system

Abstract

The system according to this embodiment aims to allow working adults to easily make friends. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a checking unit. The collection unit collects user profile information. The suggestion unit suggests the most suitable friend candidates based on the information collected by the collection unit. The promotion unit encourages the user to take action regarding the friend candidates suggested by the suggestion unit. The checking unit checks the progress of the actions encouraged by the promotion unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2022-180282 [Summary of the Invention] [Problems to be Solved by the Invention]

[0004] In the prior art, there is a lack of a suitable matching platform for working people to easily make friends, and there is room for improvement.

[0005] The system according to the embodiment aims to enable working people to easily make friends. [Means for Solving the Problems]

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a checking unit. The collection unit collects user profile information. The suggestion unit suggests the most suitable friend candidates based on the information collected by the collection unit. The promotion unit encourages the user to take action regarding the friend candidates suggested by the suggestion unit. The checking unit checks the progress of the actions encouraged by the promotion unit. [Effects of the Invention]

[0007] The system according to this embodiment allows working adults to easily make friends. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 matching platform according to an embodiment of the present invention is a system that utilizes AI to enable working adults to easily make friends. In this system, users enrich their profiles with "self-introduction content," "daily sharing," and "hobby suggestions," and the AI ​​suggests the most suitable friend candidates. It encourages "daily actions" and "progress checks" to support active participation. To deepen interactions among users, participation in events and offline meetups is made mandatory to support real-world connections. Activity is promoted through incentives using points and badges. For example, users create self-introduction content. The AI ​​suggests a self-introduction template, which users can use to easily convey their personality. For example, they can express themselves through "self-introduction videos" or "real-time posts." Next, as daily sharing, the AI ​​suggests a "diary system" where users share "today's events" every day. For example, by posting about lunch or the movie they watched today, making friends progresses naturally. The AI ​​also suggests unknown hobbies based on the user's interests. For example, suggestions such as "Let's try playing a board game today" or "Let's find running buddies next weekend" are sent to actively expand activities. In the AI ​​matching function, the AI ​​suggests the most suitable friend candidates based on the user's profile, daily posts, and hobbies. Users receive reminders for video calls and offline meetups with people they have a high matching rate with. In addition, users are given daily tasks as mandatory actions, such as "send a thank-you message to one person today" or "find an event to attend this weekend." Regular online "weekly buddy challenges" are held, and offline meetups are actively suggested and participation is strongly encouraged. With AI-generated message suggestions, the AI ​​automatically generates the first message, which the user must send. The content is instructed to include a self-introduction and mention of shared hobbies. The AI ​​adjusts the content and tone of the message to suit the user's personality. Users can use the points they earn to participate in online events and offline meetups, and additional incentives are available, such as participation in "VIP events," once they reach a certain point level.This makes it possible to make real friends through compulsory activities, and the game-like process of making friends is fun and likely to yield results. The compulsory tasks make it easier to continue activities, allowing users to smoothly continue making friends and actively deepen real connections. AI support and connections between active users enable the creation of trustworthy friendships. As a result, the matching platform can provide a system that makes it easy for working adults to make friends.

[0029] The matching platform according to this embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a checking unit. The collection unit collects user profile information. Profile information includes, but is not limited to, name, age, gender, hobbies, and interests. The collection unit, for example, stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. The suggestion unit suggests the most suitable friend candidates based on the information collected by the collection unit. The suggestion unit selects friend candidates based on criteria such as common hobbies, geographical proximity, and compatibility. The suggestion unit can use AI to analyze the user's profile information and suggest the most suitable friend candidates. The promotion unit encourages the user to take action towards the friend candidates suggested by the suggestion unit. The promotion unit encourages the user to take actions such as sending messages, sending friend requests, or participating in events. The promotion unit can use AI to encourage the user to take daily actions and check their progress. The checking unit checks the progress of the actions encouraged by the promotion unit. The checking unit checks, for example, the completion status of actions and whether or not there has been a response. The checking unit can use AI to check the progress of user actions and provide feedback. As a result, the matching platform according to the embodiment can collect user profile information, suggest optimal friend candidates, encourage actions, and check their progress.

[0030] The data collection unit collects user profile information. This profile information includes, but is not limited to, name, age, gender, hobbies, and interests. The data collection unit stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. Specifically, it stores the information entered by the user in the registration form as structured data. For example, basic information such as name and age is stored in text format, and information such as hobbies and interests is stored with tags. This allows the AI ​​to analyze the data efficiently. Furthermore, the data collection unit can also collect information from the user's social media accounts. If the user gives permission, the data collection unit retrieves information such as social media posts, friend lists, and groups the user belongs to and adds it to the profile information. This allows for the collection of more detailed and accurate profile information. The data collection unit regularly updates the collected information to reflect the user's current situation. For example, if the user starts a new hobby or moves, the profile information is updated to always keep it up-to-date. This allows the data collection unit to collect accurate and detailed user profile information and store it in the database.

[0031] The suggestion department proposes the most suitable friend candidates based on the information collected by the data collection department. The suggestion department selects friend candidates based on criteria such as shared hobbies, geographical proximity, and compatibility. Specifically, it uses AI to analyze the user's profile information and identify users with shared hobbies and interests. For example, it can find users who enjoy the same sport or who like the same music genre. It also considers geographical proximity and prioritizes suggesting users who live nearby. This increases the opportunities for actual meetings and interactions. Furthermore, the AI ​​analyzes the user's past behavior history and reactions to suggest compatible friend candidates. For example, it identifies compatible users based on the history of message exchanges and event participation with previously suggested friend candidates. The suggestion department comprehensively analyzes this information to propose the most suitable friend candidates. The suggestion department presents the user with multiple friend candidates, allowing the user to choose. This allows the user to select a friend candidate that suits their preferences. The suggestion department regularly updates its suggestions and proposes new friend candidates to keep the user interested. In this way, the suggestion department can propose the most suitable friend candidates to the user and increase the success rate of matching.

[0032] The Promotion Department encourages users to take action with the friend candidates suggested by the Proposal Department. Specifically, it prompts users to take actions such as sending messages, sending friend requests, and participating in events. The Promotion Department can use AI to prompt users to take daily actions and check their progress. For example, it can send reminders to users to send messages to suggested friend candidates. When sending friend requests, it provides notifications at the appropriate time to create an environment where users can easily take action. Furthermore, to encourage event participation, it provides event information that matches the user's interests and encourages participation. The Promotion Department can analyze the user's behavior history and reactions to prompt action at the optimal time. For example, it can send reminders at the optimal time based on the time of day and frequency of messages the user has sent in the past. It also monitors user reactions in real time and adjusts the content of the prompts as needed. In this way, the Promotion Department can support users in taking proactive action and increase the success rate of matching.

[0033] The checking unit monitors the progress of actions prompted by the promotion unit. Specifically, it checks the completion status of actions and whether or not there has been a response. The checking unit can use AI to check the progress of user actions and provide feedback. For example, it can check whether a user has sent a message, whether a friend request has been accepted, or whether they have participated in an event. This allows the checking unit to understand the progress of user actions and provide additional support or reminders as needed. The checking unit analyzes the user's behavior history to understand the success rate of actions and response trends. For example, it collects and analyzes data such as when messages are most often sent and what kind of friend requests are more likely to be accepted. This allows the checking unit to understand user behavior patterns and provide more effective support. Furthermore, the checking unit collects feedback from users and uses it to improve the overall system. For example, it collects problems and areas for improvement that users experienced when taking actions and uses this to improve the system's functions and interface. In this way, the checking unit can effectively check the progress of user actions and provide feedback, thereby improving the overall system performance.

[0034] The data collection unit can collect information such as user self-introduction content, daily sharing, and hobby suggestions. For example, the data collection unit can have a user create self-introduction content, which the AI ​​then analyzes and stores in a database. The data collection unit can also collect information such as diaries, photos, and activity reports posted by users as daily sharing. The data collection unit can also collect information on hobbies that users are interested in and related events as hobby suggestions. In this way, the data collection unit can collect information such as user self-introduction content, daily sharing, and hobby suggestions. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user-entered self-introduction content into a generating AI, which then analyzes and stores it in a database.

[0035] The suggestion unit can propose the most suitable friend candidates based on the collected information. For example, the suggestion unit analyzes collected profile information, self-introduction content, and daily sharing information, and selects friend candidates based on criteria such as common hobbies, geographical proximity, and compatibility. The suggestion unit can use AI to analyze the user's profile information and propose the most suitable friend candidates. For example, the suggestion unit can propose friend candidates with common hobbies. It can also propose friend candidates who are geographically close. It can also propose friend candidates who are compatible. In this way, the suggestion unit can propose the most suitable friend candidates based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input collected profile information into a generating AI, which can then analyze and propose the most suitable friend candidates.

[0036] The promotion unit can prompt users to take daily actions and check their progress. For example, the promotion unit can prompt users to update their diaries, interact with friends, or engage in hobby activities. The promotion unit can use AI to prompt users to take daily actions and check their progress. For example, the promotion unit can prompt users to update their diaries. The promotion unit can also prompt users to interact with friends. The promotion unit can also prompt users to engage in hobby activities. In this way, the promotion unit can prompt users to take daily actions and check their progress. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input a message prompting the user to update their diary into a generating AI, and the generating AI can generate a message and send it to the user.

[0037] The checking unit can check the progress of user actions. For example, the checking unit can check the completion status of actions and whether or not there is a response. The checking unit can use AI to check the progress of user actions and provide feedback. For example, the checking unit can check whether the user has updated their diary. The checking unit can also check whether the user has interacted with friends. The checking unit can also check whether the user has engaged in hobby activities. In this way, the checking unit can check the progress of user actions. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the progress of user actions into a generating AI, which can then analyze the progress and provide feedback.

[0038] The suggestion unit can suggest unknown hobbies based on the user's interests. For example, the suggestion unit analyzes information such as the user's profile information, self-introduction content, and daily sharing, and suggests new hobbies based on the user's interests. The suggestion unit can use AI to analyze the user's interests and suggest unknown hobbies. For example, the suggestion unit can suggest a new sport. It can also suggest a new art form. It can also suggest a new type of music. In this way, the suggestion unit can suggest unknown hobbies based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's interests into a generating AI, which can then analyze and suggest unknown hobbies.

[0039] The promotion unit can encourage users to participate in events and meetups. For example, the promotion unit can encourage users to participate in online events, offline events, and social gatherings. The promotion unit can use AI to encourage users to participate in events and meetups. For example, the promotion unit can encourage users to participate in online events. The promotion unit can also encourage users to participate in offline events. The promotion unit can also encourage users to participate in social gatherings. In this way, the promotion unit can encourage users to participate in events and meetups. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input a message encouraging the user to participate in an event into a generation AI, and the generation AI can generate a message and send it to the user.

[0040] The checking unit can award points or badges based on the progress of user actions. For example, the checking unit can award points or badges based on the completion status of an action or whether or not there is a response. The checking unit can use AI to check the progress of user actions and award points or badges. For example, the checking unit can award points when a user updates their diary. The checking unit can also award badges when a user interacts with friends. The checking unit can also award points when a user engages in hobby activities. In this way, the checking unit can award points or badges based on the progress of user actions. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the progress of user actions into a generating AI, which can analyze the progress and award points or badges.

[0041] The data collection unit can analyze a user's past self-introduction content and daily sharing history to select the optimal data collection method. For example, the data collection unit can analyze a user's past self-introduction content, and the AI ​​can suggest a new self-introduction template. The data collection unit can also analyze a user's daily sharing history, and the AI ​​can suggest new sharing methods based on the posts that received the most positive responses. The data collection unit can also analyze a user's hobby suggestion history, and the AI ​​can suggest new hobbies based on the user's interests. In this way, the data collection unit can analyze a user's past self-introduction content and daily sharing history to select the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's past self-introduction content into a generating AI, which can then analyze it and suggest a new self-introduction template.

[0042] The data collection unit can filter profile information based on the user's current lifestyle and areas of interest. For example, if a user enters their current work situation, the AI ​​can suggest relevant hobbies and activities based on that information. If a user enters their recent areas of interest, the AI ​​can also suggest relevant friend candidates based on that information. If a user enters their current lifestyle, the AI ​​can also suggest the best way to create self-introduction content based on that information. This allows the data collection unit to filter information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current lifestyle into a generating AI, which can then analyze and suggest relevant hobbies and activities.

[0043] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, if a user lives in a specific region, the AI ​​can suggest hobbies and activities related to that region. If a user is traveling, the AI ​​can also encourage the user to create self-introduction content related to their travel destination. If a user is participating in a specific event, the AI ​​can also encourage the user to share daily events related to that event. This allows the data collection unit to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze and suggest relevant hobbies and activities.

[0044] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze what a user has shared on social media and use AI to suggest self-introduction content based on that information. The data collection unit can also analyze posts that a user has shown interest in on social media and use AI to suggest hobbies based on that information. The data collection unit can also analyze events that a user has participated in on social media and use AI to encourage daily sharing based on that information. In this way, the data collection unit can analyze a user's social media activity and collect relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then analyze and collect relevant information.

[0045] The suggestion unit can adjust the level of detail in a suggestion based on the importance of the potential friend. For example, if a potential friend has a high matching rate, the AI ​​can provide detailed profile information. If a potential friend has a moderate matching rate, the AI ​​can provide concise profile information. If a potential friend has a low matching rate, the AI ​​can provide only an overview. This allows the suggestion unit to adjust the level of detail in a suggestion based on the importance of the potential friend. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the importance of the potential friend into a generating AI, which can then analyze and adjust the level of detail in the suggestion.

[0046] The suggestion unit can apply different suggestion algorithms depending on the category of the potential friend when making suggestions. For example, the suggestion unit can use AI to make suggestions based on shared hobbies for potential friends with similar interests. The suggestion unit can also use AI to make suggestions based on shared jobs for potential friends with similar jobs. The suggestion unit can also use AI to make suggestions based on shared lifestyles for potential friends with similar lifestyles. This allows the suggestion unit to apply different suggestion algorithms depending on the category of the potential friend. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the category of potential friend into a generating AI, which can then analyze and apply a different suggestion algorithm.

[0047] The suggestion unit can prioritize suggestions based on when the friend candidates submitted their suggestions. For example, if a friend candidate has recently updated their profile, the AI ​​will prioritize suggesting that candidate. If a friend candidate has not updated their profile for a long time, the AI ​​can also postpone suggesting that candidate. The suggestion unit can also prioritize suggesting a friend candidate if they are planning to attend a specific event. This allows the suggestion unit to prioritize suggestions based on when the friend candidates submitted their suggestions. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the friend candidate submission times into a generating AI, which can then analyze and determine the suggestion priority.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the friend candidates when making suggestions. For example, if a friend candidate has a high matching rate, the AI ​​may suggest that friend candidate first. If a friend candidate has a moderate matching rate, the AI ​​may also suggest that friend candidate in the middle. If a friend candidate has a low matching rate, the AI ​​may also suggest that friend candidate last. In this way, the suggestion unit can adjust the order of suggestions based on the relevance of the friend candidates. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the relevance of the friend candidates into a generating AI, which can then analyze and adjust the order of suggestions.

[0049] The promotion unit can analyze the user's past action history to select the optimal promotion method during promotion. For example, the promotion unit can use AI to suggest similar actions based on actions the user has successfully taken in the past. The promotion unit can also use AI to suggest a different approach based on actions the user has failed at in the past. The promotion unit can also analyze the user's past action history and use AI to select the most effective promotion method. In this way, the promotion unit can analyze the user's past action history and select the optimal promotion method. Some or all of the above processes in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's past action history into a generating AI, which can then analyze and select the optimal promotion method.

[0050] The promotion unit can customize the means of action based on the user's current living situation during promotion. For example, if the user inputs their current work situation, the AI ​​can suggest the most appropriate action based on that information. If the user inputs their recent areas of interest, the AI ​​can also suggest relevant actions based on that information. If the user inputs their current living situation, the AI ​​can also suggest the most appropriate means of action based on that information. In this way, the promotion unit can customize the means of action based on the user's current living situation. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's current living situation into a generating AI, which can then analyze and suggest the most appropriate action.

[0051] The promotion unit can select the optimal action during promotion, taking into account the user's geographical location. For example, if the user lives in a specific region, the AI ​​can suggest actions related to that region. If the user is traveling, the AI ​​can also suggest actions related to the travel destination. If the user is participating in a specific event, the AI ​​can also suggest actions related to that event. This allows the promotion unit to select the optimal action, taking into account the user's geographical location. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's geographical location into a generating AI, which can then analyze the data and suggest the optimal action.

[0052] The promotion unit can analyze the user's social media activity and propose actions during the promotion process. For example, the promotion unit can analyze what the user has shared on social media, and the AI ​​can propose actions based on that information. The promotion unit can also analyze posts that the user has shown interest in on social media, and the AI ​​can propose relevant actions based on that information. The promotion unit can also analyze events that the user has participated in on social media, and the AI ​​can propose actions based on that information. In this way, the promotion unit can analyze the user's social media activity and propose actions. Some or all of the above processing in the promotion unit may be performed using AI, or not. For example, the promotion unit can input the user's social media activity into a generating AI, which can then analyze it and propose actions.

[0053] The checking unit can select the optimal checking method by referring to the user's past action history when checking progress. For example, the checking unit can use AI to perform similar progress checks based on actions the user has successfully performed in the past. The checking unit can also use AI to perform progress checks with a different approach based on actions the user has failed at in the past. The checking unit can also analyze the user's past action history and use AI to select the most effective progress checking method. In this way, the checking unit can select the optimal checking method by referring to the user's past action history. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's past action history into a generating AI, which can then analyze and select the optimal checking method.

[0054] The checking unit can customize the checking method based on the user's current life situation when checking progress. For example, if the user inputs their current work situation, the AI ​​can perform the optimal progress check based on that information. If the user inputs their recent areas of interest, the AI ​​can also perform relevant progress checks based on that information. If the user inputs their current life situation, the AI ​​can also suggest the optimal progress check method based on that information. In this way, the checking unit can customize the checking method based on the user's current life situation. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's current life situation into a generating AI, which can then analyze it and perform the optimal progress check.

[0055] The checking unit can select the optimal checking method when checking progress, taking into account the user's geographical location information. For example, if the user lives in a specific region, the AI ​​can perform progress checks related to that region. If the user is traveling, the AI ​​can also perform progress checks related to the travel destination. If the user is participating in a specific event, the AI ​​can also perform progress checks related to that event. This allows the checking unit to select the optimal checking method, taking into account the user's geographical location information. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's geographical location information into a generating AI, which can then analyze and select the optimal checking method.

[0056] The checking unit can analyze the user's social media activity and propose checking methods during progress checks. For example, the checking unit can analyze content shared by the user on social media, and the AI ​​can perform progress checks based on that information. The checking unit can also analyze posts that the user has shown interest in on social media, and the AI ​​can perform relevant progress checks based on that information. The checking unit can also analyze events that the user has participated in on social media, and the AI ​​can perform progress checks based on that information. In this way, the checking unit can analyze the user's social media activity and propose checking methods. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input the user's social media activity into a generating AI, which can then analyze it and propose checking methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can collect more accurate information by referring to the user's past behavioral history when gathering user profile information. For example, by analyzing what hobbies a user has had in the past and what events they have participated in, and comparing this with their current profile information, it can more accurately identify their hobbies and interests. The data collection unit can also refer to what kind of self-introduction content a user has created in the past and use this to help create their current self-introduction content. Furthermore, the data collection unit can analyze what kind of daily sharing a user has done in the past and understand the trends in their current sharing content. In this way, the data collection unit can collect more accurate information by referring to the user's past behavioral history.

[0059] The suggestion function can analyze a user's profile information and refer to their past activity history to suggest more appropriate friend candidates. For example, by analyzing a user's past hobbies and events they have attended and comparing this with their current profile information, it can identify potential friends with similar interests. The suggestion function can also refer to a user's past self-introduction content and compare it with their current self-introduction content to identify compatible friend candidates. Furthermore, by analyzing a user's past daily sharing and comparing it with their current sharing content, it can identify potential friends with common interests. In this way, the suggestion function can suggest more appropriate friend candidates by referring to a user's past activity history.

[0060] The promotion department can refer to a user's past behavior history to suggest more effective actions when prompting them to take action. For example, it can analyze what actions a user has taken in the past and what actions were successful, and use this information to suggest current actions. The promotion department can also refer to what events a user has participated in in the past to help encourage participation in current events. Furthermore, it can analyze what messages a user has sent in the past to help suggest current messages. In this way, the promotion department can refer to a user's past behavior history to suggest more effective actions.

[0061] The checking unit can perform more accurate progress checks by referring to the user's past behavior history when monitoring the progress of user actions. For example, by analyzing what actions the user has taken in the past and what actions were successful, and comparing this with the current action progress, the progress can be accurately understood. The checking unit can also refer to what events the user has participated in in the past and use this to check their current event participation status. Furthermore, the checking unit can analyze what messages the user has sent in the past and use this to check their current message sending status. In this way, the checking unit can perform more accurate progress checks by referring to the user's past behavior history.

[0062] The data collection unit can prioritize the information to collect by considering the user's current living situation when gathering user profile information. For example, if a user enters their current work situation, the AI ​​can suggest related hobbies and activities based on that information. If a user enters their recent areas of interest, the AI ​​can also suggest relevant friend candidates based on that information. If a user enters their current living situation, the AI ​​can also suggest the best way to create self-introduction content based on that information. In this way, the data collection unit can prioritize the information to collect by considering the user's current living situation.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects user profile information. This profile information includes name, age, gender, hobbies, interests, etc. The data collection unit stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. Step 2: The suggestion unit proposes the most suitable friend candidates based on the information collected by the collection unit. The suggestion unit selects friend candidates based on criteria such as shared hobbies, geographical proximity, and compatibility, and uses AI to analyze the user's profile information. Step 3: The Promotion Department encourages users to take action with the friend candidates suggested by the Proposal Department. The Promotion Department prompts users to take actions such as sending messages, sending friend requests, and participating in events, and uses AI to encourage daily actions and progress checks. Step 4: The checking unit checks the progress of the actions prompted by the facilitating unit. The checking unit verifies the completion status of the actions and whether or not there are any responses, uses AI to check the progress of the user's actions, and provides feedback.

[0065] (Example of form 2) The matching platform according to an embodiment of the present invention is a system that utilizes AI to enable working adults to easily make friends. In this system, users enrich their profiles with "self-introduction content," "daily sharing," and "hobby suggestions," and the AI ​​suggests the most suitable friend candidates. It encourages "daily actions" and "progress checks" to support active participation. To deepen interactions among users, participation in events and offline meetups is made mandatory to support real-world connections. Activity is promoted through incentives using points and badges. For example, users create self-introduction content. The AI ​​suggests a self-introduction template, which users can use to easily convey their personality. For example, they can express themselves through "self-introduction videos" or "real-time posts." Next, as daily sharing, the AI ​​suggests a "diary system" where users share "today's events" every day. For example, by posting about lunch or the movie they watched today, making friends progresses naturally. The AI ​​also suggests unknown hobbies based on the user's interests. For example, suggestions such as "Let's try playing a board game today" or "Let's find running buddies next weekend" are sent to actively expand activities. In the AI ​​matching function, the AI ​​suggests the most suitable friend candidates based on the user's profile, daily posts, and hobbies. Users receive reminders for video calls and offline meetups with people they have a high matching rate with. In addition, users are given daily tasks as mandatory actions, such as "send a thank-you message to one person today" or "find an event to attend this weekend." Regular online "weekly buddy challenges" are held, and offline meetups are actively suggested and participation is strongly encouraged. With AI-generated message suggestions, the AI ​​automatically generates the first message, which the user must send. The content is instructed to include a self-introduction and mention of shared hobbies. The AI ​​adjusts the content and tone of the message to suit the user's personality. Users can use the points they earn to participate in online events and offline meetups, and additional incentives are available, such as participation in "VIP events," once they reach a certain point level.This makes it possible to make real friends through compulsory activities, and the game-like process of making friends is fun and likely to yield results. The compulsory tasks make it easier to continue activities, allowing users to smoothly continue making friends and actively deepen real connections. AI support and connections between active users enable the creation of trustworthy friendships. As a result, the matching platform can provide a system that makes it easy for working adults to make friends.

[0066] The matching platform according to this embodiment comprises a collection unit, a suggestion unit, a promotion unit, and a checking unit. The collection unit collects user profile information. Profile information includes, but is not limited to, name, age, gender, hobbies, and interests. The collection unit, for example, stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. The suggestion unit suggests the most suitable friend candidates based on the information collected by the collection unit. The suggestion unit selects friend candidates based on criteria such as common hobbies, geographical proximity, and compatibility. The suggestion unit can use AI to analyze the user's profile information and suggest the most suitable friend candidates. The promotion unit encourages the user to take action towards the friend candidates suggested by the suggestion unit. The promotion unit encourages the user to take actions such as sending messages, sending friend requests, or participating in events. The promotion unit can use AI to encourage the user to take daily actions and check their progress. The checking unit checks the progress of the actions encouraged by the promotion unit. The checking unit checks, for example, the completion status of actions and whether or not there has been a response. The checking unit can use AI to check the progress of user actions and provide feedback. As a result, the matching platform according to the embodiment can collect user profile information, suggest optimal friend candidates, encourage actions, and check their progress.

[0067] The data collection unit collects user profile information. This profile information includes, but is not limited to, name, age, gender, hobbies, and interests. The data collection unit stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. Specifically, it stores the information entered by the user in the registration form as structured data. For example, basic information such as name and age is stored in text format, and information such as hobbies and interests is stored with tags. This allows the AI ​​to analyze the data efficiently. Furthermore, the data collection unit can also collect information from the user's social media accounts. If the user gives permission, the data collection unit retrieves information such as social media posts, friend lists, and groups the user belongs to and adds it to the profile information. This allows for the collection of more detailed and accurate profile information. The data collection unit regularly updates the collected information to reflect the user's current situation. For example, if the user starts a new hobby or moves, the profile information is updated to always keep it up-to-date. This allows the data collection unit to collect accurate and detailed user profile information and store it in the database.

[0068] The suggestion department proposes the most suitable friend candidates based on the information collected by the data collection department. The suggestion department selects friend candidates based on criteria such as shared hobbies, geographical proximity, and compatibility. Specifically, it uses AI to analyze the user's profile information and identify users with shared hobbies and interests. For example, it can find users who enjoy the same sport or who like the same music genre. It also considers geographical proximity and prioritizes suggesting users who live nearby. This increases the opportunities for actual meetings and interactions. Furthermore, the AI ​​analyzes the user's past behavior history and reactions to suggest compatible friend candidates. For example, it identifies compatible users based on the history of message exchanges and event participation with previously suggested friend candidates. The suggestion department comprehensively analyzes this information to propose the most suitable friend candidates. The suggestion department presents the user with multiple friend candidates, allowing the user to choose. This allows the user to select a friend candidate that suits their preferences. The suggestion department regularly updates its suggestions and proposes new friend candidates to keep the user interested. In this way, the suggestion department can propose the most suitable friend candidates to the user and increase the success rate of matching.

[0069] The Promotion Department encourages users to take action with the friend candidates suggested by the Proposal Department. Specifically, it prompts users to take actions such as sending messages, sending friend requests, and participating in events. The Promotion Department can use AI to prompt users to take daily actions and check their progress. For example, it can send reminders to users to send messages to suggested friend candidates. When sending friend requests, it provides notifications at the appropriate time to create an environment where users can easily take action. Furthermore, to encourage event participation, it provides event information that matches the user's interests and encourages participation. The Promotion Department can analyze the user's behavior history and reactions to prompt action at the optimal time. For example, it can send reminders at the optimal time based on the time of day and frequency of messages the user has sent in the past. It also monitors user reactions in real time and adjusts the content of the prompts as needed. In this way, the Promotion Department can support users in taking proactive action and increase the success rate of matching.

[0070] The checking unit monitors the progress of actions prompted by the promotion unit. Specifically, it checks the completion status of actions and whether or not there has been a response. The checking unit can use AI to check the progress of user actions and provide feedback. For example, it can check whether a user has sent a message, whether a friend request has been accepted, or whether they have participated in an event. This allows the checking unit to understand the progress of user actions and provide additional support or reminders as needed. The checking unit analyzes the user's behavior history to understand the success rate of actions and response trends. For example, it collects and analyzes data such as when messages are most often sent and what kind of friend requests are more likely to be accepted. This allows the checking unit to understand user behavior patterns and provide more effective support. Furthermore, the checking unit collects feedback from users and uses it to improve the overall system. For example, it collects problems and areas for improvement that users experienced when taking actions and uses this to improve the system's functions and interface. In this way, the checking unit can effectively check the progress of user actions and provide feedback, thereby improving the overall system performance.

[0071] The data collection unit can collect information such as user self-introduction content, daily sharing, and hobby suggestions. For example, the data collection unit can have a user create self-introduction content, which the AI ​​then analyzes and stores in a database. The data collection unit can also collect information such as diaries, photos, and activity reports posted by users as daily sharing. The data collection unit can also collect information on hobbies that users are interested in and related events as hobby suggestions. In this way, the data collection unit can collect information such as user self-introduction content, daily sharing, and hobby suggestions. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user-entered self-introduction content into a generating AI, which then analyzes and stores it in a database.

[0072] The suggestion unit can propose the most suitable friend candidates based on the collected information. For example, the suggestion unit analyzes collected profile information, self-introduction content, and daily sharing information, and selects friend candidates based on criteria such as common hobbies, geographical proximity, and compatibility. The suggestion unit can use AI to analyze the user's profile information and propose the most suitable friend candidates. For example, the suggestion unit can propose friend candidates with common hobbies. It can also propose friend candidates who are geographically close. It can also propose friend candidates who are compatible. In this way, the suggestion unit can propose the most suitable friend candidates based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input collected profile information into a generating AI, which can then analyze and propose the most suitable friend candidates.

[0073] The promotion unit can prompt users to take daily actions and check their progress. For example, the promotion unit can prompt users to update their diaries, interact with friends, or engage in hobby activities. The promotion unit can use AI to prompt users to take daily actions and check their progress. For example, the promotion unit can prompt users to update their diaries. The promotion unit can also prompt users to interact with friends. The promotion unit can also prompt users to engage in hobby activities. In this way, the promotion unit can prompt users to take daily actions and check their progress. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input a message prompting the user to update their diary into a generating AI, and the generating AI can generate a message and send it to the user.

[0074] The checking unit can check the progress of user actions. For example, the checking unit can check the completion status of actions and whether or not there is a response. The checking unit can use AI to check the progress of user actions and provide feedback. For example, the checking unit can check whether the user has updated their diary. The checking unit can also check whether the user has interacted with friends. The checking unit can also check whether the user has engaged in hobby activities. In this way, the checking unit can check the progress of user actions. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the progress of user actions into a generating AI, which can then analyze the progress and provide feedback.

[0075] The suggestion unit can suggest unknown hobbies based on the user's interests. For example, the suggestion unit analyzes information such as the user's profile information, self-introduction content, and daily sharing, and suggests new hobbies based on the user's interests. The suggestion unit can use AI to analyze the user's interests and suggest unknown hobbies. For example, the suggestion unit can suggest a new sport. It can also suggest a new art form. It can also suggest a new type of music. In this way, the suggestion unit can suggest unknown hobbies based on the user's interests. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's interests into a generating AI, which can then analyze and suggest unknown hobbies.

[0076] The promotion unit can encourage users to participate in events and meetups. For example, the promotion unit can encourage users to participate in online events, offline events, and social gatherings. The promotion unit can use AI to encourage users to participate in events and meetups. For example, the promotion unit can encourage users to participate in online events. The promotion unit can also encourage users to participate in offline events. The promotion unit can also encourage users to participate in social gatherings. In this way, the promotion unit can encourage users to participate in events and meetups. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input a message encouraging the user to participate in an event into a generation AI, and the generation AI can generate a message and send it to the user.

[0077] The checking unit can award points or badges based on the progress of user actions. For example, the checking unit can award points or badges based on the completion status of an action or whether or not there is a response. The checking unit can use AI to check the progress of user actions and award points or badges. For example, the checking unit can award points when a user updates their diary. The checking unit can also award badges when a user interacts with friends. The checking unit can also award points when a user engages in hobby activities. In this way, the checking unit can award points or badges based on the progress of user actions. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the progress of user actions into a generating AI, which can analyze the progress and award points or badges.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated emotions. For example, if the user is relaxed, the AI ​​can select a time to prompt the user to create self-introduction content. If the user is busy, the AI ​​can postpone sharing daily activities and collect information at a time when the user has free time. If the user is excited, the AI ​​can prioritize collecting hobby suggestions and select a time to capture the user's interest. In this way, the data collection unit can adjust the timing of profile information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0079] The data collection unit can analyze a user's past self-introduction content and daily sharing history to select the optimal data collection method. For example, the data collection unit can analyze a user's past self-introduction content, and the AI ​​can suggest a new self-introduction template. The data collection unit can also analyze a user's daily sharing history, and the AI ​​can suggest new sharing methods based on the posts that received the most positive responses. The data collection unit can also analyze a user's hobby suggestion history, and the AI ​​can suggest new hobbies based on the user's interests. In this way, the data collection unit can analyze a user's past self-introduction content and daily sharing history to select the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's past self-introduction content into a generating AI, which can then analyze it and suggest a new self-introduction template.

[0080] The data collection unit can filter profile information based on the user's current lifestyle and areas of interest. For example, if a user enters their current work situation, the AI ​​can suggest relevant hobbies and activities based on that information. If a user enters their recent areas of interest, the AI ​​can also suggest relevant friend candidates based on that information. If a user enters their current lifestyle, the AI ​​can also suggest the best way to create self-introduction content based on that information. This allows the data collection unit to filter information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current lifestyle into a generating AI, which can then analyze and suggest relevant hobbies and activities.

[0081] The data collection unit can estimate the user's emotions and determine the priority of profile information to collect based on the estimated emotions. For example, if the user is relaxed, the AI ​​might prioritize collecting self-introduction content. If the user is busy, the AI ​​might postpone collecting daily shares. If the user is excited, the AI ​​might prioritize collecting hobby suggestions. This allows the data collection unit to determine the priority of profile information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of profile information to collect.

[0082] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information. For example, if a user lives in a specific region, the AI ​​can suggest hobbies and activities related to that region. If a user is traveling, the AI ​​can also encourage the user to create self-introduction content related to their travel destination. If a user is participating in a specific event, the AI ​​can also encourage the user to share daily events related to that event. This allows the data collection unit to prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze and suggest relevant hobbies and activities.

[0083] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information. For example, the data collection unit can analyze what a user has shared on social media and use AI to suggest self-introduction content based on that information. The data collection unit can also analyze posts that a user has shown interest in on social media and use AI to suggest hobbies based on that information. The data collection unit can also analyze events that a user has participated in on social media and use AI to encourage daily sharing based on that information. In this way, the data collection unit can analyze a user's social media activity and collect relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input a user's social media activity into a generating AI, which can then analyze and collect relevant information.

[0084] The suggestion unit can estimate the user's emotions and adjust how it suggests potential friends based on those emotions. For example, if the user is relaxed, the AI ​​can provide detailed information about potential friends. If the user is busy, the AI ​​can provide concise information about potential friends. If the user is excited, the AI ​​can provide visually appealing information about potential friends. This allows the suggestion unit to adjust how it suggests potential friends based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust how it suggests potential friends.

[0085] The suggestion unit can adjust the level of detail in a suggestion based on the importance of the potential friend. For example, if a potential friend has a high matching rate, the AI ​​can provide detailed profile information. If a potential friend has a moderate matching rate, the AI ​​can provide concise profile information. If a potential friend has a low matching rate, the AI ​​can provide only an overview. This allows the suggestion unit to adjust the level of detail in a suggestion based on the importance of the potential friend. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the importance of the potential friend into a generating AI, which can then analyze and adjust the level of detail in the suggestion.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the potential friend when making suggestions. For example, the suggestion unit can use AI to make suggestions based on shared hobbies for potential friends with similar interests. The suggestion unit can also use AI to make suggestions based on shared jobs for potential friends with similar jobs. The suggestion unit can also use AI to make suggestions based on shared lifestyles for potential friends with similar lifestyles. This allows the suggestion unit to apply different suggestion algorithms depending on the category of the potential friend. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the category of potential friend into a generating AI, which can then analyze and apply a different suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the AI ​​can provide detailed suggestions. If the user is busy, the AI ​​can provide concise suggestions. If the user is excited, the AI ​​can provide visually appealing suggestions. This allows the suggestion unit to adjust the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestions.

[0088] The suggestion unit can prioritize suggestions based on when the friend candidates submitted their suggestions. For example, if a friend candidate has recently updated their profile, the AI ​​will prioritize suggesting that candidate. If a friend candidate has not updated their profile for a long time, the AI ​​can also postpone suggesting that candidate. The suggestion unit can also prioritize suggesting a friend candidate if they are planning to attend a specific event. This allows the suggestion unit to prioritize suggestions based on when the friend candidates submitted their suggestions. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the friend candidate submission times into a generating AI, which can then analyze and determine the suggestion priority.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the friend candidates when making suggestions. For example, if a friend candidate has a high matching rate, the AI ​​may suggest that friend candidate first. If a friend candidate has a moderate matching rate, the AI ​​may also suggest that friend candidate in the middle. If a friend candidate has a low matching rate, the AI ​​may also suggest that friend candidate last. In this way, the suggestion unit can adjust the order of suggestions based on the relevance of the friend candidates. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the relevance of the friend candidates into a generating AI, which can then analyze and adjust the order of suggestions.

[0090] The facilitator can estimate the user's emotions and adjust how it promotes actions based on those emotions. For example, if the user is relaxed, the AI ​​can provide a detailed description of the action. If the user is busy, the AI ​​can provide a concise description of the action. If the user is excited, the AI ​​can provide a visually appealing description of the action. This allows the facilitator to adjust how it promotes actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the facilitator may be performed using or without AI. For example, the facilitator can input user emotion data into a generative AI, which can estimate the emotion and adjust how it promotes actions.

[0091] The promotion unit can analyze the user's past action history to select the optimal promotion method during promotion. For example, the promotion unit can use AI to suggest similar actions based on actions the user has successfully taken in the past. The promotion unit can also use AI to suggest a different approach based on actions the user has failed at in the past. The promotion unit can also analyze the user's past action history and use AI to select the most effective promotion method. In this way, the promotion unit can analyze the user's past action history and select the optimal promotion method. Some or all of the above processes in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's past action history into a generating AI, which can then analyze and select the optimal promotion method.

[0092] The promotion unit can customize the means of action based on the user's current living situation during promotion. For example, if the user inputs their current work situation, the AI ​​can suggest the most appropriate action based on that information. If the user inputs their recent areas of interest, the AI ​​can also suggest relevant actions based on that information. If the user inputs their current living situation, the AI ​​can also suggest the most appropriate means of action based on that information. In this way, the promotion unit can customize the means of action based on the user's current living situation. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's current living situation into a generating AI, which can then analyze and suggest the most appropriate action.

[0093] The facilitator can estimate the user's emotions and prioritize actions based on those emotions. For example, if the user is relaxed, the facilitator might prioritize actions that are of high importance. If the user is busy, the facilitator might also prioritize actions that are easy for the AI ​​to perform. If the user is excited, the facilitator might also prioritize visually appealing actions. This allows the facilitator to prioritize actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the facilitator may be performed using AI or not. For example, the facilitator can input user emotion data into a generative AI, which can then estimate the emotion and determine the priority of actions.

[0094] The promotion unit can select the optimal action during promotion, taking into account the user's geographical location. For example, if the user lives in a specific region, the AI ​​can suggest actions related to that region. If the user is traveling, the AI ​​can also suggest actions related to the travel destination. If the user is participating in a specific event, the AI ​​can also suggest actions related to that event. This allows the promotion unit to select the optimal action, taking into account the user's geographical location. Some or all of the above processing in the promotion unit may be performed using AI or not. For example, the promotion unit can input the user's geographical location into a generating AI, which can then analyze the data and suggest the optimal action.

[0095] The promotion unit can analyze the user's social media activity and propose actions during the promotion process. For example, the promotion unit can analyze what the user has shared on social media, and the AI ​​can propose actions based on that information. The promotion unit can also analyze posts that the user has shown interest in on social media, and the AI ​​can propose relevant actions based on that information. The promotion unit can also analyze events that the user has participated in on social media, and the AI ​​can propose actions based on that information. In this way, the promotion unit can analyze the user's social media activity and propose actions. Some or all of the above processing in the promotion unit may be performed using AI, or not. For example, the promotion unit can input the user's social media activity into a generating AI, which can then analyze it and propose actions.

[0096] The checking unit can estimate the user's emotions and adjust the progress checking method based on the estimated user emotions. For example, if the user is relaxed, the AI ​​can perform a detailed progress check. If the user is busy, the AI ​​can perform a concise progress check. If the user is excited, the AI ​​can perform a visually engaging progress check. In this way, the checking unit can adjust the progress checking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the progress checking method.

[0097] The checking unit can select the optimal checking method by referring to the user's past action history when checking progress. For example, the checking unit can use AI to perform similar progress checks based on actions the user has successfully performed in the past. The checking unit can also use AI to perform progress checks with a different approach based on actions the user has failed at in the past. The checking unit can also analyze the user's past action history and use AI to select the most effective progress checking method. In this way, the checking unit can select the optimal checking method by referring to the user's past action history. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's past action history into a generating AI, which can then analyze and select the optimal checking method.

[0098] The checking unit can customize the checking method based on the user's current life situation when checking progress. For example, if the user inputs their current work situation, the AI ​​can perform the optimal progress check based on that information. If the user inputs their recent areas of interest, the AI ​​can also perform relevant progress checks based on that information. If the user inputs their current life situation, the AI ​​can also suggest the optimal progress check method based on that information. In this way, the checking unit can customize the checking method based on the user's current life situation. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's current life situation into a generating AI, which can then analyze it and perform the optimal progress check.

[0099] The checking unit can estimate the user's emotions and determine the priority of progress checks based on the estimated emotions. For example, if the user is relaxed, the AI ​​will prioritize high-priority progress checks. If the user is busy, the AI ​​can also prioritize progress checks that are easy to perform. If the user is excited, the AI ​​can also prioritize progress checks that are visually appealing. In this way, the checking unit can determine the priority of progress checks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of progress checks.

[0100] The checking unit can select the optimal checking method when checking progress, taking into account the user's geographical location information. For example, if the user lives in a specific region, the AI ​​can perform progress checks related to that region. If the user is traveling, the AI ​​can also perform progress checks related to the travel destination. If the user is participating in a specific event, the AI ​​can also perform progress checks related to that event. This allows the checking unit to select the optimal checking method, taking into account the user's geographical location information. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input the user's geographical location information into a generating AI, which can then analyze and select the optimal checking method.

[0101] The checking unit can analyze the user's social media activity and propose checking methods during progress checks. For example, the checking unit can analyze content shared by the user on social media, and the AI ​​can perform progress checks based on that information. The checking unit can also analyze posts that the user has shown interest in on social media, and the AI ​​can perform relevant progress checks based on that information. The checking unit can also analyze events that the user has participated in on social media, and the AI ​​can perform progress checks based on that information. In this way, the checking unit can analyze the user's social media activity and propose checking methods. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input the user's social media activity into a generating AI, which can then analyze it and propose checking methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can collect more accurate information by referring to the user's past behavioral history when gathering user profile information. For example, by analyzing what hobbies a user has had in the past and what events they have participated in, and comparing this with their current profile information, it can more accurately identify their hobbies and interests. The data collection unit can also refer to what kind of self-introduction content a user has created in the past and use this to help create their current self-introduction content. Furthermore, the data collection unit can analyze what kind of daily sharing a user has done in the past and understand the trends in their current sharing content. In this way, the data collection unit can collect more accurate information by referring to the user's past behavioral history.

[0104] The suggestion function can analyze a user's profile information and refer to their past activity history to suggest more appropriate friend candidates. For example, by analyzing a user's past hobbies and events they have attended and comparing this with their current profile information, it can identify potential friends with similar interests. The suggestion function can also refer to a user's past self-introduction content and compare it with their current self-introduction content to identify compatible friend candidates. Furthermore, by analyzing a user's past daily sharing and comparing it with their current sharing content, it can identify potential friends with common interests. In this way, the suggestion function can suggest more appropriate friend candidates by referring to a user's past activity history.

[0105] The promotion department can refer to a user's past behavior history to suggest more effective actions when prompting them to take action. For example, it can analyze what actions a user has taken in the past and what actions were successful, and use this information to suggest current actions. The promotion department can also refer to what events a user has participated in in the past to help encourage participation in current events. Furthermore, it can analyze what messages a user has sent in the past to help suggest current messages. In this way, the promotion department can refer to a user's past behavior history to suggest more effective actions.

[0106] The checking unit can perform more accurate progress checks by referring to the user's past behavior history when monitoring the progress of user actions. For example, by analyzing what actions the user has taken in the past and what actions were successful, and comparing this with the current action progress, the progress can be accurately understood. The checking unit can also refer to what events the user has participated in in the past and use this to check their current event participation status. Furthermore, the checking unit can analyze what messages the user has sent in the past and use this to check their current message sending status. In this way, the checking unit can perform more accurate progress checks by referring to the user's past behavior history.

[0107] The data collection unit can prioritize the information to collect by considering the user's current living situation when gathering user profile information. For example, if a user enters their current work situation, the AI ​​can suggest related hobbies and activities based on that information. If a user enters their recent areas of interest, the AI ​​can also suggest relevant friend candidates based on that information. If a user enters their current living situation, the AI ​​can also suggest the best way to create self-introduction content based on that information. In this way, the data collection unit can prioritize the information to collect by considering the user's current living situation.

[0108] The suggestion unit can estimate the user's emotions and adjust how it suggests potential friends based on those emotions. For example, if the user is relaxed, the AI ​​can provide detailed information about potential friends. If the user is busy, the AI ​​can provide concise information about potential friends. If the user is excited, the AI ​​can provide visually appealing information about potential friends. This allows the suggestion unit to adjust how it suggests potential friends based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without AI. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust how it suggests potential friends.

[0109] The facilitator can estimate the user's emotions and adjust how it promotes actions based on those emotions. For example, if the user is relaxed, the AI ​​can provide a detailed description of the action. If the user is busy, the AI ​​can provide a concise description of the action. If the user is excited, the AI ​​can provide a visually appealing description of the action. This allows the facilitator to adjust how it promotes actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the facilitator may be performed using or without AI. For example, the facilitator can input user emotion data into a generative AI, which can estimate the emotion and adjust how it promotes the action.

[0110] The checking unit can estimate the user's emotions and adjust the progress checking method based on the estimated emotions. For example, if the user is relaxed, the AI ​​can perform a detailed progress check. If the user is busy, the AI ​​can perform a concise progress check. If the user is excited, the AI ​​can perform a visually engaging progress check. In this way, the checking unit can adjust the progress checking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI or not. For example, the checking unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the progress checking method.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated emotions. For example, if the user is relaxed, the AI ​​can select a time to prompt the user to create self-introduction content. If the user is busy, the AI ​​can postpone sharing daily activities and collect information during their free time. If the user is excited, the AI ​​can prioritize collecting hobby suggestions and select a time to capture the user's interest. In this way, the data collection unit can adjust the timing of profile information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0112] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the AI ​​can provide detailed suggestions. If the user is busy, the AI ​​can provide concise suggestions. If the user is excited, the AI ​​can provide visually appealing suggestions. This allows the suggestion unit to adjust the length of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the length of the suggestions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects user profile information. This profile information includes name, age, gender, hobbies, interests, etc. The data collection unit stores the information entered by the user in a database and converts it into a format that is easy for AI to analyze. Step 2: The suggestion unit proposes the most suitable friend candidates based on the information collected by the collection unit. The suggestion unit selects friend candidates based on criteria such as shared hobbies, geographical proximity, and compatibility, and uses AI to analyze the user's profile information. Step 3: The Promotion Department encourages users to take action with the friend candidates suggested by the Proposal Department. The Promotion Department prompts users to take actions such as sending messages, sending friend requests, and participating in events, and uses AI to encourage daily actions and progress checks. Step 4: The checking unit checks the progress of the actions prompted by the facilitating unit. The checking unit verifies the completion status of the actions and whether or not there are any responses, uses AI to check the progress of the user's actions, and provides feedback.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and check unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user profile information using the control unit 46A of the smart device 14 and stores it in the database 24 using the identification processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests friend candidates based on the collected information. The promotion unit prompts the user to take action using the control unit 46A of the smart device 14, and the check unit checks the progress of the action using the identification processing unit 290 of the data processing unit 12. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and checking unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user profile information using the control unit 46A of the smart glasses 214 and stores it in the database 24 using the identification processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests friend candidates based on the collected information. The promotion unit prompts the user to take action using the control unit 46A of the smart glasses 214, and the checking unit checks the progress of the action using the identification processing unit 290 of the data processing unit 12. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and checking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user profile information using the control unit 46A of the headset terminal 314 and stores it in the database 24 using the identification processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests friend candidates based on the collected information. The promotion unit prompts the user to take action using the control unit 46A of the headset terminal 314, and the checking unit checks the progress of the action using the identification processing unit 290 of the data processing unit 12. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the collection unit, suggestion unit, promotion unit, and checking unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user profile information by the control unit 46A of the robot 414 and stores it in the database 24 by the identification processing unit 290 of the data processing unit 12. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests friend candidates based on the collected information. The promotion unit prompts the user to take action by the control unit 46A of the robot 414, and the checking unit checks the progress of the action by the identification processing unit 290 of the data processing unit 12. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A collection unit that collects user profile information, Based on the information collected by the aforementioned collection unit, a suggestion unit proposes the most suitable friend candidates, The promotion unit encourages the user to take action regarding the friend candidates proposed by the proposal unit, The system includes a checking unit that checks the progress of the action prompted by the aforementioned acceleration unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information such as user self-introduction content, daily life sharing, and hobby suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the information collected, we suggest the most suitable friend candidates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned promotion unit is Encourage users to take daily actions and check their progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned checking unit is Check the progress of user actions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Suggesting unknown hobbies based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned promotion unit is Encouraging users to participate in events and meetups The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned checking unit is Points or badges are awarded based on the progress of user actions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of profile information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We analyze users' past self-introduction content and daily sharing history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting profile information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of profile information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting profile information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting profile information, we analyze the user's social media activity and collect relevant information. 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 how friend suggestions are made based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the potential friend. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the potential friend. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When making a proposal, prioritize it based on when potential friends submitted their proposals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the potential friends. 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 adjusts how actions are promoted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned promotion unit is During promotion, the system analyzes the user's past action history to select the most suitable promotion method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned promotion unit is During promotion, the means of action are customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned promotion unit is It estimates the user's emotions and prioritizes actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned promotion unit is During promotion, the optimal action is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned promotion unit is During the promotion phase, we analyze users' social media activity and suggest appropriate actions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned checking unit is We estimate the user's emotions and adjust the progress check method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned checking unit is During progress checks, the system selects the most suitable checking method by referring to the user's past action history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned checking unit is When checking progress, customize the checking method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned checking unit is The system estimates the user's emotions and prioritizes progress checks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned checking unit is When checking progress, the optimal checking method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned checking unit is During progress checks, we analyze users' social media activity and propose methods for monitoring it. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user profile information, Based on the information collected by the aforementioned collection unit, a suggestion unit proposes the most suitable friend candidates, The promotion unit encourages the user to take action regarding the friend candidates proposed by the proposal unit, The system includes a checking unit that checks the progress of the action prompted by the aforementioned acceleration unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect information such as user self-introduction content, daily life sharing, and hobby suggestions. The system according to feature 1.

3. The aforementioned proposal section is, Based on the information collected, we suggest the most suitable friend candidates. The system according to feature 1.

4. The aforementioned promotion unit is Encourage users to take daily actions and check their progress. The system according to feature 1.

5. The aforementioned checking unit is Check the progress of user actions. The system according to feature 1.

6. The aforementioned proposal section is, Suggesting unknown hobbies based on the user's interests. The system according to feature 1.

7. The aforementioned promotion unit is Encouraging users to participate in events and meetups The system according to feature 1.

8. The aforementioned checking unit is Points or badges are awarded based on the progress of user actions. The system according to feature 1.

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