system

Abstract

The system according to this embodiment aims to help senior citizens find friends with common hobbies and interests and to promote interaction among them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a recommendation unit, a verification unit, an interaction unit, a health unit, and a support unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends friends based on the analysis results obtained by the analysis unit. The verification unit performs age verification and identity verification. The interaction unit provides online and offline interaction opportunities. The health unit links hobby activities and health management. The support unit provides a 24-hour AI chatbot.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for the senior generation to find peers with common hobbies and interests, and the opportunities for communication are limited.

[0005] The system according to the embodiment aims to find peers with common hobbies and interests for the senior generation and promote communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a verification unit, an interaction unit, a health unit, and a support unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends partners based on the analysis results obtained by the analysis unit. The verification unit performs age verification and identity verification. The interaction unit provides online and offline interaction opportunities. The health unit links hobby activities and health management. The support unit provides a 24-hour AI chatbot. [Effects of the Invention]

[0007] The system according to this embodiment allows senior citizens to find friends with similar hobbies and interests and to promote interaction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​Senior Hobby Partner Matching System according to an embodiment of the present invention is a system that helps seniors aged 60 and over find partners with common hobbies and interests and promotes interaction. This system analyzes the user's hobbies, interests, personality, and lifestyle, and recommends the most suitable hobby partners. For example, the user inputs information such as their hobbies, interests, personality, and lifestyle. Next, the AI ​​analyzes this information and recommends compatible partners. For example, it recommends people with the same hobbies or similar lifestyles. Furthermore, the AI ​​thoroughly verifies age and identity to provide a safe environment. It also integrates online and offline activities, providing diverse opportunities for interaction such as virtual hobby classes, online events, and arranging in-person meetups. In addition, it links hobby activities with health management to support a healthy lifestyle while enjoying oneself. For example, it provides health advice related to hobby activities and visualizes activity levels and social participation. A 24-hour AI chatbot also answers questions about how to use the service and about hobbies. Through this system, seniors can find new hobby partners and enjoy fulfilling days. Furthermore, by maintaining and expanding connections with society, it contributes to alleviating loneliness and maintaining cognitive function. For example, this system is extremely beneficial for those who want to spend their time after retirement meaningfully, those who want to try new hobbies or activities, and those who seek interaction with peers of the same generation. It is also suitable for those who can perform basic operations on smartphones and tablets, and those who are interested in maintaining their health. This system aims to achieve many goals, including creating a sense of purpose for seniors, extending healthy life expectancy, promoting intergenerational interaction, improving seniors' digital literacy, revitalizing local communities, utilizing the knowledge and experience of seniors, alleviating loneliness and isolation, and proposing new ways of living and working. For example, it supports new lifestyles for seniors, such as side jobs and social contribution activities through hobbies. Through this system, we aim to realize a society where seniors can actively participate and thrive. In this way, the AI ​​Senior Hobby Partner Matching System can help seniors find hobby partners and promote interaction.

[0029] The AI ​​senior hobby partner matching system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a verification unit, an interaction unit, a health unit, and a support unit. The collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and health information. The collection unit collects user information using, for example, questionnaires. The collection unit can also collect user health information using sensors. Furthermore, the collection unit can collect user interests using online data. For example, the collection unit collects the history of pages the user has viewed online to identify interests. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these. For example, the analysis unit analyzes the user's hobby trends using statistical analysis. The analysis unit can also analyze the user's personality using machine learning algorithms. Furthermore, the analysis unit can analyze the user's lifestyle using data mining techniques. For example, the analysis unit analyzes the user's lifestyle data to identify their lifestyle. The recommendation unit recommends partners based on the analysis results obtained by the analysis unit. Recommendations are made based on, for example, compatibility evaluation criteria or recommendation algorithms. For example, the recommendation unit recommends the best partners for a user using compatibility evaluation criteria. The recommendation unit can also recommend partners using recommendation algorithms. Furthermore, the recommendation unit can recommend partners based on a user's past interaction history. For example, the recommendation unit recommends new partners based on information about partners the user has interacted with in the past. The verification unit performs age verification and identity verification. Verification is performed using, for example, identification document verification or digital authentication. For example, the verification unit verifies the user's age using an identification document. The verification unit can also perform identity verification using digital authentication. Furthermore, the verification unit can perform identity verification using facial recognition technology. For example, the verification unit analyzes the user's facial image to verify their identity. The interaction unit provides online and offline interaction opportunities.Interaction opportunities include, but are not limited to, virtual hobby classes, online events, and in-person meetups. For example, the Interaction Department could host virtual hobby classes, providing users with opportunities to enjoy their hobbies online. It could also host online events, providing users with opportunities to interact with other users. Furthermore, the Interaction Department could arrange in-person meetups, providing users with opportunities to meet and interact directly. For example, the Interaction Department could coordinate the location of an in-person meetup based on the user's place of residence. The Health Department would link hobby activities with health management. Health management would include, but is not limited to, providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department could provide health advice related to hobby activities. It could also monitor users' activity levels and understand their health status. Furthermore, the Health Department could visualize users' social participation to support health management. For example, the Health Department could record the number of events a user attends and evaluate their social participation. The Support Department would provide a 24 / 7 AI chatbot. The AI ​​chatbot can, for example, answer questions about how to use the service or about hobbies, but is not limited to such examples. For instance, the support department can use the AI ​​chatbot to respond to user inquiries 24 hours a day. The support department can also use the AI ​​chatbot to troubleshoot problems. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. This enables the AI ​​senior hobby partner matching system according to this embodiment to efficiently collect, analyze, recommend, verify, interact with, manage health, and support users' information.

[0030] The data collection unit collects user information. This information includes, but is not limited to, personal information, hobbies, interests, and health information. For example, the unit collects user information using questionnaires. These questionnaires are provided through online forms or applications, allowing users to provide detailed answers about their hobbies, interests, and health status. The unit can also collect user health information using sensors. For example, wearable devices can be used to collect data such as heart rate, steps, and sleep patterns. Furthermore, the unit can collect user interests using online data. For example, it can collect a user's browsing history to identify their interests. This allows the unit to understand what hobbies and activities the user is interested in. The unit centrally manages this data and creates user profiles. This allows for efficient collection of user information and provision to the analytics and recommendation units. The unit implements encryption technology and access control to ensure data privacy and security. This prevents unauthorized access to users' personal information and provides a secure environment for using the service.

[0031] The analysis department analyzes the information collected by the data collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis department can use statistical analysis to analyze users' hobby trends, thereby understanding what hobbies users have and what activities they are interested in. The analysis department can also use machine learning algorithms to analyze users' personalities, for example, by identifying personality traits based on user response data and behavioral data, providing foundational information for finding compatible partners. Furthermore, the analysis department can use data mining techniques to analyze users' lifestyles, for example, by analyzing users' lifestyle data to identify their lifestyles, thereby understanding when users are active and what their daily rhythms are. Based on these analysis results, the analysis department provides information to recommend the most suitable partners to users. To ensure data accuracy and reliability, the analysis department preprocesses and cleans the data to remove noise and outliers. The analysis department also visualizes the analysis results, providing them in a format easily understood by users and other departments. This allows the analysis department to effectively utilize the collected data and improve the overall system performance.

[0032] The recommendation team recommends partners based on the analysis results obtained by the analysis team. Recommendations are based on, but are not limited to, compatibility evaluation criteria or recommendation algorithms. For example, the recommendation team recommends the best partners for a user using compatibility evaluation criteria. Compatibility evaluation criteria include the degree of shared hobbies, personality compatibility, and lifestyle compatibility. The recommendation team can also recommend partners using recommendation algorithms. For example, collaborative filtering or content-based recommendation algorithms can be used to find the best partners for a user. Furthermore, the recommendation team can recommend partners based on a user's past interaction history. For example, the recommendation team recommends new partners based on information about partners the user has interacted with in the past. This provides users with the opportunity to reconnect with partners with whom they have built good relationships in the past. The recommendation team has implemented a feedback loop to improve the accuracy and reliability of recommendation results. It collects feedback from users and uses it to improve the recommendation algorithm. This allows the recommendation team to recommend more appropriate partners to users and improve their satisfaction.

[0033] The verification unit performs age and identity verification. Verification is performed using, for example, identification document verification or digital authentication. For example, the verification unit verifies the user's age using identification documents. Users upload identification documents such as driver's licenses or passports, and the verification unit verifies the information. The verification unit can also perform identity verification using digital authentication. For example, an authentication code is sent to the email address or phone number provided by the user during registration, and identity verification is performed by entering that code. Furthermore, the verification unit can also perform identity verification using facial recognition technology. For example, the verification unit analyzes the user's facial image to verify their identity. Facial recognition technology uses a highly accurate algorithm to identify the user's facial features and compare them with registered information. This allows the verification unit to reliably verify the user's age and identity and prevent fraudulent use. To ensure the transparency and reliability of the verification process, the verification unit explains the details of the verification procedure to the user and provides necessary support. This allows the verification unit to provide an environment in which users can use the service with peace of mind.

[0034] The Community Club provides online and offline opportunities for interaction. These opportunities include, but are not limited to, virtual hobby classes, online events, and in-person meetups. For example, the Community Club can host virtual hobby classes, providing users with the opportunity to enjoy their hobbies online. In virtual hobby classes, professional instructors provide lessons on hobbies, and users can participate from the comfort of their homes. The Community Club can also host online events, providing opportunities for users to interact with other users. Online events may include hobby-related discussions, workshops, and games, providing a space for users to interact in a fun way. Furthermore, the Community Club can arrange in-person meetups, providing opportunities for users to meet and interact in person. For example, the Community Club can coordinate the location of in-person meetups based on users' locations, creating an environment that is easy for users to participate in. In-person meetups feature hobby-related activities and social events, allowing users to build deeper relationships through direct interaction. Through these opportunities for interaction, the Community Club supports users in enjoying their hobbies, meeting new friends, and having fulfilling experiences.

[0035] The Health Department links recreational activities with health management. Health management includes, but is not limited to, providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department provides health advice related to recreational activities. By providing health advice tailored to the user's recreational activities, it supports users in enjoying their hobbies in a healthy way. The Health Department can also monitor users' activity levels and understand their health status. For example, it can use wearable devices to monitor the user's steps, heart rate, calories burned, etc., and evaluate their health status. Furthermore, the Health Department can visualize users' social participation and support health management. For example, the Health Department records the number of events a user participates in and evaluates their social participation. This allows the Health Department to understand how socially active a user is and provide support as needed. Through these health management functions, the Health Department supports users in enjoying their hobbies in a healthy way while also being socially active.

[0036] The support department provides a 24 / 7 AI chatbot. The AI ​​chatbot answers questions about service usage and hobbies, among other things, but is not limited to these examples. For instance, the support department uses the AI ​​chatbot to respond to user inquiries 24 / 7. The AI ​​chatbot uses natural language processing technology to understand user questions and provide appropriate answers. The support department can also use the AI ​​chatbot for troubleshooting. For example, if a user encounters a problem while using the service, the AI ​​chatbot can identify the cause of the problem and guide them to a solution. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. Based on the user's hobbies and interests, the AI ​​chatbot can suggest relevant information and activities. This allows the support department to provide support to users so they can effectively use the service and enjoy their hobbies. The support department collects user feedback to continuously improve the performance of the AI ​​chatbot and uses it for improvement. This allows the support department to provide better support to users and increase their satisfaction.

[0037] The data collection unit can collect information about the user's hobbies, interests, personality, and lifestyle. For example, the data collection unit collects information about the user's hobbies and interests that the user has entered. The data collection unit can also collect information about the user's personality. Furthermore, the data collection unit can collect information about the user's lifestyle. For example, the data collection unit collects information about the user's daily activities and habits. As a result, the data collection unit can recommend more suitable partners by collecting information about the user's hobbies, interests, personality, and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information entered by the user into a generating AI, which can then analyze and collect the information.

[0038] The analysis unit can analyze the collected information and identify compatible partners. For example, it can analyze collected information on hobbies and interests to identify partners with similar interests. It can also analyze collected personality information to identify partners with a high degree of personality compatibility. Furthermore, it can analyze collected lifestyle information to identify partners with similar lifestyles. For example, it can analyze users' lifestyle data to identify partners with similar lifestyles. In this way, the analysis unit can identify compatible partners by analyzing the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI, which can analyze the information to identify compatible partners.

[0039] The recommendation system can recommend partners based on analysis results. For example, the recommendation system can recommend the most suitable partners to a user using compatibility evaluation criteria. It can also recommend partners using recommendation algorithms. Furthermore, the recommendation system can recommend partners based on a user's past interaction history. For example, it can recommend new partners based on information about partners the user has interacted with in the past. This allows the recommendation system to provide the user with the most suitable partners by recommending them based on analysis results. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input analysis results into a generating AI, which can then recommend partners.

[0040] The verification unit can perform age verification and identity verification. For example, the verification unit can verify the user's age using an identification document. The verification unit can also perform identity verification using digital authentication. Furthermore, the verification unit can perform identity verification using facial recognition technology. For example, the verification unit can analyze the user's facial image to verify their identity. In this way, the verification unit can provide a secure environment by performing age verification and identity verification. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input image data of an identification document into a generating AI, which can then perform age verification and identity verification.

[0041] The interaction department can organize virtual hobby classes and online events, and coordinate in-person meetups. For example, the interaction department can organize virtual hobby classes, providing users with opportunities to enjoy their hobbies online. It can also organize online events, providing users with opportunities to interact with other users. Furthermore, the interaction department can coordinate in-person meetups, providing users with opportunities to meet and interact in person. For example, the interaction department can coordinate the location of an in-person meetup based on the user's place of residence. In this way, the interaction department can provide diverse opportunities for interaction by organizing virtual hobby classes and online events, and coordinating in-person meetups. Some or all of the above processes in the interaction department may be performed using AI, for example, or not using AI. For example, the interaction department can input the user's place of residence information into a generating AI, which can then recommend the optimal meetup location.

[0042] The Health Department can provide health advice related to hobby activities and visualize activity levels and social participation. For example, the Health Department can provide health advice related to hobby activities. The Health Department can also monitor users' activity levels and understand their health status. Furthermore, the Health Department can visualize users' social participation and support health management. For example, the Health Department can record the number of events a user participates in and evaluate their social participation. In this way, the Health Department can provide health advice related to hobby activities and visualize activity levels and social participation, supporting users in leading a healthy life while having fun. Some or all of the above processes in the Health Department may be performed using AI, for example, or not using AI. For example, the Health Department can input user activity data into a generating AI, and the generating AI can provide health advice.

[0043] The support department provides a 24 / 7 AI chatbot that can answer questions about how to use the service and about hobbies. For example, the support department can use the AI ​​chatbot to respond to user questions 24 hours a day. The support department can also use the AI ​​chatbot to troubleshoot. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. In this way, by providing a 24 / 7 AI chatbot, the support department can answer users' questions about how to use the service and about hobbies at any time. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input a user's question into a generating AI, and the generating AI can generate an answer.

[0044] The data collection unit can analyze the user's past hobby activity history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the user's past event participation history. The data collection unit can also collect information about hobbies the user has shown interest in in the past. Furthermore, the data collection unit can prioritize using information sources that the user has frequently accessed in the past. For example, the data collection unit can analyze the user's past hobby activity history and select the optimal information collection method. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past hobby activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past hobby activity history data into a generating AI, which can then select the optimal information collection method.

[0045] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to areas of interest that the user is currently interested in. The data collection unit can also filter information appropriately according to the user's current lifestyle. Furthermore, the data collection unit can collect relevant information based on the user's current health status. For example, the data collection unit filters information based on the user's current lifestyle and areas of interest. This allows the data collection unit to collect more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI, which can then filter the information.

[0046] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. The data collection unit can also collect region-specific hobby activity information based on the user's geographical location. Furthermore, the data collection unit can collect relevant information by referring to the user's travel history. For example, the data collection unit can analyze the user's geographical location and prioritize the collection of highly relevant information. In this way, the data collection unit can provide more relevant information by considering the user's geographical location when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant information.

[0047] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant hobby activity information based on information the user has shared on social media. The data collection unit can also collect relevant information by referring to the activity of accounts the user follows. Furthermore, the data collection unit can analyze the user's interests on social media and collect relevant information. For example, the data collection unit can analyze the user's social media activity and collect highly relevant information. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI, and the generating AI can collect relevant information.

[0048] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, the analysis unit can determine the priority of the analysis based on its importance. For example, it can evaluate the importance of the information and perform the analysis at the optimal level of detail. This allows the analysis unit to perform a more detailed analysis on more important information by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0049] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a hobby-specific analysis algorithm to information about hobbies. It can also apply a psychological analysis algorithm to information about personality. Furthermore, it can apply a lifestyle analysis algorithm to information about lifestyle. For example, the analysis unit can classify the information categories and apply the most appropriate analysis algorithm. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the information category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI, which can then apply the most appropriate analysis algorithm.

[0050] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. Furthermore, the analysis unit can determine the priority of analysis according to when the information was collected. For example, the analysis unit may evaluate the timing of information collection and perform analysis with the optimal priority. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and perform the analysis in the optimal order. This allows the analysis unit to prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect not only the user's hobbies and interests, but also their past hobby activity history. For example, it can collect a history of events and workshops the user has participated in and use this to analyze their hobby trends. It can also collect a history of hobby-related items the user has purchased in the past. Furthermore, it can collect a history of hobby-related websites and videos the user has viewed in the past. By collecting a history of the user's past hobby activities, the data collection unit can recommend hobby partners with greater accuracy.

[0054] The recommendation system can recommend friends based on the user's hobbies and interests, as well as their health status. For example, it can recommend friends with similar health goals based on the user's exercise habits and health status. It can also recommend friends with similar eating habits and nutritional status based on the user's diet. Furthermore, it can recommend friends with similar sleep patterns based on the user's sleep habits. In this way, the recommendation system can promote healthier interactions by recommending friends that take the user's health status into consideration.

[0055] The interaction section can provide opportunities for interaction by considering not only the user's hobbies and interests, but also their social network. For example, the interaction section can introduce users to people with similar hobbies based on their network of friends and family. It can also introduce users to people in the same region or workplace based on their workplace or local community network. Furthermore, the interaction section can introduce users to online friends with similar hobbies based on their online social network. In this way, the interaction section can promote broader interaction by providing opportunities for interaction while considering the user's social network.

[0056] The support department can provide support considering not only the user's hobbies and interests, but also their technical skill level. For example, the support department can provide beginner-level support based on the user's digital literacy. If the user is at an intermediate level, they can provide more advanced support. Furthermore, if the user is an advanced user, they can provide expert support. This allows the support department to provide more effective support by considering the user's technical skill level.

[0057] The analysis unit can perform analyses that take into account not only the user's hobbies and interests, but also their learning style. For example, if the user is a visual learner, the analysis unit will prioritize visual data in its analysis. If the user is an auditory learner, it can prioritize audio data in its analysis. Furthermore, if the user is an experiential learner, it can prioritize actual experience data in its analysis. By considering the user's learning style, the analysis unit can provide more appropriate analysis results.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects user information. User information includes, for example, personal information, hobbies, interests, and health information. The data collection unit collects this information using surveys, sensors, and online data. For example, the data collection unit collects the history of pages the user has viewed online to identify their interests. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit can use statistical analysis to analyze users' hobbies and machine learning algorithms to analyze users' personalities. Step 3: The recommendation team recommends partners based on the analysis results obtained by the analysis team. Recommendations are made based on compatibility evaluation criteria and recommendation algorithms. For example, the recommendation team can recommend the most suitable partners to a user using compatibility evaluation criteria, and can also recommend new partners based on past interaction history. Step 4: The verification unit performs age verification and identity verification. Verification is carried out using identification documents, digital authentication, and facial recognition technology. For example, the verification unit verifies the user's age using an identification document and verifies their identity by analyzing their facial image. Step 5: The interaction department provides online and offline opportunities for interaction. These opportunities include virtual hobby classes, online events, and in-person meetups. For example, the interaction department could host virtual hobby classes, providing users with opportunities to enjoy their hobbies online. Step 6: The Health Department links recreational activities with health management. Health management includes providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department provides health advice related to recreational activities and monitors users' activity levels. Step 7: The support department provides a 24 / 7 AI chatbot. The AI ​​chatbot answers questions about how to use the service and about hobbies. For example, the support department uses the AI ​​chatbot to respond to user questions 24 / 7, providing troubleshooting and advice on hobbies.

[0060] (Example of form 2) The AI ​​Senior Hobby Partner Matching System according to an embodiment of the present invention is a system that helps seniors aged 60 and over find partners with common hobbies and interests and promotes interaction. This system analyzes the user's hobbies, interests, personality, and lifestyle, and recommends the most suitable hobby partners. For example, the user inputs information such as their hobbies, interests, personality, and lifestyle. Next, the AI ​​analyzes this information and recommends compatible partners. For example, it recommends people with the same hobbies or similar lifestyles. Furthermore, the AI ​​thoroughly verifies age and identity to provide a safe environment. It also integrates online and offline activities, providing diverse opportunities for interaction such as virtual hobby classes, online events, and arranging in-person meetups. In addition, it links hobby activities with health management to support a healthy lifestyle while enjoying oneself. For example, it provides health advice related to hobby activities and visualizes activity levels and social participation. A 24-hour AI chatbot also answers questions about how to use the service and about hobbies. Through this system, seniors can find new hobby partners and enjoy fulfilling days. Furthermore, by maintaining and expanding connections with society, it contributes to alleviating loneliness and maintaining cognitive function. For example, this system is extremely beneficial for those who want to spend their time after retirement meaningfully, those who want to try new hobbies or activities, and those who seek interaction with peers of the same generation. It is also suitable for those who can perform basic operations on smartphones and tablets, and those who are interested in maintaining their health. This system aims to achieve many goals, including creating a sense of purpose for seniors, extending healthy life expectancy, promoting intergenerational interaction, improving seniors' digital literacy, revitalizing local communities, utilizing the knowledge and experience of seniors, alleviating loneliness and isolation, and proposing new ways of living and working. For example, it supports new lifestyles for seniors, such as side jobs and social contribution activities through hobbies. Through this system, we aim to realize a society where seniors can actively participate and thrive. In this way, the AI ​​Senior Hobby Partner Matching System can help seniors find hobby partners and promote interaction.

[0061] The AI ​​senior hobby partner matching system according to this embodiment comprises a collection unit, an analysis unit, a recommendation unit, a verification unit, an interaction unit, a health unit, and a support unit. The collection unit collects user information. User information includes, but is not limited to, personal information, hobbies, interests, and health information. The collection unit collects user information using, for example, questionnaires. The collection unit can also collect user health information using sensors. Furthermore, the collection unit can collect user interests using online data. For example, the collection unit collects the history of pages the user has viewed online to identify interests. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, statistical analysis or machine learning algorithms, but is not limited to these. For example, the analysis unit analyzes the user's hobby trends using statistical analysis. The analysis unit can also analyze the user's personality using machine learning algorithms. Furthermore, the analysis unit can analyze the user's lifestyle using data mining techniques. For example, the analysis unit analyzes the user's lifestyle data to identify their lifestyle. The recommendation unit recommends partners based on the analysis results obtained by the analysis unit. Recommendations are made based on, for example, compatibility evaluation criteria or recommendation algorithms. For example, the recommendation unit recommends the best partners for a user using compatibility evaluation criteria. The recommendation unit can also recommend partners using recommendation algorithms. Furthermore, the recommendation unit can recommend partners based on a user's past interaction history. For example, the recommendation unit recommends new partners based on information about partners the user has interacted with in the past. The verification unit performs age verification and identity verification. Verification is performed using, for example, identification document verification or digital authentication. For example, the verification unit verifies the user's age using an identification document. The verification unit can also perform identity verification using digital authentication. Furthermore, the verification unit can perform identity verification using facial recognition technology. For example, the verification unit analyzes the user's facial image to verify their identity. The interaction unit provides online and offline interaction opportunities.Interaction opportunities include, but are not limited to, virtual hobby classes, online events, and in-person meetups. For example, the Interaction Department could host virtual hobby classes, providing users with opportunities to enjoy their hobbies online. It could also host online events, providing users with opportunities to interact with other users. Furthermore, the Interaction Department could arrange in-person meetups, providing users with opportunities to meet and interact directly. For example, the Interaction Department could coordinate the location of an in-person meetup based on the user's place of residence. The Health Department would link hobby activities with health management. Health management would include, but is not limited to, providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department could provide health advice related to hobby activities. It could also monitor users' activity levels and understand their health status. Furthermore, the Health Department could visualize users' social participation to support health management. For example, the Health Department could record the number of events a user attends and evaluate their social participation. The Support Department would provide a 24 / 7 AI chatbot. The AI ​​chatbot can, for example, answer questions about how to use the service or about hobbies, but is not limited to such examples. For instance, the support department can use the AI ​​chatbot to respond to user inquiries 24 hours a day. The support department can also use the AI ​​chatbot to troubleshoot problems. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. This enables the AI ​​senior hobby partner matching system according to this embodiment to efficiently collect, analyze, recommend, verify, interact with, manage health, and support users' information.

[0062] The data collection unit collects user information. This information includes, but is not limited to, personal information, hobbies, interests, and health information. For example, the unit collects user information using questionnaires. These questionnaires are provided through online forms or applications, allowing users to provide detailed answers about their hobbies, interests, and health status. The unit can also collect user health information using sensors. For example, wearable devices can be used to collect data such as heart rate, steps, and sleep patterns. Furthermore, the unit can collect user interests using online data. For example, it can collect a user's browsing history to identify their interests. This allows the unit to understand what hobbies and activities the user is interested in. The unit centrally manages this data and creates user profiles. This allows for efficient collection of user information and provision to the analytics and recommendation units. The unit implements encryption technology and access control to ensure data privacy and security. This prevents unauthorized access to users' personal information and provides a secure environment for using the service.

[0063] The analysis department analyzes the information collected by the data collection department. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis department can use statistical analysis to analyze users' hobby trends, thereby understanding what hobbies users have and what activities they are interested in. The analysis department can also use machine learning algorithms to analyze users' personalities, for example, by identifying personality traits based on user response data and behavioral data, providing foundational information for finding compatible partners. Furthermore, the analysis department can use data mining techniques to analyze users' lifestyles, for example, by analyzing users' lifestyle data to identify their lifestyles, thereby understanding when users are active and what their daily rhythms are. Based on these analysis results, the analysis department provides information to recommend the most suitable partners to users. To ensure data accuracy and reliability, the analysis department preprocesses and cleans the data to remove noise and outliers. The analysis department also visualizes the analysis results, providing them in a format easily understood by users and other departments. This allows the analysis department to effectively utilize the collected data and improve the overall system performance.

[0064] The recommendation team recommends partners based on the analysis results obtained by the analysis team. Recommendations are based on, but are not limited to, compatibility evaluation criteria or recommendation algorithms. For example, the recommendation team recommends the best partners for a user using compatibility evaluation criteria. Compatibility evaluation criteria include the degree of shared hobbies, personality compatibility, and lifestyle compatibility. The recommendation team can also recommend partners using recommendation algorithms. For example, collaborative filtering or content-based recommendation algorithms can be used to find the best partners for a user. Furthermore, the recommendation team can recommend partners based on a user's past interaction history. For example, the recommendation team recommends new partners based on information about partners the user has interacted with in the past. This provides users with the opportunity to reconnect with partners with whom they have built good relationships in the past. The recommendation team has implemented a feedback loop to improve the accuracy and reliability of recommendation results. It collects feedback from users and uses it to improve the recommendation algorithm. This allows the recommendation team to recommend more appropriate partners to users and improve their satisfaction.

[0065] The verification unit performs age and identity verification. Verification is performed using, for example, identification document verification or digital authentication. For example, the verification unit verifies the user's age using identification documents. Users upload identification documents such as driver's licenses or passports, and the verification unit verifies the information. The verification unit can also perform identity verification using digital authentication. For example, an authentication code is sent to the email address or phone number provided by the user during registration, and identity verification is performed by entering that code. Furthermore, the verification unit can also perform identity verification using facial recognition technology. For example, the verification unit analyzes the user's facial image to verify their identity. Facial recognition technology uses a highly accurate algorithm to identify the user's facial features and compare them with registered information. This allows the verification unit to reliably verify the user's age and identity and prevent fraudulent use. To ensure the transparency and reliability of the verification process, the verification unit explains the details of the verification procedure to the user and provides necessary support. This allows the verification unit to provide an environment in which users can use the service with peace of mind.

[0066] The Community Club provides online and offline opportunities for interaction. These opportunities include, but are not limited to, virtual hobby classes, online events, and in-person meetups. For example, the Community Club can host virtual hobby classes, providing users with the opportunity to enjoy their hobbies online. In virtual hobby classes, professional instructors provide lessons on hobbies, and users can participate from the comfort of their homes. The Community Club can also host online events, providing opportunities for users to interact with other users. Online events may include hobby-related discussions, workshops, and games, providing a space for users to interact in a fun way. Furthermore, the Community Club can arrange in-person meetups, providing opportunities for users to meet and interact in person. For example, the Community Club can coordinate the location of in-person meetups based on users' locations, creating an environment that is easy for users to participate in. In-person meetups feature hobby-related activities and social events, allowing users to build deeper relationships through direct interaction. Through these opportunities for interaction, the Community Club supports users in enjoying their hobbies, meeting new friends, and having fulfilling experiences.

[0067] The Health Department links recreational activities with health management. Health management includes, but is not limited to, providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department provides health advice related to recreational activities. By providing health advice tailored to the user's recreational activities, it supports users in enjoying their hobbies in a healthy way. The Health Department can also monitor users' activity levels and understand their health status. For example, it can use wearable devices to monitor the user's steps, heart rate, calories burned, etc., and evaluate their health status. Furthermore, the Health Department can visualize users' social participation and support health management. For example, the Health Department records the number of events a user participates in and evaluates their social participation. This allows the Health Department to understand how socially active a user is and provide support as needed. Through these health management functions, the Health Department supports users in enjoying their hobbies in a healthy way while also being socially active.

[0068] The support department provides a 24 / 7 AI chatbot. The AI ​​chatbot answers questions about service usage and hobbies, among other things, but is not limited to these examples. For instance, the support department uses the AI ​​chatbot to respond to user inquiries 24 / 7. The AI ​​chatbot uses natural language processing technology to understand user questions and provide appropriate answers. The support department can also use the AI ​​chatbot for troubleshooting. For example, if a user encounters a problem while using the service, the AI ​​chatbot can identify the cause of the problem and guide them to a solution. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. Based on the user's hobbies and interests, the AI ​​chatbot can suggest relevant information and activities. This allows the support department to provide support to users so they can effectively use the service and enjoy their hobbies. The support department collects user feedback to continuously improve the performance of the AI ​​chatbot and uses it for improvement. This allows the support department to provide better support to users and increase their satisfaction.

[0069] The data collection unit can collect information about the user's hobbies, interests, personality, and lifestyle. For example, the data collection unit collects information about the user's hobbies and interests that the user has entered. The data collection unit can also collect information about the user's personality. Furthermore, the data collection unit can collect information about the user's lifestyle. For example, the data collection unit collects information about the user's daily activities and habits. As a result, the data collection unit can recommend more suitable partners by collecting information about the user's hobbies, interests, personality, and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information entered by the user into a generating AI, which can then analyze and collect the information.

[0070] The analysis unit can analyze the collected information and identify compatible partners. For example, it can analyze collected information on hobbies and interests to identify partners with similar interests. It can also analyze collected personality information to identify partners with a high degree of personality compatibility. Furthermore, it can analyze collected lifestyle information to identify partners with similar lifestyles. For example, it can analyze users' lifestyle data to identify partners with similar lifestyles. In this way, the analysis unit can identify compatible partners by analyzing the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI, which can analyze the information to identify compatible partners.

[0071] The recommendation system can recommend partners based on analysis results. For example, the recommendation system can recommend the most suitable partners to a user using compatibility evaluation criteria. It can also recommend partners using recommendation algorithms. Furthermore, the recommendation system can recommend partners based on a user's past interaction history. For example, it can recommend new partners based on information about partners the user has interacted with in the past. This allows the recommendation system to provide the user with the most suitable partners by recommending them based on analysis results. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input analysis results into a generating AI, which can then recommend partners.

[0072] The verification unit can perform age verification and identity verification. For example, the verification unit can verify the user's age using an identification document. The verification unit can also perform identity verification using digital authentication. Furthermore, the verification unit can perform identity verification using facial recognition technology. For example, the verification unit can analyze the user's facial image to verify their identity. In this way, the verification unit can provide a secure environment by performing age verification and identity verification. Some or all of the above processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input image data of an identification document into a generating AI, which can then perform age verification and identity verification.

[0073] The interaction department can organize virtual hobby classes and online events, and coordinate in-person meetups. For example, the interaction department can organize virtual hobby classes, providing users with opportunities to enjoy their hobbies online. It can also organize online events, providing users with opportunities to interact with other users. Furthermore, the interaction department can coordinate in-person meetups, providing users with opportunities to meet and interact in person. For example, the interaction department can coordinate the location of an in-person meetup based on the user's place of residence. In this way, the interaction department can provide diverse opportunities for interaction by organizing virtual hobby classes and online events, and coordinating in-person meetups. Some or all of the above processes in the interaction department may be performed using AI, for example, or not using AI. For example, the interaction department can input the user's place of residence information into a generating AI, which can then recommend the optimal meetup location.

[0074] The Health Department can provide health advice related to hobby activities and visualize activity levels and social participation. For example, the Health Department can provide health advice related to hobby activities. The Health Department can also monitor users' activity levels and understand their health status. Furthermore, the Health Department can visualize users' social participation and support health management. For example, the Health Department can record the number of events a user participates in and evaluate their social participation. In this way, the Health Department can provide health advice related to hobby activities and visualize activity levels and social participation, supporting users in leading a healthy life while having fun. Some or all of the above processes in the Health Department may be performed using AI, for example, or not using AI. For example, the Health Department can input user activity data into a generating AI, and the generating AI can provide health advice.

[0075] The support department provides a 24 / 7 AI chatbot that can answer questions about how to use the service and about hobbies. For example, the support department can use the AI ​​chatbot to respond to user questions 24 hours a day. The support department can also use the AI ​​chatbot to troubleshoot. Furthermore, the support department can use the AI ​​chatbot to provide advice to users. For example, the support department can use the AI ​​chatbot to provide advice on hobbies. In this way, by providing a 24 / 7 AI chatbot, the support department can answer users' questions about how to use the service and about hobbies at any time. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input a user's question into a generating AI, and the generating AI can generate an answer.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit can collect information during times when the user is relaxed. It can also refrain from collecting information when the user is stressed. Furthermore, the data collection unit can collect information during times when the user is active. For example, the data collection unit can monitor the user's emotional state in real time and collect information at the optimal time. This allows the data collection unit to collect information at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0077] The data collection unit can analyze the user's past hobby activity history and select the optimal information collection method. For example, the data collection unit can prioritize collecting relevant information based on the user's past event participation history. The data collection unit can also collect information about hobbies the user has shown interest in in the past. Furthermore, the data collection unit can prioritize using information sources that the user has frequently accessed in the past. For example, the data collection unit can analyze the user's past hobby activity history and select the optimal information collection method. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past hobby activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past hobby activity history data into a generating AI, which can then select the optimal information collection method.

[0078] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to areas of interest that the user is currently interested in. The data collection unit can also filter information appropriately according to the user's current lifestyle. Furthermore, the data collection unit can collect relevant information based on the user's current health status. For example, the data collection unit filters information based on the user's current lifestyle and areas of interest. This allows the data collection unit to collect more relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current lifestyle data into a generating AI, which can then filter the information.

[0079] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting information that is of interest. If the user is calm, the data collection unit may also prioritize collecting detailed information. Furthermore, if the user is tired, the data collection unit may also prioritize collecting concise information. For example, the data collection unit can monitor the user's emotional state in real time and prioritize collecting the most relevant information. This allows the data collection unit to prioritize collecting more appropriate information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of information.

[0080] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, the data collection unit can prioritize the collection of nearby event information based on the user's current location. The data collection unit can also collect region-specific hobby activity information based on the user's geographical location. Furthermore, the data collection unit can collect relevant information by referring to the user's travel history. For example, the data collection unit can analyze the user's geographical location and prioritize the collection of highly relevant information. In this way, the data collection unit can provide more relevant information by considering the user's geographical location when collecting data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant hobby activity information based on information the user has shared on social media. The data collection unit can also collect relevant information by referring to the activity of accounts the user follows. Furthermore, the data collection unit can analyze the user's interests on social media and collect relevant information. For example, the data collection unit can analyze the user's social media activity and collect highly relevant information. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI, and the generating AI can collect relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can monitor the user's emotional state in real time and provide analysis results in the most appropriate presentation. This allows the analysis unit to provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of its analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a concise analysis on less important information. Furthermore, the analysis unit can determine the priority of the analysis based on its importance. For example, it can evaluate the importance of the information and perform the analysis at the optimal level of detail. This allows the analysis unit to perform a more detailed analysis on more important information by adjusting the level of detail based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input information importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a hobby-specific analysis algorithm to information about hobbies. It can also apply a psychological analysis algorithm to information about personality. Furthermore, it can apply a lifestyle analysis algorithm to information about lifestyle. For example, the analysis unit can classify the information categories and apply the most appropriate analysis algorithm. This allows the analysis unit to provide more appropriate analysis results by applying different analysis algorithms depending on the information category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI, which can then apply the most appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. For example, the analysis unit can monitor the user's emotional state in real time and provide an analysis result of the optimal length. This allows the analysis unit to provide an analysis result of a more appropriate length by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also analyze older information as needed. Furthermore, the analysis unit can determine the priority of analysis according to when the information was collected. For example, the analysis unit may evaluate the timing of information collection and perform analysis with the optimal priority. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the timing of information collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. For example, the analysis unit can evaluate the relevance of the information and perform the analysis in the optimal order. This allows the analysis unit to prioritize the analysis of more relevant information by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can collect not only the user's hobbies and interests, but also their past hobby activity history. For example, it can collect a history of events and workshops the user has participated in and use this to analyze their hobby trends. It can also collect a history of hobby-related items the user has purchased in the past. Furthermore, it can collect a history of hobby-related websites and videos the user has viewed in the past. By collecting a history of the user's past hobby activities, the data collection unit can recommend hobby partners with greater accuracy.

[0090] The analytics unit can estimate the user's emotions and adjust the analysis priorities based on those estimates. For example, if the user is excited, the analytics unit will prioritize analyzing information that interests them. If the user is relaxed, it can prioritize analyzing detailed information. Furthermore, if the user is stressed, it can prioritize analyzing concise information. In this way, the analytics unit can provide more relevant information by adjusting the analysis priorities based on the user's emotions.

[0091] The recommendation system can recommend friends based on the user's hobbies and interests, as well as their health status. For example, it can recommend friends with similar health goals based on the user's exercise habits and health status. It can also recommend friends with similar eating habits and nutritional status based on the user's diet. Furthermore, it can recommend friends with similar sleep patterns based on the user's sleep habits. In this way, the recommendation system can promote healthier interactions by recommending friends that take the user's health status into consideration.

[0092] The verification unit can estimate the user's emotions and adjust the verification method based on those emotions. For example, if the user is relaxed, the verification unit can perform a detailed verification procedure. If the user is in a hurry, it can perform a concise verification procedure. Furthermore, if the user is stressed, it can temporarily postpone the verification procedure. In this way, the verification unit can provide a more appropriate verification procedure by adjusting the verification method based on the user's emotions.

[0093] The interaction section can provide opportunities for interaction by considering not only the user's hobbies and interests, but also their social network. For example, the interaction section can introduce users to people with similar hobbies based on their network of friends and family. It can also introduce users to people in the same region or workplace based on their workplace or local community network. Furthermore, the interaction section can introduce users to online friends with similar hobbies based on their online social network. In this way, the interaction section can promote broader interaction by providing opportunities for interaction while considering the user's social network.

[0094] The health department can estimate the user's emotions and adjust the content of health advice based on those emotions. For example, if the user is relaxed, the health department can provide detailed health advice. If the user is in a hurry, it can provide concise health advice. Furthermore, if the user is stressed, it can provide advice on relaxation methods and stress relief techniques. In this way, the health department can provide more appropriate health support by adjusting the content of health advice based on the user's emotions.

[0095] The support department can provide support considering not only the user's hobbies and interests, but also their technical skill level. For example, the support department can provide beginner-level support based on the user's digital literacy. If the user is at an intermediate level, they can provide more advanced support. Furthermore, if the user is an advanced user, they can provide expert support. This allows the support department to provide more effective support by considering the user's technical skill level.

[0096] The data collection unit can estimate the user's emotions and adjust its information collection methods based on those estimates. For example, if the user is relaxed, the unit can collect information using a detailed questionnaire. If the user is in a hurry, it can use a concise questionnaire. Furthermore, if the user is stressed, it can temporarily refrain from collecting information. In this way, the data collection unit can collect more relevant information by adjusting its information collection methods based on the user's emotions.

[0097] The analysis unit can perform analyses that take into account not only the user's hobbies and interests, but also their learning style. For example, if the user is a visual learner, the analysis unit will prioritize visual data in its analysis. If the user is an auditory learner, it can prioritize audio data in its analysis. Furthermore, if the user is an experiential learner, it can prioritize actual experience data in its analysis. By considering the user's learning style, the analysis unit can provide more appropriate analysis results.

[0098] The recommendation system can estimate the user's emotions and adjust the timing of recommendations based on those emotions. For example, if the user is relaxed, the recommendation system will provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations. Furthermore, if the user is stressed, it can temporarily refrain from providing recommendations. In this way, the recommendation system can provide recommendations at more appropriate times by adjusting the timing based on the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects user information. User information includes, for example, personal information, hobbies, interests, and health information. The data collection unit collects this information using surveys, sensors, and online data. For example, the data collection unit collects the history of pages the user has viewed online to identify their interests. Step 2: The analysis unit analyzes the information collected by the data collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and data mining techniques. For example, the analysis unit can use statistical analysis to analyze users' hobbies and machine learning algorithms to analyze users' personalities. Step 3: The recommendation team recommends partners based on the analysis results obtained by the analysis team. Recommendations are made based on compatibility evaluation criteria and recommendation algorithms. For example, the recommendation team can recommend the most suitable partners to a user using compatibility evaluation criteria, and can also recommend new partners based on past interaction history. Step 4: The verification unit performs age verification and identity verification. Verification is carried out using identification documents, digital authentication, and facial recognition technology. For example, the verification unit verifies the user's age using an identification document and verifies their identity by analyzing their facial image. Step 5: The interaction department provides online and offline opportunities for interaction. These opportunities include virtual hobby classes, online events, and in-person meetups. For example, the interaction department could host virtual hobby classes, providing users with opportunities to enjoy their hobbies online. Step 6: The Health Department links recreational activities with health management. Health management includes providing health advice, monitoring activity levels, and visualizing social participation. For example, the Health Department provides health advice related to recreational activities and monitors users' activity levels. Step 7: The support department provides a 24 / 7 AI chatbot. The AI ​​chatbot answers questions about how to use the service and about hobbies. For example, the support department uses the AI ​​chatbot to respond to user questions 24 / 7, providing troubleshooting and advice on hobbies.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, verification unit, interaction unit, health unit, and support unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and survey functions of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable partners based on the analysis results. The verification unit performs age verification and identity verification using the camera and digital authentication functions of the smart device 14. The interaction unit uses the communication functions of the smart device 14 to coordinate online events and face-to-face meetups. The health unit monitors activity levels using the sensors of the smart device 14 and provides health advice. The support unit provides 24-hour support using the AI ​​chatbot function of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, verification unit, interaction unit, health unit, and support unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and survey functions of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable partners based on the analysis results. The verification unit performs age verification and identity verification using the camera and digital authentication functions of the smart glasses 214. The interaction unit uses the communication functions of the smart glasses 214 to coordinate online events and face-to-face meetups. The health unit monitors activity levels using the sensors of the smart glasses 214 and provides health advice. The support unit provides 24-hour support using the AI ​​chatbot function of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, verification unit, interaction unit, health unit, and support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user information using the sensors and survey functions of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends the most suitable partners based on the analysis results. The verification unit performs age verification and identity verification using the camera and digital authentication functions of the headset terminal 314. The interaction unit uses the communication functions of the headset terminal 314 to coordinate online events and face-to-face meetups. The health unit monitors activity levels using the sensors of the headset terminal 314 and provides health advice. The support unit provides 24-hour support using the AI ​​chatbot function of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, verification unit, interaction unit, health unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user information using the robot 414's sensors and questionnaire function. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and recommends the most suitable companions based on the analysis results. The verification unit performs age verification and identity verification using, for example, the robot 414's camera and digital authentication function. The interaction unit uses the robot 414's communication function to coordinate online events and face-to-face meetups. The health unit monitors activity levels using the robot 414's sensors and provides health advice. The support unit provides 24-hour support using the robot 414's AI chatbot function. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the recommendation department recommends colleagues, A verification unit that performs age verification and identity verification, The Exchange Department provides opportunities for online and offline interaction, The Health Department coordinates hobby activities and health management, It includes a support department that provides a 24-hour AI chatbot. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the user's hobbies, interests, personality, and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected information is analyzed to identify compatible partners. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, Recommend colleagues based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is Age verification and identity verification will be performed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned AC unit is We organize virtual hobby classes and online events, and coordinate in-person meetups. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned health department, Provides health advice related to hobby activities and visualizes activity levels and social participation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit is We provide a 24 / 7 AI chatbot to answer questions about how to use the service and your hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past hobby activity history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the recommendation department recommends colleagues, A verification unit that performs age verification and identity verification, The Exchange Department provides opportunities for online and offline interaction, The Health Department coordinates hobby activities and health management, It includes a support department that provides a 24-hour AI chatbot. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the user's hobbies, interests, personality, and lifestyle. The system according to feature 1.

3. The aforementioned analysis unit is The collected information is analyzed to identify compatible partners. The system according to feature 1.

4. The aforementioned recommendation department, Recommend colleagues based on the analysis results. The system according to feature 1.

5. The aforementioned verification unit is Age verification and identity verification will be performed. The system according to feature 1.

6. The aforementioned AC unit is We organize virtual hobby classes and online events, and coordinate in-person meetups. The system according to feature 1.

7. The aforementioned health department, Provides health advice related to hobby activities and visualizes activity levels and social participation. The system according to feature 1.

8. The aforementioned support unit is We provide a 24-hour AI chatbot that answers questions about how to use the service and about hobbies. The system according to feature 1.

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