performed using generative AI, or it may be performed without using generative AI.

[0055] The communications department can customize the content of communications based on the interests of senior and younger generations. For example, the communications department can analyze the hobbies and interests of senior generations and reflect them in the content of communications. The communications department can also analyze the learning fields and career goals of younger generations and reflect them in the content of communications. The communications department can also analyze the past project experience of senior generations and reflect it in the content of communications. By customizing the content of communications based on interests, more appropriate communications become possible. Some or all of the above processing in the communications department may be performed using generative AI, or it may be performed without using generative AI.

[0056] The Communication Department can select the optimal method of communication by considering the geographical information of senior citizens and younger generations. For example, the Communication Department can analyze the residential information of senior citizens and propose communication methods with younger generations who are geographically close. The Communication Department can also analyze the commuting routes of younger generations and propose communication methods with senior citizens who are geographically close. The Communication Department can also analyze the community activity history of senior citizens and propose communication methods with younger generations who are geographically close. By considering geographical information, more appropriate communication becomes possible. Some or all of the above processing in the Communication Department may be performed using generative AI, or it may be performed without using generative AI.

[0057] The communications department can analyze the social media activities of senior citizens and younger generations and reflect this in their communications. For example, the communications department can analyze the social media activity history of senior citizens and reflect it in the content of their communications. The communications department can also analyze the interests of younger generations on social media and reflect it in the content of their communications. The communications department can also analyze the number of followers and influence of senior citizens on social media and reflect it in the content of their communications. By analyzing social media activities, more appropriate communications become possible. Some or all of the above processing in the communications department may be performed using generative AI, or it may be performed without using generative AI.

[0058] The instruction department can select the optimal method during instruction by referring to the past instruction history of senior and younger participants. For example, the instruction department can analyze the past instruction history of senior participants and propose the optimal method. The instruction department can also analyze the past instruction history of younger participants and propose the optimal method. The instruction department can also analyze the past instruction success rates of senior and younger participants and propose the optimal method. In this way, the optimal instruction method is selected by referring to past instruction history. Some or all of the above processing in the instruction department may be performed using generative AI, or it may be performed without using generative AI.

[0059] The instruction department can customize the content of instruction based on the interests of senior and younger participants. For example, the instruction department can analyze the hobbies and interests of senior participants and reflect them in the instruction. The instruction department can also analyze the learning areas and career goals of younger participants and reflect them in the instruction. The instruction department can also analyze the past project experience of senior participants and reflect it in the instruction. By customizing the content of instruction based on interests, more appropriate instruction becomes possible. Some or all of the above processes in the instruction department may be performed using generative AI, or they may not be performed using generative AI.

[0060] The guidance department can select the optimal method during guidance by considering the geographical information of both senior and young people. For example, the guidance department can analyze the residential information of senior people and propose guidance methods for young people who are geographically close. The guidance department can also analyze the commuting routes of young people and propose guidance methods for senior people who are geographically close. The guidance department can also analyze the community activity history of senior people and propose guidance methods for young people who are geographically close. By considering geographical information, more appropriate guidance becomes possible. Some or all of the above processing by the guidance department may be performed using generative AI, or it may be performed without using generative AI.

[0061] The leadership team can analyze the social media activities of senior and younger generations during the guidance process and incorporate this analysis into their guidance. For example, the leadership team can analyze the social media activity history of senior generations and incorporate this into their guidance. They can also analyze the social media interests of younger generations and incorporate this into their guidance. Furthermore, they can analyze the number of followers and influence of senior generations on social media and incorporate this into their guidance. This allows for more appropriate guidance by analyzing social media activities. Some or all of the above-described processes in the leadership team may be performed using generative AI, or they may be performed without using generative AI.

[0062] The payment unit can select the optimal payment method by referring to the past payment history of senior citizens and young adults at the time of payment. For example, the payment unit can analyze the past payment history of senior citizens and propose the optimal method. The payment unit can also analyze the past payment history of young adults and propose the optimal method. The payment unit can also analyze the past payment success rates of senior citizens and young adults and propose the optimal method. In this way, the optimal payment method is selected by referring to past payment history. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0063] The payment unit can customize payment methods based on attribute information of senior and younger users during the payment process. For example, the payment unit can suggest payment methods based on the senior user's work history and hobbies. The payment unit can also suggest payment methods based on the younger user's field of study and career goals. The payment unit can also suggest payment methods based on the common interests of senior and younger users. By customizing payment methods based on attribute information, more appropriate payments become possible. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0064] The payment unit can select the optimal payment method by considering the geographical information of senior citizens and young people during the payment process. For example, the payment unit can analyze the residential information of senior citizens and propose a payment method with young people who are geographically close. The payment unit can also analyze the commuting routes of young people and propose a payment method with senior citizens who are geographically close. The payment unit can also analyze the community activity history of senior citizens and propose a payment method with young people who are geographically close. By considering geographical information, more appropriate payments become possible. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0065] The payment processing unit can analyze the social media activity of senior citizens and younger generations during the payment process and reflect this in the payment. For example, the payment processing unit can analyze the social media activity history of senior citizens and reflect this in the payment details. The payment processing unit can also analyze the social media interests of younger generations and reflect this in the payment details. The payment processing unit can also analyze the number of followers and influence of senior citizens on social media and reflect this in the payment details. This allows for more appropriate payments by analyzing social media activity. Some or all of the above processing in the payment processing unit may be performed using generative AI, or it may be performed without using generative AI.

[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 analysis unit can consider real-time health data when analyzing profiles of senior and younger demographics. For example, it can collect health data such as heart rate and blood pressure from senior citizens and incorporate it into their profiles. It can also analyze the exercise habits and sleep patterns of younger citizens and incorporate them into their profiles. Furthermore, it can provide data for appropriate matching based on the health status of senior citizens. This allows for more accurate profile analysis by considering health data.

[0068] The analysis unit can analyze the past activity history of senior and younger generations to improve the level of detail in their profiles. For example, it can analyze the past volunteer activity history of senior generations and reflect it in their profiles. It can also analyze the past learning history of younger generations and reflect it in their profiles. It can also analyze the past work experience of senior generations and reflect it in their profiles. In this way, analyzing past activity history improves the level of detail in the profiles.

[0069] The calculation unit can improve the accuracy of the compatibility score calculation by referring to the past matching history of senior and younger groups. For example, it can analyze the past matching history of senior groups and reflect it in the compatibility score. It can also analyze the past matching history of younger groups and reflect it in the compatibility score. It can also analyze the past matching success rate of senior and younger groups and reflect it in the compatibility score. In this way, the accuracy of the compatibility score calculation is improved by referring to past matching history.

[0070] The matching unit can improve the accuracy of matching by considering the relationships between senior and younger generations during the matching process. For example, it can analyze the past matching history of senior and younger generations and perform matching while considering these relationships. It can also analyze the common interests of senior and younger generations and perform matching while considering these relationships. It can also analyze the past communication history of senior and younger generations and perform matching while considering these relationships. In this way, the accuracy of matching is improved by considering these relationships.

[0071] The communications department can select the optimal communication method by referring to the past communication history of both senior and younger generations. For example, it can analyze the past communication history of senior generations and propose the optimal method. It can also analyze the past communication history of younger generations and propose the optimal method. It can also analyze the past communication success rates of senior and younger generations and propose the optimal method. In this way, the optimal communication method is selected by referring to past communication history.

[0072] The instruction department can customize the content of instruction based on the interests of senior and younger participants. For example, they can analyze the hobbies and interests of senior participants and incorporate them into the instruction. They can also analyze the learning areas and career goals of younger participants and incorporate them into the instruction. Furthermore, they can analyze the past project experience of senior participants and incorporate it into the instruction. By customizing the content of instruction based on interests, more appropriate instruction becomes possible.

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

[0074] Step 1: The analysis unit analyzes the profiles of senior and younger generations. The analysis unit collects information such as age, occupation, hobbies, and skills of senior and younger generations, and creates profiles. The analysis unit uses a generative AI to analyze the content of the profiles and provides data for calculating compatibility scores. Step 2: The calculation unit calculates a compatibility score based on the profile analyzed by the analysis unit. The calculation unit calculates the compatibility score by considering, for example, common hobbies and interests between seniors and younger people. The calculation unit uses a generation AI to improve the accuracy of the compatibility score calculation. Step 3: The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. For example, the matching unit matches based on common hobbies and interests between seniors and younger people. The matching unit uses a generation AI to improve the accuracy of the matching. Step 4: The Communication Department provides a platform for seniors and younger generations, matched by the Matching Department, to communicate online. The Communication Department provides communication in various forms, such as chat, video calls, and forums. The Communication Department can use generative AI to adjust the communication methods. Step 5: The leadership team enables senior members to share their knowledge and experience with younger members in a forum provided by the communications team. For example, senior members can give online lectures to younger members. The leadership team can use generative AI to adjust the teaching methods. Step 6: The payment department implements a secure tipping system to receive rewards for guidance and advice provided by the instruction department. For example, the payment department can receive rewards using an electronic payment system for guidance and advice provided by seniors to younger generations. The payment department can use generative AI to adjust the payment method.

[0075] (Example of form 2) The platform according to an embodiment of the present invention is a platform that utilizes a sophisticated matching system using generative AI and an electronic payment system to solve the challenges faced by seniors and young people. This platform manages the profiles of seniors and young people, and the generative AI calculates compatibility scores to perform optimal matching. Furthermore, it provides a space where seniors can share their knowledge and experience with young people, and young people can acquire practical knowledge and skills. In addition, a secure tipping system is implemented using the electronic payment system, allowing seniors to receive compensation for the guidance and advice they provide to young people. As a result, seniors can gain a sense of social contribution and self-affirmation, and young people can acquire practical knowledge and skills. For example, the generative AI analyzes the profiles of seniors and young people and calculates compatibility scores. Next, optimal matching is performed based on the compatibility scores, and a space is provided where seniors and young people can communicate online. Furthermore, seniors can share their knowledge and experience with young people, and young people can acquire practical knowledge and skills. Finally, a secure tipping system is implemented using the electronic payment system, allowing seniors to receive compensation for the guidance and advice they provide to young people. This system allows seniors to gain a sense of social contribution and self-esteem, while younger generations can acquire practical knowledge and skills. Furthermore, it promotes community building between seniors and younger generations, alleviating feelings of isolation and career anxiety. In addition, it supports younger generations' adaptation to local environments through the provision of local information and career advice. Thus, the platform solves the challenges faced by both seniors and younger generations, enabling them to gain a sense of social contribution and self-esteem.

[0076] The platform according to this embodiment comprises an analysis unit, a calculation unit, a matching unit, a communication unit, a guidance unit, and a settlement unit. The analysis unit analyzes the profiles of senior citizens and young people. The analysis unit collects information such as age, occupation, hobbies, and skills of senior citizens and young people, and creates profiles. The analysis unit uses a generative AI to analyze the content of the profiles and provides data for calculating a compatibility score. The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. The calculation unit calculates a compatibility score considering, for example, common hobbies and interests of senior citizens and young people. The calculation unit uses a generative AI to improve the accuracy of the compatibility score calculation. The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. The matching unit performs matching based on, for example, common hobbies and interests of senior citizens and young people. The matching unit uses a generative AI to improve the accuracy of the matching. The communication unit provides a platform for senior citizens and young people matched by the matching unit to communicate online. The communication unit provides communication in the form of, for example, chat, video calls, forums, etc. The Communication Department can adjust communication methods using generative AI. The Instruction Department facilitates the sharing of knowledge and experience between seniors and younger generations in the forums provided by the Communication Department. For example, the Instruction Department allows seniors to give online lectures to younger generations. The Instruction Department can adjust instruction methods using generative AI. The Payment Department implements a secure tipping system for receiving compensation for instruction and advice provided by the Instruction Department. For example, the Payment Department allows seniors to receive compensation for instruction and advice provided to younger generations using an electronic payment system. The Payment Department can adjust payment methods using generative AI. As a result, the platform according to this embodiment can solve the challenges faced by seniors and younger generations and enable them to contribute to society and gain a sense of self-affirmation.

[0077] The analysis unit analyzes the profiles of senior and young people. For example, the analysis unit collects information such as age, occupation, hobbies, and skills of senior and young people to create profiles. Specifically, it collects detailed information on senior people's age, work history, hobbies, special skills, and past experiences, and similarly for young people's age, education level, current occupation, interests, and skill sets. This information is obtained through data entered by users when they register on the platform, data collection from social media, and surveys. The analysis unit uses generative AI to analyze the content of the profiles and provides data for calculating compatibility scores. The generative AI uses natural language processing technology to analyze user input data and extract meaning from text data. For example, if a senior person's hobby is "gardening" and a young person's hobby is "plant cultivation," the generative AI will determine that these hobbies are common areas of interest and reflect this in the calculation of the compatibility score. The generative AI also analyzes the user's past behavior history and communication patterns to create more accurate profiles. This allows the analysis unit to create detailed profiles of senior and younger users and provide the data necessary to calculate compatibility scores. Furthermore, the analysis unit can maintain accurate profiles at all times by regularly updating the profile data and reflecting the latest user information.

[0078] The calculation unit calculates a compatibility score based on the profiles analyzed by the analysis unit. For example, the calculation unit considers common hobbies and interests between seniors and younger users when calculating the compatibility score. Specifically, it uses a generative AI to compare the profile data of seniors and younger users and extract commonalities and differences. The generative AI uses a machine learning algorithm to learn from past matching data and improve the accuracy of compatibility score calculation. For example, if seniors and younger users have many common hobbies or their skill sets are complementary, the compatibility score will be higher. The generative AI also considers the user's personality traits and communication style to calculate a more accurate compatibility score. For example, if seniors have a calm and easy-to-talk-to personality and younger users are proactive and have a strong desire to learn, the compatibility score will be higher. The calculation unit comprehensively evaluates these factors and calculates the compatibility score between seniors and younger users. Furthermore, the calculation unit periodically reviews the compatibility score calculation results and incorporates user feedback and new data to always provide compatibility scores based on the latest information. This allows the calculation unit to support the optimal matching of senior citizens and younger generations, maximizing the platform's effectiveness.

[0079] The matching unit performs optimal matching based on compatibility scores calculated by the calculation unit. For example, the matching unit matches users based on shared hobbies and interests between seniors and younger generations. Specifically, it uses a generative AI to analyze profile data of seniors and younger generations and select the most suitable matching candidates. The generative AI considers not only compatibility scores but also the user's past matching history and feedback to achieve more accurate matching. For example, if a senior has had good results matching with a younger generation with a specific hobby in the past, matching with younger generations with similar hobbies will be prioritized. The generative AI also considers the user's geographical location information and prioritizes matching users who live nearby to facilitate actual interaction. The matching unit comprehensively evaluates these factors to perform optimal matching between seniors and younger generations. Furthermore, the matching unit notifies the user of the matching results and supports the user in quickly contacting their matched partner. For example, as soon as the matching results are notified, the chat and video call functions of the communication unit become available. This allows the matching unit to promote effective interaction between seniors and younger generations and enhance the value of using the platform.

[0080] The Communication Department provides a platform for seniors and younger generations, matched by the Matching Department, to communicate online. The Communication Department offers communication in various formats, such as chat, video calls, and forums. Specifically, it uses generative AI to suggest the optimal communication method based on the user's communication style and preferences. For example, if seniors prefer text chat, the chat function is prioritized; if younger generations prefer video calls, the video call function is recommended. The generative AI also analyzes the content of the communication and provides appropriate advice and support. For instance, if the conversation stalls or runs out of topics, the generative AI suggests new topics or provides hints to keep the conversation going. Furthermore, the Communication Department incorporates security features to protect user privacy, ensuring safe and secure communication between users. For example, chat and video call content is encrypted to prevent access by third parties. It also provides a function for users to report inappropriate behavior, and prompt action is taken to maintain a healthy communication environment. This allows the Communication Department to provide a platform for seniors and younger generations to effectively interact and deepen their mutual understanding.

[0081] The leadership team facilitates the sharing of knowledge and experience between senior and younger generations in forums provided by the communications team. For example, senior members can conduct online lectures for younger generations. Specifically, generative AI is used to suggest teaching methods that effectively convey the expertise and experience of senior members. For instance, when senior members teach technical skills, the generative AI assists in creating appropriate teaching materials and presentations, and supports the progress of the lecture. The generative AI also analyzes the understanding and reactions of younger generations in real time and provides appropriate feedback to senior members. For example, if younger generations find a particular topic difficult to understand, the generative AI suggests supplementary explanations or alternative approaches to senior members. Furthermore, the leadership team promotes two-way communication between senior and younger generations, providing an environment where younger generations can actively ask questions and express their opinions. For example, features such as real-time question acceptance during online lectures and discussion forums provide a space where younger generations can freely exchange opinions. This allows the leadership team to effectively convey the knowledge and experience of senior members to younger generations and support their growth.

[0082] The Payment Department will implement a secure tipping system for receiving rewards for guidance and advice provided by the Guidance Department. For example, the Payment Department can receive rewards using an electronic payment system for guidance and advice provided by seniors to younger generations. Specifically, it will use generative AI to analyze users' payment history and behavioral patterns and suggest the optimal payment method. For example, it will select the most suitable payment method from credit cards, debit cards, e-money, bank transfers, etc., based on payment methods used in the past and current usage. The generative AI will also be used to detect and prevent fraudulent activity. For example, if an abnormal payment pattern or unauthorized access is detected, the generative AI will immediately issue a warning and take necessary measures. Furthermore, the Payment Department will employ advanced encryption technology to ensure user privacy and security, preventing payment information from being leaked to third parties. For example, payment information will be protected using the SSL / TLS encryption protocol, and users' personal and payment information will be managed securely. This will enable the Payment Department to provide an environment where seniors and younger generations can conduct transactions and receive rewards with peace of mind.

[0083] The analysis unit can analyze the profiles of senior and young people using generative AI. For example, the analysis unit collects information such as age, occupation, hobbies, and skills of senior and young people and inputs it into the generative AI. The generative AI analyzes the profiles based on the collected information and provides data for calculating compatibility scores. This improves the accuracy of profile analysis by using the generative AI. The generative AI can analyze profiles using, for example, natural language processing technology and machine learning algorithms. The generative AI can analyze the profile content in detail and identify common hobbies and interests between senior and young people. This allows the analysis unit to improve the accuracy of profile analysis using the generative AI.

[0084] The calculation unit can calculate compatibility scores using generative AI. For example, the calculation unit calculates compatibility scores by considering common hobbies and interests between senior citizens and younger people. The generative AI executes an algorithm to calculate compatibility scores based on the profile content. This improves the accuracy of compatibility score calculation by using generative AI. The generative AI can calculate compatibility scores using, for example, machine learning algorithms or data mining techniques. The generative AI identifies common hobbies and interests between senior citizens and younger people and calculates compatibility scores based on them. This allows the calculation unit to improve the accuracy of compatibility score calculation by using generative AI.

[0085] The matching unit can perform optimal matching using generative AI. For example, the matching unit matches based on shared hobbies and interests between seniors and younger generations. The generative AI executes an algorithm to perform optimal matching based on compatibility scores. This improves the accuracy of matching through the use of generative AI. The generative AI can perform optimal matching using, for example, machine learning algorithms and data mining techniques. The generative AI identifies shared hobbies and interests between seniors and younger generations and performs optimal matching based on these. This allows the matching unit to improve the accuracy of matching using generative AI.

[0086] The Communication Department can provide a platform for seniors and young people to communicate online. The Communication Department provides communication in various forms, such as chat, video calls, and forums. The Communication Department can also use generative AI to adjust the communication methods. For example, the generative AI can analyze the emotions of seniors and young people and suggest the most suitable communication method. This facilitates interaction between seniors and young people through online communication. Some or all of the above-described processes in the Communication Department may be performed using generative AI, or they may not.

[0087] The leadership team allows senior members to share their knowledge and experience with younger members. For example, senior members can give online lectures to younger members. The leadership team can also use generative AI to adjust teaching methods. For example, generative AI can analyze the emotions of senior and younger members and suggest the most suitable teaching method. This facilitates learning for younger members by allowing senior members to share their knowledge and experience. Some or all of the above processes in the leadership team may be performed using generative AI or not.

[0088] The payment unit can implement a secure tipping system that allows seniors to receive compensation for the guidance and advice they provide to younger generations. For example, the payment unit can receive compensation from seniors for the guidance and advice they provide to younger generations using an electronic payment system. The payment unit can also use generative AI to adjust the payment method. For example, the generative AI can analyze the emotions of seniors and younger generations and suggest the optimal payment method. This allows seniors to receive compensation by implementing a secure tipping system. Some or all of the above processing in the payment unit may be performed using generative AI or not.

[0089] The analysis unit can estimate the emotions of senior and younger generations and adjust the accuracy of the profile analysis based on the estimated emotions. For example, if a senior generation feels lonely, the analysis unit's generative AI will analyze their emotions and perform a profile analysis to alleviate the loneliness. If a younger generation feels anxious, the analysis unit's generative AI can analyze their emotions and perform a profile analysis to alleviate the anxiety. If a senior generation feels satisfied, the analysis unit's generative AI can analyze their emotions and perform a profile analysis to maintain that satisfaction. By adjusting the accuracy of the profile analysis based on emotions, a more appropriate profile analysis becomes possible. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without the generative AI.

[0090] The analysis unit can analyze the past activity history of senior and younger generations to improve the level of detail in their profiles. For example, the analysis unit can analyze the past volunteer activity history of senior generations and reflect it in their profiles. The analysis unit can also analyze the past learning history of younger generations and reflect it in their profiles. The analysis unit can also analyze the past work experience of senior generations and reflect it in their profiles. In this way, the level of detail in the profiles is improved by analyzing past activity history. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0091] The analysis unit can customize its analysis algorithm based on the interests of senior and younger generations during profile analysis. For example, the analysis unit can analyze the hobbies and interests of senior generations and reflect them in their profiles. The analysis unit can also analyze the learning fields and career goals of younger generations and reflect them in their profiles. The analysis unit can also analyze the past project experience of senior generations and reflect it in their profiles. By customizing the analysis algorithm based on interests, more appropriate profile analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0092] The analysis unit can estimate the emotions of senior and younger generations and determine the priority of profile analysis based on the estimated emotions. For example, if a senior generation feels lonely, the analysis unit can use a generative AI to analyze their emotions and prioritize profile analysis to alleviate loneliness. Similarly, if a younger generation feels anxious, the analysis unit can use a generative AI to analyze their emotions and prioritize profile analysis to alleviate anxiety. If a senior generation feels satisfied, the analysis unit can use a generative AI to analyze their emotions and prioritize profile analysis to maintain that satisfaction. By determining the priority of profile analysis based on emotions, more appropriate profile analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not.

[0093] The analysis unit can perform profile analysis while considering the geographical information of senior citizens and young people. For example, the analysis unit can analyze the residential information of senior citizens and reflect it in their profiles. The analysis unit can also analyze the commuting routes of young people and reflect them in their profiles. The analysis unit can also analyze the community activity history of senior citizens and reflect it in their profiles. By considering geographical information during the analysis, more appropriate profile analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0094] The analysis unit can analyze the social media activities of senior citizens and younger generations during profile analysis and reflect them in the profiles. For example, the analysis unit can analyze the social media activity history of senior citizens and reflect it in the profiles. The analysis unit can also analyze the social media interests of younger generations and reflect them in the profiles. The analysis unit can also analyze the number of followers and influence of senior citizens on social media and reflect them in the profiles. This improves the level of detail in the profiles by analyzing social media activities. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI.

[0095] The calculation unit can estimate the emotions of senior and younger generations and adjust the compatibility score calculation method based on the estimated emotions. For example, if a senior generation feels lonely, the calculation unit's generating AI analyzes their emotions and calculates a compatibility score to alleviate loneliness. If a younger generation feels anxious, the calculation unit's generating AI can analyze their emotions and calculate a compatibility score to alleviate anxiety. If a senior generation feels satisfied, the calculation unit's generating AI can analyze their emotions and calculate a compatibility score to maintain that satisfaction. By adjusting the compatibility score calculation method based on emotions, a more appropriate compatibility score can be calculated. 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 calculation unit may be performed using a generating AI or not.

[0096] The calculation unit can improve the accuracy of the compatibility score calculation by referring to the past matching history of senior and young people. For example, the calculation unit can analyze the past matching history of senior people and reflect it in the compatibility score. The calculation unit can also analyze the past matching history of young people and reflect it in the compatibility score. The calculation unit can also analyze the past matching success rate of senior and young people and reflect it in the compatibility score. In this way, the accuracy of the compatibility score calculation is improved by referring to past matching history. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0097] The calculation unit can apply different calculation algorithms to senior and younger groups when calculating compatibility scores based on their attribute information. For example, the calculation unit can calculate compatibility scores based on senior groups' work history and hobbies. The calculation unit can also calculate compatibility scores based on younger groups' areas of study and career goals. The calculation unit can also calculate compatibility scores based on the common interests of senior and younger groups. By applying different calculation algorithms based on attribute information, a more appropriate compatibility score can be calculated. Some or all of the above-described processes in the calculation unit may be performed using a generative AI, or they may be performed without using a generative AI.

[0098] The calculation unit can estimate the emotions of senior and younger users and adjust the display method of the compatibility score based on the estimated emotions. For example, if a senior user is feeling lonely, the calculation unit's generating AI analyzes their emotions and displays a compatibility score to alleviate loneliness. If a younger user is feeling anxious, the calculation unit's generating AI can analyze their emotions and display a compatibility score to alleviate anxiety. If a senior user is feeling satisfied, the calculation unit's generating AI can analyze their emotions and display a compatibility score to maintain that satisfaction. By adjusting the display method of the compatibility score based on emotions, a more appropriate compatibility score is displayed. 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 processing in the calculation unit may be performed using a generating AI or not.

[0099] The calculation unit can perform compatibility score calculations while considering the geographical distribution of senior citizens and young people. For example, the calculation unit can analyze the residential information of senior citizens and reflect it in the compatibility score. The calculation unit can also analyze the commuting routes of young people and reflect them in the compatibility score. The calculation unit can also analyze the community activity history of senior citizens and reflect it in the compatibility score. By performing calculations while considering geographical distribution, a more appropriate compatibility score can be calculated. Some or all of the above processing in the calculation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0100] The calculation unit can improve the accuracy of the compatibility score calculation by referring to relevant literature for senior and young people. For example, the calculation unit can analyze past research papers of senior people and reflect them in the compatibility score. The calculation unit can also analyze literature related to the learning fields of young people and reflect it in the compatibility score. The calculation unit can also calculate the compatibility score based on common research themes for senior and young people. This improves the accuracy of the compatibility score calculation by referring to relevant literature. Some or all of the above processing in the calculation unit may be performed using a generative AI, or it may be performed without using a generative AI.

[0101] The matching unit can estimate the emotions of senior and younger generations and adjust the matching criteria based on the estimated emotions. For example, if a senior generation feels lonely, the matching unit's generative AI analyzes their emotions and sets matching criteria to alleviate loneliness. If a younger generation feels anxious, the matching unit's generative AI can analyze their emotions and set matching criteria to alleviate anxiety. If a senior generation feels satisfied, the matching unit's generative AI can analyze their emotions and set matching criteria to maintain that satisfaction. By adjusting the matching criteria based on emotions, more appropriate matching becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using or without the generative AI.

[0102] The matching unit can improve the accuracy of matching by considering the interrelationships between senior and younger groups during the matching process. For example, the matching unit can analyze the past matching history of senior and younger groups and perform matching while considering their interrelationships. The matching unit can also analyze the common interests of senior and younger groups and perform matching while considering their interrelationships. The matching unit can also analyze the past communication history of senior and younger groups and perform matching while considering their interrelationships. This improves the accuracy of matching by considering their interrelationships. Some or all of the above-described processes in the matching unit may be performed using generative AI, or they may be performed without using generative AI.

[0103] The matching unit can perform matching while considering the attribute information of senior citizens and young people. For example, the matching unit can perform matching based on the work history and hobbies of senior citizens. The matching unit can also perform matching based on the learning fields and career goals of young people. The matching unit can also perform matching based on the common interests of senior citizens and young people. This allows for more appropriate matching by considering attribute information. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0104] The matching unit can estimate the emotions of senior and younger users and adjust the order in which matching results are displayed based on the estimated emotions. For example, if a senior user is feeling lonely, the matching unit's generative AI will analyze their emotions and prioritize displaying matching results that alleviate loneliness. Similarly, if a younger user is feeling anxious, the matching unit's generative AI will analyze their emotions and prioritize displaying matching results that alleviate anxiety. If a senior user is feeling satisfied, the matching unit's generative AI will analyze their emotions and prioritize displaying matching results that maintain satisfaction. By adjusting the order in which matching results are displayed based on emotions, more appropriate matching results are displayed. 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 processing in the matching unit may be performed using or without a generative AI.

[0105] The matching unit can perform matching while considering the geographical distribution of senior citizens and young people. For example, the matching unit can analyze the residential information of senior citizens and match them with young people who are geographically close. The matching unit can also analyze the commuting routes of young people and match them with senior citizens who are geographically close. The matching unit can also analyze the community activity history of senior citizens and match them with young people who are geographically close. This allows for more appropriate matching by considering geographical distribution. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0106] The matching unit can improve the accuracy of matching by referring to relevant literature for senior and younger generations during the matching process. For example, the matching unit can analyze past research papers of senior generations and match them with relevant younger generations. The matching unit can also analyze literature related to the learning fields of younger generations and match them with relevant senior generations. The matching unit can also perform matching based on common research themes for senior and younger generations. This improves the accuracy of matching by referring to relevant literature. Some or all of the above processing in the matching unit may be performed using generative AI, or it may be performed without using generative AI.

[0107] The communication department can estimate the emotions of senior and younger generations and adjust communication methods based on the estimated emotions. For example, if a senior generation feels lonely, the communication department's generative AI can analyze their emotions and suggest communication methods to alleviate loneliness. If a younger generation feels anxious, the communication department's generative AI can analyze their emotions and suggest communication methods to alleviate anxiety. If a senior generation feels satisfied, the communication department's generative AI can analyze their emotions and suggest communication methods to maintain that satisfaction. This allows for more appropriate communication by adjusting communication methods based on 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 communication department may be performed using or without generative AI.

[0108] The communication department can select the optimal method of communication by referring to the past communication history of senior and younger generations. For example, the communication department can analyze the past communication history of senior generations and propose the optimal method. The communication department can also analyze the past communication history of younger generations and propose the optimal method. The communication department can also analyze the past communication success rates of senior and younger generations and propose the optimal method. In this way, the optimal communication method is selected by referring to past communication history. Some or all of the above processing in the communication department may be performed using generative AI, or it may be performed without using generative AI.

[0109] The communications department can customize the content of communications based on the interests of senior and younger generations. For example, the communications department can analyze the hobbies and interests of senior generations and reflect them in the content of communications. The communications department can also analyze the learning fields and career goals of younger generations and reflect them in the content of communications. The communications department can also analyze the past project experience of senior generations and reflect it in the content of communications. By customizing the content of communications based on interests, more appropriate communications become possible. Some or all of the above processing in the communications department may be performed using generative AI, or it may be performed without using generative AI.

[0110] The communication department can estimate the emotions of senior and younger generations and prioritize communication based on the estimated emotions. For example, if a senior generation is feeling lonely, the communication department's generative AI will analyze their emotions and prioritize communication to alleviate loneliness. If a younger generation is feeling anxious, the communication department's generative AI will analyze their emotions and prioritize communication to alleviate anxiety. If a senior generation is feeling satisfied, the communication department's generative AI will analyze their emotions and prioritize communication to maintain that satisfaction. This allows for more appropriate communication by prioritizing communication based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication department may be performed using or without generative AI.

[0111] The Communication Department can select the optimal method of communication by considering the geographical information of senior citizens and younger generations. For example, the Communication Department can analyze the residential information of senior citizens and propose communication methods with younger generations who are geographically close. The Communication Department can also analyze the commuting routes of younger generations and propose communication methods with senior citizens who are geographically close. The Communication Department can also analyze the community activity history of senior citizens and propose communication methods with younger generations who are geographically close. By considering geographical information, more appropriate communication becomes possible. Some or all of the above processing in the Communication Department may be performed using generative AI, or it may be performed without using generative AI.

[0112] The communications department can analyze the social media activities of senior citizens and younger generations and reflect this in their communications. For example, the communications department can analyze the social media activity history of senior citizens and reflect it in the content of their communications. The communications department can also analyze the interests of younger generations on social media and reflect it in the content of their communications. The communications department can also analyze the number of followers and influence of senior citizens on social media and reflect it in the content of their communications. By analyzing social media activities, more appropriate communications become possible. Some or all of the above processing in the communications department may be performed using generative AI, or it may be performed without using generative AI.

[0113] The leadership team can estimate the emotions of senior and younger generations and adjust their teaching methods based on these estimated emotions. For example, if a senior generation feels lonely, the leadership team can use a generative AI to analyze their emotions and suggest teaching methods to alleviate loneliness. If a younger generation feels anxious, the leadership team can use a generative AI to analyze their emotions and suggest teaching methods to alleviate anxiety. If a senior generation feels satisfied, the leadership team can use a generative AI to analyze their emotions and suggest teaching methods to maintain that satisfaction. This allows for more appropriate teaching by adjusting teaching methods based on 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 leadership team may be performed using or without generative AI.

[0114] The instruction department can select the optimal method during instruction by referring to the past instruction history of senior and younger participants. For example, the instruction department can analyze the past instruction history of senior participants and propose the optimal method. The instruction department can also analyze the past instruction history of younger participants and propose the optimal method. The instruction department can also analyze the past instruction success rates of senior and younger participants and propose the optimal method. In this way, the optimal instruction method is selected by referring to past instruction history. Some or all of the above processing in the instruction department may be performed using generative AI, or it may be performed without using generative AI.

[0115] The instruction department can customize the content of instruction based on the interests of senior and younger participants. For example, the instruction department can analyze the hobbies and interests of senior participants and reflect them in the instruction. The instruction department can also analyze the learning areas and career goals of younger participants and reflect them in the instruction. The instruction department can also analyze the past project experience of senior participants and reflect it in the instruction. By customizing the content of instruction based on interests, more appropriate instruction becomes possible. Some or all of the above processes in the instruction department may be performed using generative AI, or they may not be performed using generative AI.

[0116] The leadership team can estimate the emotions of senior and younger generations and prioritize instruction based on these estimated emotions. For example, if senior generations are feeling lonely, the leadership team can use a generative AI to analyze their emotions and prioritize instruction to alleviate loneliness. If younger generations are feeling anxious, the leadership team can use a generative AI to analyze their emotions and prioritize instruction to alleviate anxiety. If senior generations are feeling satisfied, the leadership team can use a generative AI to analyze their emotions and prioritize instruction to maintain that satisfaction. This allows for more appropriate instruction by prioritizing instruction based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the leadership team may be performed using or without generative AI.

[0117] The guidance department can select the optimal method during guidance by considering the geographical information of both senior and young people. For example, the guidance department can analyze the residential information of senior people and propose guidance methods for young people who are geographically close. The guidance department can also analyze the commuting routes of young people and propose guidance methods for senior people who are geographically close. The guidance department can also analyze the community activity history of senior people and propose guidance methods for young people who are geographically close. By considering geographical information, more appropriate guidance becomes possible. Some or all of the above processing by the guidance department may be performed using generative AI, or it may be performed without using generative AI.

[0118] The leadership team can analyze the social media activities of senior and younger generations during the guidance process and incorporate this analysis into their guidance. For example, the leadership team can analyze the social media activity history of senior generations and incorporate this into their guidance. They can also analyze the social media interests of younger generations and incorporate this into their guidance. Furthermore, they can analyze the number of followers and influence of senior generations on social media and incorporate this into their guidance. This allows for more appropriate guidance by analyzing social media activities. Some or all of the above-described processes in the leadership team may be performed using generative AI, or they may be performed without using generative AI.

[0119] The payment unit can estimate the emotions of senior and younger generations and adjust the payment method based on the estimated emotions. For example, if a senior generation feels lonely, the payment unit's generative AI analyzes their emotions and proposes a payment method to alleviate loneliness. If a younger generation feels anxious, the payment unit's generative AI can analyze their emotions and propose a payment method to alleviate anxiety. If a senior generation feels satisfied, the payment unit's generative AI can analyze their emotions and propose a payment method to maintain that satisfaction. By adjusting the payment method based on emotions, more appropriate payments become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the payment unit may be performed using or without the generative AI.

[0120] The payment unit can select the optimal payment method by referring to the past payment history of senior citizens and young adults at the time of payment. For example, the payment unit can analyze the past payment history of senior citizens and propose the optimal method. The payment unit can also analyze the past payment history of young adults and propose the optimal method. The payment unit can also analyze the past payment success rates of senior citizens and young adults and propose the optimal method. In this way, the optimal payment method is selected by referring to past payment history. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0121] The payment unit can customize payment methods based on attribute information of senior and younger users during the payment process. For example, the payment unit can suggest payment methods based on the senior user's work history and hobbies. The payment unit can also suggest payment methods based on the younger user's field of study and career goals. The payment unit can also suggest payment methods based on the common interests of senior and younger users. By customizing payment methods based on attribute information, more appropriate payments become possible. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0122] The payment unit can estimate the emotions of senior and younger generations and determine payment priorities based on the estimated emotions. For example, if a senior generation feels lonely, the payment unit's generative AI analyzes their emotions and prioritizes payments that alleviate loneliness. If a younger generation feels anxious, the payment unit's generative AI analyzes their emotions and prioritizes payments that alleviate anxiety. If a senior generation feels satisfied, the payment unit's generative AI analyzes their emotions and prioritizes payments that maintain that satisfaction. This allows for more appropriate payments by determining payment priorities based on emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the payment unit may be performed using or without generative AI.

[0123] The payment unit can select the optimal payment method by considering the geographical information of senior citizens and young people during the payment process. For example, the payment unit can analyze the residential information of senior citizens and propose a payment method with young people who are geographically close. The payment unit can also analyze the commuting routes of young people and propose a payment method with senior citizens who are geographically close. The payment unit can also analyze the community activity history of senior citizens and propose a payment method with young people who are geographically close. By considering geographical information, more appropriate payments become possible. Some or all of the above processing in the payment unit may be performed using generative AI, or it may be performed without using generative AI.

[0124] The payment processing unit can analyze the social media activity of senior citizens and younger generations during the payment process and reflect this in the payment. For example, the payment processing unit can analyze the social media activity history of senior citizens and reflect this in the payment details. The payment processing unit can also analyze the social media interests of younger generations and reflect this in the payment details. The payment processing unit can also analyze the number of followers and influence of senior citizens on social media and reflect this in the payment details. This allows for more appropriate payments by analyzing social media activity. Some or all of the above processing in the payment processing unit may be performed using generative AI, or it may be performed without using generative AI.

[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 analysis unit can consider real-time health data when analyzing profiles of senior and younger demographics. For example, it can collect health data such as heart rate and blood pressure from senior citizens and incorporate it into their profiles. It can also analyze the exercise habits and sleep patterns of younger citizens and incorporate them into their profiles. Furthermore, it can provide data for appropriate matching based on the health status of senior citizens. This allows for more accurate profile analysis by considering health data.

[0127] The calculation unit can estimate the emotions of senior and younger generations and adjust the compatibility score calculation method based on the estimated emotions. For example, if a senior generation feels lonely, the generating AI analyzes their emotions and calculates a compatibility score to alleviate loneliness. If a younger generation feels anxious, the generating AI can also analyze their emotions and calculate a compatibility score to alleviate anxiety. If a senior generation feels satisfied, the generating AI can also analyze their emotions and calculate a compatibility score to maintain that satisfaction. By adjusting the compatibility score calculation method based on emotions, a more appropriate compatibility score can be calculated.

[0128] The matching unit can estimate the emotions of senior and younger generations and adjust the matching criteria based on these estimated emotions. For example, if a senior generation feels lonely, the generating AI analyzes their emotions and sets matching criteria to alleviate loneliness. If a younger generation feels anxious, the generating AI can also analyze their emotions and set matching criteria to alleviate anxiety. If a senior generation feels satisfied, the generating AI can also analyze their emotions and set matching criteria to maintain that satisfaction. By adjusting the matching criteria based on emotions, more appropriate matching becomes possible.

[0129] The communication department can estimate the emotions of senior and younger generations and adjust communication methods based on those estimated emotions. For example, if a senior generation feels lonely, the generative AI can analyze their emotions and suggest communication methods to alleviate loneliness. If a younger generation feels anxious, the generative AI can also analyze their emotions and suggest communication methods to reduce anxiety. If a senior generation feels satisfied, the generative AI can analyze their emotions and suggest communication methods to maintain that satisfaction. By adjusting communication methods based on emotions, more appropriate communication becomes possible.

[0130] The leadership team can estimate the emotions of senior and younger generations and adjust their teaching methods based on these estimates. For example, if a senior generation feels lonely, the generative AI can analyze their emotions and suggest teaching methods to alleviate loneliness. If a younger generation feels anxious, the generative AI can analyze their emotions and suggest teaching methods to reduce anxiety. If a senior generation feels satisfied, the generative AI can analyze their emotions and suggest teaching methods to maintain that satisfaction. By adjusting teaching methods based on emotions, more appropriate guidance becomes possible.

[0131] The analysis unit can analyze the past activity history of senior and younger generations to improve the level of detail in their profiles. For example, it can analyze the past volunteer activity history of senior generations and reflect it in their profiles. It can also analyze the past learning history of younger generations and reflect it in their profiles. It can also analyze the past work experience of senior generations and reflect it in their profiles. In this way, analyzing past activity history improves the level of detail in the profiles.

[0132] The calculation unit can improve the accuracy of the compatibility score calculation by referring to the past matching history of senior and younger groups. For example, it can analyze the past matching history of senior groups and reflect it in the compatibility score. It can also analyze the past matching history of younger groups and reflect it in the compatibility score. It can also analyze the past matching success rate of senior and younger groups and reflect it in the compatibility score. In this way, the accuracy of the compatibility score calculation is improved by referring to past matching history.

[0133] The matching unit can improve the accuracy of matching by considering the relationships between senior and younger generations during the matching process. For example, it can analyze the past matching history of senior and younger generations and perform matching while considering these relationships. It can also analyze the common interests of senior and younger generations and perform matching while considering these relationships. It can also analyze the past communication history of senior and younger generations and perform matching while considering these relationships. In this way, the accuracy of matching is improved by considering these relationships.

[0134] The communications department can select the optimal communication method by referring to the past communication history of both senior and younger generations. For example, it can analyze the past communication history of senior generations and propose the optimal method. It can also analyze the past communication history of younger generations and propose the optimal method. It can also analyze the past communication success rates of senior and younger generations and propose the optimal method. In this way, the optimal communication method is selected by referring to past communication history.

[0135] The instruction department can customize the content of instruction based on the interests of senior and younger participants. For example, they can analyze the hobbies and interests of senior participants and incorporate them into the instruction. They can also analyze the learning areas and career goals of younger participants and incorporate them into the instruction. Furthermore, they can analyze the past project experience of senior participants and incorporate it into the instruction. By customizing the content of instruction based on interests, more appropriate instruction becomes possible.

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

[0137] Step 1: The analysis unit analyzes the profiles of senior and younger generations. The analysis unit collects information such as age, occupation, hobbies, and skills of senior and younger generations, and creates profiles. The analysis unit uses a generative AI to analyze the content of the profiles and provides data for calculating compatibility scores. Step 2: The calculation unit calculates a compatibility score based on the profile analyzed by the analysis unit. The calculation unit calculates the compatibility score by considering, for example, common hobbies and interests between seniors and younger people. The calculation unit uses a generation AI to improve the accuracy of the compatibility score calculation. Step 3: The matching unit performs optimal matching based on the compatibility score calculated by the calculation unit. For example, the matching unit matches based on common hobbies and interests between seniors and younger people. The matching unit uses a generation AI to improve the accuracy of the matching. Step 4: The Communication Department provides a platform for seniors and younger generations, matched by the Matching Department, to communicate online. The Communication Department provides communication in various forms, such as chat, video calls, and forums. The Communication Department can use generative AI to adjust the communication methods. Step 5: The leadership team enables senior members to share their knowledge and experience with younger members in a forum provided by the communications team. For example, senior members can give online lectures to younger members. The leadership team can use generative AI to adjust the teaching methods. Step 6: The payment department implements a secure tipping system to receive rewards for guidance and advice provided by the instruction department. For example, the payment department can receive rewards using an electronic payment system for guidance and advice provided by seniors to younger generations. The payment department can use generative AI to adjust the payment method.

[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 analysis unit, calculation unit, matching unit, communication unit, guidance unit, and settlement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the processor 28 of the data processing unit 12, and analyzes the profiles of senior and younger users. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, and calculates a compatibility score. The matching unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and performs optimal matching. The communication unit is implemented by the control unit 46A of the smart device 14, and provides online communication. The guidance unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and facilitates the sharing of knowledge and experience between senior and younger users. The settlement unit is implemented by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and provides a secure tipping system. 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 analysis unit, calculation unit, matching unit, communication unit, guidance unit, and settlement unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the processor 28 of the data processing unit 12, and analyzes the profiles of senior and younger users. The calculation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and calculates a compatibility score. The matching unit is implemented, for example, by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and performs optimal matching. The communication unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides online communication. The guidance unit is implemented, for example, by the control unit 46A of the smart glasses 214 and the identification processing unit 290 of the data processing unit 12, and enables senior users to share their knowledge and experience with younger users. The payment section is implemented, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, providing a secure tipping system. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[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 analysis unit, calculation unit, matching unit, communication unit, guidance unit, and settlement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the processor 28 of the data processing unit 12, and analyzes the profiles of senior and younger users. The calculation unit is implemented by the identification processing unit 290 of the data processing unit 12, and calculates a compatibility score. The matching unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and performs optimal matching. The communication unit is implemented by the control unit 46A of the headset terminal 314, and provides online communication. The guidance unit is implemented by the control unit 46A of the headset terminal 314 and the identification processing unit 290 of the data processing unit 12, and facilitates the sharing of knowledge and experience between senior users and younger users. The payment section is implemented, for example, by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12, providing a secure tipping system. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[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 analysis unit, calculation unit, matching unit, communication unit, guidance unit, and settlement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the processor 28 of the data processing unit 12, and analyzes the profiles of senior and younger users. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, and calculates a compatibility score. The matching unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and performs optimal matching. The communication unit is implemented by the control unit 46A of the robot 414, and provides online communication. The guidance unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and facilitates the sharing of knowledge and experience between senior and younger users. The settlement unit is implemented by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and provides a secure tipping system. 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) The analysis department analyzes the profiles of senior citizens and young people, A calculation unit calculates a compatibility score based on the profile analyzed by the aforementioned analysis unit, A matching unit that performs optimal matching based on the compatibility score calculated by the calculation unit, The Communication Department provides a platform for seniors and young people, who have been matched by the Matching Department, to communicate online. In the forum provided by the aforementioned communications department, senior members share their knowledge and experience with younger members, and the leadership department The system includes a settlement unit for receiving compensation for the guidance and advice provided by the aforementioned leadership unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We will use generative AI to analyze the profiles of senior citizens and younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The calculation unit described above, Calculate compatibility score using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Optimal matching is performed using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned communications department, Providing a platform for seniors and young people to communicate online. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned leadership, Seniors share their knowledge and experience with younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned settlement unit, Implement a secure tipping system that allows seniors to receive compensation for the guidance and advice they provide to younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the emotions of senior and younger demographics and adjust the accuracy of profile analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Analyze the past activity history of senior and younger generations to improve the level of detail in their profiles. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During profile analysis, the analysis algorithm is customized based on the interests of senior and younger demographics. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the emotions of senior and younger demographics and determine the priority of profile analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When performing profile analysis, the analysis takes into account the geographical information of both senior and younger demographics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During profile analysis, the social media activity of senior citizens and younger generations is analyzed and reflected in the profiles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The calculation unit described above, We estimate the emotions of senior and younger generations and adjust the compatibility score calculation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The calculation unit described above, When calculating compatibility scores, we improve the accuracy of the calculation by referring to the past matching history of senior and younger users. The system described in Appendix 1, characterized by the features described herein. (Note 16) The calculation unit described above, When calculating compatibility scores, different calculation algorithms are applied based on the attribute information of senior and younger demographics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The calculation unit described above, We estimate the emotions of senior and younger demographics and adjust how compatibility scores are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit described above, When calculating compatibility scores, the geographical distribution of senior and younger demographics is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit described above, When calculating compatibility scores, we improve the accuracy of the calculation by referring to relevant literature on senior and younger demographics. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is We estimate the sentiments of senior and younger demographics and adjust the matching criteria based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is To improve the accuracy of matching, we consider the interrelationship between senior citizens and younger generations during the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, attribute information of both senior citizens and younger people is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is The system estimates the sentiments of senior and younger demographics and adjusts the order in which matching results are displayed based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is During the matching process, the geographical distribution of senior citizens and younger generations is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature for senior citizens and younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications department, It estimates the emotions of senior and younger generations and adjusts communication methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications department, When communicating, the optimal method is selected by referring to the past communication history of both senior and younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications department, When communicating, customize the content of the communication based on the interests of senior citizens and younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications department, It estimates the emotions of senior and younger generations and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned communications department, When communicating, consider the geographical information of both senior and younger demographics to select the most appropriate method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned communications department, When communicating, analyze the social media activity of senior citizens and younger generations and reflect that analysis in your communication. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned leadership, We estimate the emotions of senior and younger generations and adjust our teaching methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned leadership, During instruction, the optimal method is selected by referring to the past instruction history of both senior and younger students. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned leadership, During instruction, the content of the lesson will be customized based on the interests of senior citizens and younger people. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned leadership, The system estimates the emotions of senior and younger generations and determines the priority of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned leadership, When providing instruction, the most suitable method will be selected considering the geographical information of both senior and younger groups. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned leadership, During instruction, analyze the social media activities of senior citizens and younger generations and incorporate that analysis into the instruction. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned settlement unit, The system estimates the sentiments of senior citizens and younger generations, and adjusts payment methods based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned settlement unit, During payment, the system selects the optimal method by referencing the past payment history of both senior and younger customers. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned settlement unit, At the time of payment, the payment method will be customized based on the attribute information of senior citizens and younger people. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned settlement unit, The system estimates the sentiments of senior and younger demographics and determines payment priorities based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned settlement unit, When making a payment, the optimal method will be selected considering the geographical information of both senior citizens and younger generations. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned settlement unit, At the time of payment, the social media activity of senior citizens and younger generations will be analyzed and reflected in the payment process. 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. The analysis department analyzes the profiles of senior citizens and young people, A calculation unit calculates a compatibility score based on the profile analyzed by the aforementioned analysis unit, A matching unit that performs optimal matching based on the compatibility score calculated by the calculation unit, The Communication Department provides a platform for seniors and young people, who have been matched by the Matching Department, to communicate online. In the forum provided by the aforementioned communications department, senior members share their knowledge and experience with younger members, and the leadership department The system includes a settlement unit for receiving compensation for the guidance and advice provided by the aforementioned leadership unit. A system characterized by the following features.

2. The aforementioned analysis unit, We will use generative AI to analyze the profiles of senior citizens and younger generations. The system according to feature 1.

3. The calculation unit described above, Compatibility scores are calculated using generative AI. The system according to feature 1.

4. The matching unit is Optimal matching is performed using generative AI. The system according to feature 1.

5. The aforementioned communications department, Providing a platform for seniors and young people to communicate online. The system according to feature 1.

6. The aforementioned leadership, Seniors share their knowledge and experience with younger generations. The system according to feature 1.

7. The aforementioned settlement unit, Implement a secure tipping system that allows seniors to receive compensation for the guidance and advice they provide to younger generations. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the emotions of senior and younger demographics and adjust the accuracy of profile analysis based on the estimated emotions. The system according to feature 1.

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