system

Abstract

The system according to this embodiment aims to understand users' preferences and tendencies and effectively match them with compatible users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a hint provision unit. The collection unit collects user profile information and past action data. The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and tendencies. The suggestion unit matches and suggests compatible users based on the preferences and tendencies understood by the analysis unit. The hint provision unit provides hints on how to start a conversation and communication.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately grasp the preferences and tendencies of users and effectively match compatible users.

[0005] The system according to the embodiment aims to grasp the preferences and tendencies of users and effectively match compatible users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a hint provision unit. The data collection unit collects user profile information and past action data. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and tendencies. The suggestion unit suggests matching compatible users based on the preferences and tendencies understood by the analysis unit. The hint provision unit provides hints on how to start a conversation and communication. [Effects of the Invention]

[0007] The system according to this embodiment can understand users' preferences and tendencies and effectively match them with compatible users. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​matching assistant for a matching app according to an embodiment of the present invention is a system that analyzes compatibility from user profiles and past actions and makes matching suggestions based on the user's preferences and tendencies. The AI ​​matching assistant for a matching app collects user profile information and past action data, and the AI ​​analyzes this data to understand the user's preferences and tendencies. Furthermore, based on the understood preferences and tendencies, it suggests matching with highly compatible users and provides hints on how to start a conversation and how to communicate, thereby supporting smooth communication between users. For example, the AI ​​matching assistant for a matching app collects user profile information and past action data. In this process, it collects data such as profile information entered by the user and the user's behavior history within the app. For example, this includes the user's age, gender, hobbies, interests, and information on people the user has "liked" in the past (an example of people the user has given a positive evaluation to). This allows for a detailed understanding of the user's profile information and behavior patterns. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​understands the user's preferences and tendencies. For example, it analyzes the characteristics of people the user has "liked" in the past and the content of message exchanges to identify what type of person the user prefers. This allows for a detailed understanding of the user's preferences and tendencies. Furthermore, based on these understood preferences and tendencies, the system suggests matching users with high compatibility. The AI ​​identifies and suggests compatible partners based on the user's preferences and tendencies. For example, it suggests partners with common hobbies or interests, or partners with similar characteristics to those the user has previously "liked." This allows users to be matched with people who are a good fit for them. Finally, it provides tips on how to start conversations and communication techniques. To support smooth communication between users, the AI ​​provides tips on how to start conversations and communication techniques. For example, it includes suggesting topics based on common hobbies and interests, and suggesting questions based on the other person's profile information. This is expected to facilitate smoother communication between users.This allows AI matching assistants in dating apps to match users with compatible partners and enjoy smooth communication. For example, users can meet someone with similar hobbies and have engaging conversations about those hobbies. Furthermore, by utilizing communication tips provided by the AI, conversations with people you've just met can proceed more smoothly. Based on the user's profile information and past action data, the AI ​​matching assistant in dating apps can suggest highly compatible users and support smooth communication.

[0029] The AI ​​matching assistant of the matching app according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a hint provision unit. The data collection unit collects user profile information and past action data. The data collection unit collects data including, for example, the user's age, gender, hobbies, interests, and information about people the user has previously "liked". The data collection unit collects data such as profile information entered by the user and the user's behavior history within the app. The data collection unit collects data such as the user's age, gender, hobbies, interests, and information about people the user has previously "liked". The data collection unit can, for example, grasp the user's detailed profile information and behavior patterns. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and tendencies. The analysis unit can, for example, understand the user's preferences and tendencies based on the collected data. The analysis unit can, for example, analyze the characteristics of people the user has previously "liked" and the content of message exchanges to identify what type of person the user prefers. The analysis unit can, for example, grasp the user's preferences and tendencies in detail. The suggestion unit matches and proposes highly compatible users based on preferences and tendencies identified by the analysis unit. For example, the suggestion unit identifies highly compatible partners based on the user's preferences and tendencies and proposes matching. For example, the suggestion unit proposes partners with common hobbies or interests, or partners with similar characteristics to those the user has previously "liked." For example, the suggestion unit allows the user to be matched with a suitable partner. The hint provision unit provides hints on how to start a conversation and communication. For example, the hint provision unit provides hints on how to start a conversation and communication to support smooth communication between users. For example, the hint provision unit includes suggesting topics based on common hobbies and interests, and suggesting questions based on the other person's profile information. For example, the hint provision unit is expected to facilitate smooth communication between users. As a result, the AI ​​matching assistant of the matching app according to the embodiment can propose highly compatible users based on the user's profile information and past action data, and support smooth communication.

[0030] The data collection unit collects user profile information and past action data. Specifically, it collects data including the user's age, gender, hobbies, interests, and information about people they have previously "liked." The data collection unit collects data such as profile information entered by the user and their activity history within the app. For example, in addition to basic information such as age, gender, hobbies, and interests entered by the user in their profile, it also collects information such as the characteristics of people they have previously "liked" and the content of message exchanges. This allows the data collection unit to understand detailed profile information and behavioral patterns of users. Furthermore, the data collection unit analyzes the user's activity log and collects detailed behavioral data such as when they are active and what kind of content they are interested in. This allows for a deeper understanding of the user's usage trends and behavioral patterns. The collected data is securely stored on a cloud server and managed so that the analysis and recommendation units can access it. The frequency and accuracy of data collection are adjusted to achieve optimal matching while respecting user privacy. For example, if a user adds new profile information or their behavioral patterns change, the data collection unit immediately updates the data to maintain the latest information. This allows the data collection unit to always support highly accurate matching based on the latest data.

[0031] The analysis unit analyzes the data collected by the data collection unit to understand user preferences and tendencies. Specifically, it gains a detailed understanding of user preferences and tendencies based on the collected data. For example, it analyzes the characteristics of people the user has "liked" in the past and the content of message exchanges to identify what type of person the user prefers. The AI ​​uses machine learning algorithms to extract patterns from user behavior data and model preferences and tendencies. For example, if a user tends to "like" people in a specific age group or with specific hobbies, the AI ​​learns that pattern and uses it for future matching. It also analyzes the content of message exchanges using natural language processing technology to identify what topics the user is interested in. This allows the analysis unit to quickly and accurately understand user preferences and tendencies. Furthermore, the analysis unit can predict user preferences and tendencies based on past matching data and success stories. For example, it analyzes the characteristics of users who have successfully matched in the past and suggests partners with similar characteristics. In addition, the analysis unit can use anomaly detection algorithms to detect unusual behavior patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department matches users with highly compatible partners based on preferences and tendencies identified by the Analysis Department. Specifically, it identifies highly compatible partners based on the user's preferences and tendencies and makes matching suggestions. For example, it suggests partners with common hobbies or interests, or partners with similar characteristics to those the user has previously "liked." The Proposal Department uses AI to analyze the user's preferences and tendencies and identify the optimal match. For example, if a user is interested in outdoor activities, it prioritizes suggesting partners with the same hobby. It also identifies combinations with a high success rate based on past matching data and suggests them to the user. This allows the Proposal Department to match users with partners who are a good fit for them. Furthermore, the Proposal Department can continuously revise its suggestions based on real-time updated data to adapt to the latest situation. For example, if a user's behavior patterns or preferences change, the Proposal Department immediately incorporates new data and updates its suggestions. The Proposal Department can also make more accurate matching suggestions by considering regional characteristics and past matching history. This allows the Proposal Department to always provide highly accurate matching suggestions based on the latest information, improving user satisfaction.

[0033] The hint-providing unit provides tips on how to start conversations and improve communication. Specifically, it provides hints on how to start conversations and improve communication to support smooth communication between users. For example, this includes suggesting topics based on shared hobbies and interests, and suggesting questions based on the other person's profile information. The hint-providing unit uses AI to analyze the user's profile information and past message content to suggest the best way to start a conversation and topics. For example, if a user is interested in music, it will suggest asking about the other person's favorite artists. It also identifies topics that the user is interested in based on past message content and suggests topics accordingly. This allows the hint-providing unit to facilitate smoother communication between users. Furthermore, the hint-providing unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it analyzes how users reacted to suggested topics and incorporates this into future suggestions. In addition, the hint-providing unit can provide hints to users at the appropriate time using multiple communication methods. For example, it can display hints via pop-up notifications before sending messages, providing information that is helpful when users actually start communicating. This allows the hint-providing section to support smooth communication between users and improve the success rate of matching.

[0034] The data collection unit can collect data including the user's age, gender, hobbies, interests, and information about people the user has previously "liked." For example, the data collection unit can collect the user's age. For example, the data collection unit can collect the user's gender. For example, the data collection unit can collect the user's hobbies. For example, the data collection unit can collect information about people the user has previously "liked." This allows the data collection unit to understand the user's detailed profile information and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's profile information into AI, and the AI ​​can automatically collect the data.

[0035] The analysis unit can understand user preferences and tendencies based on the collected data. For example, the analysis unit can understand user preferences based on the collected data. For example, the analysis unit can understand user tendencies based on the collected data. For example, the analysis unit can analyze the characteristics of people the user has "liked" in the past. For example, the analysis unit can analyze the content of message exchanges. This allows the analysis unit to understand user preferences and tendencies in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can automatically analyze the data.

[0036] The suggestion unit can suggest people who share common hobbies and interests, or people who have similar characteristics to people the user has previously "liked." For example, the suggestion unit can suggest people who share common hobbies. For example, the suggestion unit can suggest people who share common interests. For example, the suggestion unit can suggest people who have similar characteristics to people the user has previously "liked." This allows the suggestion unit to match the user with a suitable partner. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's preferences and tendencies into the AI, which can then automatically suggest highly compatible partners.

[0037] The hint-providing unit can suggest topics based on shared hobbies and interests, or suggest questions based on the other party's profile information. For example, the hint-providing unit can suggest topics based on shared hobbies. For example, the hint-providing unit can suggest topics based on shared interests. For example, the hint-providing unit can suggest questions based on the other party's profile information. This allows the hint-providing unit to expect smoother communication between users. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's profile information into AI, which can then automatically provide communication hints.

[0038] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit may prioritize using question formats that the user has frequently answered in the past. For example, the data collection unit may avoid questions that the user has skipped in the past and collect information in an alternative way. For example, the data collection unit may prioritize using interfaces (voice, text, etc.) that the user has preferred to use in the past. This allows the data collection unit to select the optimal data collection method based on the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past behavior data into AI, which can then automatically select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, the unit may prioritize questions related to topics the user is currently interested in. For example, the unit may collect information related to the user's current lifestyle (work, hobbies, family, etc.). For example, the unit may ask questions based on events or activities the user has recently participated in. This allows the unit to collect optimal data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit may input the user's lifestyle data into an AI, which can then automatically filter the data collection.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of information related to the user's current location. For example, the data collection unit asks region-specific questions based on the user's geographical location. For example, the data collection unit collects information related to places the user has visited in the past. This allows the data collection unit to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into AI, which can then automatically collect highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can ask relevant questions based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity history and collect information related to topics of interest. For example, the data collection unit can collect relevant information based on accounts followed by the user on social media. In this way, the data collection unit can collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI, which can then automatically collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can automatically apply the most suitable analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can automatically determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize analyzing highly relevant data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can automatically adjust the order of analysis.

[0046] The suggestion unit can adjust the level of detail of its suggestions based on the user's preferences and tendencies. For example, the suggestion unit can provide detailed suggestions based on the user's preferences. For example, the suggestion unit can provide concise suggestions based on the user's tendencies. For example, the suggestion unit can adjust the level of detail of its suggestions according to the user's preferences and tendencies. In this way, the suggestion unit can adjust the level of detail of its suggestions according to the user's preferences and tendencies. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's preferences and tendencies into the AI, which can then automatically adjust the level of detail of its suggestions.

[0047] The suggestion unit can make optimal suggestions by referring to the user's past matching history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the characteristics of partners the user has previously matched with. For example, the suggestion unit can make suggestions with a high success rate based on the user's past matching history. For example, the suggestion unit can analyze the user's past matching history and suggest the most compatible partner. In this way, the suggestion unit can make optimal suggestions based on the user's past matching history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's matching history data into AI, and the AI ​​can automatically make optimal suggestions.

[0048] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, the suggestion unit can make suggestions related to the user's current location. For example, the suggestion unit can make region-specific suggestions based on the user's geographical location. For example, the suggestion unit can make suggestions related to places the user has visited in the past. In this way, the suggestion unit can make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into AI, which can then automatically make optimal suggestions.

[0049] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information the user has shared on social media. For example, the suggestion unit can analyze the user's social media activity history and make suggestions that are of interest to them. For example, the suggestion unit can make relevant suggestions based on the accounts the user follows on social media. In this way, the suggestion unit can make relevant suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into AI, and the AI ​​can automatically make relevant suggestions.

[0050] The hint-providing unit can provide optimal hints by referring to the user's past communication history when providing hints. For example, the hint-providing unit can provide optimal hints based on patterns of successful communication the user has had in the past. For example, the hint-providing unit can provide hints with a high success rate from the user's past communication history. For example, the hint-providing unit can analyze the user's past communication history and provide the most effective hints. In this way, the hint-providing unit can provide optimal hints based on the user's past communication history. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's communication history data into AI, and the AI ​​can automatically provide optimal hints.

[0051] The hint-providing unit can customize the content of hints based on the user's current life circumstances when providing hints. For example, the hint-providing unit can provide hints related to topics the user is currently interested in. For example, the hint-providing unit can provide hints related to the user's current life circumstances (work, hobbies, family, etc.). For example, the hint-providing unit can provide hints based on events or activities the user has recently participated in. This allows the hint-providing unit to provide the most appropriate hints based on the user's current life circumstances. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's life circumstances data into AI, which can then automatically customize the content of the hints.

[0052] The hint-providing unit can provide optimal hints by considering the user's geographical location information when providing hints. For example, the hint-providing unit can provide hints related to the user's current location. For example, the hint-providing unit can provide region-specific hints based on the user's geographical location. For example, the hint-providing unit can provide hints related to places the user has visited in the past. In this way, the hint-providing unit can provide optimal hints based on the user's geographical location information. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's geographical location data into AI, and the AI ​​can automatically provide optimal hints.

[0053] The hint-providing unit can analyze the user's social media activity and provide relevant hints when providing hints. For example, the hint-providing unit can provide relevant hints based on information shared by the user on social media. For example, the hint-providing unit can analyze the user's social media activity history and provide hints that are of interest. For example, the hint-providing unit can provide relevant hints based on accounts that the user follows on social media. In this way, the hint-providing unit can provide relevant hints based on the user's social media activity. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's social media data into AI, and the AI ​​can automatically provide relevant hints.

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

[0055] The AI ​​matching assistant in a dating app can analyze a user's past matching history and identify matching patterns with a high success rate. For example, it can extract the characteristics of past successful matches and prioritize suggesting partners with similar characteristics. It can also analyze the causes of past unsuccessful matches and make suggestions to avoid similar failures. Furthermore, if the user's past matching history reveals that they have a higher success rate at certain times or days of the week, the AI ​​can tailor suggestions to those times. This allows for more effective matching suggestions based on the user's past matching history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's matching history data into the AI, which can then automatically make optimal suggestions.

[0056] The AI ​​matching assistant in a dating app can make region-specific matching suggestions by considering the user's geographical location. For example, it can suggest other users participating in events or activities related to the user's current location. It can also suggest partners with similar experiences based on information related to places the user has visited in the past. Furthermore, it can suggest partners with region-specific hobbies and interests based on the user's geographical location. This allows for more relevant matching suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into the AI, which can then automatically make optimal suggestions.

[0057] The AI ​​matching assistant in a dating app can analyze a user's social media activity and collect relevant data. For example, it can ask relevant questions based on information the user has shared on social media. It can also analyze the user's social media activity history and collect information related to topics of interest. Furthermore, it can collect relevant information based on the accounts the user follows on social media. This allows for the collection of more relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into the AI, which can then automatically collect the relevant data.

[0058] The AI ​​matching assistant in a dating app can customize data collection based on the user's current lifestyle and areas of interest. For example, it can prioritize questions related to topics the user is currently interested in. It can also collect information related to the user's current lifestyle (work, hobbies, family, etc.). Furthermore, it can ask questions based on events and activities the user has recently participated in. This allows for the collection of more relevant data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into the AI, which can then automatically customize data collection.

[0059] The AI ​​matching assistant in a dating app can analyze a user's past behavior patterns and select the optimal data collection method. For example, it can prioritize using question formats that the user has frequently answered in the past. It can also avoid questions that the user has previously skipped and collect information in an alternative way. Furthermore, it can prioritize using interfaces (voice, text, etc.) that the user has preferred to use in the past. This allows for the selection of a more effective data collection method based on the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior data into the AI, which can then automatically select the optimal data collection method.

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

[0061] Step 1: The data collection unit collects user profile information and past action data. For example, it collects data including the user's age, gender, hobbies, interests, and information about people they have previously "liked." The data collection unit collects data such as profile information entered by the user and their activity history within the app. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and tendencies. For example, it analyzes the characteristics of people the user has previously "liked" and the content of message exchanges to identify what type of person the user prefers. Step 3: The suggestion unit matches users with highly compatible profiles based on the preferences and trends identified by the analysis unit. For example, it suggests users with similar hobbies or interests, or users with similar characteristics to those the user has previously "liked." Step 4: The hint section provides tips on how to start a conversation and improve communication. This may include suggestions for topics based on shared hobbies and interests, or suggestions for questions based on the other person's profile information.

[0062] (Example of form 2) The AI ​​matching assistant for a matching app according to an embodiment of the present invention is a system that analyzes compatibility from user profiles and past actions and makes matching suggestions based on the user's preferences and tendencies. The AI ​​matching assistant for a matching app collects user profile information and past action data, and the AI ​​analyzes this data to understand the user's preferences and tendencies. Furthermore, based on the understood preferences and tendencies, it suggests matching with highly compatible users and provides hints on how to start a conversation and how to communicate, thereby supporting smooth communication between users. For example, the AI ​​matching assistant for a matching app collects user profile information and past action data. In this process, it collects data such as profile information entered by the user and the user's behavior history within the app. For example, this includes the user's age, gender, hobbies, interests, and information about people the user has "liked" in the past. This allows for a detailed understanding of the user's profile information and behavior patterns. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​understands the user's preferences and tendencies. For example, it analyzes the characteristics of people the user has "liked" in the past and the content of message exchanges to identify what type of person the user prefers. This allows for a detailed understanding of the user's preferences and tendencies. Furthermore, based on the user's identified preferences and tendencies, the AI ​​suggests matching users with high compatibility. The AI ​​identifies and suggests compatible partners based on the user's preferences and tendencies. For example, it suggests partners with similar hobbies or interests, or partners with similar characteristics to those the user has previously "liked." This allows users to be matched with people who are a good fit for them. Finally, it provides tips on how to start conversations and communication techniques. To support smooth communication between users, the AI ​​provides tips on how to start conversations and communication techniques. For example, it includes suggesting topics based on shared hobbies and interests, and suggesting questions based on the other person's profile information. This is expected to facilitate smoother communication between users.This allows AI matching assistants in dating apps to match users with compatible partners and enjoy smooth communication. For example, users can meet someone with similar hobbies and have engaging conversations about those hobbies. Furthermore, by utilizing communication tips provided by the AI, conversations with people you've just met can proceed more smoothly. Based on the user's profile information and past action data, the AI ​​matching assistant in dating apps can suggest highly compatible users and support smooth communication.

[0063] The AI ​​matching assistant in the matching app according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, and a hint provision unit. The data collection unit collects user profile information and past action data. The data collection unit collects data including, for example, the user's age, gender, hobbies, interests, and information about people the user has previously "liked" (an example of people the user has given a positive evaluation to). The data collection unit collects data such as profile information entered by the user and the user's behavior history within the app. The data collection unit collects data such as the user's age, gender, hobbies, interests, and information about people the user has previously "liked". The data collection unit can, for example, grasp the user's detailed profile information and behavior patterns. The analysis unit analyzes the data collected by the data collection unit to understand the user's preferences and tendencies. The analysis unit, for example, understands the user's preferences and tendencies based on the collected data. The analysis unit analyzes, for example, the characteristics of people the user has previously "liked" and the content of message exchanges to identify what type of person the user prefers. The analysis unit can, for example, grasp the user's preferences and tendencies in detail. The suggestion unit suggests matching users with high compatibility based on the preferences and tendencies grasped by the analysis unit. The suggestion unit, for example, identifies highly compatible partners based on the user's preferences and tendencies and makes matching suggestions. The suggestion unit suggests partners with common hobbies or interests, or partners with similar characteristics to those the user has previously "liked." The suggestion unit allows the user to be matched with a suitable partner. The hint provision unit provides hints on how to start a conversation and communication. The hint provision unit provides hints on how to start a conversation and communication to support smooth communication between users. The hint provision unit includes, for example, suggesting topics based on common hobbies and interests, and suggesting questions based on the other person's profile information. The hint provision unit is expected to facilitate smooth communication between users. As a result, the AI ​​matching assistant in the matching app according to this embodiment can suggest highly compatible users based on the user's profile information and past action data, and support smooth communication.

[0064] The data collection unit collects user profile information and past action data. Specifically, it collects data including the user's age, gender, hobbies, interests, and information about people they have previously "liked." The data collection unit collects data such as profile information entered by the user and their activity history within the app. For example, in addition to basic information such as age, gender, hobbies, and interests entered by the user in their profile, it also collects information such as the characteristics of people they have previously "liked" and the content of message exchanges. This allows the data collection unit to understand detailed profile information and behavioral patterns of users. Furthermore, the data collection unit analyzes the user's activity log and collects detailed behavioral data such as when they are active and what kind of content they are interested in. This allows for a deeper understanding of the user's usage trends and behavioral patterns. The collected data is securely stored on a cloud server and managed so that the analysis and recommendation units can access it. The frequency and accuracy of data collection are adjusted to achieve optimal matching while respecting user privacy. For example, if a user adds new profile information or their behavioral patterns change, the data collection unit immediately updates the data to maintain the latest information. This allows the data collection unit to always support highly accurate matching based on the latest data.

[0065] The analysis unit analyzes the data collected by the data collection unit to understand user preferences and tendencies. Specifically, it gains a detailed understanding of user preferences and tendencies based on the collected data. For example, it analyzes the characteristics of people the user has "liked" in the past and the content of message exchanges to identify what type of person the user prefers. The AI ​​uses machine learning algorithms to extract patterns from user behavior data and model preferences and tendencies. For example, if a user tends to "like" people in a specific age group or with specific hobbies, the AI ​​learns that pattern and uses it for future matching. It also analyzes the content of message exchanges using natural language processing technology to identify what topics the user is interested in. This allows the analysis unit to quickly and accurately understand user preferences and tendencies. Furthermore, the analysis unit can predict user preferences and tendencies based on past matching data and success stories. For example, it analyzes the characteristics of users who have successfully matched in the past and suggests partners with similar characteristics. In addition, the analysis unit can use anomaly detection algorithms to detect unusual behavior patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The Proposal Department matches users with highly compatible partners based on preferences and tendencies identified by the Analysis Department. Specifically, it identifies highly compatible partners based on the user's preferences and tendencies and makes matching suggestions. For example, it suggests partners with common hobbies or interests, or partners with similar characteristics to those the user has previously "liked." The Proposal Department uses AI to analyze the user's preferences and tendencies and identify the optimal match. For example, if a user is interested in outdoor activities, it prioritizes suggesting partners with the same hobby. It also identifies combinations with a high success rate based on past matching data and suggests them to the user. This allows the Proposal Department to match users with partners who are a good fit for them. Furthermore, the Proposal Department can continuously revise its suggestions based on real-time updated data to adapt to the latest situation. For example, if a user's behavior patterns or preferences change, the Proposal Department immediately incorporates new data and updates its suggestions. The Proposal Department can also make more accurate matching suggestions by considering regional characteristics and past matching history. This allows the Proposal Department to always provide highly accurate matching suggestions based on the latest information, improving user satisfaction.

[0067] The hint-providing unit provides tips on how to start conversations and improve communication. Specifically, it provides hints on how to start conversations and improve communication to support smooth communication between users. For example, this includes suggesting topics based on shared hobbies and interests, and suggesting questions based on the other person's profile information. The hint-providing unit uses AI to analyze the user's profile information and past message content to suggest the best way to start a conversation and topics. For example, if a user is interested in music, it will suggest asking about the other person's favorite artists. It also identifies topics that the user is interested in based on past message content and suggests topics accordingly. This allows the hint-providing unit to facilitate smoother communication between users. Furthermore, the hint-providing unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it analyzes how users reacted to suggested topics and incorporates this into future suggestions. In addition, the hint-providing unit can provide hints to users at the appropriate time using multiple communication methods. For example, it can display hints via pop-up notifications before sending messages, providing information that is helpful when users actually start communicating. This allows the hint-providing section to support smooth communication between users and improve the success rate of matching.

[0068] The data collection unit can collect data including the user's age, gender, hobbies, interests, and information about people the user has previously "liked." For example, the data collection unit can collect the user's age. For example, the data collection unit can collect the user's gender. For example, the data collection unit can collect the user's hobbies. For example, the data collection unit can collect information about people the user has previously "liked." This allows the data collection unit to understand the user's detailed profile information and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's profile information into AI, and the AI ​​can automatically collect the data.

[0069] The analysis unit can understand user preferences and tendencies based on the collected data. For example, the analysis unit can understand user preferences based on the collected data. For example, the analysis unit can understand user tendencies based on the collected data. For example, the analysis unit can analyze the characteristics of people the user has "liked" in the past. For example, the analysis unit can analyze the content of message exchanges. This allows the analysis unit to understand user preferences and tendencies in detail. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can automatically analyze the data.

[0070] The suggestion unit can suggest people who share common hobbies and interests, or people who have similar characteristics to people the user has previously "liked." For example, the suggestion unit can suggest people who share common hobbies. For example, the suggestion unit can suggest people who share common interests. For example, the suggestion unit can suggest people who have similar characteristics to people the user has previously "liked." This allows the suggestion unit to match the user with a suitable partner. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's preferences and tendencies into the AI, which can then automatically suggest highly compatible partners.

[0071] The hint-providing unit can suggest topics based on shared hobbies and interests, or suggest questions based on the other party's profile information. For example, the hint-providing unit can suggest topics based on shared hobbies. For example, the hint-providing unit can suggest topics based on shared interests. For example, the hint-providing unit can suggest questions based on the other party's profile information. This allows the hint-providing unit to expect smoother communication between users. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's profile information into AI, which can then automatically provide communication hints.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit collects detailed profile information when the user is relaxed. For example, when the user is stressed, the data collection unit asks only simple questions and postpones the collection of detailed information. For example, when the user is excited, the data collection unit asks engaging questions to collect information. This allows the data collection unit to collect data 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can automatically adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit may prioritize using question formats that the user has frequently answered in the past. For example, the data collection unit may avoid questions that the user has skipped in the past and collect information in an alternative way. For example, the data collection unit may prioritize using interfaces (voice, text, etc.) that the user has preferred to use in the past. This allows the data collection unit to select the optimal data collection method based on the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's past behavior data into AI, which can then automatically select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, the unit may prioritize questions related to topics the user is currently interested in. For example, the unit may collect information related to the user's current lifestyle (work, hobbies, family, etc.). For example, the unit may ask questions based on events or activities the user has recently participated in. This allows the unit to collect optimal data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit may input the user's lifestyle data into an AI, which can then automatically filter the data collection.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, when the user is relaxed, the data collection unit prioritizes collecting detailed profile information. For example, when the user is stressed, the data collection unit prioritizes collecting only basic information. For example, when the user is excited, the data collection unit prioritizes collecting information that is of interest. In this way, the data collection unit can determine the priority of data to collect 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, and the AI ​​can automatically determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of information related to the user's current location. For example, the data collection unit asks region-specific questions based on the user's geographical location. For example, the data collection unit collects information related to places the user has visited in the past. This allows the data collection unit to collect highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into AI, which can then automatically collect highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can ask relevant questions based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity history and collect information related to topics of interest. For example, the data collection unit can collect relevant information based on accounts followed by the user on social media. In this way, the data collection unit can collect relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI, which can then automatically collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is excited, the analysis unit provides visually appealing analysis results. This allows the analysis unit to provide analysis results in the most appropriate presentation 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can automatically adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can apply an image recognition algorithm to image data. For example, the analysis unit can apply a statistical analysis algorithm to numerical data. This allows the analysis unit to apply the most suitable analysis algorithm depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can automatically apply the most suitable analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the user is excited, the analysis unit provides visually appealing analysis results. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can automatically adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze the most recent data while referring to past data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can automatically determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize analyzing highly relevant data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can automatically adjust the order of analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. If the user is excited, the suggestion unit will provide visually appealing suggestions. This allows the suggestion unit to present suggestions in the most appropriate way 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then automatically adjust the way it presents its suggestions.

[0085] The suggestion unit can adjust the level of detail of its suggestions based on the user's preferences and tendencies. For example, the suggestion unit can provide detailed suggestions based on the user's preferences. For example, the suggestion unit can provide concise suggestions based on the user's tendencies. For example, the suggestion unit can adjust the level of detail of its suggestions according to the user's preferences and tendencies. In this way, the suggestion unit can adjust the level of detail of its suggestions according to the user's preferences and tendencies. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's preferences and tendencies into the AI, which can then automatically adjust the level of detail of its suggestions.

[0086] The suggestion unit can make optimal suggestions by referring to the user's past matching history when making suggestions. For example, the suggestion unit can make optimal suggestions based on the characteristics of partners the user has previously matched with. For example, the suggestion unit can make suggestions with a high success rate based on the user's past matching history. For example, the suggestion unit can analyze the user's past matching history and suggest the most compatible partner. In this way, the suggestion unit can make optimal suggestions based on the user's past matching history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's matching history data into AI, and the AI ​​can automatically make optimal suggestions.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will prioritize detailed suggestions. If the user is stressed, the suggestion unit will prioritize concise suggestions. If the user is excited, the suggestion unit will prioritize visually appealing suggestions. In this way, the suggestion unit can determine the priority of suggestions 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, and the AI ​​can automatically determine the priority of suggestions.

[0088] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, the suggestion unit can make suggestions related to the user's current location. For example, the suggestion unit can make region-specific suggestions based on the user's geographical location. For example, the suggestion unit can make suggestions related to places the user has visited in the past. In this way, the suggestion unit can make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into AI, which can then automatically make optimal suggestions.

[0089] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make relevant suggestions based on information the user has shared on social media. For example, the suggestion unit can analyze the user's social media activity history and make suggestions that are of interest to them. For example, the suggestion unit can make relevant suggestions based on the accounts the user follows on social media. In this way, the suggestion unit can make relevant suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media data into AI, and the AI ​​can automatically make relevant suggestions.

[0090] The hint-providing unit can estimate the user's emotions and adjust the way hints are provided based on the estimated emotions. For example, if the user is relaxed, the hint-providing unit provides detailed hints. If the user is stressed, the hint-providing unit provides concise and to-the-point hints. If the user is excited, the hint-providing unit provides visually appealing hints. This allows the hint-providing unit to provide hints in the most optimal way 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the hint-providing unit may be performed using AI or not. For example, the hint-providing unit can input user emotion data into an AI, which can then automatically adjust the way hints are provided.

[0091] The hint-providing unit can provide optimal hints by referring to the user's past communication history when providing hints. For example, the hint-providing unit can provide optimal hints based on patterns of successful communication the user has had in the past. For example, the hint-providing unit can provide hints with a high success rate from the user's past communication history. For example, the hint-providing unit can analyze the user's past communication history and provide the most effective hints. In this way, the hint-providing unit can provide optimal hints based on the user's past communication history. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's communication history data into AI, and the AI ​​can automatically provide optimal hints.

[0092] The hint-providing unit can customize the content of hints based on the user's current life circumstances when providing hints. For example, the hint-providing unit can provide hints related to topics the user is currently interested in. For example, the hint-providing unit can provide hints related to the user's current life circumstances (work, hobbies, family, etc.). For example, the hint-providing unit can provide hints based on events or activities the user has recently participated in. This allows the hint-providing unit to provide the most appropriate hints based on the user's current life circumstances. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's life circumstances data into AI, which can then automatically customize the content of the hints.

[0093] The hint provider can estimate the user's emotions and determine the priority of hints based on the estimated emotions. For example, if the user is relaxed, the hint provider will prioritize detailed hints. If the user is stressed, the hint provider will prioritize concise hints. If the user is excited, the hint provider will prioritize visually appealing hints. In this way, the hint provider can determine the priority of hints 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 hint provider may be performed using AI or not using AI. For example, the hint provider can input user emotion data into an AI, and the AI ​​can automatically determine the priority of hints.

[0094] The hint-providing unit can provide optimal hints by considering the user's geographical location information when providing hints. For example, the hint-providing unit can provide hints related to the user's current location. For example, the hint-providing unit can provide region-specific hints based on the user's geographical location. For example, the hint-providing unit can provide hints related to places the user has visited in the past. In this way, the hint-providing unit can provide optimal hints based on the user's geographical location information. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's geographical location data into AI, and the AI ​​can automatically provide optimal hints.

[0095] The hint-providing unit can analyze the user's social media activity and provide relevant hints when providing hints. For example, the hint-providing unit can provide relevant hints based on information shared by the user on social media. For example, the hint-providing unit can analyze the user's social media activity history and provide hints that are of interest. For example, the hint-providing unit can provide relevant hints based on accounts that the user follows on social media. In this way, the hint-providing unit can provide relevant hints based on the user's social media activity. Some or all of the above processing in the hint-providing unit may be performed using AI, for example, or without AI. For example, the hint-providing unit can input the user's social media data into AI, and the AI ​​can automatically provide relevant hints.

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

[0097] The AI ​​matching assistant in a dating app can estimate the user's emotions and adjust the timing of matching suggestions based on those emotions. For example, when the user is relaxed, it can provide detailed matching suggestions. When the user is stressed, it can provide concise suggestions, postponing detailed suggestions. When the user is excited, it can provide visually appealing suggestions. This allows for matching suggestions to be made at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion engine 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 processing described above in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into the AI, which can then automatically adjust the timing of suggestions.

[0098] The AI ​​matching assistant in a dating app can analyze a user's past matching history and identify matching patterns with a high success rate. For example, it can extract the characteristics of past successful matches and prioritize suggesting partners with similar characteristics. It can also analyze the causes of past unsuccessful matches and make suggestions to avoid similar failures. Furthermore, if the user's past matching history reveals that they have a higher success rate at certain times or days of the week, the AI ​​can tailor suggestions to those times. This allows for more effective matching suggestions based on the user's past matching history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's matching history data into the AI, which can then automatically make optimal suggestions.

[0099] The AI ​​matching assistant in a dating app can estimate the user's emotions and customize communication hints based on those emotions. For example, if the user is relaxed, it can provide detailed communication hints. If the user is stressed, it can provide concise and to-the-point hints. If the user is excited, it can provide visually appealing hints. This allows for communication hints to be provided in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion engine 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 processing described above in the hint-providing unit may be performed using AI or not. For example, the hint-providing unit can input user emotion data into the AI, which can then automatically adjust how hints are provided.

[0100] The AI ​​matching assistant in a dating app can make region-specific matching suggestions by considering the user's geographical location. For example, it can suggest other users participating in events or activities related to the user's current location. It can also suggest partners with similar experiences based on information related to places the user has visited in the past. Furthermore, it can suggest partners with region-specific hobbies and interests based on the user's geographical location. This allows for more relevant matching suggestions based on the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into the AI, which can then automatically make optimal suggestions.

[0101] The AI ​​matching assistant in a dating app can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, when the user is relaxed, detailed profile information can be collected. When the user is stressed, only simple questions can be asked, and detailed information collection can be postponed. Also, when the user is excited, engaging questions can be asked to collect information. This allows data to be collected in the most optimal way according to the user's emotions. Emotion estimation is achieved 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 collection unit may be performed using AI or not. For example, the collection unit can input the user's emotion data into the AI, and the AI ​​can automatically adjust the data collection method.

[0102] The AI ​​matching assistant in a dating app can analyze a user's social media activity and collect relevant data. For example, it can ask relevant questions based on information the user has shared on social media. It can also analyze the user's social media activity history and collect information related to topics of interest. Furthermore, it can collect relevant information based on the accounts the user follows on social media. This allows for the collection of more relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into the AI, which can then automatically collect the relevant data.

[0103] The AI ​​matching assistant in a dating app can estimate the user's emotions and prioritize analysis based on those emotions. For example, if the user is relaxed, detailed analysis can be prioritized. If the user is stressed, concise analysis can be prioritized. If the user is excited, visually appealing analysis can be prioritized. This allows for analysis to be performed with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion engine 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI, which can then automatically determine the analysis priority.

[0104] The AI ​​matching assistant in a dating app can customize data collection based on the user's current lifestyle and areas of interest. For example, it can prioritize questions related to topics the user is currently interested in. It can also collect information related to the user's current lifestyle (work, hobbies, family, etc.). Furthermore, it can ask questions based on events and activities the user has recently participated in. This allows for the collection of more relevant data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into the AI, which can then automatically customize data collection.

[0105] The AI ​​matching assistant in a dating app can estimate the user's emotions and adjust the level of detail in suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is stressed, it can provide concise suggestions. If the user is excited, it can provide visually appealing suggestions. This allows for suggestions to be made with the optimal level of detail according to the user's emotions. Emotion estimation is achieved using an emotion engine 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 processing described above in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into the AI, which can then automatically adjust the level of detail in the suggestions.

[0106] The AI ​​matching assistant in a dating app can analyze a user's past behavior patterns and select the optimal data collection method. For example, it can prioritize using question formats that the user has frequently answered in the past. It can also avoid questions that the user has previously skipped and collect information in an alternative way. Furthermore, it can prioritize using interfaces (voice, text, etc.) that the user has preferred to use in the past. This allows for the selection of a more effective data collection method based on the user's past behavior patterns. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior data into the AI, which can then automatically select the optimal data collection method.

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

[0108] Step 1: The data collection unit collects user profile information and past action data. For example, it collects data including the user's age, gender, hobbies, interests, and information about people they have previously "liked." The data collection unit collects data such as profile information entered by the user and their activity history within the app. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's preferences and tendencies. For example, it analyzes the characteristics of people the user has previously "liked" and the content of message exchanges to identify what type of person the user prefers. Step 3: The suggestion unit matches users with highly compatible profiles based on the preferences and trends identified by the analysis unit. For example, it suggests users with similar hobbies or interests, or users with similar characteristics to those the user has previously "liked." Step 4: The hint section provides tips on how to start a conversation and improve communication. This may include suggestions for topics based on shared hobbies and interests, or suggestions for questions based on the other person's profile information.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] For example, the data collection unit can collect user profile information and past action data using the camera 42 and microphone 38B of the smart device 14. The analysis unit can analyze the collected data using the identification processing unit 290 of the data processing device 12 to understand the user's preferences and tendencies. The suggestion unit can suggest highly compatible users using the identification processing unit 290 of the data processing device 12. The hint provision unit can provide hints on how to start a conversation and communication tips 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 described above and can be modified in various ways.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] For example, the data collection unit can collect user profile information and past action data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit can analyze the data collected by the identification processing unit 290 of the data processing device 12 to understand the user's preferences and tendencies. The suggestion unit can suggest highly compatible users by matching them using the identification processing unit 290 of the data processing device 12. The hint provision unit can provide hints on how to start a conversation and communication tips by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the data collection unit can collect user profile information and past action data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's preferences and tendencies. The suggestion unit can suggest highly compatible users by matching them using the specific processing unit 290 of the data processing device 12. The hint provision unit can provide hints on how to start a conversation and communication tips by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] For example, the data collection unit can collect user profile information and past action data using the camera 42 and microphone 238 of the robot 414. The analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's preferences and tendencies. The suggestion unit can suggest highly compatible users by matching them using the specific processing unit 290 of the data processing device 12. The hint provision unit can provide hints on how to start a conversation and communication tips by 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.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection unit that collects user profile information and past action data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand user preferences and trends, A proposal unit that matches and suggests compatible users based on the preferences and trends identified by the aforementioned analysis unit, It includes a hints section that provides tips on how to start a conversation and communication techniques. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data including the user's age, gender, hobbies, interests, and information about people the user has previously given positive feedback to. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we understand user preferences and trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest people who share common hobbies and interests, or who have similar characteristics to people the user has previously given positive feedback to. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned hint-providing unit, Suggest topics of conversation based on shared hobbies and interests, or suggest questions based on the other person's profile information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is By estimating user sentiment and adjusting the timing of data collection based on that estimated sentiment, data is collected at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is By estimating user sentiment and prioritizing the data to collect based on that estimated sentiment, we can collect the right data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, By estimating the user's emotions and adjusting the representation of the analysis based on those estimated emotions, the analysis results are provided in an appropriate format. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, By estimating the user's emotions and adjusting the analysis length based on those emotions, the analysis is performed at an appropriate length. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, By estimating the user's emotions and adjusting the way suggestions are presented based on those estimated emotions, the system delivers suggestions in an appropriate manner. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's preferences and tendencies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we refer to the user's past matching history to make the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, By estimating the user's emotions and prioritizing suggestions based on those emotions, we can make appropriate recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned hint-providing unit, By estimating the user's emotions and adjusting how hints are provided based on those emotions, hints are delivered in an appropriate manner. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned hint-providing unit, When providing hints, we refer to the user's past communication history to provide the most appropriate hints. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned hint-providing unit, When providing hints, customize the content of the hints based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned hint-providing unit, By estimating the user's emotions and prioritizing hints based on those emotions, the system provides appropriate hints. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned hint-providing unit, When providing hints, we take the user's geographical location into consideration to provide the most appropriate hints. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned hint-providing unit, When providing hints, we analyze the user's social media activity and provide relevant hints. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user profile information and past action data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand user preferences and trends, A proposal unit that matches and suggests compatible users based on the preferences and trends identified by the aforementioned analysis unit, It includes a hints section that provides tips on how to start a conversation and communication techniques. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data including the user's age, gender, hobbies, interests, and information about people the user has previously given positive feedback to. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we understand user preferences and trends. The system according to feature 1.

4. The aforementioned proposal section is, We suggest people who share common hobbies and interests, or who have similar characteristics to people the user has previously given positive feedback to. The system according to feature 1.

5. The aforementioned hint-providing unit, Suggest topics of conversation based on shared hobbies and interests, or suggest questions based on the other person's profile information. The system according to feature 1.

6. The aforementioned collection unit is By estimating user sentiment and adjusting the timing of data collection based on that estimated sentiment, data is collected at the appropriate time. The system according to feature 1.

7. The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system according to feature 1.

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

9. The aforementioned collection unit is By estimating user sentiment and prioritizing the data to collect based on that estimated sentiment, we can collect the right data. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

Images