system

Abstract

The system according to this embodiment aims to prevent social isolation among the elderly and people who feel lonely. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a matching unit. The collection unit collects information about the user's interests and hobbies. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate topics and activities. The promotion unit facilitates connections between users based on the topics and activities suggested by the suggestion unit. The matching unit matches users with local communities and volunteer activities.

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 no sufficient means to effectively prevent the social isolation of the elderly and people who feel lonely, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent the social isolation of the elderly and people who feel lonely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a matching unit. The collection unit collects information about the user's interests and hobbies. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate topics and activities. The promotion unit facilitates connections between users based on the topics and activities suggested by the suggestion unit. The matching unit matches users with local communities and volunteer activities. [Effects of the Invention]

[0007] The system according to this embodiment can prevent social isolation among the elderly and people who feel lonely. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The communication support system according to an embodiment of the present invention is a system that utilizes generative AI to prevent social isolation among the elderly and people who feel lonely. This system proposes appropriate topics and activities based on the user's interests and hobbies, and promotes connections between users. It also provides a matching function with local communities and volunteer activities. For example, a user registers with the application and inputs information about their interests and hobbies. For example, if their hobby is gardening, they input that information into the generative AI. The generative AI analyzes the user's interests and hobbies based on the input information and proposes appropriate topics and activities. For example, it can propose the latest information and events related to gardening. Next, it provides a function to promote connections between users. Based on the user's interests and hobbies, the generative AI recommends other users who have common topics. For example, it matches users who enjoy gardening, promoting online interaction. This allows users to make new friends and strengthen their social connections. Furthermore, it also has a matching function with local communities and volunteer activities. The generative AI analyzes local event information and volunteer activities and proposes community activities suitable for the user. For example, it proposes events that the user can participate in, such as gardening workshops or local cleanup activities. This allows users to deepen their connections with their local community and reduce feelings of isolation. The communication support system provides an environment where the elderly and those experiencing loneliness can build rich relationships without becoming socially isolated. It promotes communication based on individual interests and hobbies, strengthening psychological well-being and social bonds. Ultimately, the goal is to create a society where no one feels isolated and where people support one another. The communication support system can prevent social isolation by suggesting appropriate topics and activities based on the user's interests and hobbies.

[0029] The communication support system according to the embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a matching unit. The collection unit collects information about the user's interests and hobbies. For example, the collection unit collects information about the user's interests and hobbies when the user registers for the application. For example, if the user selects gardening as a hobby, the collection unit can collect that information. The collection unit can also collect information about other interests and hobbies, such as the user's musical preferences or sports hobbies. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate topics and activities. For example, the suggestion unit analyzes the user's interests and hobbies using generative AI. For example, the suggestion unit can suggest the latest information and events related to gardening. The suggestion unit can also suggest online discussion topics and local events based on the user's interests and hobbies. The promotion unit promotes connections between users based on the topics and activities suggested by the suggestion unit. For example, the promotion unit recommends other users who share common interests. For example, the promotion unit matches users who enjoy gardening and promotes online interaction. Furthermore, the promotion unit can also provide online chat and discussion forums for users to connect with each other. The matching unit matches users with local communities and volunteer activities. The matching unit analyzes local event information and volunteer activities, for example, using generative AI. For instance, the matching unit can suggest events that users can participate in, such as gardening workshops or local cleanup activities. The matching unit can also suggest community activities that are suitable for the user. As a result, the communication support system according to this embodiment can suggest appropriate topics and activities based on the user's interests and hobbies, thereby preventing social isolation.

[0030] The data collection unit collects information about users' interests and hobbies. For example, it collects information about users' interests and hobbies when they register for an application. Specifically, when a user registers for an application for the first time, it collects information about the user's interests and hobbies through detailed questionnaires and question-based input forms. For example, if a user selects gardening as a hobby, that information can be collected. The data collection unit can also collect information about other interests and hobbies, such as the user's musical preferences or sports hobbies. Furthermore, the data collection unit can collect data about users' interests and hobbies from their online activities, social media posts, and browsing history. This allows the data collection unit to collect information about users' interests and hobbies from multiple perspectives and build a more accurate database. The collected data is stored on a secure server and encrypted to protect privacy. The data collection unit can periodically send notifications to users prompting them to update their interests and hobbies, thus maintaining up-to-date information. This allows the data collection unit to continuously update information about users' interests and hobbies, improving the accuracy and reliability of the entire system.

[0031] The suggestion department analyzes the information collected by the data collection department and proposes appropriate topics and activities. For example, the suggestion department uses generative AI to analyze the user's interests and hobbies. Specifically, the generative AI uses natural language processing technology to analyze the user's input data and extract keywords and topics related to their interests and hobbies. For example, the suggestion department can propose the latest information and events related to gardening. The generative AI collects the latest gardening information from news articles, blogs, and social media posts on the internet and provides it to the user. The suggestion department can also propose online discussion topics and local events based on the user's interests and hobbies. For example, a user interested in gardening can be provided with information on online gardening forums and local gardening clubs. Furthermore, the suggestion department can personalize its suggestions based on the user's past activity history and feedback, making them more appropriate. This allows the suggestion department to provide highly accurate suggestions based on the user's interests and hobbies, improving user satisfaction.

[0032] The Facilitation Department promotes connections between users based on topics and activities proposed by the Proposal Department. For example, the Facilitation Department recommends other users with common interests. Specifically, the Facilitation Department uses algorithms that automatically search for and recommend other users with common interests based on the user's interests and hobbies. For example, it matches users who enjoy gardening together to facilitate online interaction. The Facilitation Department can also provide online chat and discussion forums for users to connect with each other. This allows users to easily interact with other users who share common interests. Furthermore, the Facilitation Department can regularly hold events and activities to promote user interaction. For example, it can host online gardening workshops and discussion sessions to deepen interactions between users. The Facilitation Department can also collect user feedback and make improvements to enhance the quality of interactions. This allows the Facilitation Department to strengthen connections between users and improve the quality of communication.

[0033] The matching department connects users with local communities and volunteer activities. For example, it uses generative AI to analyze local event information and volunteer opportunities. Specifically, the generative AI collects information from local event calendars and volunteer recruitment websites, and suggests appropriate events and activities based on the user's interests and hobbies. For instance, the matching department can suggest events the user can participate in, such as gardening workshops or local cleanup activities. The generative AI selects more appropriate events based on the user's past participation history and feedback. The matching department can also suggest community activities suitable for the user. For example, it provides information on local gardening clubs and volunteer groups, creating an environment that makes it easy for users to participate. Furthermore, the matching department can monitor the user's participation status and provide reminders and follow-ups as needed. This allows the matching department to support users in actively participating in local communities and volunteer activities, preventing social isolation.

[0034] The data collection unit collects information about the user's interests and hobbies when the user registers for the application. For example, the data collection unit requests the user to input information about their hobbies and interests when registering for the application. For example, the data collection unit collects information when the user selects hobbies such as gardening, music, or sports. The data collection unit can also collect information if the user prefers a particular genre of books. In this way, the data collection unit can efficiently collect information about the user's interests and hobbies. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the information entered by the user into a generative AI, which can then analyze and collect that information.

[0035] The suggestion unit analyzes the user's interests and hobbies based on the collected information and proposes appropriate topics and activities. The suggestion unit analyzes the collected information, for example, using generative AI. For example, if the user is interested in gardening, the suggestion unit will propose the latest information and events related to gardening. Also, if the user is interested in music, the suggestion unit can propose discussion topics and concert information related to music. Furthermore, the suggestion unit can propose online discussion topics and local events based on the user's interests and hobbies. In this way, the suggestion unit can propose appropriate topics and activities based on the user's interests and hobbies. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the collected information into a generative AI, which can then analyze that information and propose appropriate topics and activities.

[0036] The promotion unit recommends other users with common interests based on proposed topics and activities, thereby facilitating online interaction. For example, the promotion unit uses generative AI to recommend other users with common interests. For instance, the promotion unit matches users who share a hobby like gardening, facilitating online interaction. The promotion unit can also provide online chat and discussion forums for users to connect with each other. Furthermore, the promotion unit can propose events for users to interact with other users who share their interests. In this way, the promotion unit can promote connections between users and prevent social isolation. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input other users with common interests into a generative AI based on proposed topics and activities, and the generative AI can analyze that information and make recommendations.

[0037] The matching unit analyzes local event information and volunteer activities and proposes community activities suitable for the user. For example, the matching unit uses generative AI to analyze local event information and volunteer activities. For example, the matching unit proposes events that the user can participate in, such as gardening workshops or local cleanup activities. The matching unit can also propose community activities suitable for the user. Furthermore, the matching unit can propose events and activities that help the user deepen their connection with the local community. In this way, the matching unit can help the user deepen their connection with the local community and reduce feelings of isolation. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input local event information and volunteer activities into the generative AI, which can then analyze that information and propose community activities suitable for the user.

[0038] The data collection unit analyzes the user's past activity history and selects the optimal information collection method. For example, the data collection unit uses generative AI to analyze the user's past activity history. For example, the data collection unit prioritizes collecting information sources that the user has frequently used in the past. The data collection unit can also analyze the user's past activity patterns and collect information at the optimal timing. Furthermore, the data collection unit can collect relevant information based on topics that the user has shown interest in in the past. This allows the data collection unit to select the optimal information collection method based on the user's past activity history. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's past activity history data into a generative AI, which can then analyze the data and select the optimal information collection method.

[0039] The data collection unit filters the information based on the user's current living situation and areas of interest. For example, the data collection unit uses generative AI to analyze the user's current living situation and areas of interest. For example, the data collection unit prioritizes collecting information related to projects the user is currently working on. The data collection unit can also filter relevant information based on the user's current living situation (e.g., work, family). Furthermore, the data collection unit can eliminate unnecessary information and collect only the necessary information based on the user's areas of interest. This allows the data collection unit to collect only the necessary information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input data on the user's current living situation and areas of interest into the generative AI, which can then analyze the data and filter the information.

[0040] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, the data collection unit analyzes the user's geographical location using a generative AI. For instance, it prioritizes collecting nearby event information based on the user's current location. It can also prioritize collecting local news and topics based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist information and restaurant information for their travel destination. This allows the data collection unit to prioritize collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or without one. For example, the data collection unit can input the user's geographical location into a generative AI, which can then analyze the information and prioritize collecting highly relevant information.

[0041] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit uses generative AI to analyze the user's social media activity. For instance, the data collection unit prioritizes collecting information on accounts the user follows on social media. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. Furthermore, the data collection unit can collect information on groups and communities the user participates in on social media. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI, or without it. For example, the data collection unit can input the user's social media activity data into a generative AI, which then analyzes the data to collect relevant information.

[0042] The proposal department adjusts the level of detail of a proposal based on the importance of the topic or activity. For example, the proposal department uses generative AI to analyze the importance of topics and activities. For example, the proposal department provides detailed information on important topics and activities. It can also provide concise information on general topics and activities. Furthermore, the proposal department can adjust the level of detail of a proposal according to the user's level of interest. This allows the proposal department to adjust the level of detail of a proposal based on the importance of the topic or activity. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input topic and activity importance data into the generative AI, which can then analyze the data and adjust the level of detail of the proposal.

[0043] The suggestion unit applies different suggestion algorithms depending on the category of topic or activity when making suggestions. For example, the suggestion unit uses generative AI to analyze the category of topic or activity. For example, for suggestions related to hobbies, the suggestion unit applies an algorithm based on the user's past activity history. The suggestion unit can also apply an algorithm based on real-time event information for suggestions related to events. Furthermore, for suggestions related to volunteer activities, the suggestion unit can apply an algorithm based on local volunteer information. This allows the suggestion unit to apply different suggestion algorithms depending on the category of topic or activity. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input topic or activity category data into a generative AI, which can then analyze that data and apply different suggestion algorithms.

[0044] The proposal department determines the priority of proposals based on the submission timing of topics and activities. For example, the proposal department uses generative AI to analyze the submission timing of topics and activities. For instance, the proposal department prioritizes proposals for recent events and activities. It can also postpone proposals for long-term activities and projects. Furthermore, the proposal department can adjust the priority of proposals based on the user's schedule. This allows the proposal department to determine the priority of proposals based on the submission timing of topics and activities. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input topic and activity submission timing data into a generative AI, which can then analyze that data to determine the priority of proposals.

[0045] The suggestion unit adjusts the order of suggestions based on the relevance of topics and activities. The suggestion unit analyzes the relevance of topics and activities, for example, using generative AI. For example, the suggestion unit suggests the topics and activities most relevant to the user's interests first. The suggestion unit can also postpone less relevant topics and activities. Furthermore, the suggestion unit can adjust the order of suggestions based on the user's past preferences. This allows the suggestion unit to adjust the order of suggestions based on the relevance of topics and activities. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input topic and activity relevance data into a generative AI, which can then analyze the data and adjust the order of suggestions.

[0046] The promotion unit analyzes the user's past interaction history to select the optimal promotion method when promoting interaction. For example, the promotion unit uses generative AI to analyze the user's past interaction history. For example, the promotion unit prioritizes suggesting interaction methods that the user has been successful with in the past. The promotion unit can also promote interaction at the optimal timing based on the user's past interaction history. Furthermore, the promotion unit can suggest relevant methods based on interaction methods that the user has shown interest in in the past. In this way, the promotion unit can select the optimal interaction promotion method based on the user's past interaction history. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input the user's past interaction history data into a generative AI, which can then analyze the data to select the optimal interaction promotion method.

[0047] The facilitator customizes the means of interaction based on the user's current lifestyle when facilitating interaction. For example, the facilitator analyzes the user's current lifestyle using generative AI. For instance, if the user is busy, the facilitator suggests a short interaction method. If the user has free time, the facilitator can also suggest a longer interaction method. Furthermore, depending on the user's lifestyle, the facilitator can suggest online or offline interaction methods. This allows the facilitator to provide the optimal means of interaction based on the user's current lifestyle. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's current lifestyle data into the generative AI, which can then analyze the data to customize the means of interaction.

[0048] The promotion unit selects the optimal interaction method when promoting interaction, taking into account the user's geographical location information. The promotion unit analyzes the user's geographical location information, for example, using generative AI. For example, the promotion unit suggests nearby interaction events based on the user's current location. The promotion unit can also suggest local interaction methods based on the user's geographical location information. Furthermore, if the user is traveling, the promotion unit can suggest interaction methods at their travel destination. In this way, the promotion unit can provide the optimal interaction method based on the user's geographical location information. Some or all of the above processing in the promotion unit may be performed using generative AI, or it may be performed without generative AI. For example, the promotion unit can input the user's geographical location information data into a generative AI, which can then analyze the data to select the optimal interaction method.

[0049] The promotion unit analyzes the user's social media activity and proposes means of interaction when promoting interaction. For example, the promotion unit uses generative AI to analyze the user's social media activity. For example, the promotion unit proposes interaction events of accounts that the user follows on social media. The promotion unit can also analyze the content of the user's social media posts and propose relevant interaction methods. Furthermore, the promotion unit can propose ways of interacting with groups and communities that the user participates in on social media. In this way, the promotion unit can provide the optimal means of interaction based on the user's social media activity. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose means of interaction.

[0050] The matching unit analyzes the user's past participation history to select the optimal matching method during the matching process. For example, the matching unit may use a generative AI to analyze the user's past participation history. For instance, the matching unit may suggest relevant events based on the user's past event participation history. The matching unit can also select the optimal matching method based on the user's past participation history. Furthermore, the matching unit may suggest relevant matching methods based on events and activities the user has shown interest in in the past. This allows the matching unit to select the optimal matching method based on the user's past participation history. Some or all of the above-described processes in the matching unit may be performed using a generative AI, or they may not. For example, the matching unit may input the user's past participation history data into a generative AI, which then analyzes the data to select the optimal matching method.

[0051] The matching unit customizes the matching method based on the user's current lifestyle during the matching process. For example, the matching unit analyzes the user's current lifestyle using a generative AI. For instance, if the user is busy, the matching unit suggests a short-duration matching method. Conversely, if the user has free time, the matching unit can suggest a longer matching method. Furthermore, the matching unit can suggest online or offline matching methods depending on the user's lifestyle. This allows the matching unit to provide the optimal matching method based on the user's current lifestyle. Some or all of the above processing in the matching unit may be performed using a generative AI, or without one. For example, the matching unit can input the user's current lifestyle data into a generative AI, which then analyzes the data to customize the matching method.

[0052] The matching unit selects the optimal matching method when matching users, taking into account the user's geographical location information. The matching unit analyzes the user's geographical location information, for example, using a generative AI. For example, the matching unit suggests nearby events and activities based on the user's current location. The matching unit can also suggest local events and activities based on the user's geographical location information. Furthermore, if the user is traveling, the matching unit can suggest events and activities at their travel destination. This allows the matching unit to provide the optimal matching method based on the user's geographical location information. Some or all of the above processing in the matching unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the matching unit can input the user's geographical location data into a generative AI, which can then analyze the data to select the optimal matching method.

[0053] The matching unit analyzes the user's social media activity during the matching process and proposes matching methods. For example, the matching unit uses generative AI to analyze the user's social media activity. For example, the matching unit proposes events and activities of accounts that the user follows on social media. The matching unit can also analyze the content of the user's social media posts and propose relevant events and activities. Furthermore, the matching unit can propose events and activities of groups and communities that the user participates in on social media. This allows the matching unit to provide the optimal matching method based on the user's social media activity. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose matching methods.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The communication support system can also include an educational support section to further enhance users' motivation to learn. This educational support section suggests learning content based on the user's interests and hobbies. For example, if a user is interested in gardening, it can suggest online courses or workshops related to gardening. Furthermore, the educational support section can monitor the user's learning progress and provide appropriate feedback. It can also support users in maintaining their motivation to achieve their learning goals. In this way, the communication support system can increase users' motivation to learn and promote their personal growth.

[0056] The communication support system can also include a lifestyle rhythm management unit that monitors the user's daily rhythm and makes suggestions at the appropriate time. This unit monitors the user's sleep patterns and activity levels and analyzes them using AI. For example, it can determine whether the user is a morning person or a night owl and make suggestions accordingly. It can also suggest relaxing activities if the user is tired. Furthermore, it can suggest energetic activities that match the user's most active times. This allows the lifestyle rhythm management unit to provide more effective support by making suggestions tailored to the user's daily rhythm.

[0057] The communication support system can also include a travel suggestion unit that proposes personalized travel plans based on the user's hobbies and interests. The travel suggestion unit proposes the most suitable travel plan to the user based on information collected by the data collection unit. For example, if the user is interested in gardening, the travel suggestion unit can propose a travel plan that includes gardening-related tourist destinations and events. Furthermore, the travel suggestion unit can customize the optimal travel plan according to the user's budget and schedule. In addition, the travel suggestion unit can analyze the user's past travel history and propose relevant travel plans. This allows the communication support system to provide personalized travel plans based on the user's hobbies and interests.

[0058] The communication support system can also include a nutrition management unit that monitors the user's diet and nutritional status to support a healthy lifestyle. The nutrition management unit collects the user's dietary data and analyzes it using AI. For example, the nutrition management unit can record the user's meals and evaluate their nutritional balance. It can also propose appropriate meal plans based on the user's health status and goals. Furthermore, the nutrition management unit can provide advice and recipes to help the user maintain a healthy diet. In this way, the communication support system can comprehensively support the user's health.

[0059] The communication support system can also include a shopping suggestion unit that provides personalized shopping recommendations based on the user's hobbies and interests. The shopping suggestion unit proposes the most suitable products and services to the user based on information collected by the data collection unit. For example, if the user is interested in gardening, the shopping suggestion unit can suggest gardening-related products and services. Furthermore, the shopping suggestion unit can customize the optimal shopping plan according to the user's budget and preferences. In addition, the shopping suggestion unit can analyze the user's past purchase history and suggest related products and services. This allows the communication support system to provide personalized shopping recommendations based on the user's hobbies and interests.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects information about the user's interests and hobbies. For example, when a user registers for the application, it collects information such as gardening, musical preferences, and sports hobbies. Step 2: The proposal department analyzes the information collected by the collection department and proposes appropriate topics and activities. For example, it uses generative AI to suggest the latest information and events related to gardening, online discussion topics, and local events. Step 3: The Facilitation Department promotes connections between users based on the topics and activities proposed by the Proposal Department. For example, it recommends other users with common interests and provides chat and discussion forums to facilitate online interaction. Step 4: The matching department matches users with local communities and volunteer activities. For example, it uses generative AI to analyze local event information and volunteer activities, and suggests events that users can participate in, such as gardening workshops or community cleanup activities.

[0062] (Example of form 2) The communication support system according to an embodiment of the present invention is a system that utilizes generative AI to prevent social isolation among the elderly and people who feel lonely. This system proposes appropriate topics and activities based on the user's interests and hobbies, and promotes connections between users. It also provides a matching function with local communities and volunteer activities. For example, a user registers with the application and inputs information about their interests and hobbies. For example, if their hobby is gardening, they input that information into the generative AI. The generative AI analyzes the user's interests and hobbies based on the input information and proposes appropriate topics and activities. For example, it can propose the latest information and events related to gardening. Next, it provides a function to promote connections between users. Based on the user's interests and hobbies, the generative AI recommends other users who have common topics. For example, it matches users who enjoy gardening, promoting online interaction. This allows users to make new friends and strengthen their social connections. Furthermore, it also has a matching function with local communities and volunteer activities. The generative AI analyzes local event information and volunteer activities and proposes community activities suitable for the user. For example, it proposes events that the user can participate in, such as gardening workshops or local cleanup activities. This allows users to deepen their connections with their local community and reduce feelings of isolation. The communication support system provides an environment where the elderly and those experiencing loneliness can build rich relationships without becoming socially isolated. It promotes communication based on individual interests and hobbies, strengthening psychological well-being and social bonds. Ultimately, the goal is to create a society where no one feels isolated and where people support one another. The communication support system can prevent social isolation by suggesting appropriate topics and activities based on the user's interests and hobbies.

[0063] The communication support system according to the embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a matching unit. The collection unit collects information about the user's interests and hobbies. For example, the collection unit collects information about the user's interests and hobbies when the user registers for the application. For example, if the user selects gardening as a hobby, the collection unit can collect that information. The collection unit can also collect information about other interests and hobbies, such as the user's musical preferences or sports hobbies. The suggestion unit analyzes the information collected by the collection unit and suggests appropriate topics and activities. For example, the suggestion unit analyzes the user's interests and hobbies using generative AI. For example, the suggestion unit can suggest the latest information and events related to gardening. The suggestion unit can also suggest online discussion topics and local events based on the user's interests and hobbies. The promotion unit promotes connections between users based on the topics and activities suggested by the suggestion unit. For example, the promotion unit recommends other users who share common interests. For example, the promotion unit matches users who enjoy gardening and promotes online interaction. Furthermore, the promotion unit can also provide online chat and discussion forums for users to connect with each other. The matching unit matches users with local communities and volunteer activities. The matching unit analyzes local event information and volunteer activities, for example, using generative AI. For instance, the matching unit can suggest events that users can participate in, such as gardening workshops or local cleanup activities. The matching unit can also suggest community activities that are suitable for the user. As a result, the communication support system according to this embodiment can suggest appropriate topics and activities based on the user's interests and hobbies, thereby preventing social isolation.

[0064] The data collection unit collects information about users' interests and hobbies. For example, it collects information about users' interests and hobbies when they register for an application. Specifically, when a user registers for an application for the first time, it collects information about the user's interests and hobbies through detailed questionnaires and question-based input forms. For example, if a user selects gardening as a hobby, that information can be collected. The data collection unit can also collect information about other interests and hobbies, such as the user's musical preferences or sports hobbies. Furthermore, the data collection unit can collect data about users' interests and hobbies from their online activities, social media posts, and browsing history. This allows the data collection unit to collect information about users' interests and hobbies from multiple perspectives and build a more accurate database. The collected data is stored on a secure server and encrypted to protect privacy. The data collection unit can periodically send notifications to users prompting them to update their interests and hobbies, thus maintaining up-to-date information. This allows the data collection unit to continuously update information about users' interests and hobbies, improving the accuracy and reliability of the entire system.

[0065] The suggestion department analyzes the information collected by the data collection department and proposes appropriate topics and activities. For example, the suggestion department uses generative AI to analyze the user's interests and hobbies. Specifically, the generative AI uses natural language processing technology to analyze the user's input data and extract keywords and topics related to their interests and hobbies. For example, the suggestion department can propose the latest information and events related to gardening. The generative AI collects the latest gardening information from news articles, blogs, and social media posts on the internet and provides it to the user. The suggestion department can also propose online discussion topics and local events based on the user's interests and hobbies. For example, a user interested in gardening can be provided with information on online gardening forums and local gardening clubs. Furthermore, the suggestion department can personalize its suggestions based on the user's past activity history and feedback, making them more appropriate. This allows the suggestion department to provide highly accurate suggestions based on the user's interests and hobbies, improving user satisfaction.

[0066] The Facilitation Department promotes connections between users based on topics and activities proposed by the Proposal Department. For example, the Facilitation Department recommends other users with common interests. Specifically, the Facilitation Department uses algorithms that automatically search for and recommend other users with common interests based on the user's interests and hobbies. For example, it matches users who enjoy gardening together to facilitate online interaction. The Facilitation Department can also provide online chat and discussion forums for users to connect with each other. This allows users to easily interact with other users who share common interests. Furthermore, the Facilitation Department can regularly hold events and activities to promote user interaction. For example, it can host online gardening workshops and discussion sessions to deepen interactions between users. The Facilitation Department can also collect user feedback and make improvements to enhance the quality of interactions. This allows the Facilitation Department to strengthen connections between users and improve the quality of communication.

[0067] The matching department connects users with local communities and volunteer activities. For example, it uses generative AI to analyze local event information and volunteer opportunities. Specifically, the generative AI collects information from local event calendars and volunteer recruitment websites, and suggests appropriate events and activities based on the user's interests and hobbies. For instance, the matching department can suggest events the user can participate in, such as gardening workshops or local cleanup activities. The generative AI selects more appropriate events based on the user's past participation history and feedback. The matching department can also suggest community activities suitable for the user. For example, it provides information on local gardening clubs and volunteer groups, creating an environment that makes it easy for users to participate. Furthermore, the matching department can monitor the user's participation status and provide reminders and follow-ups as needed. This allows the matching department to support users in actively participating in local communities and volunteer activities, preventing social isolation.

[0068] The data collection unit collects information about the user's interests and hobbies when the user registers for the application. For example, the data collection unit requests the user to input information about their hobbies and interests when registering for the application. For example, the data collection unit collects information when the user selects hobbies such as gardening, music, or sports. The data collection unit can also collect information if the user prefers a particular genre of books. In this way, the data collection unit can efficiently collect information about the user's interests and hobbies. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the information entered by the user into a generative AI, which can then analyze and collect that information.

[0069] The suggestion unit analyzes the user's interests and hobbies based on the collected information and proposes appropriate topics and activities. The suggestion unit analyzes the collected information, for example, using generative AI. For example, if the user is interested in gardening, the suggestion unit will propose the latest information and events related to gardening. Also, if the user is interested in music, the suggestion unit can propose discussion topics and concert information related to music. Furthermore, the suggestion unit can propose online discussion topics and local events based on the user's interests and hobbies. In this way, the suggestion unit can propose appropriate topics and activities based on the user's interests and hobbies. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the collected information into a generative AI, which can then analyze that information and propose appropriate topics and activities.

[0070] The promotion unit recommends other users with common interests based on proposed topics and activities, thereby facilitating online interaction. For example, the promotion unit uses generative AI to recommend other users with common interests. For instance, the promotion unit matches users who share a hobby like gardening, facilitating online interaction. The promotion unit can also provide online chat and discussion forums for users to connect with each other. Furthermore, the promotion unit can propose events for users to interact with other users who share their interests. In this way, the promotion unit can promote connections between users and prevent social isolation. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input other users with common interests into a generative AI based on proposed topics and activities, and the generative AI can analyze that information and make recommendations.

[0071] The matching unit analyzes local event information and volunteer activities and proposes community activities suitable for the user. For example, the matching unit uses generative AI to analyze local event information and volunteer activities. For example, the matching unit proposes events that the user can participate in, such as gardening workshops or local cleanup activities. The matching unit can also propose community activities suitable for the user. Furthermore, the matching unit can propose events and activities that help the user deepen their connection with the local community. In this way, the matching unit can help the user deepen their connection with the local community and reduce feelings of isolation. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input local event information and volunteer activities into the generative AI, which can then analyze that information and propose community activities suitable for the user.

[0072] The data collection unit estimates the user's emotions and adjusts the timing of information gathering based on the estimated emotions of the user. The data collection unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the data collection unit is feeling stressed, it will gather information during times when the user can relax. Also, if the data collection unit is excited, it can start gathering information immediately and provide topics that will interest the user. Furthermore, if the data collection unit is tired, it can gather information after the user has rested to reduce the burden. In this way, the data collection unit can provide more appropriate information by adjusting the timing of information gathering according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's emotion data into a generative AI, which can then analyze the data and adjust the timing of information gathering.

[0073] The data collection unit analyzes the user's past activity history and selects the optimal information collection method. For example, the data collection unit uses generative AI to analyze the user's past activity history. For example, the data collection unit prioritizes collecting information sources that the user has frequently used in the past. The data collection unit can also analyze the user's past activity patterns and collect information at the optimal timing. Furthermore, the data collection unit can collect relevant information based on topics that the user has shown interest in in the past. This allows the data collection unit to select the optimal information collection method based on the user's past activity history. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input the user's past activity history data into a generative AI, which can then analyze the data and select the optimal information collection method.

[0074] The data collection unit filters the information based on the user's current living situation and areas of interest. For example, the data collection unit uses generative AI to analyze the user's current living situation and areas of interest. For example, the data collection unit prioritizes collecting information related to projects the user is currently working on. The data collection unit can also filter relevant information based on the user's current living situation (e.g., work, family). Furthermore, the data collection unit can eliminate unnecessary information and collect only the necessary information based on the user's areas of interest. This allows the data collection unit to collect only the necessary information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input data on the user's current living situation and areas of interest into the generative AI, which can then analyze the data and filter the information.

[0075] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. The data collection unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is excited, the data collection unit will prioritize collecting the latest news and trending information. The data collection unit can also prioritize collecting information related to hobbies and entertainment if the user is relaxed. Furthermore, if the user is stressed, the data collection unit can prioritize collecting relaxing content. In this way, the data collection unit can provide more appropriate information by prioritizing information according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then analyze the data to determine the priority of information.

[0076] The data collection unit prioritizes collecting highly relevant information based on the user's geographical location. For example, the data collection unit analyzes the user's geographical location using a generative AI. For instance, it prioritizes collecting nearby event information based on the user's current location. It can also prioritize collecting local news and topics based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can prioritize collecting tourist information and restaurant information for their travel destination. This allows the data collection unit to prioritize collecting highly relevant information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generative AI, or without one. For example, the data collection unit can input the user's geographical location into a generative AI, which can then analyze the information and prioritize collecting highly relevant information.

[0077] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit uses generative AI to analyze the user's social media activity. For instance, the data collection unit prioritizes collecting information on accounts the user follows on social media. The data collection unit can also analyze the content of the user's social media posts and collect relevant information. Furthermore, the data collection unit can collect information on groups and communities the user participates in on social media. This allows the data collection unit to collect relevant information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using generative AI, or without it. For example, the data collection unit can input the user's social media activity data into a generative AI, which then analyzes the data to collect relevant information.

[0078] The suggestion unit estimates the user's emotions and adjusts the way it presents its suggestions based on those emotions. The suggestion unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, the suggestion unit will present suggestions in a gentle manner. If the user is excited, the suggestion unit can present suggestions in an energetic manner. Furthermore, if the user is stressed, the suggestion unit can present suggestions in a calm manner. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze that data and adjust the way it presents its suggestions.

[0079] The proposal department adjusts the level of detail of a proposal based on the importance of the topic or activity. For example, the proposal department uses generative AI to analyze the importance of topics and activities. For example, the proposal department provides detailed information on important topics and activities. It can also provide concise information on general topics and activities. Furthermore, the proposal department can adjust the level of detail of a proposal according to the user's level of interest. This allows the proposal department to adjust the level of detail of a proposal based on the importance of the topic or activity. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input topic and activity importance data into the generative AI, which can then analyze the data and adjust the level of detail of the proposal.

[0080] The suggestion unit applies different suggestion algorithms depending on the category of topic or activity when making suggestions. For example, the suggestion unit uses generative AI to analyze the category of topic or activity. For example, for suggestions related to hobbies, the suggestion unit applies an algorithm based on the user's past activity history. The suggestion unit can also apply an algorithm based on real-time event information for suggestions related to events. Furthermore, for suggestions related to volunteer activities, the suggestion unit can apply an algorithm based on local volunteer information. This allows the suggestion unit to apply different suggestion algorithms depending on the category of topic or activity. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input topic or activity category data into a generative AI, which can then analyze that data and apply different suggestion algorithms.

[0081] The suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. The suggestion unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is in a hurry, the suggestion unit will make short, to-the-point suggestions. If the user is relaxed, the suggestion unit may also make longer suggestions that include detailed explanations. Furthermore, if the user is excited, the suggestion unit may add visually stimulating effects to the suggestions. In this way, the suggestion unit can make more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then analyze the data and adjust the length of the suggestions.

[0082] The proposal department determines the priority of proposals based on the submission timing of topics and activities. For example, the proposal department uses generative AI to analyze the submission timing of topics and activities. For instance, the proposal department prioritizes proposals for recent events and activities. It can also postpone proposals for long-term activities and projects. Furthermore, the proposal department can adjust the priority of proposals based on the user's schedule. This allows the proposal department to determine the priority of proposals based on the submission timing of topics and activities. Some or all of the above processing in the proposal department may be performed using generative AI or not. For example, the proposal department can input topic and activity submission timing data into a generative AI, which can then analyze that data to determine the priority of proposals.

[0083] The suggestion unit adjusts the order of suggestions based on the relevance of topics and activities. The suggestion unit analyzes the relevance of topics and activities, for example, using generative AI. For example, the suggestion unit suggests the topics and activities most relevant to the user's interests first. The suggestion unit can also postpone less relevant topics and activities. Furthermore, the suggestion unit can adjust the order of suggestions based on the user's past preferences. This allows the suggestion unit to adjust the order of suggestions based on the relevance of topics and activities. Some or all of the above processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input topic and activity relevance data into a generative AI, which can then analyze the data and adjust the order of suggestions.

[0084] The facilitator estimates the user's emotions and adjusts the interaction facilitation method based on the estimated user emotions. The facilitator estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, the facilitator suggests a casual interaction method. The facilitator can also suggest a formal interaction method if the user is tense. Furthermore, if the user is excited, the facilitator can suggest an active interaction method. In this way, the facilitator can facilitate more appropriate interactions by adjusting the interaction facilitation method according to the user's emotions. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's emotion data into a generative AI, which can then analyze the data and adjust the interaction facilitation method.

[0085] The promotion unit analyzes the user's past interaction history to select the optimal promotion method when promoting interaction. For example, the promotion unit uses generative AI to analyze the user's past interaction history. For example, the promotion unit prioritizes suggesting interaction methods that the user has been successful with in the past. The promotion unit can also promote interaction at the optimal timing based on the user's past interaction history. Furthermore, the promotion unit can suggest relevant methods based on interaction methods that the user has shown interest in in the past. In this way, the promotion unit can select the optimal interaction promotion method based on the user's past interaction history. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input the user's past interaction history data into a generative AI, which can then analyze the data to select the optimal interaction promotion method.

[0086] The facilitator customizes the means of interaction based on the user's current lifestyle when facilitating interaction. For example, the facilitator analyzes the user's current lifestyle using generative AI. For instance, if the user is busy, the facilitator suggests a short interaction method. If the user has free time, the facilitator can also suggest a longer interaction method. Furthermore, depending on the user's lifestyle, the facilitator can suggest online or offline interaction methods. This allows the facilitator to provide the optimal means of interaction based on the user's current lifestyle. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input the user's current lifestyle data into the generative AI, which can then analyze the data to customize the means of interaction.

[0087] The facilitator estimates the user's emotions and determines the priority of interactions based on the estimated emotions. The facilitator estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the facilitator is excited, it will immediately facilitate an interaction. Alternatively, if the user is relaxed, it can facilitate an interaction slowly. Furthermore, if the user is stressed, the facilitator can prioritize suggesting relaxing interaction methods. In this way, the facilitator can facilitate more appropriate interactions by determining the priority of interactions according to the user's emotions. Some or all of the above processing in the facilitator may be performed using generative AI or not. For example, the facilitator can input user emotion data into a generative AI, which can then analyze the data to determine the priority of interactions.

[0088] The promotion unit selects the optimal interaction method when promoting interaction, taking into account the user's geographical location information. The promotion unit analyzes the user's geographical location information, for example, using generative AI. For example, the promotion unit suggests nearby interaction events based on the user's current location. The promotion unit can also suggest local interaction methods based on the user's geographical location information. Furthermore, if the user is traveling, the promotion unit can suggest interaction methods at their travel destination. In this way, the promotion unit can provide the optimal interaction method based on the user's geographical location information. Some or all of the above processing in the promotion unit may be performed using generative AI, or it may be performed without generative AI. For example, the promotion unit can input the user's geographical location information data into a generative AI, which can then analyze the data to select the optimal interaction method.

[0089] The promotion unit analyzes the user's social media activity and proposes means of interaction when promoting interaction. For example, the promotion unit uses generative AI to analyze the user's social media activity. For example, the promotion unit proposes interaction events of accounts that the user follows on social media. The promotion unit can also analyze the content of the user's social media posts and propose relevant interaction methods. Furthermore, the promotion unit can propose ways of interacting with groups and communities that the user participates in on social media. In this way, the promotion unit can provide the optimal means of interaction based on the user's social media activity. Some or all of the above processing in the promotion unit may be performed using generative AI or not. For example, the promotion unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose means of interaction.

[0090] The matching unit estimates the user's emotions and adjusts the matching method based on the estimated emotions. The matching unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is relaxed, the matching unit suggests a casual matching method. The matching unit can also suggest a formal matching method if the user is tense. Furthermore, if the user is excited, the matching unit can suggest an active matching method. In this way, the matching unit can perform more appropriate matching by adjusting the matching method according to the user's emotions. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input user emotion data into a generative AI, which can then analyze the data and adjust the matching method.

[0091] The matching unit analyzes the user's past participation history to select the optimal matching method during the matching process. For example, the matching unit may use a generative AI to analyze the user's past participation history. For instance, the matching unit may suggest relevant events based on the user's past event participation history. The matching unit can also select the optimal matching method based on the user's past participation history. Furthermore, the matching unit may suggest relevant matching methods based on events and activities the user has shown interest in in the past. This allows the matching unit to select the optimal matching method based on the user's past participation history. Some or all of the above-described processes in the matching unit may be performed using a generative AI, or they may not. For example, the matching unit may input the user's past participation history data into a generative AI, which then analyzes the data to select the optimal matching method.

[0092] The matching unit customizes the matching method based on the user's current lifestyle during the matching process. For example, the matching unit analyzes the user's current lifestyle using a generative AI. For instance, if the user is busy, the matching unit suggests a short-duration matching method. Conversely, if the user has free time, the matching unit can suggest a longer matching method. Furthermore, the matching unit can suggest online or offline matching methods depending on the user's lifestyle. This allows the matching unit to provide the optimal matching method based on the user's current lifestyle. Some or all of the above processing in the matching unit may be performed using a generative AI, or without one. For example, the matching unit can input the user's current lifestyle data into a generative AI, which then analyzes the data to customize the matching method.

[0093] The matching unit estimates the user's emotions and determines matching priorities based on the estimated emotions. The matching unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, if the user is excited, the matching unit will perform a match immediately. Alternatively, if the user is relaxed, the matching unit can perform a match slowly. Furthermore, if the user is stressed, the matching unit can prioritize suggesting a relaxing matching method. In this way, the matching unit can perform more appropriate matches by determining matching priorities according to the user's emotions. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input user emotion data into a generative AI, which can then analyze the data to determine matching priorities.

[0094] The matching unit selects the optimal matching method when matching users, taking into account the user's geographical location information. The matching unit analyzes the user's geographical location information, for example, using a generative AI. For example, the matching unit suggests nearby events and activities based on the user's current location. The matching unit can also suggest local events and activities based on the user's geographical location information. Furthermore, if the user is traveling, the matching unit can suggest events and activities at their travel destination. This allows the matching unit to provide the optimal matching method based on the user's geographical location information. Some or all of the above processing in the matching unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the matching unit can input the user's geographical location data into a generative AI, which can then analyze the data to select the optimal matching method.

[0095] The matching unit analyzes the user's social media activity during the matching process and proposes matching methods. For example, the matching unit uses generative AI to analyze the user's social media activity. For example, the matching unit proposes events and activities of accounts that the user follows on social media. The matching unit can also analyze the content of the user's social media posts and propose relevant events and activities. Furthermore, the matching unit can propose events and activities of groups and communities that the user participates in on social media. This allows the matching unit to provide the optimal matching method based on the user's social media activity. Some or all of the above processing in the matching unit may be performed using generative AI or not. For example, the matching unit can input the user's social media activity data into a generative AI, which can then analyze the data and propose matching methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The communication support system can also include a health management unit that monitors the user's health status. This unit collects user health data and analyzes it using AI. For example, it can monitor the user's heart rate and sleep patterns and estimate their stress level. Furthermore, based on the user's health status, the health management unit can suggest relaxing activities and stress reduction measures. It can also provide advice and reminders to help users lead a healthy lifestyle. This allows the communication support system to offer more comprehensive support by providing suggestions that take the user's health status into consideration.

[0098] The communication support system can also include an educational support section to further enhance users' motivation to learn. This educational support section suggests learning content based on the user's interests and hobbies. For example, if a user is interested in gardening, it can suggest online courses or workshops related to gardening. Furthermore, the educational support section can monitor the user's learning progress and provide appropriate feedback. It can also support users in maintaining their motivation to achieve their learning goals. In this way, the communication support system can increase users' motivation to learn and promote their personal growth.

[0099] The communication support system may also include an entertainment section that estimates the user's emotions and suggests music and video content based on those emotions. The entertainment section estimates the user's emotions using an emotion engine or generative AI. For example, if the user is relaxed, the entertainment section suggests relaxing music and videos. If the user is excited, it can suggest energetic music and videos. Furthermore, if the user is stressed, it can suggest content that helps reduce stress. This allows the entertainment section to provide entertainment content tailored to the user's emotions, thereby enhancing the user's psychological well-being.

[0100] The communication support system can also include a lifestyle rhythm management unit that monitors the user's daily rhythm and makes suggestions at the appropriate time. This unit monitors the user's sleep patterns and activity levels and analyzes them using AI. For example, it can determine whether the user is a morning person or a night owl and make suggestions accordingly. It can also suggest relaxing activities if the user is tired. Furthermore, it can suggest energetic activities that match the user's most active times. This allows the lifestyle rhythm management unit to provide more effective support by making suggestions tailored to the user's daily rhythm.

[0101] The communication support system may further include a tone adjustment unit that estimates the user's emotions and adjusts the tone of communication based on the estimated emotions. The tone adjustment unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is relaxed, the tone adjustment unit will communicate in a soft tone. If the user is excited, it can communicate in an energetic tone. Furthermore, if the user is stressed, it can communicate in a calm tone. In this way, the tone adjustment unit can communicate in a tone appropriate to the user's emotions, enabling more effective dialogue.

[0102] The communication support system can also include a travel suggestion unit that proposes personalized travel plans based on the user's hobbies and interests. The travel suggestion unit proposes the most suitable travel plan to the user based on information collected by the data collection unit. For example, if the user is interested in gardening, the travel suggestion unit can propose a travel plan that includes gardening-related tourist destinations and events. Furthermore, the travel suggestion unit can customize the optimal travel plan according to the user's budget and schedule. In addition, the travel suggestion unit can analyze the user's past travel history and propose relevant travel plans. This allows the communication support system to provide personalized travel plans based on the user's hobbies and interests.

[0103] The communication support system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. The feedback adjustment unit estimates the user's emotions using an emotion engine or generative AI. For example, if the user is relaxed, the feedback adjustment unit will provide positive feedback. If the user is excited, it can also provide energetic feedback. Furthermore, if the user is stressed, it can provide encouraging feedback. In this way, the feedback adjustment unit can provide feedback that is appropriate to the user's emotions and increase the user's motivation.

[0104] The communication support system can also include a nutrition management unit that monitors the user's diet and nutritional status to support a healthy lifestyle. The nutrition management unit collects the user's dietary data and analyzes it using AI. For example, the nutrition management unit can record the user's meals and evaluate their nutritional balance. It can also propose appropriate meal plans based on the user's health status and goals. Furthermore, the nutrition management unit can provide advice and recipes to help the user maintain a healthy diet. In this way, the communication support system can comprehensively support the user's health.

[0105] The communication support system can also include a fitness section that estimates the user's emotions and proposes an exercise plan based on those emotions. The fitness section estimates the user's emotions using an emotion engine or generative AI. For example, if the user is relaxed, the fitness section might suggest relaxing exercises such as yoga or stretching. If the user is excited, it might suggest energetic exercises such as running or dancing. Furthermore, if the user is stressed, it might suggest exercises that help reduce stress. This allows the fitness section to provide an exercise plan tailored to the user's emotions, thereby improving the user's health and well-being.

[0106] The communication support system can also include a shopping suggestion unit that provides personalized shopping recommendations based on the user's hobbies and interests. The shopping suggestion unit proposes the most suitable products and services to the user based on information collected by the data collection unit. For example, if the user is interested in gardening, the shopping suggestion unit can suggest gardening-related products and services. Furthermore, the shopping suggestion unit can customize the optimal shopping plan according to the user's budget and preferences. In addition, the shopping suggestion unit can analyze the user's past purchase history and suggest related products and services. This allows the communication support system to provide personalized shopping recommendations based on the user's hobbies and interests.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects information about the user's interests and hobbies. For example, when a user registers for the application, it collects information such as gardening, musical preferences, and sports hobbies. Step 2: The proposal department analyzes the information collected by the collection department and proposes appropriate topics and activities. For example, it uses generative AI to suggest the latest information and events related to gardening, online discussion topics, and local events. Step 3: The Facilitation Department promotes connections between users based on the topics and activities proposed by the Proposal Department. For example, it recommends other users with common interests and provides chat and discussion forums to facilitate online interaction. Step 4: The matching department matches users with local communities and volunteer activities. For example, it uses generative AI to analyze local event information and volunteer activities, and suggests events that users can participate in, such as gardening workshops or community cleanup activities.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information about the user's interests and hobbies using the receiving device 38 of the smart device 14. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to suggest appropriate topics and activities. The promotion unit is implemented in the control unit 46A of the smart device 14, for example, and promotes connections between users based on the suggested topics and activities. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and matches users with local communities and volunteer activities. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and matching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the smart glasses 214 to collect information about the user's interests and hobbies. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to suggest appropriate topics and activities. The promotion unit is implemented in the control unit 46A of the smart glasses 214, for example, and promotes connections between users based on the suggested topics and activities. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and matches users with local communities and volunteer activities. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and matching unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the headset terminal 314 to collect information about the user's interests and hobbies. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected information and suggest appropriate topics and activities. The promotion unit is implemented in the control unit 46A of the headset terminal 314, for example, to facilitate connections between users based on the suggested topics and activities. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to match users with local communities and volunteer activities. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and matching unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the microphone 238 of the robot 414 to collect information about the user's interests and hobbies. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to suggest appropriate topics and activities. The promotion unit is implemented in the control unit 46A of the robot 414, for example, and facilitates connections between users based on the suggested topics and activities. The matching unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and matches users with local communities and volunteer activities. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects information about users' interests and hobbies, The aforementioned collection unit analyzes the information collected and proposes appropriate topics and activities, Based on the topics and activities proposed by the aforementioned proposal unit, the promotion unit facilitates connections between users, It includes a matching department that connects people with local communities and volunteer activities. A system characterized by the following features. (Note 2) The aforementioned collection unit is When a user registers for the application, information about their interests and hobbies is collected. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected information, we analyze the user's interests and hobbies and suggest appropriate topics and activities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned promotion unit is Based on suggested topics and activities, we recommend other users with common interests to facilitate online interaction. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is We analyze local event information and volunteer activities to suggest community activities that are suitable for the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information gathering related to their interests and hobbies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) 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 10) The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the topic or activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the topic or activity. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on the topic and the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the topics and activities. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned promotion unit is It estimates the user's emotions and adjusts the interaction facilitation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned promotion unit is When promoting interaction, the system analyzes the user's past interaction history to select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned promotion unit is When facilitating interaction, customize the means of interaction based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned promotion unit is It estimates the user's emotions and determines the priority of interactions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned promotion unit is When promoting interaction, the system selects the optimal interaction method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned promotion unit is When promoting interaction, we analyze users' social media activity and suggest ways to facilitate interaction. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is It estimates the user's emotions and adjusts the matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is During the matching process, the system analyzes the user's past participation history to select the most suitable matching method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, the matching method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, the system selects the optimal matching method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is During the matching process, we analyze the user's social media activity and suggest matching methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 information about users' interests and hobbies, The aforementioned collection unit analyzes the information collected and proposes appropriate topics and activities, Based on the topics and activities proposed by the aforementioned proposal unit, the promotion unit facilitates connections between users, It includes a matching department that connects people with local communities and volunteer activities. A system characterized by the following features.

2. The aforementioned collection unit is When a user registers for the application, information about their interests and hobbies is collected. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected information, we analyze the user's interests and hobbies and suggest appropriate topics and activities. The system according to feature 1.

4. The aforementioned promotion unit is Based on suggested topics and activities, we recommend other users with common interests to facilitate online interaction. The system according to feature 1.

5. The matching unit is We analyze local event information and volunteer activities to suggest community activities that are suitable for the user. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information gathering related to their interests and hobbies based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past activity history and select the optimal method for collecting information. The system according to feature 1.

8. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system according to feature 1.

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