system

Abstract

The system according to this embodiment aims to provide a reliable communication environment where users can make new friends. [Solution] The system according to the embodiment comprises a recommendation unit, an announcement unit, an evaluation unit, a monitoring unit, a transition unit, and an advertising unit. The recommendation unit recommends the most suitable open chats and events based on the user's profile and behavioral history. The announcement unit makes announcements and recruits participants for events within the open chat. The evaluation unit implements a user evaluation system to visualize highly reliable users. The monitoring unit monitors inappropriate behavior using AI. The transition unit facilitates the transition of users who have become friends in the open chat to SNS groups. The advertising unit delivers appropriate advertisements to the open chat groups in a recommendation format.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the opportunity for a user to make new friends is scarce and it is difficult to provide a highly reliable communication environment.

[0005] The system according to the embodiment aims to enable a user to make new friends and provide a highly reliable communication environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recommendation unit, an announcement unit, an evaluation unit, a monitoring unit, a transition unit, and an advertising unit. The recommendation unit recommends the most suitable open chats and events based on the user's profile and behavioral history. The announcement unit makes announcements and recruits participants for events within the open chat. The evaluation unit implements a user evaluation system to visualize highly reliable users. The monitoring unit uses AI to monitor inappropriate behavior. The transition unit facilitates the transition of users who have become friends in the open chat to SNS groups. The advertising unit delivers appropriate advertisements to the open chat groups in a recommendation format. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to make new friends and provide a reliable communication environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The friend-making support system according to an embodiment of the present invention is a system that solves the problem of limited opportunities to make new friends in modern society. This system allows people living in the same neighborhood to easily make friends through SNS open chats and enjoy shared hobbies. Users can easily log in with their SNS accounts and participate in open chats categorized by region or hobby. Furthermore, the friend-making support system utilizes AI to recommend the most suitable open chats and events based on the user's profile and activity history. Event announcements and recruitment of participants can be conducted within the open chat, and real-time feedback can be shared. A user evaluation system is introduced to visualize highly reliable users, while also monitoring inappropriate behavior by AI. Users who become friends in an open chat can move to an SNS group if they wish, enabling more intimate communication. This allows for the building of richer relationships while enhancing safety and convenience. As a revenue model, a method of displaying appropriate advertisements in a recommendation format to open chat groups is adopted. For example, for a group of people who enjoy mountain climbing, if a user asks the conversational bot pre-installed in the group, "I want to go to the mountains in Nagano Prefecture on October 2nd, could you suggest some preparations and model routes?", the bot will suggest recommended climbing gear in addition to a list of necessary items, and provide links to e-commerce sites. Companies selling outdoor goods pay advertising fees for this recommendation function, allowing them to directly reach their target audience and generate revenue. In this way, a sustainable business model is built by providing valuable information to users while also offering an effective advertising tool for companies. This revenue model creates a win-win situation by providing users with the information they want while also offering companies an effective advertising tool. As a result, the friend-making support system allows users to make new friends and enjoy shared hobbies.

[0029] The friend-making support system according to this embodiment comprises a recommendation unit, an announcement unit, an evaluation unit, a monitoring unit, a migration unit, and an advertising unit. The recommendation unit recommends the most suitable open chats and events based on the user's profile and behavioral history. For example, the recommendation unit analyzes the user's interests and past behavioral history to recommend the most suitable open chats and events. The recommendation unit can also use AI to analyze the user's profile and recommend the most suitable open chats and events. For example, the recommendation unit recommends relevant open chats and events based on the user's hobbies and interests. The announcement unit makes announcements and recruits participants for events within open chats. For example, the announcement unit announces detailed event information within open chats and recruits participants. The announcement unit can also use AI to optimize the method of announcing events. For example, the announcement unit selects the most suitable announcement method based on the user's interests. The evaluation unit introduces a user evaluation system to visualize highly reliable users. For example, the evaluation unit visualizes highly reliable users based on the user's evaluation score. The evaluation unit can also use AI to analyze user evaluations and visualize highly reliable users. For example, the evaluation unit can identify highly reliable users based on their past behavior history. The monitoring unit monitors for inappropriate behavior using AI. For example, the monitoring unit can use AI to detect inappropriate behavior within open chats and issue warnings. The monitoring unit can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. For example, the monitoring unit can optimize its monitoring algorithm based on past inappropriate behavior data. The migration unit helps users who have become friends in open chats to move to SNS groups. For example, the migration unit can suggest moving to an SNS group if the interaction within the open chat meets certain criteria. The migration unit can also use AI to analyze the user's interaction status and suggest the optimal timing for the migration. For example, the migration unit can suggest moving to an SNS group based on the user's interaction frequency and message content. The advertising unit displays appropriate advertisements in a recommendation format to open chat groups. For example, the advertising unit selects the most suitable advertisements based on the user's interests and preferences and displays them within the open chat.Furthermore, the advertising department can use AI to optimize how ads are displayed. For example, the advertising department can select and display the most suitable ads based on the user's behavioral history. As a result, the friend-making support system according to this embodiment allows users to participate in the most suitable open chats and events and interact with trustworthy users.

[0030] The recommendation team recommends the most suitable open chats and events based on the user's profile and activity history. Specifically, it analyzes the user's past participation history in events and open chats to identify their interests. For example, if a user is interested in music, it will recommend music-related open chats and concert events. The recommendation team also uses AI to analyze user profiles and provide more accurate recommendations. The AI ​​analyzes the user's hobbies and interests using natural language processing technology and extracts relevant keywords. This allows the team to automatically select open chats and events that the user is likely to be interested in. Furthermore, the recommendation team considers not only the user's activity history but also the ratings and feedback of other users. For example, it prioritizes recommending open chats and events that have received high ratings from other users with similar interests. This makes it easier for users to participate in open chats and events that are right for them, increasing their opportunities to make friends.

[0031] The announcement team will announce events and recruit participants within the open chat. Specifically, they will announce event details and recruit participants within the open chat. For example, they will provide information such as the date, time, location, and participation requirements in text, image, and video formats. The announcement team will also use AI to optimize event announcement methods. The AI ​​will select the most suitable announcement method based on user interests. For example, if a user prefers visual content, announcements will heavily utilize images and videos. The announcement team will also analyze user behavior history to select the optimal announcement timing. For example, if a user is active at night, event announcements will be made at night. This allows the announcement team to effectively communicate event information to users and increase their willingness to participate. Furthermore, the announcement team will collect user feedback and use it to improve announcement methods. For example, they will analyze user reactions to announcements and reflect them in future announcements. This allows the announcement team to consistently provide the most optimal announcement method and increase user participation.

[0032] The evaluation department will implement a user evaluation system to visualize highly reliable users. Specifically, it will visualize highly reliable users based on their evaluation scores. Evaluation scores are calculated based on feedback from other users, participation history, and behavioral history. For example, users who have received high ratings from other users or who actively participate in events will have high evaluation scores. The evaluation department can also use AI to analyze user evaluations and visualize highly reliable users. The AI ​​analyzes users' past behavioral history and feedback to identify highly reliable users. This allows other users to interact with highly reliable users with confidence. Furthermore, the evaluation department will regularly update user evaluation scores to provide the latest information. For example, evaluation scores will be updated whenever new feedback or behavioral history is added, ensuring that the evaluation information is always up-to-date. This allows the evaluation department to accurately assess user reliability and provide reliable information to other users.

[0033] The monitoring department uses AI to monitor inappropriate behavior. Specifically, it uses AI to detect inappropriate behavior within open chats and issue warnings. The AI ​​analyzes chat content using natural language processing technology to identify inappropriate behavior. For example, it can detect discriminatory remarks or violent behavior and issue immediate warnings. The monitoring department can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. The AI ​​optimizes the monitoring algorithm based on past data of inappropriate behavior. This allows the monitoring department to detect inappropriate behavior with higher accuracy and respond quickly. Furthermore, the monitoring department accepts reports from users and strengthens its response to inappropriate behavior. For example, if a user reports inappropriate behavior, the AI ​​analyzes the content and takes appropriate action. In this way, the monitoring department can maintain a healthy environment within open chats and provide a safe space for users to interact.

[0034] The transition unit helps users who have become friends in open chats move to social networking groups (SNS groups). Specifically, it suggests a move to an SNS group when the interaction within the open chat meets certain criteria. For example, it might suggest a move if the number of messages exchanged between users exceeds a certain limit, or if specific keywords are used frequently. The transition unit can also use AI to analyze user interaction patterns and suggest the optimal timing for the move. The AI ​​analyzes the frequency of user interaction and message content to identify the appropriate time to move to an SNS group. This allows users to move to the SNS group naturally and continue deeper interactions. Furthermore, the transition unit provides follow-up support after the move, helping users adapt smoothly to the new environment. For example, it monitors activity in the SNS group after the move and provides support as needed. In this way, the transition unit can help users smoothly transition to the SNS group and continue the process of making friends.

[0035] The advertising department will recommend appropriate advertisements to open chat groups. Specifically, it will select the most suitable advertisements based on the user's interests and preferences and display them within the open chat. For example, if a user is interested in sports, advertisements for sports-related products and events will be displayed. The advertising department can also use AI to optimize how advertisements are displayed. The AI ​​analyzes the user's behavior history to select and display the most suitable advertisements. For example, it will display relevant advertisements based on advertisements the user has clicked on in the past and their purchase history. This allows the advertising department to effectively deliver advertisements to users and maximize their effectiveness. Furthermore, the advertising department will regularly evaluate and improve the performance of advertisements. For example, it will analyze the click-through rate and conversion rate of advertisements and prioritize the display of high-performing advertisements. This allows the advertising department to always provide the most suitable advertisements and attract user interest.

[0036] The recommendation unit includes a collection unit that collects user profiles and behavioral history. The recommendation unit collects profile information such as the user's age, gender, interests, and past behavioral history. The recommendation unit can also collect behavioral history such as events the user has participated in, message exchanges, and browsing history. For example, the recommendation unit collects the types and frequency of events the user has participated in and recommends the most suitable open chats and events. By collecting user profiles and behavioral history, more accurate recommendations become possible. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user profile information into a generating AI and have the generating AI perform analysis of the profile information.

[0037] The monitoring unit includes a detection unit that uses AI to detect inappropriate behavior. The monitoring unit, for example, uses AI to detect inappropriate behavior within an open chat and issues a warning. The monitoring unit can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. For example, the monitoring unit optimizes its monitoring algorithm based on past inappropriate behavior data. This provides a safe communication environment by detecting inappropriate behavior. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input message data from the open chat into a generating AI and have the generating AI perform the detection of inappropriate behavior.

[0038] The recommendation system analyzes the user's past participation history and selects the optimal recommendation method. For example, the recommendation system analyzes the types of events the user has participated in in the past and recommends similar events. It can also analyze the activity frequency of open chats the user has participated in in the past and recommend chats with an appropriate frequency. Furthermore, the recommendation system can recommend events with high ratings based on the user's ratings of past events. This allows the system to recommend the most suitable open chats and events to the user based on their past participation history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's participation history data into a generating AI and have the generating AI select the optimal recommendation method.

[0039] The recommendation system filters recommendations based on the user's current lifestyle and areas of interest. For example, if a user enters their current lifestyle, the recommendation system will recommend events that match that lifestyle. The recommendation system can also extract the user's areas of interest from their profile and recommend relevant open chats. Furthermore, the recommendation system can recommend events at appropriate times, according to the user's daily rhythm. This allows for the recommendation of open chats and events that are tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0040] The recommendation system prioritizes recommending highly relevant open chats and events, taking into account the user's geographical location. For example, it might prioritize recommending events held near the user's current location. It can also recommend regional open chats based on the user's geographical location. Furthermore, it can recommend events held near places the user frequently visits. This allows for the recommendation of highly relevant open chats and events based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system could input the user's geographical location into a generating AI and have the AI ​​recommend highly relevant open chats and events.

[0041] The recommendation unit analyzes the user's social media activity and recommends relevant open chats and events. For example, the recommendation unit recommends open chats related to topics the user has shown interest in on social media. It can also recommend relevant events based on events the user follows on social media. Furthermore, the recommendation unit can analyze the frequency of the user's social media activity and recommend open chats at an appropriate frequency. This allows for the recommendation of relevant open chats and events based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI perform the recommendation of relevant open chats and events.

[0042] The notification unit adjusts the level of detail in the notification based on the importance of the event. For example, for important events, the notification unit will provide a notification with detailed information. For general events, the notification unit can also provide a notification with concise information. Furthermore, for urgent events, the notification unit can provide a notification quickly. This enables optimal notification according to the importance of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input event importance data into a generating AI and have the generating AI adjust the level of detail in the notification.

[0043] The notification unit applies different notification algorithms depending on the event category when making an announcement. For example, in the case of a sports event, the notification unit will create a visually dynamic announcement. In the case of a cultural event, the notification unit may also create an announcement that includes detailed descriptions. Furthermore, in the case of a social event, the notification unit may also create an announcement that includes participant profiles. This enables optimal announcements tailored to the event category. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input event category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0044] The notification unit determines the priority of notifications based on the event location when making a notification. For example, the notification unit will prioritize notifying users of events held near the user's current location. It can also prioritize notifying users of events held near places the user frequently visits. Furthermore, the notification unit can prioritize notifying users of events in specific regions based on the user's geographical location information. This enables optimal notifications based on the event location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI determine the notification priority.

[0045] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit may refer to the evaluation methods used for events that the user has previously given high ratings to. The evaluation unit may also avoid evaluation methods used for events that the user has previously given low ratings to. Furthermore, the evaluation unit may analyze the user's evaluation history and propose the optimal evaluation method. This allows the evaluation unit to provide the optimal evaluation method based on past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input the user's evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0046] The evaluation unit weights the evaluation based on the user's attribute information during the evaluation process. For example, the evaluation unit may weight the evaluation based on the user's age. It can also weight the evaluation based on the user's gender. Furthermore, it can weight the evaluation based on the user's occupation. This allows for the provision of optimal evaluation weighting based on the user's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's attribute information into a generating AI and have the generating AI perform the evaluation weighting.

[0047] The evaluation unit adjusts the display method of evaluations while considering the user's geographical location information. For example, the evaluation unit prioritizes displaying evaluations for locations close to the user's current location. It can also prioritize displaying evaluations for locations the user frequently visits. Furthermore, the evaluation unit can display evaluations for each region based on the user's geographical location information. This allows for the provision of an optimal evaluation display method based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the evaluation display method.

[0048] The monitoring unit optimizes the monitoring algorithm by referring to past inappropriate behavior data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm based on past inappropriate behavior data. The monitoring unit can also analyze patterns of past inappropriate behavior and adjust the monitoring algorithm accordingly. Furthermore, the monitoring unit can optimize the monitoring algorithm based on the frequency of past inappropriate behavior. This allows the monitoring unit to provide an optimal monitoring algorithm based on past inappropriate behavior data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past inappropriate behavior data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0049] The monitoring unit improves the accuracy of monitoring by considering user attribute information during monitoring. For example, the monitoring unit improves the accuracy of monitoring based on the user's age. It can also improve the accuracy of monitoring based on the user's gender. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's occupation. This allows for the provision of optimal monitoring accuracy based on user attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user attribute information into a generating AI and have the generating AI perform the monitoring accuracy improvement.

[0050] The monitoring unit performs monitoring while taking the user's geographical location information into consideration. For example, the monitoring unit prioritizes monitoring inappropriate behavior in locations close to the user's current location. It can also prioritize monitoring inappropriate behavior in locations frequently visited by the user. Furthermore, the monitoring unit can perform regional monitoring based on the user's geographical location information. This allows for optimal monitoring based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0051] The migration unit selects the optimal migration method during the migration process by referring to the user's past communication history. For example, the migration unit may select the optimal migration method based on migration methods previously used by the user. The migration unit can also analyze the user's past communication history and propose the optimal migration method. Furthermore, the migration unit can select the optimal migration method based on the user's past migration history. This allows the system to provide the optimal migration method based on past communication history. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's communication history data into a generating AI and have the generating AI select the optimal migration method.

[0052] The migration unit customizes the migration method based on the user's attribute information during the migration process. For example, the migration unit may customize the migration method based on the user's age. It can also customize the migration method based on the user's gender. Furthermore, the migration unit may customize the migration method based on the user's occupation. This allows for the provision of the optimal migration method based on the user's attribute information. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's attribute information into a generating AI and have the generating AI perform the customization of the migration method.

[0053] The migration unit selects the optimal migration method during migration, taking into account the user's geographical location information. For example, the migration unit may prioritize migrations to locations close to the user's current location. It can also prioritize migrations to locations frequently visited by the user. Furthermore, the migration unit can perform region-specific migrations based on the user's geographical location information. This allows the system to provide the optimal migration method based on the user's geographical location information. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal migration method.

[0054] The advertising department selects the most suitable advertisements by referring to the user's past purchase history when displaying ads. For example, the advertising department displays ads related to products the user has previously purchased. The advertising department can also analyze the user's purchase history and advertise products that the user might be interested in. Furthermore, the advertising department can prioritize displaying ads for products that the user has previously given high ratings to. This allows the advertising department to provide optimal advertisements based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the user's purchase history data into a generating AI and have the generating AI select the most suitable advertisements.

[0055] The advertising department weights ads based on user attribute information when displaying them. For example, the advertising department may weight ads based on the user's age. It can also weight ads based on the user's gender. Furthermore, it can weight ads based on the user's occupation. This allows for the provision of optimal ad weighting based on user attribute information. Some or all of the above processing in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input user attribute information into a generating AI and have the generating AI perform the ad weighting.

[0056] The advertising department displays the most suitable advertisements when showing them, taking into account the user's geographical location. For example, the advertising department can display advertisements for products that can be purchased near the user's current location. It can also display advertisements for products that can be purchased in places the user frequently visits. Furthermore, the advertising department can display region-specific advertisements based on the user's geographical location. This allows for the provision of optimal advertisements based on the user's geographical location. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the user's geographical location information into a generating AI and have the generating AI execute the display of optimal advertisements.

[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 friend-making support system can also recommend open chats and events based on the user's musical preferences. For example, if a user likes a particular genre of music, it can recommend events and chats related to that genre. Similarly, if a user follows a specific artist, it can recommend events and chats related to that artist. Furthermore, if a user is interested in music festivals, it can recommend chats that provide information related to festivals. This allows the system to recommend the most suitable open chats and events based on the user's musical tastes.

[0059] The friend-making support system can also recommend open chats and events based on the user's reading history. For example, if a user prefers a particular genre of books, it can recommend events and chats related to that genre. Similarly, if a user follows a specific author, it can recommend events and chats related to that author. Furthermore, if a user is interested in book clubs, it can recommend chats that provide information related to book clubs. This allows the system to recommend the most suitable open chats and events based on the user's reading history.

[0060] The friend-making support system can also recommend open chats and events based on the user's travel history. For example, if a user has visited a particular region, it can recommend events and chats related to that region. Similarly, if a user is interested in a specific tourist destination, it can recommend events and chats related to that destination. Furthermore, if a user is planning a trip, it can recommend chats that provide travel-related information. This allows the system to recommend the most suitable open chats and events based on the user's travel history.

[0061] The friend-making support system can also recommend open chats and events based on the user's occupation. For example, if a user works in a specific profession, it can recommend events and chats related to that profession. If a user is aiming for career advancement, it can recommend chats that provide career-related information. Furthermore, if a user wants to learn skills related to their profession, it can recommend chats that provide information on skill development. This allows the system to recommend the most suitable open chats and events for each user's occupation.

[0062] The friend-making support system can also recommend open chats and events based on the user's interests. For example, if a user likes a particular sport, it can recommend events and chats related to that sport. Similarly, if a user is interested in a particular art form, it can recommend events and chats related to that art. Furthermore, if a user is interested in cooking, it can recommend chats that provide information about cooking. This allows the system to recommend the most suitable open chats and events based on the user's interests.

[0063] The friend-making support system can also recommend open chats and events based on the user's educational background. For example, if a user holds a specific degree, it can recommend events and chats related to that degree. Similarly, if a user is interested in a particular field, it can recommend events and chats related to that field. Furthermore, if a user is seeking academic information, it can recommend chats that provide academic information. This allows the system to recommend the most suitable open chats and events based on the user's educational background.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The recommendation team recommends the most suitable open chats and events based on the user's profile and activity history. For example, it analyzes the user's interests and past activity history, uses AI to analyze the user's profile, and recommends relevant open chats and events. Step 2: The announcement team will announce events and recruit participants within the open chat. For example, they will announce event details and recruit participants within the open chat. They will also use AI to select the most suitable announcement method based on user interests and preferences. Step 3: The evaluation department implements a user evaluation system to visualize highly reliable users. For example, it uses AI to identify and visualize highly reliable users based on user evaluation scores and past behavioral history. Step 4: The monitoring unit uses AI to monitor for inappropriate behavior. For example, it uses AI to detect inappropriate behavior within open chats and issue warnings. It also optimizes the monitoring algorithm based on past data on inappropriate behavior to improve monitoring accuracy. Step 5: The transition phase involves users who have become friends in the open chat moving to the SNS group. For example, if the interaction within the open chat meets certain criteria, a transition to the SNS group will be suggested. AI will also be used to analyze the users' interaction status and suggest the optimal timing for the transition. Step 6: The advertising department will deliver appropriate ads to the open chat group in a recommendation format. For example, they will select the most suitable ads based on the user's interests and use AI to optimize how the ads are displayed.

[0066] (Example of form 2) The friend-making support system according to an embodiment of the present invention is a system that solves the problem of limited opportunities to make new friends in modern society. This system allows people living in the same neighborhood to easily make friends through SNS open chats and enjoy shared hobbies. Users can easily log in with their SNS accounts and participate in open chats categorized by region or hobby. Furthermore, the friend-making support system utilizes AI to recommend the most suitable open chats and events based on the user's profile and activity history. Event announcements and recruitment of participants can be conducted within the open chat, and real-time feedback can be shared. A user evaluation system is introduced to visualize highly reliable users, while also monitoring inappropriate behavior by AI. Users who become friends in an open chat can move to an SNS group if they wish, enabling more intimate communication. This allows for the building of richer relationships while enhancing safety and convenience. As a revenue model, a method of displaying appropriate advertisements in a recommendation format to open chat groups is adopted. For example, for a group of people who enjoy mountain climbing, if a user asks the conversational bot pre-installed in the group, "I want to go to the mountains in Nagano Prefecture on October 2nd, could you suggest some preparations and model routes?", the bot will suggest recommended climbing gear in addition to a list of necessary items, and provide links to e-commerce sites. Companies selling outdoor goods pay advertising fees for this recommendation function, allowing them to directly reach their target audience and generate revenue. In this way, a sustainable business model is built by providing valuable information to users while also offering an effective advertising tool for companies. This revenue model creates a win-win situation by providing users with the information they want while also offering companies an effective advertising tool. As a result, the friend-making support system allows users to make new friends and enjoy shared hobbies.

[0067] The friend-making support system according to this embodiment comprises a recommendation unit, an announcement unit, an evaluation unit, a monitoring unit, a migration unit, and an advertising unit. The recommendation unit recommends the most suitable open chats and events based on the user's profile and behavioral history. For example, the recommendation unit analyzes the user's interests and past behavioral history to recommend the most suitable open chats and events. The recommendation unit can also use AI to analyze the user's profile and recommend the most suitable open chats and events. For example, the recommendation unit recommends relevant open chats and events based on the user's hobbies and interests. The announcement unit makes announcements and recruits participants for events within open chats. For example, the announcement unit announces detailed event information within open chats and recruits participants. The announcement unit can also use AI to optimize the method of announcing events. For example, the announcement unit selects the most suitable announcement method based on the user's interests. The evaluation unit introduces a user evaluation system to visualize highly reliable users. For example, the evaluation unit visualizes highly reliable users based on the user's evaluation score. The evaluation unit can also use AI to analyze user evaluations and visualize highly reliable users. For example, the evaluation unit can identify highly reliable users based on their past behavior history. The monitoring unit monitors for inappropriate behavior using AI. For example, the monitoring unit can use AI to detect inappropriate behavior within open chats and issue warnings. The monitoring unit can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. For example, the monitoring unit can optimize its monitoring algorithm based on past inappropriate behavior data. The migration unit helps users who have become friends in open chats to move to SNS groups. For example, the migration unit can suggest moving to an SNS group if the interaction within the open chat meets certain criteria. The migration unit can also use AI to analyze the user's interaction status and suggest the optimal timing for the migration. For example, the migration unit can suggest moving to an SNS group based on the user's interaction frequency and message content. The advertising unit displays appropriate advertisements in a recommendation format to open chat groups. For example, the advertising unit selects the most suitable advertisements based on the user's interests and preferences and displays them within the open chat.Furthermore, the advertising department can use AI to optimize how ads are displayed. For example, the advertising department can select and display the most suitable ads based on the user's behavioral history. As a result, the friend-making support system according to this embodiment allows users to participate in the most suitable open chats and events and interact with trustworthy users.

[0068] The recommendation team recommends the most suitable open chats and events based on the user's profile and activity history. Specifically, it analyzes the user's past participation history in events and open chats to identify their interests. For example, if a user is interested in music, it will recommend music-related open chats and concert events. The recommendation team also uses AI to analyze user profiles and provide more accurate recommendations. The AI ​​analyzes the user's hobbies and interests using natural language processing technology and extracts relevant keywords. This allows the team to automatically select open chats and events that the user is likely to be interested in. Furthermore, the recommendation team considers not only the user's activity history but also the ratings and feedback of other users. For example, it prioritizes recommending open chats and events that have received high ratings from other users with similar interests. This makes it easier for users to participate in open chats and events that are right for them, increasing their opportunities to make friends.

[0069] The announcement team will announce events and recruit participants within the open chat. Specifically, they will announce event details and recruit participants within the open chat. For example, they will provide information such as the date, time, location, and participation requirements in text, image, and video formats. The announcement team will also use AI to optimize event announcement methods. The AI ​​will select the most suitable announcement method based on user interests. For example, if a user prefers visual content, announcements will heavily utilize images and videos. The announcement team will also analyze user behavior history to select the optimal announcement timing. For example, if a user is active at night, event announcements will be made at night. This allows the announcement team to effectively communicate event information to users and increase their willingness to participate. Furthermore, the announcement team will collect user feedback and use it to improve announcement methods. For example, they will analyze user reactions to announcements and reflect them in future announcements. This allows the announcement team to consistently provide the most optimal announcement method and increase user participation.

[0070] The evaluation department will implement a user evaluation system to visualize highly reliable users. Specifically, it will visualize highly reliable users based on their evaluation scores. Evaluation scores are calculated based on feedback from other users, participation history, and behavioral history. For example, users who have received high ratings from other users or who actively participate in events will have high evaluation scores. The evaluation department can also use AI to analyze user evaluations and visualize highly reliable users. The AI ​​analyzes users' past behavioral history and feedback to identify highly reliable users. This allows other users to interact with highly reliable users with confidence. Furthermore, the evaluation department will regularly update user evaluation scores to provide the latest information. For example, evaluation scores will be updated whenever new feedback or behavioral history is added, ensuring that the evaluation information is always up-to-date. This allows the evaluation department to accurately assess user reliability and provide reliable information to other users.

[0071] The monitoring department uses AI to monitor inappropriate behavior. Specifically, it uses AI to detect inappropriate behavior within open chats and issue warnings. The AI ​​analyzes chat content using natural language processing technology to identify inappropriate behavior. For example, it can detect discriminatory remarks or violent behavior and issue immediate warnings. The monitoring department can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. The AI ​​optimizes the monitoring algorithm based on past data of inappropriate behavior. This allows the monitoring department to detect inappropriate behavior with higher accuracy and respond quickly. Furthermore, the monitoring department accepts reports from users and strengthens its response to inappropriate behavior. For example, if a user reports inappropriate behavior, the AI ​​analyzes the content and takes appropriate action. In this way, the monitoring department can maintain a healthy environment within open chats and provide a safe space for users to interact.

[0072] The transition unit helps users who have become friends in open chats move to social networking groups (SNS groups). Specifically, it suggests a move to an SNS group when the interaction within the open chat meets certain criteria. For example, it might suggest a move if the number of messages exchanged between users exceeds a certain limit, or if specific keywords are used frequently. The transition unit can also use AI to analyze user interaction patterns and suggest the optimal timing for the move. The AI ​​analyzes the frequency of user interaction and message content to identify the appropriate time to move to an SNS group. This allows users to move to the SNS group naturally and continue deeper interactions. Furthermore, the transition unit provides follow-up support after the move, helping users adapt smoothly to the new environment. For example, it monitors activity in the SNS group after the move and provides support as needed. In this way, the transition unit can help users smoothly transition to the SNS group and continue the process of making friends.

[0073] The advertising department will recommend appropriate advertisements to open chat groups. Specifically, it will select the most suitable advertisements based on the user's interests and preferences and display them within the open chat. For example, if a user is interested in sports, advertisements for sports-related products and events will be displayed. The advertising department can also use AI to optimize how advertisements are displayed. The AI ​​analyzes the user's behavior history to select and display the most suitable advertisements. For example, it will display relevant advertisements based on advertisements the user has clicked on in the past and their purchase history. This allows the advertising department to effectively deliver advertisements to users and maximize their effectiveness. Furthermore, the advertising department will regularly evaluate and improve the performance of advertisements. For example, it will analyze the click-through rate and conversion rate of advertisements and prioritize the display of high-performing advertisements. This allows the advertising department to always provide the most suitable advertisements and attract user interest.

[0074] The recommendation unit includes a collection unit that collects user profiles and behavioral history. The recommendation unit collects profile information such as the user's age, gender, interests, and past behavioral history. The recommendation unit can also collect behavioral history such as events the user has participated in, message exchanges, and browsing history. For example, the recommendation unit collects the types and frequency of events the user has participated in and recommends the most suitable open chats and events. By collecting user profiles and behavioral history, more accurate recommendations become possible. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user profile information into a generating AI and have the generating AI perform analysis of the profile information.

[0075] The monitoring unit includes a detection unit that uses AI to detect inappropriate behavior. The monitoring unit, for example, uses AI to detect inappropriate behavior within an open chat and issues a warning. The monitoring unit can also use AI to learn patterns of inappropriate behavior and improve monitoring accuracy. For example, the monitoring unit optimizes its monitoring algorithm based on past inappropriate behavior data. This provides a safe communication environment by detecting inappropriate behavior. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input message data from the open chat into a generating AI and have the generating AI perform the detection of inappropriate behavior.

[0076] The recommendation system estimates the user's emotions and adjusts the content of recommended open chats and events based on the estimated emotions. For example, if a user is feeling stressed, the recommendation system will recommend relaxing hobby-related open chats and events. It can also recommend active events and chats if the user is feeling excited. Furthermore, if a user is feeling lonely, the recommendation system can recommend active open chats. This allows for the recommendation of the most suitable open chats and events based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the recommendation system may be performed using AI or not. For example, the recommendation system can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0077] The recommendation system analyzes the user's past participation history and selects the optimal recommendation method. For example, the recommendation system analyzes the types of events the user has participated in in the past and recommends similar events. It can also analyze the activity frequency of open chats the user has participated in in the past and recommend chats with an appropriate frequency. Furthermore, the recommendation system can recommend events with high ratings based on the user's ratings of past events. This allows the system to recommend the most suitable open chats and events to the user based on their past participation history. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's participation history data into a generating AI and have the generating AI select the optimal recommendation method.

[0078] The recommendation system filters recommendations based on the user's current lifestyle and areas of interest. For example, if a user enters their current lifestyle, the recommendation system will recommend events that match that lifestyle. The recommendation system can also extract the user's areas of interest from their profile and recommend relevant open chats. Furthermore, the recommendation system can recommend events at appropriate times, according to the user's daily rhythm. This allows for the recommendation of open chats and events that are tailored to the user's lifestyle and areas of interest. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0079] The recommendation system estimates the user's emotions and prioritizes the open chats and events to recommend based on those estimated emotions. For example, if the user is tired, the recommendation system will prioritize recommending relaxing events. It can also prioritize recommending active events if the user is excited. Furthermore, if the user is feeling lonely, the recommendation system can prioritize recommending active open chats. This allows for the recommendation of open chats and events with priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0080] The recommendation system prioritizes recommending highly relevant open chats and events, taking into account the user's geographical location. For example, it might prioritize recommending events held near the user's current location. It can also recommend regional open chats based on the user's geographical location. Furthermore, it can recommend events held near places the user frequently visits. This allows for the recommendation of highly relevant open chats and events based on the user's geographical location. Some or all of the above processing in the recommendation system may be performed using AI, or not. For example, the recommendation system could input the user's geographical location into a generating AI and have the AI ​​recommend highly relevant open chats and events.

[0081] The recommendation unit analyzes the user's social media activity and recommends relevant open chats and events. For example, the recommendation unit recommends open chats related to topics the user has shown interest in on social media. It can also recommend relevant events based on events the user follows on social media. Furthermore, the recommendation unit can analyze the frequency of the user's social media activity and recommend open chats at an appropriate frequency. This allows for the recommendation of relevant open chats and events based on the user's social media activity. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input the user's social media activity data into a generating AI and have the generating AI perform the recommendation of relevant open chats and events.

[0082] The notification unit estimates the user's emotions and adjusts the event notification method based on the estimated emotions. For example, if the user is excited, the notification unit will deliver a visually stimulating notification. If the user is relaxed, the notification unit can deliver a notification in a calm tone. Furthermore, if the user is stressed, the notification unit can deliver a simple and easy-to-understand notification. This allows for the provision of the most appropriate notification method according to 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0083] The notification unit adjusts the level of detail in the notification based on the importance of the event. For example, for important events, the notification unit will provide a notification with detailed information. For general events, the notification unit can also provide a notification with concise information. Furthermore, for urgent events, the notification unit can provide a notification quickly. This enables optimal notification according to the importance of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input event importance data into a generating AI and have the generating AI adjust the level of detail in the notification.

[0084] The notification unit applies different notification algorithms depending on the event category when making an announcement. For example, in the case of a sports event, the notification unit will create a visually dynamic announcement. In the case of a cultural event, the notification unit may also create an announcement that includes detailed descriptions. Furthermore, in the case of a social event, the notification unit may also create an announcement that includes participant profiles. This enables optimal announcements tailored to the event category. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input event category data into a generating AI and have the generating AI execute the application of the notification algorithm.

[0085] The notification unit estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit adjusts the timing of the notification. It can also deliver notifications at an appropriate time if the user is busy. Furthermore, if the user is excited, the notification unit can deliver notifications immediately. This allows for notifications to be delivered at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0086] The notification unit determines the priority of notifications based on the event location when making a notification. For example, the notification unit will prioritize notifying users of events held near the user's current location. It can also prioritize notifying users of events held near places the user frequently visits. Furthermore, the notification unit can prioritize notifying users of events in specific regions based on the user's geographical location information. This enables optimal notifications based on the event location. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's geographical location information into a generating AI and have the generating AI determine the notification priority.

[0087] The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation based on the estimated emotions. For example, if the user is nervous, the evaluation unit provides a simple and highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. This allows for the provision of an optimal evaluation display method tailored to 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 evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0088] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit may refer to the evaluation methods used for events that the user has previously given high ratings to. The evaluation unit may also avoid evaluation methods used for events that the user has previously given low ratings to. Furthermore, the evaluation unit may analyze the user's evaluation history and propose the optimal evaluation method. This allows the evaluation unit to provide the optimal evaluation method based on past evaluation history. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may input the user's evaluation history data into a generating AI and have the generating AI select the optimal evaluation method.

[0089] The evaluation unit weights the evaluation based on the user's attribute information during the evaluation process. For example, the evaluation unit may weight the evaluation based on the user's age. It can also weight the evaluation based on the user's gender. Furthermore, it can weight the evaluation based on the user's occupation. This allows for the provision of optimal evaluation weighting based on the user's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's attribute information into a generating AI and have the generating AI perform the evaluation weighting.

[0090] The evaluation unit estimates the user's emotions and determines the evaluation priority based on the estimated emotions. For example, if the user is excited, the evaluation unit will give a higher evaluation priority. Conversely, if the user is relaxed, the evaluation unit may also give a lower evaluation priority. Furthermore, if the user is stressed, the evaluation unit may adjust the evaluation priority. This allows for the provision of an optimal evaluation priority according to 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 evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0091] The evaluation unit adjusts the display method of evaluations while considering the user's geographical location information. For example, the evaluation unit prioritizes displaying evaluations for locations close to the user's current location. It can also prioritize displaying evaluations for locations the user frequently visits. Furthermore, the evaluation unit can display evaluations for each region based on the user's geographical location information. This allows for the provision of an optimal evaluation display method based on the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the evaluation display method.

[0092] The monitoring unit estimates the user's emotions and adjusts the monitoring criteria based on the estimated emotions. For example, if the user is excited, the monitoring unit applies strict monitoring criteria. It can also apply lenient monitoring criteria if the user is relaxed. Furthermore, if the user is stressed, the monitoring unit can adjust the monitoring criteria appropriately. This allows for the provision of optimal monitoring criteria tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0093] The monitoring unit optimizes the monitoring algorithm by referring to past inappropriate behavior data during monitoring. For example, the monitoring unit optimizes the monitoring algorithm based on past inappropriate behavior data. The monitoring unit can also analyze patterns of past inappropriate behavior and adjust the monitoring algorithm accordingly. Furthermore, the monitoring unit can optimize the monitoring algorithm based on the frequency of past inappropriate behavior. This allows the monitoring unit to provide an optimal monitoring algorithm based on past inappropriate behavior data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past inappropriate behavior data into a generating AI and have the generating AI perform the optimization of the monitoring algorithm.

[0094] The monitoring unit improves the accuracy of monitoring by considering user attribute information during monitoring. For example, the monitoring unit improves the accuracy of monitoring based on the user's age. It can also improve the accuracy of monitoring based on the user's gender. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the user's occupation. This allows for the provision of optimal monitoring accuracy based on user attribute information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user attribute information into a generating AI and have the generating AI perform the monitoring accuracy improvement.

[0095] The monitoring unit estimates the user's emotions and adjusts the order in which the monitoring results are displayed based on the estimated emotions. For example, if the user is excited, the monitoring unit can immediately display the monitoring results. It can also delay displaying the results if the user is relaxed. Furthermore, if the user is stressed, the monitoring unit can display the monitoring results at an appropriate time. This provides an optimal display order of monitoring results tailored to 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-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0096] The monitoring unit performs monitoring while taking the user's geographical location information into consideration. For example, the monitoring unit prioritizes monitoring inappropriate behavior in locations close to the user's current location. It can also prioritize monitoring inappropriate behavior in locations frequently visited by the user. Furthermore, the monitoring unit can perform regional monitoring based on the user's geographical location information. This allows for optimal monitoring based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI perform the monitoring.

[0097] The transition unit estimates the user's emotions and adjusts the timing of the transition based on the estimated emotions. For example, if the user is relaxed, the transition unit adjusts the timing of the transition. If the user is excited, the transition unit can also perform the transition immediately. Furthermore, if the user is stressed, the transition unit can perform the transition at an appropriate time. This allows for the provision of optimal transition timing according to 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 transition unit may be performed using AI, or not using AI. For example, the transition unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0098] The migration unit selects the optimal migration method during the migration process by referring to the user's past communication history. For example, the migration unit may select the optimal migration method based on migration methods previously used by the user. The migration unit can also analyze the user's past communication history and propose the optimal migration method. Furthermore, the migration unit can select the optimal migration method based on the user's past migration history. This allows the system to provide the optimal migration method based on past communication history. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's communication history data into a generating AI and have the generating AI select the optimal migration method.

[0099] The migration unit customizes the migration method based on the user's attribute information during the migration process. For example, the migration unit may customize the migration method based on the user's age. It can also customize the migration method based on the user's gender. Furthermore, the migration unit may customize the migration method based on the user's occupation. This allows for the provision of the optimal migration method based on the user's attribute information. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's attribute information into a generating AI and have the generating AI perform the customization of the migration method.

[0100] The transition unit estimates the user's emotions and determines the transition priority based on the estimated emotions. For example, if the user is excited, the transition unit will prioritize the transition higher. Conversely, if the user is relaxed, the transition unit may also prioritize the transition lower. Furthermore, if the user is stressed, the transition unit may adjust the transition priority. This allows for the provision of an optimal transition priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transition unit may be performed using AI or not. For example, the transition unit can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0101] The migration unit selects the optimal migration method during migration, taking into account the user's geographical location information. For example, the migration unit may prioritize migrations to locations close to the user's current location. It can also prioritize migrations to locations frequently visited by the user. Furthermore, the migration unit can perform region-specific migrations based on the user's geographical location information. This allows the system to provide the optimal migration method based on the user's geographical location information. Some or all of the above-described processes in the migration unit may be performed using AI, for example, or without AI. For example, the migration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal migration method.

[0102] The advertising department estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. For example, if the user is relaxed, the advertising department will display ads in a calm tone. If the user is excited, the advertising department may also display visually stimulating ads. Furthermore, if the user is stressed, the advertising department may display simple and easy-to-understand ads. This allows for the provision of the most appropriate ad display method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0103] The advertising department selects the most suitable advertisements by referring to the user's past purchase history when displaying ads. For example, the advertising department displays ads related to products the user has previously purchased. The advertising department can also analyze the user's purchase history and advertise products that the user might be interested in. Furthermore, the advertising department can prioritize displaying ads for products that the user has previously given high ratings to. This allows the advertising department to provide optimal advertisements based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input the user's purchase history data into a generating AI and have the generating AI select the most suitable advertisements.

[0104] The advertising department weights ads based on user attribute information when displaying them. For example, the advertising department may weight ads based on the user's age. It can also weight ads based on the user's gender. Furthermore, it can weight ads based on the user's occupation. This allows for the provision of optimal ad weighting based on user attribute information. Some or all of the above processing in the advertising department may be performed using AI, for example, or not using AI. For example, the advertising department can input user attribute information into a generating AI and have the generating AI perform the ad weighting.

[0105] The advertising department estimates the user's emotions and determines the priority of advertisements based on the estimated emotions. For example, if the user is excited, the advertising department will give a higher priority to the advertisement. Conversely, if the user is relaxed, the advertising department may give a lower priority to the advertisement. Furthermore, if the user is stressed, the advertising department may adjust the advertisement priority. This allows for the provision of optimal advertisement priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI, or not using AI. For example, the advertising department can input user message data into a generative AI and have the generative AI perform emotion estimation.

[0106] The advertising department displays the most suitable advertisements when showing them, taking into account the user's geographical location. For example, the advertising department can display advertisements for products that can be purchased near the user's current location. It can also display advertisements for products that can be purchased in places the user frequently visits. Furthermore, the advertising department can display region-specific advertisements based on the user's geographical location. This allows for the provision of optimal advertisements based on the user's geographical location. Some or all of the above processes in the advertising department may be performed using AI, for example, or not. For example, the advertising department can input the user's geographical location information into a generating AI and have the generating AI execute the display of optimal advertisements.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The friend-making support system can also collect user health data and recommend the most suitable open chats and events based on their health status. For example, if a user is not getting enough exercise, it can recommend events and chats that encourage exercise. Similarly, if a user is feeling stressed, it can recommend events and chats that help them relax. Furthermore, if a user wants to adopt a healthy diet, it can recommend chats that provide health-related information. This allows the system to recommend the most suitable open chats and events based on the user's health condition.

[0109] The friend-making support system can also recommend open chats and events based on the user's musical preferences. For example, if a user likes a particular genre of music, it can recommend events and chats related to that genre. Similarly, if a user follows a specific artist, it can recommend events and chats related to that artist. Furthermore, if a user is interested in music festivals, it can recommend chats that provide information related to festivals. This allows the system to recommend the most suitable open chats and events based on the user's musical tastes.

[0110] The friend-making support system can also recommend open chats and events based on the user's reading history. For example, if a user prefers a particular genre of books, it can recommend events and chats related to that genre. Similarly, if a user follows a specific author, it can recommend events and chats related to that author. Furthermore, if a user is interested in book clubs, it can recommend chats that provide information related to book clubs. This allows the system to recommend the most suitable open chats and events based on the user's reading history.

[0111] The friend-making support system can also recommend open chats and events based on the user's travel history. For example, if a user has visited a particular region, it can recommend events and chats related to that region. Similarly, if a user is interested in a specific tourist destination, it can recommend events and chats related to that destination. Furthermore, if a user is planning a trip, it can recommend chats that provide travel-related information. This allows the system to recommend the most suitable open chats and events based on the user's travel history.

[0112] The friend-making support system can also estimate the user's emotions and send messages encouraging participation in open chats and events based on those emotions. For example, if a user is feeling lonely, it can send messages recommending active chats and events. If a user is feeling stressed, it can send messages recommending relaxing events and chats. Furthermore, if a user is excited, it can send messages recommending active events and chats. This allows for optimal participation in open chats and events tailored to the user's emotions.

[0113] The friend-making support system can also recommend open chats and events based on the user's occupation. For example, if a user works in a specific profession, it can recommend events and chats related to that profession. If a user is aiming for career advancement, it can recommend chats that provide career-related information. Furthermore, if a user wants to learn skills related to their profession, it can recommend chats that provide information on skill development. This allows the system to recommend the most suitable open chats and events for each user's occupation.

[0114] The friend-making support system can also estimate the user's emotions and adjust feedback in open chats and events based on those emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is excited, it can provide concise feedback. Furthermore, if the user is stressed, it can provide positive feedback. This allows for the provision of optimal feedback tailored to the user's emotions.

[0115] The friend-making support system can also recommend open chats and events based on the user's interests. For example, if a user likes a particular sport, it can recommend events and chats related to that sport. Similarly, if a user is interested in a particular art form, it can recommend events and chats related to that art. Furthermore, if a user is interested in cooking, it can recommend chats that provide information about cooking. This allows the system to recommend the most suitable open chats and events based on the user's interests.

[0116] The friend-making support system can also estimate the user's emotions and adjust the way open chats and events are notified based on those emotions. For example, if the user is relaxed, notifications will be sent in a calm tone. If the user is excited, visually stimulating notifications can be sent. Furthermore, if the user is stressed, simple and easy-to-understand notifications can be sent. This allows the system to provide the most appropriate notification method according to the user's emotions.

[0117] The friend-making support system can also recommend open chats and events based on the user's educational background. For example, if a user holds a specific degree, it can recommend events and chats related to that degree. Similarly, if a user is interested in a particular field, it can recommend events and chats related to that field. Furthermore, if a user is seeking academic information, it can recommend chats that provide academic information. This allows the system to recommend the most suitable open chats and events based on the user's educational background.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The recommendation team recommends the most suitable open chats and events based on the user's profile and activity history. For example, it analyzes the user's interests and past activity history, uses AI to analyze the user's profile, and recommends relevant open chats and events. Step 2: The announcement team will announce events and recruit participants within the open chat. For example, they will announce event details and recruit participants within the open chat. They will also use AI to select the most suitable announcement method based on user interests and preferences. Step 3: The evaluation department implements a user evaluation system to visualize highly reliable users. For example, AI is used to identify and visualize highly reliable users based on user evaluation scores and past behavioral history. Step 4: The monitoring unit uses AI to monitor for inappropriate behavior. For example, it uses AI to detect inappropriate behavior within open chats and issue warnings. It also optimizes the monitoring algorithm based on past data on inappropriate behavior to improve monitoring accuracy. Step 5: The transition phase involves users who have become friends in the open chat moving to the SNS group. For example, if the interaction within the open chat meets certain criteria, a transition to the SNS group will be suggested. AI will also be used to analyze the users' interaction status and suggest the optimal timing for the transition. Step 6: The advertising department will deliver appropriate ads to the open chat group in a recommendation format. For example, they will select the most suitable ads based on the user's interests and use AI to optimize how the ads are displayed.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements mentioned above, including the recommendation unit, notification unit, evaluation unit, monitoring unit, migration unit, and advertising unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the recommendation unit analyzes the user's profile and behavioral history using the control unit 46A of the smart device 14 and recommends the most suitable open chats and events. The notification unit uses the control unit 46A of the smart device 14 to announce events and recruit participants. The evaluation unit analyzes user evaluations using the specific processing unit 290 of the data processing unit 12 and visualizes highly reliable users. The monitoring unit monitors inappropriate behavior using the specific processing unit 290 of the data processing unit 12. The migration unit proposes migration from open chats to SNS groups using the control unit 46A of the smart device 14. The advertising unit selects the most suitable advertisements using the specific processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example above and can be modified in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the recommendation unit, notification unit, evaluation unit, monitoring unit, transition unit, and advertising unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the recommendation unit analyzes the user's profile and behavioral history using the control unit 46A of the smart glasses 214 and recommends the most suitable open chats and events. The notification unit uses the control unit 46A of the smart glasses 214 to announce events and recruit participants. The evaluation unit analyzes user evaluations using the specific processing unit 290 of the data processing unit 12 and visualizes highly reliable users. The monitoring unit monitors inappropriate behavior using the specific processing unit 290 of the data processing unit 12. The transition unit proposes migration from open chats to SNS groups using the control unit 46A of the smart glasses 214. The advertising unit selects the most suitable advertisements using the specific processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the smart glasses 214. 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.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements mentioned above, including the recommendation unit, notification unit, evaluation unit, monitoring unit, migration unit, and advertising unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the recommendation unit analyzes the user's profile and behavioral history using the control unit 46A of the headset terminal 314 and recommends the most suitable open chats and events. The notification unit uses the control unit 46A of the headset terminal 314 to announce events and recruit participants. The evaluation unit analyzes user evaluations using the specific processing unit 290 of the data processing unit 12 and visualizes highly reliable users. The monitoring unit monitors inappropriate behavior using the specific processing unit 290 of the data processing unit 12. The migration unit proposes migration from open chats to SNS groups using the control unit 46A of the headset terminal 314. The advertising unit selects the most suitable advertisements using the specific processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the headset terminal 314. 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.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements mentioned above, including the recommendation unit, notification unit, evaluation unit, monitoring unit, transition unit, and advertising unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the recommendation unit analyzes the user's profile and behavioral history using the control unit 46A of the robot 414 and recommends the most suitable open chats and events. The notification unit uses the control unit 46A of the robot 414 to announce events and recruit participants. The evaluation unit analyzes user evaluations using the specific processing unit 290 of the data processing unit 12 and visualizes highly reliable users. The monitoring unit monitors inappropriate behavior using the specific processing unit 290 of the data processing unit 12. The transition unit proposes migration from open chats to SNS groups using the control unit 46A of the robot 414. The advertising unit selects the most suitable advertisements using the specific processing unit 290 of the data processing unit 12 and displays them using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A recommendation team that suggests the most suitable open chats and events based on the user's profile and activity history, The open chat includes an announcement section that handles event announcements and participant recruitment, and The evaluation department has introduced a user evaluation system to visualize highly reliable users, The monitoring department uses AI to monitor inappropriate behavior, The transition part is when users who have become friends in an open chat move to an SNS group, It includes an advertising department that displays appropriate advertisements in a recommendation format to open chat groups. A system characterized by the following features. (Note 2) The aforementioned recommendation department, It includes a data collection unit that collects user profiles and behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, It is equipped with an AI detection unit that detects inappropriate language and behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, It estimates user sentiment and adjusts the content of recommended open chats and events based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Analyze the user's past participation history to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, When making recommendations, 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 7) The aforementioned recommendation department, It estimates user sentiment and prioritizes recommended open chats and events based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending highly relevant open chats and events, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recommendation department, When making recommendations, the system analyzes the user's social media activity and recommends relevant open chats and events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned notification section, We estimate user sentiment and adjust how we announce events based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned notification section, When making an announcement, adjust the level of detail in the announcement based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification section, When announcing an event, a different announcement algorithm will be applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification section, The system estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification section, When making an announcement, prioritize the announcement based on the event's location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit described above, It estimates the user's sentiment and adjusts how ratings are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit described above, During the evaluation process, the system selects the most suitable evaluation method by referring to the user's past evaluation history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit described above, During the evaluation process, weights are assigned to the evaluation based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit described above, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit described above, When evaluating, the display method of the evaluation will be adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, We estimate user sentiment and adjust monitoring criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to past data on inappropriate behavior. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, During monitoring, consider user attribute information to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, It estimates the user's sentiment and adjusts the order in which monitoring results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, During monitoring, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transition section is It estimates the user's emotions and adjusts the timing of transitions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned transition section is During the migration process, the optimal migration method is selected by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transition section is During migration, customize the migration method based on user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transition section is It estimates user sentiment and determines migration priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transition section is During the migration, the optimal migration method will be selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advertising department, When displaying ads, the system selects the most suitable ads by referencing the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advertising department, When displaying ads, weight the ads based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advertising department, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advertising department, When displaying ads, the system takes into account the user's geographical location to show the most relevant ads. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 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 recommendation team that suggests the most suitable open chats and events based on the user's profile and activity history, The open chat includes an announcement section that handles event announcements and participant recruitment, and The evaluation department has introduced a user evaluation system to visualize highly reliable users, The monitoring department uses AI to monitor inappropriate behavior, The transition part is when users who have become friends in an open chat move to an SNS group, It includes an advertising department that displays appropriate advertisements in a recommendation format to open chat groups. A system characterized by the following features.

2. The aforementioned recommendation department, It includes a data collection unit that collects user profiles and behavioral history. The system according to feature 1.

3. The aforementioned monitoring unit, The system includes a detection unit that uses AI to detect inappropriate language and behavior. The system according to feature 1.

4. The aforementioned recommendation department, It estimates user sentiment and adjusts the content of recommended open chats and events based on that estimated sentiment. The system according to feature 1.

5. The aforementioned recommendation department, Analyze the user's past participation history to select the most suitable recommendation method. The system according to feature 1.

6. The aforementioned recommendation department, When making recommendations, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned recommendation department, It estimates user sentiment and prioritizes recommended open chats and events based on that estimated sentiment. The system according to feature 1.

8. The aforementioned recommendation department, When making recommendations, the system prioritizes recommending highly relevant open chats and events, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned recommendation department, When making recommendations, the system analyzes the user's social media activity and recommends relevant open chats and events. The system according to feature 1.

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