system

Abstract

The system according to this embodiment aims to suggest the most suitable partner based on the user's values ​​and hobbies, manage the progress of the matchmaking process, and suggest areas for improvement. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, a dialogue unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects the user's values ​​and hobbies. The suggestion unit suggests a suitable partner based on the information collected by the collection unit. The dialogue unit promotes self-understanding through dialogue with the user. The analysis unit analyzes the results obtained by the dialogue unit. The generation unit automatically generates a date plan tailored to the partner's hobbies. The tracking unit tracks the progress of the matchmaking activity. The feedback unit suggests improvements based on the progress tracked by the tracking unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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, in matchmaking, optimal partner proposals based on the user's values and hobbies, as well as progress management and feedback, are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal partner based on the user's values and hobbies, manage the progress of matchmaking, and propose improvement points.

Means for Solving the Problems

[0007] The system according to this embodiment can suggest the most suitable partner based on the user's values ​​and hobbies, manage the progress of the matchmaking process, and suggest areas for improvement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 matchmaking support agent system according to an embodiment of the present invention is a system that collects the user's values ​​and hobbies, proposes the most suitable partner, deepens self-understanding, automatically generates date plans, tracks the progress of matchmaking, and suggests areas for improvement. This matchmaking support agent system utilizes AI to analyze the user's values ​​and hobbies and provides an individually optimized matching function that proposes the most suitable partner. Next, it provides an interactive self-analysis function that deepens self-understanding through dialogue with the AI. Furthermore, it provides a date plan proposal function that automatically generates date plans tailored to the partner's hobbies. Finally, it provides a progress management and feedback function that tracks the progress of matchmaking and suggests areas for improvement. For example, the matchmaking support agent system uses AI to analyze the user's values ​​and hobbies based on the profile information and past behavioral history registered by the user and proposes the most suitable partner. This allows the user to find a partner that meets their ideal conditions. Next, the matchmaking support agent system uses AI to ask the user questions and analyze the user's personality and values ​​based on the answers. This allows the user to deepen their self-understanding and make better choices. Furthermore, the matchmaking support agent system uses AI to propose the most suitable date plan based on the partner's hobbies and interests. This allows the user to create a date plan that will please the other person. Finally, the matchmaking agent system uses AI to analyze dating experiences and provide advice for the next date. This allows users to manage their matchmaking progress and make effective improvements. As a result, users can engage in matchmaking more efficiently and make better choices by deepening their self-understanding. It also reduces the stress of matchmaking and enables service providers to improve customer satisfaction through highly accurate matching and operate efficiently. In this way, the matchmaking agent system collects users' values ​​and hobbies, suggests the most suitable partner, deepens self-understanding, automatically generates date plans, tracks matchmaking progress, and suggests areas for improvement, thereby achieving efficient and effective matchmaking support.

[0029] The matchmaking support agent system according to this embodiment comprises a collection unit, a proposal unit, a dialogue unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects the user's values ​​and hobbies. The collection unit analyzes the user's values ​​and hobbies, for example, based on the profile information registered by the user and their past behavioral history. The collection unit can use AI to analyze the user's values ​​and hobbies in detail. The proposal unit proposes the most suitable partner based on the information collected by the collection unit. The proposal unit, for example, uses AI to select the most suitable partner based on the collected information and proposes it to the user. The proposal unit can use AI to propose a partner that matches the user's values ​​and hobbies with high accuracy. The dialogue unit deepens self-understanding through dialogue with the user. The dialogue unit, for example, uses AI to ask the user questions and analyzes the user's personality and values ​​based on their answers. The dialogue unit can use AI to conduct dialogues to deepen the user's self-understanding. The analysis unit analyzes the results obtained by the dialogue unit. The analysis unit, for example, uses AI to analyze the results of conversations and analyze the user's personality and values ​​in detail. The analysis unit can analyze the results of conversations with high accuracy using AI. The generation unit automatically generates date plans tailored to the other person's hobbies. The generation unit, for example, uses AI to propose the optimal date plan based on the other person's hobbies and interests. The generation unit can generate date plans that will please the other person with high accuracy using AI. The tracking unit tracks the progress of the matchmaking process. The tracking unit, for example, uses AI to analyze the user's impressions of the date and provide advice for the next date. The tracking unit can track the progress of the matchmaking process in detail using AI. The feedback unit suggests areas for improvement based on the progress tracked by the tracking unit. The feedback unit, for example, uses AI to analyze the progress and suggest areas for improvement to the user. The feedback unit can provide effective feedback based on the progress of the matchmaking process using AI. As a result, the matchmaking support agent system according to this embodiment can collect the user's values ​​and hobbies, suggest the most suitable partner, deepen self-understanding, automatically generate date plans, track the progress of matchmaking, and suggest areas for improvement, thereby achieving efficient and effective matchmaking support.

[0030] The data collection unit collects user values ​​and hobbies. For example, it analyzes users' values ​​and hobbies based on their registered profile information and past activity history. Specifically, the profile information entered by users includes age, occupation, hobbies, interests, values, and ideal partner type. This information is centrally managed by the system, and a detailed user profile is created. Furthermore, data such as what events the user has attended, who they have matched with, and what messages they have sent and received is also collected as past activity history. This data is analyzed using AI to reveal the user's behavior patterns and preference tendencies. For example, if a user frequently participates in events related to a particular hobby, it is determined that this hobby is an important part of the user's values. Also, by analyzing the characteristics of people the user has matched with in the past, it is possible to identify what type of person the user is interested in. Based on this information, the data collection unit gains a detailed understanding of users' values ​​and hobbies and provides foundational data to improve the accuracy of the entire system. Furthermore, the data collection unit implements strict security measures in data collection and management to protect user privacy. This allows users to use the system with peace of mind.

[0031] The proposal department suggests the most suitable partner based on the information collected by the data collection department. For example, the proposal department uses AI to select the most suitable partner based on the collected information and proposes it to the user. Specifically, the AI ​​analyzes the user's values, hobbies, and past behavioral history, and searches the database for partners that match this information. The AI ​​uses a matching algorithm to evaluate the compatibility between the user and the partner and select the most suitable partner. For example, if the user enjoys outdoor activities, a partner who also enjoys outdoor activities will be suggested. Also, if the user values ​​a particular set of values, a partner who shares those values ​​will be prioritized in the suggestions. The proposal department can use AI to suggest partners that match the user's values ​​and hobbies with high accuracy. Furthermore, the proposal department collects user feedback and continuously improves the accuracy of its suggestions. For example, by providing the user's matching results and dating impressions with suggested partners, the AI ​​learns from this information and incorporates it into future suggestions. In this way, the proposal department can suggest the most suitable partner according to the user's needs and increase the success rate of matchmaking.

[0032] The dialogue function deepens self-understanding through dialogue with the user. For example, the dialogue function uses AI to ask questions to the user and analyze the user's personality and values ​​based on their answers. Specifically, the AI ​​asks the user questions about their hobbies, interests, and values, and analyzes their answers. For example, through questions such as "How do you spend your holidays?" or "What is your ideal partner like?", it gains a detailed understanding of the user's personality and values. The AI ​​uses natural language processing technology to analyze the user's answers and extract keywords and emotional tendencies. This allows for a highly accurate analysis of the user's personality and values. The dialogue function uses AI to conduct dialogues that deepen the user's self-understanding. Furthermore, the dialogue function provides feedback based on the user's answers to further enhance self-understanding. For example, if a user values ​​a particular set of values, it provides advice and information related to those values. This allows the user to deepen their self-understanding and gain help in finding a suitable partner. The dialogue function plays a crucial role in increasing the success rate of matchmaking through dialogue with users.

[0033] The analysis department analyzes the results obtained by the dialogue department. For example, the analysis department uses AI to analyze the dialogue results and conduct a detailed analysis of the user's personality and values. Specifically, the AI ​​analyzes the user's response data collected by the dialogue department to reveal the user's personality traits and value tendencies. The AI ​​uses natural language processing technology and machine learning algorithms to extract keywords and emotional patterns from the user's responses and analyze the user's personality and values ​​with high accuracy. For example, the AI ​​can identify the user's personality traits and value tendencies from the words and expressions the user frequently uses in the dialogue. Based on this information, the analysis department creates a detailed user profile and provides it to the suggestion and generation departments. This improves the accuracy and effectiveness of the entire system. Furthermore, the analysis department collects user feedback and continuously improves the accuracy of the analysis results. For example, based on the feedback provided by the user, the AI ​​adjusts the analysis algorithm and reflects it in the next analysis. In this way, the analysis department plays a crucial role in increasing the success rate of matchmaking by analyzing the user's personality and values ​​with high accuracy.

[0034] The generation unit automatically generates date plans tailored to the other person's hobbies. For example, the generation unit uses AI to suggest the optimal date plan based on the other person's hobbies and interests. Specifically, the AI ​​analyzes the other person's profile information and past activity history to identify their hobbies and interests. For example, if the other person enjoys outdoor activities, it will suggest date plans such as hiking or camping. If the other person is interested in cooking, it will suggest date plans such as cooking classes or gourmet tours. Using AI, the generation unit can generate date plans that will please the other person with high accuracy. Furthermore, the generation unit collects user feedback and continuously improves the accuracy of the date plans. For example, based on the user's feedback on the date, the AI ​​adjusts the content of the date plan and reflects it in the next suggestion. In this way, the generation unit can provide date plans that are highly satisfying for both the user and the other person. The generation unit plays an important role in supporting the user's search for a partner and can increase the success rate of their search.

[0035] The tracking unit tracks the progress of the user's matchmaking efforts. For example, the tracking unit uses AI to analyze dating feedback and provide advice for the next date. Specifically, the AI ​​analyzes the user's dating feedback and opinions to identify success factors and areas for improvement. For instance, it records the user's favorite activities and conversations during the date and uses this to suggest plans for the next date. It also analyzes any problems or dissatisfactions that arose during the date and provides solutions. The tracking unit uses AI to track the progress of the matchmaking efforts in detail. Furthermore, the tracking unit centrally manages the user's matchmaking progress and provides advice and support at the appropriate time. For example, if a user hasn't gone on a date for a certain period, the tracking unit sends a reminder to encourage them to continue their matchmaking efforts. Also, if a user is dating multiple people, the tracking unit manages the progress of each relationship and provides optimal advice. In this way, the tracking unit can efficiently support the user's matchmaking efforts and increase their chances of success.

[0036] The feedback unit suggests improvements based on the progress tracked by the tracking unit. For example, the feedback unit uses AI to analyze progress and suggest improvements to the user. Specifically, the AI ​​analyzes dating impressions and feedback collected by the tracking unit to identify areas for improvement in the user's behavior and communication. For example, if the user showed little reaction to a particular topic during a date, the feedback unit will provide advice on deepening their knowledge of that topic. Also, if the user was nervous and couldn't talk well during a date, the feedback unit will provide advice on how to relax and improve their communication skills. The feedback unit can use AI to provide effective feedback based on the progress of the matchmaking process. Furthermore, the feedback unit collects user feedback and continuously improves the accuracy and effectiveness of the entire system. For example, based on the feedback provided by the user, the AI ​​adjusts the feedback algorithm and reflects it in the next feedback. In this way, the feedback unit plays a crucial role in effectively supporting the user's matchmaking efforts and increasing the success rate of their matchmaking.

[0037] The data collection unit can collect user profile information and past activity history. For example, the data collection unit collects profile information registered by the user. The data collection unit can collect basic information such as the user's age, gender, and occupation. The data collection unit can also collect the user's past activity history. For example, the data collection unit collects the user's past activity and dating history. Based on this information, the data collection unit can analyze the user's values ​​and hobbies in detail. This allows the data collection unit to perform more accurate matching by collecting user profile information and past activity history. 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 profile information and activity history into an AI, which can analyze this information to extract the user's values ​​and hobbies.

[0038] The suggestion unit can propose the most suitable partner based on the collected information. For example, the suggestion unit can use AI to select the most suitable partner based on the collected information and propose it to the user. The suggestion unit can use AI to propose partners that match the user's values ​​and hobbies with high accuracy. For example, the suggestion unit can select the most suitable partner based on criteria such as shared hobbies and matching values. The suggestion unit can use AI to analyze the user's values ​​and hobbies in detail and propose the most suitable partner. In this way, the suggestion unit can find the ideal partner for the user by proposing the most suitable partner based on the collected information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input collected information into AI, and the AI ​​can select and propose the most suitable partner.

[0039] The dialogue unit can ask the user questions and analyze the user's personality and values ​​based on their answers. For example, the dialogue unit's AI can ask the user questions about their hobbies and values. Based on the user's answers, the AI ​​can analyze the user's personality and values ​​in detail. The dialogue unit can use AI to conduct dialogues that deepen the user's self-understanding. For example, the dialogue unit's AI can ask the user a series of questions in order to analyze the user's personality and values. The dialogue unit can use AI to analyze the user's personality and values ​​with high accuracy. In this way, the dialogue unit can deepen the user's self-understanding by asking the user questions and analyzing the user's personality and values ​​based on their answers. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's answers into the AI, and the AI ​​can analyze the user's personality and values.

[0040] The generation unit can propose the optimal date plan based on the other person's hobbies and interests. For example, the generation unit uses AI to propose the optimal date plan based on the other person's hobbies and interests. The generation unit can use AI to generate a date plan that will please the other person with high accuracy. For example, the generation unit can make restaurant reservations and suggest activities based on the other person's hobbies and interests. The generation unit can use AI to analyze the other person's hobbies and interests in detail and propose the optimal date plan. As a result, by having the generation unit propose the optimal date plan based on the other person's hobbies and interests, the user can realize a date plan that will please the other person. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into AI, and the AI ​​can generate the optimal date plan.

[0041] The tracking unit can analyze the feedback from the date and provide advice for the next date. For example, the tracking unit can use AI to analyze the feedback from the date and provide advice for the next date. The tracking unit can use AI to analyze the feedback from the date in detail and provide effective advice. For example, the tracking unit can analyze the satisfaction level of the date and the impression of the other person and suggest areas for improvement for the next date. The tracking unit can use AI to analyze the feedback from the date with high accuracy and provide advice for the next date. In this way, the tracking unit can manage the progress of the matchmaking process and make effective improvements by analyzing the feedback from the date and providing advice for the next date. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the feedback from the date into the AI, and the AI ​​can generate advice for the next date.

[0042] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can select the most efficient data collection method from the user's behavior history. The data collection unit can analyze the user's behavior patterns and suggest the optimal data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history into AI, which can then select the optimal data collection method.

[0043] The data collection unit can filter profile information and behavioral history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current lifestyle. The data collection unit can filter the information to be collected based on the user's areas of interest. The data collection unit can determine the priority of the information to be collected according to the user's lifestyle and areas of interest. As a result, the data collection unit can collect highly relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then filter the information.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information and behavioral history. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can determine the priority of the information to be collected by considering the user's geographical location. The data collection unit can collect the most relevant information based on the user's location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can determine the priority of the information.

[0045] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information and behavioral history. For example, the data collection unit can analyze the user's social media activity and collect relevant information. The data collection unit can filter the information to be collected based on the content of the user's social media posts. The data collection unit can collect the most relevant information by referring to the user's social media activity history. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can filter the information.

[0046] The proposal function can adjust the level of detail of a proposal based on the importance of the recipient. For example, if the recipient is important, the proposal function will provide a detailed proposal. If the recipient is not so important, the proposal function can provide a concise proposal. The proposal function can adjust the level of detail of a proposal according to the importance of the recipient. This allows the proposal function to make more effective proposals by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the proposal function may be performed using AI or not. For example, the proposal function can input the importance of the recipient into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the recipient's category when making a proposal. For example, if the recipient is a friend, the proposal unit will apply a proposal algorithm for friends. If the recipient is a business partner, the proposal unit can apply a proposal algorithm for business. If the recipient is a family member, the proposal unit can apply a proposal algorithm for family. This allows the proposal unit to make more appropriate proposals by applying different proposal algorithms depending on the recipient's category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the recipient's category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0048] The proposal unit can determine the priority of proposals based on the recipient's profile information when making a proposal. For example, the proposal unit can prioritize proposals of high importance based on the recipient's profile information. The proposal unit can determine the priority of proposals by considering the recipient's profile information. The proposal unit can make the most appropriate proposal based on the recipient's profile information. As a result, the proposal unit can make more effective proposals by determining the priority of proposals based on the recipient's profile information. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the recipient's profile information into AI, and the AI ​​can determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the recipient. For example, the proposal unit can present high-priority proposals first based on the relevance of the recipient. The proposal unit can adjust the order of proposals considering the relevance of the recipient. The proposal unit can determine the optimal order of proposals based on the relevance of the recipient. This allows the proposal unit to make more effective proposals by adjusting the order of proposals based on the relevance of the recipient. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of the recipient into the AI, and the AI ​​can adjust the order of proposals.

[0050] The dialogue unit can select the most appropriate question during a conversation by referring to the user's past conversation history. For example, the dialogue unit can select relevant questions based on the user's past conversation history. The dialogue unit can select the most effective question from the user's conversation history. The dialogue unit can analyze the user's past conversation history and suggest the most appropriate question. In this way, the dialogue unit can select the most appropriate question by referring to the user's past conversation history. Some or all of the above processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's conversation history into AI, and the AI ​​can select the most appropriate question.

[0051] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. For example, the dialogue unit can provide relevant dialogue content based on the user's current situation. The dialogue unit can customize the content of the dialogue considering the user's situation. The dialogue unit can provide optimal dialogue content according to the user's current situation. As a result, the dialogue unit can enable more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's current situation into the AI, and the AI ​​can customize the content of the dialogue.

[0052] The dialogue unit can select the most appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, the dialogue unit can provide relevant dialogue content based on the user's current location. The dialogue unit can select dialogue content considering the user's geographical location. The dialogue unit can provide the most appropriate dialogue content based on the user's location. As a result, the dialogue unit can provide highly relevant dialogue content by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's geographical location information into the AI, and the AI ​​can select the dialogue content.

[0053] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can analyze the user's social media activity and provide relevant dialogue content. The dialogue unit can suggest dialogue content based on the user's social media posts. The dialogue unit can provide optimal dialogue content by referring to the user's social media activity history. In this way, the dialogue unit can provide relevant dialogue content by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's social media activity into AI, and the AI ​​can suggest dialogue content.

[0054] The analysis unit can select the optimal analysis method by referring to the user's past behavior history during analysis. For example, the analysis unit can select the optimal analysis method based on the user's past behavior history. The analysis unit can select the most effective analysis method from the user's behavior history. The analysis unit can analyze the user's past behavior history and propose the optimal analysis method. In this way, the analysis unit can select the optimal analysis method by referring to the user's past behavior history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's behavior history into AI, and the AI ​​can select the optimal analysis method.

[0055] The analysis unit can customize the means of analysis based on the user's current situation during the analysis. For example, the analysis unit can provide relevant analysis means based on the user's current situation. The analysis unit can customize the means of analysis considering the user's situation. The analysis unit can provide the optimal analysis means according to the user's current situation. This allows the analysis unit to perform a more appropriate analysis by customizing the means of analysis based on the user's current situation. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's current situation into the AI, and the AI ​​can customize the means of analysis.

[0056] The analysis unit can select the optimal analysis method while considering the user's geographical location information during analysis. For example, the analysis unit can provide relevant analysis methods based on the user's current location. The analysis unit can select an analysis method while considering the user's geographical location information. The analysis unit can provide the optimal analysis method based on the user's location information. In this way, the analysis unit can provide highly relevant analysis methods by considering the user's geographical location information. 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 the user's geographical location information into AI, and the AI ​​can select an analysis method.

[0057] The analysis unit can analyze a user's social media activity and propose analytical methods during the analysis process. For example, the analysis unit can analyze a user's social media activity and provide relevant analytical methods. The analysis unit can propose analytical methods based on the content of a user's social media posts. The analysis unit can provide the most suitable analytical methods by referring to a user's social media activity history. In this way, the analysis unit can provide relevant analytical methods by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input a user's social media activity into an AI, which can then propose analytical methods.

[0058] The generation unit can select the optimal date plan by referring to the other party's past dating history during the generation process. For example, the generation unit can select the optimal date plan based on the other party's past dating history. The generation unit can select the most effective date plan from the other party's dating history. The generation unit can analyze the other party's past dating history and propose the optimal date plan. In this way, the generation unit can select the optimal date plan by referring to the other party's past dating history. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's dating history into AI, and the AI ​​can select the optimal date plan.

[0059] The generation unit can customize the date plan based on the other person's current hobbies and interests during the generation process. For example, the generation unit can provide relevant date plans based on the other person's current hobbies. The generation unit can customize the date plan considering the other person's interests. The generation unit can provide the optimal date plan according to the other person's current hobbies and interests. In this way, the generation unit can provide a more appropriate date plan by customizing it based on the other person's current hobbies and interests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into the AI, and the AI ​​can customize the date plan.

[0060] The generation unit can select the optimal date plan by considering the other party's geographical location information during generation. For example, the generation unit provides relevant date plans based on the other party's current location. The generation unit can select a date plan by considering the other party's geographical location information. The generation unit can provide the optimal date plan based on the other party's location information. In this way, the generation unit can provide highly relevant date plans by considering the other party's geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's geographical location information into the AI, and the AI ​​can select a date plan.

[0061] The generation unit can analyze the other party's social media activity and propose a date plan during the generation process. For example, the generation unit can analyze the other party's social media activity and provide a relevant date plan. The generation unit can propose a date plan based on the content of the other party's social media posts. The generation unit can provide the optimal date plan by referring to the other party's social media activity history. In this way, the generation unit can provide a relevant date plan by analyzing the other party's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's social media activity into AI, and the AI ​​can propose a date plan.

[0062] The tracking unit can select the optimal tracking method by referring to the user's past progress history during tracking. For example, the tracking unit can select the optimal tracking method based on the user's past progress history. The tracking unit can select the most effective tracking method from the user's progress history. The tracking unit can analyze the user's past progress history and propose the optimal tracking method. Thus, the tracking unit can select the optimal tracking method by referring to the user's past progress history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's progress history into AI, and the AI ​​can select the optimal tracking method.

[0063] The tracking unit can customize the means of tracking based on the user's current situation during tracking. For example, the tracking unit can provide relevant tracking means based on the user's current situation. The tracking unit can customize the means of tracking considering the user's situation. The tracking unit can provide the optimal tracking means according to the user's current situation. This enables more appropriate tracking by customizing the means of tracking based on the user's current situation. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's current situation into the AI, and the AI ​​can customize the means of tracking.

[0064] The tracking unit can select the optimal tracking method while considering the user's geographical location information. For example, the tracking unit provides a relevant tracking method based on the user's current location. The tracking unit can select a tracking method considering the user's geographical location information. The tracking unit can provide the optimal tracking method based on the user's location information. In this way, the tracking unit can provide a highly relevant tracking method by considering the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's geographical location information into the AI, and the AI ​​can select a tracking method.

[0065] The tracking unit can analyze the user's social media activity during tracking and propose tracking methods. For example, the tracking unit can analyze the user's social media activity and provide relevant tracking methods. The tracking unit can propose tracking methods based on the content of the user's social media posts. The tracking unit can provide the optimal tracking method by referring to the user's social media activity history. In this way, the tracking unit can provide relevant tracking methods by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's social media activity into AI, and the AI ​​can propose tracking methods.

[0066] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit can select the optimal feedback method based on the user's past feedback history. The feedback unit can select the most effective feedback method from the user's feedback history. The feedback unit can analyze the user's past feedback history and propose the optimal feedback method. In this way, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's feedback history into AI, and the AI ​​can select the optimal feedback method.

[0067] The feedback unit can customize the means of feedback based on the user's current situation when providing feedback. For example, the feedback unit provides relevant feedback means based on the user's current situation. The feedback unit can customize the means of feedback considering the user's situation. The feedback unit can customize a date plan based on the hobbies and interests of the most suitable person to provide feedback to, according to the user's current situation. The generation unit provides relevant date plans based on the other person's current hobbies. The generation unit can customize the date plan considering the other person's interests. The generation unit can provide the most suitable date plan according to the other person's current hobbies and interests. As a result, the generation unit can provide a more appropriate date plan by customizing the date plan based on the other person's current hobbies and interests. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into the AI, and the AI ​​can customize the date plan.

[0068] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can provide relevant feedback methods based on the user's current location. The feedback unit can select a feedback method considering the user's geographical location information. The feedback unit can provide the optimal feedback method based on the user's location information. As a result, the feedback unit can provide highly relevant feedback methods by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into AI, and the AI ​​can select a feedback method.

[0069] The feedback unit can analyze the user's social media activity and suggest methods of feedback when providing feedback. For example, the feedback unit can analyze the user's social media activity and provide relevant feedback methods. The feedback unit can suggest methods of feedback based on the content of the user's social media posts. The feedback unit can provide the most suitable feedback method by referring to the user's social media activity history. In this way, the feedback unit can provide relevant feedback methods by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into AI, and the AI ​​can suggest methods of feedback.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The matchmaking agent system can also include a health management department that monitors the user's health status. This department collects user health data and provides advice to reduce stress and fatigue during matchmaking activities. For example, it can monitor the user's sleep patterns and exercise levels and recommend appropriate rest and exercise. It can also analyze the user's diet and suggest nutritionally balanced meals. Furthermore, it can provide advice on relaxation methods and stress management to support the user's mental health. This allows users to engage in matchmaking activities while maintaining a healthy lifestyle.

[0072] The matchmaking agent system can also include a fashion advice department that advises users on their fashion style. This department proposes optimal fashion styles based on the user's body type and preferences. For example, it can suggest clothing and color combinations that suit the user's body type. It can also suggest outfits appropriate for the user's date situation. Furthermore, it can suggest fashion items that fit the user's budget. This allows users to approach dates with confidence.

[0073] The matchmaking agent system can also include a communication training department to further enhance users' communication skills. This department provides training to improve users' conversational skills and expressive abilities. For example, it can teach users how to ask effective questions and how to draw out information from others. It can also instruct users on the use of facial expressions and gestures. Furthermore, it can provide training to improve users' self-introduction and presentation skills. This allows users to communicate with others with confidence.

[0074] The matchmaking support agent system can also include a hobby discovery section to help users deepen their hobbies and interests. This section suggests new hobbies and activities based on the user's interests. For example, it can suggest events and workshops related to areas of interest. It can also provide online courses and learning materials to help users acquire new skills. Furthermore, it can introduce users to communities and groups where they can enjoy shared hobbies with others. This allows users to grow personally through new hobbies and become more attractive in their matchmaking efforts.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The data collection unit collects the user's values ​​and hobbies. For example, the data collection unit analyzes the user's values ​​and hobbies based on the profile information and past behavioral history registered by the user. The data collection unit can use AI to analyze the user's values ​​and hobbies in detail. Step 2: The suggestion unit proposes the most suitable partner based on the information collected by the collection unit. For example, the suggestion unit uses AI to select the most suitable partner based on the collected information and proposes it to the user. The suggestion unit can use AI to propose partners that match the user's values ​​and hobbies with high accuracy. Step 3: The dialogue unit deepens its self-understanding through dialogue with the user. For example, the dialogue unit uses AI to ask questions to the user and analyzes the user's personality and values ​​based on their answers. The dialogue unit can use AI to conduct dialogues that deepen the user's self-understanding. Step 4: The analysis unit analyzes the results obtained by the dialogue unit. For example, the analysis unit uses AI to analyze the dialogue results and analyze the user's personality and values ​​in detail. The analysis unit can use AI to analyze the dialogue results with high accuracy. Step 5: The generation unit automatically generates a date plan tailored to the other person's hobbies. For example, the generation unit uses AI to suggest the optimal date plan based on the other person's hobbies and interests. Using AI, the generation unit can generate a date plan that will please the other person with high accuracy. Step 6: The tracking unit tracks the progress of the matchmaking process. For example, the tracking unit uses AI to analyze the impressions of the date and provide advice for the next date. The tracking unit can use AI to track the progress of the matchmaking process in detail. Step 7: The feedback unit suggests improvements based on the progress tracked by the tracking unit. For example, the feedback unit uses AI to analyze progress and suggest improvements to the user. The feedback unit can use AI to provide effective feedback based on the progress of the matchmaking process.

[0077] (Example of form 2) The matchmaking support agent system according to an embodiment of the present invention is a system that collects the user's values ​​and hobbies, proposes the most suitable partner, deepens self-understanding, automatically generates date plans, tracks the progress of matchmaking, and suggests areas for improvement. This matchmaking support agent system utilizes AI to analyze the user's values ​​and hobbies and provides an individually optimized matching function that proposes the most suitable partner. Next, it provides an interactive self-analysis function that deepens self-understanding through dialogue with the AI. Furthermore, it provides a date plan proposal function that automatically generates date plans tailored to the partner's hobbies. Finally, it provides a progress management and feedback function that tracks the progress of matchmaking and suggests areas for improvement. For example, the matchmaking support agent system uses AI to analyze the user's values ​​and hobbies based on the profile information and past behavioral history registered by the user and proposes the most suitable partner. This allows the user to find a partner that meets their ideal conditions. Next, the matchmaking support agent system uses AI to ask the user questions and analyze the user's personality and values ​​based on the answers. This allows the user to deepen their self-understanding and make better choices. Furthermore, the matchmaking support agent system uses AI to propose the most suitable date plan based on the partner's hobbies and interests. This allows the user to create a date plan that will please the other person. Finally, the matchmaking agent system uses AI to analyze dating experiences and provide advice for the next date. This allows users to manage their matchmaking progress and make effective improvements. As a result, users can engage in matchmaking more efficiently and make better choices by deepening their self-understanding. It also reduces the stress of matchmaking and enables service providers to improve customer satisfaction through highly accurate matching and operate efficiently. In this way, the matchmaking agent system collects users' values ​​and hobbies, suggests the most suitable partner, deepens self-understanding, automatically generates date plans, tracks matchmaking progress, and suggests areas for improvement, thereby achieving efficient and effective matchmaking support.

[0078] The matchmaking support agent system according to this embodiment comprises a collection unit, a proposal unit, a dialogue unit, an analysis unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects the user's values ​​and hobbies. The collection unit analyzes the user's values ​​and hobbies, for example, based on the profile information registered by the user and their past behavioral history. The collection unit can use AI to analyze the user's values ​​and hobbies in detail. The proposal unit proposes the most suitable partner based on the information collected by the collection unit. The proposal unit, for example, uses AI to select the most suitable partner based on the collected information and proposes it to the user. The proposal unit can use AI to propose a partner that matches the user's values ​​and hobbies with high accuracy. The dialogue unit deepens self-understanding through dialogue with the user. The dialogue unit, for example, uses AI to ask the user questions and analyzes the user's personality and values ​​based on their answers. The dialogue unit can use AI to conduct dialogues to deepen the user's self-understanding. The analysis unit analyzes the results obtained by the dialogue unit. The analysis unit, for example, uses AI to analyze the results of conversations and analyze the user's personality and values ​​in detail. The analysis unit can analyze the results of conversations with high accuracy using AI. The generation unit automatically generates date plans tailored to the other person's hobbies. The generation unit, for example, uses AI to propose the optimal date plan based on the other person's hobbies and interests. The generation unit can generate date plans that will please the other person with high accuracy using AI. The tracking unit tracks the progress of the matchmaking process. The tracking unit, for example, uses AI to analyze the user's impressions of the date and provide advice for the next date. The tracking unit can track the progress of the matchmaking process in detail using AI. The feedback unit suggests areas for improvement based on the progress tracked by the tracking unit. The feedback unit, for example, uses AI to analyze the progress and suggest areas for improvement to the user. The feedback unit can provide effective feedback based on the progress of the matchmaking process using AI. As a result, the matchmaking support agent system according to this embodiment can collect the user's values ​​and hobbies, suggest the most suitable partner, deepen self-understanding, automatically generate date plans, track the progress of matchmaking, and suggest areas for improvement, thereby achieving efficient and effective matchmaking support.

[0079] The data collection unit collects user values ​​and hobbies. For example, it analyzes users' values ​​and hobbies based on their registered profile information and past activity history. Specifically, the profile information entered by users includes age, occupation, hobbies, interests, values, and ideal partner type. This information is centrally managed by the system, and a detailed user profile is created. Furthermore, data such as what events the user has attended, who they have matched with, and what messages they have sent and received is also collected as past activity history. This data is analyzed using AI to reveal the user's behavior patterns and preference tendencies. For example, if a user frequently participates in events related to a particular hobby, it is determined that this hobby is an important part of the user's values. Also, by analyzing the characteristics of people the user has matched with in the past, it is possible to identify what type of person the user is interested in. Based on this information, the data collection unit gains a detailed understanding of users' values ​​and hobbies and provides foundational data to improve the accuracy of the entire system. Furthermore, the data collection unit implements strict security measures in data collection and management to protect user privacy. This allows users to use the system with peace of mind.

[0080] The proposal department suggests the most suitable partner based on the information collected by the data collection department. For example, the proposal department uses AI to select the most suitable partner based on the collected information and proposes it to the user. Specifically, the AI ​​analyzes the user's values, hobbies, and past behavioral history, and searches the database for partners that match this information. The AI ​​uses a matching algorithm to evaluate the compatibility between the user and the partner and select the most suitable partner. For example, if the user enjoys outdoor activities, a partner who also enjoys outdoor activities will be suggested. Also, if the user values ​​a particular set of values, a partner who shares those values ​​will be prioritized in the suggestions. The proposal department can use AI to suggest partners that match the user's values ​​and hobbies with high accuracy. Furthermore, the proposal department collects user feedback and continuously improves the accuracy of its suggestions. For example, by providing the user's matching results and dating impressions with suggested partners, the AI ​​learns from this information and incorporates it into future suggestions. In this way, the proposal department can suggest the most suitable partner according to the user's needs and increase the success rate of matchmaking.

[0081] The dialogue function deepens self-understanding through dialogue with the user. For example, the dialogue function uses AI to ask questions to the user and analyze the user's personality and values ​​based on their answers. Specifically, the AI ​​asks the user questions about their hobbies, interests, and values, and analyzes their answers. For example, through questions such as "How do you spend your holidays?" or "What is your ideal partner like?", it gains a detailed understanding of the user's personality and values. The AI ​​uses natural language processing technology to analyze the user's answers and extract keywords and emotional tendencies. This allows for a highly accurate analysis of the user's personality and values. The dialogue function uses AI to conduct dialogues that deepen the user's self-understanding. Furthermore, the dialogue function provides feedback based on the user's answers to further enhance self-understanding. For example, if a user values ​​a particular set of values, it provides advice and information related to those values. This allows the user to deepen their self-understanding and gain help in finding a suitable partner. The dialogue function plays a crucial role in increasing the success rate of matchmaking through dialogue with users.

[0082] The analysis department analyzes the results obtained by the dialogue department. For example, the analysis department uses AI to analyze the dialogue results and conduct a detailed analysis of the user's personality and values. Specifically, the AI ​​analyzes the user's response data collected by the dialogue department to reveal the user's personality traits and value tendencies. The AI ​​uses natural language processing technology and machine learning algorithms to extract keywords and emotional patterns from the user's responses and analyze the user's personality and values ​​with high accuracy. For example, the AI ​​can identify the user's personality traits and value tendencies from the words and expressions the user frequently uses in the dialogue. Based on this information, the analysis department creates a detailed user profile and provides it to the suggestion and generation departments. This improves the accuracy and effectiveness of the entire system. Furthermore, the analysis department collects user feedback and continuously improves the accuracy of the analysis results. For example, based on the feedback provided by the user, the AI ​​adjusts the analysis algorithm and reflects it in the next analysis. In this way, the analysis department plays a crucial role in increasing the success rate of matchmaking by analyzing the user's personality and values ​​with high accuracy.

[0083] The generation unit automatically generates date plans tailored to the other person's hobbies. For example, the generation unit uses AI to suggest the optimal date plan based on the other person's hobbies and interests. Specifically, the AI ​​analyzes the other person's profile information and past activity history to identify their hobbies and interests. For example, if the other person enjoys outdoor activities, it will suggest date plans such as hiking or camping. If the other person is interested in cooking, it will suggest date plans such as cooking classes or gourmet tours. Using AI, the generation unit can generate date plans that will please the other person with high accuracy. Furthermore, the generation unit collects user feedback and continuously improves the accuracy of the date plans. For example, based on the user's feedback on the date, the AI ​​adjusts the content of the date plan and reflects it in the next suggestion. In this way, the generation unit can provide date plans that are highly satisfying for both the user and the other person. The generation unit plays an important role in supporting the user's search for a partner and can increase the success rate of their search.

[0084] The tracking unit tracks the progress of the user's matchmaking efforts. For example, the tracking unit uses AI to analyze dating feedback and provide advice for the next date. Specifically, the AI ​​analyzes the user's dating feedback and opinions to identify success factors and areas for improvement. For instance, it records the user's favorite activities and conversations during the date and uses this to suggest plans for the next date. It also analyzes any problems or dissatisfactions that arose during the date and provides solutions. The tracking unit uses AI to track the progress of the matchmaking efforts in detail. Furthermore, the tracking unit centrally manages the user's matchmaking progress and provides advice and support at the appropriate time. For example, if a user hasn't gone on a date for a certain period, the tracking unit sends a reminder to encourage them to continue their matchmaking efforts. Also, if a user is dating multiple people, the tracking unit manages the progress of each relationship and provides optimal advice. In this way, the tracking unit can efficiently support the user's matchmaking efforts and increase their chances of success.

[0085] The feedback unit suggests improvements based on the progress tracked by the tracking unit. For example, the feedback unit uses AI to analyze progress and suggest improvements to the user. Specifically, the AI ​​analyzes dating impressions and feedback collected by the tracking unit to identify areas for improvement in the user's behavior and communication. For example, if the user showed little reaction to a particular topic during a date, the feedback unit will provide advice on deepening their knowledge of that topic. Also, if the user was nervous and couldn't talk well during a date, the feedback unit will provide advice on how to relax and improve their communication skills. The feedback unit can use AI to provide effective feedback based on the progress of the matchmaking process. Furthermore, the feedback unit collects user feedback and continuously improves the accuracy and effectiveness of the entire system. For example, based on the feedback provided by the user, the AI ​​adjusts the feedback algorithm and reflects it in the next feedback. In this way, the feedback unit plays a crucial role in effectively supporting the user's matchmaking efforts and increasing the success rate of their matchmaking.

[0086] The data collection unit can collect user profile information and past activity history. For example, the data collection unit collects profile information registered by the user. The data collection unit can collect basic information such as the user's age, gender, and occupation. The data collection unit can also collect the user's past activity history. For example, the data collection unit collects the user's past activity and dating history. Based on this information, the data collection unit can analyze the user's values ​​and hobbies in detail. This allows the data collection unit to perform more accurate matching by collecting user profile information and past activity history. 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 profile information and activity history into an AI, which can analyze this information to extract the user's values ​​and hobbies.

[0087] The suggestion unit can propose the most suitable partner based on the collected information. For example, the suggestion unit can use AI to select the most suitable partner based on the collected information and propose it to the user. The suggestion unit can use AI to propose partners that match the user's values ​​and hobbies with high accuracy. For example, the suggestion unit can select the most suitable partner based on criteria such as shared hobbies and matching values. The suggestion unit can use AI to analyze the user's values ​​and hobbies in detail and propose the most suitable partner. In this way, the suggestion unit can find the ideal partner for the user by proposing the most suitable partner based on the collected information. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input collected information into AI, and the AI ​​can select and propose the most suitable partner.

[0088] The dialogue unit can ask the user questions and analyze the user's personality and values ​​based on their answers. For example, the dialogue unit's AI can ask the user questions about their hobbies and values. Based on the user's answers, the AI ​​can analyze the user's personality and values ​​in detail. The dialogue unit can use AI to conduct dialogues that deepen the user's self-understanding. For example, the dialogue unit's AI can ask the user a series of questions in order to analyze the user's personality and values. The dialogue unit can use AI to analyze the user's personality and values ​​with high accuracy. In this way, the dialogue unit can deepen the user's self-understanding by asking the user questions and analyzing the user's personality and values ​​based on their answers. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's answers into the AI, and the AI ​​can analyze the user's personality and values.

[0089] The generation unit can propose the optimal date plan based on the other person's hobbies and interests. For example, the generation unit uses AI to propose the optimal date plan based on the other person's hobbies and interests. The generation unit can use AI to generate a date plan that will please the other person with high accuracy. For example, the generation unit can make restaurant reservations and suggest activities based on the other person's hobbies and interests. The generation unit can use AI to analyze the other person's hobbies and interests in detail and propose the optimal date plan. As a result, by having the generation unit propose the optimal date plan based on the other person's hobbies and interests, the user can realize a date plan that will please the other person. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into AI, and the AI ​​can generate the optimal date plan.

[0090] The tracking unit can analyze the feedback from the date and provide advice for the next date. For example, the tracking unit can use AI to analyze the feedback from the date and provide advice for the next date. The tracking unit can use AI to analyze the feedback from the date in detail and provide effective advice. For example, the tracking unit can analyze the satisfaction level of the date and the impression of the other person and suggest areas for improvement for the next date. The tracking unit can use AI to analyze the feedback from the date with high accuracy and provide advice for the next date. In this way, the tracking unit can manage the progress of the matchmaking process and make effective improvements by analyzing the feedback from the date and providing advice for the next date. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the feedback from the date into the AI, and the AI ​​can generate advice for the next date.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of collecting profile information and behavioral history based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect information when the user is relaxed. If the user is relaxed, the data collection unit can advance the collection timing to collect detailed information. If the user is in a hurry, the data collection unit can adjust the collection timing to quickly collect the necessary information. In this way, the data collection unit can collect information at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotions into an AI, which can then adjust the collection timing.

[0092] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can select the most efficient data collection method from the user's behavior history. The data collection unit can analyze the user's behavior patterns and suggest the optimal data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history into AI, which can then select the optimal data collection method.

[0093] The data collection unit can filter profile information and behavioral history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current lifestyle. The data collection unit can filter the information to be collected based on the user's areas of interest. The data collection unit can determine the priority of the information to be collected according to the user's lifestyle and areas of interest. As a result, the data collection unit can collect highly relevant information by filtering information based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then filter the information.

[0094] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority information. If the user is relaxed, the data collection unit can prioritize collecting detailed information. If the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. In this way, the data collection unit can prioritize collecting important information by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's emotions into an AI, which can then determine the priority of information.

[0095] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information and behavioral history. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current location. The data collection unit can determine the priority of the information to be collected by considering the user's geographical location. The data collection unit can collect the most relevant information based on the user's location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can determine the priority of the information.

[0096] The data collection unit can analyze the user's social media activity and collect relevant information when collecting profile information and behavioral history. For example, the data collection unit can analyze the user's social media activity and collect relevant information. The data collection unit can filter the information to be collected based on the content of the user's social media posts. The data collection unit can collect the most relevant information by referring to the user's social media activity history. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can filter the information.

[0097] 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 nervous, the suggestion unit can provide a simple and easy-to-understand presentation. If the user is relaxed, the suggestion unit can provide a presentation that includes detailed information. If the user is in a hurry, the suggestion unit can provide a presentation that gets straight to the point. This allows the suggestion unit to provide more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's emotions into an AI, which can then adjust the presentation of its suggestions.

[0098] The proposal function can adjust the level of detail of a proposal based on the importance of the recipient. For example, if the recipient is important, the proposal function will provide a detailed proposal. If the recipient is not so important, the proposal function can provide a concise proposal. The proposal function can adjust the level of detail of a proposal according to the importance of the recipient. This allows the proposal function to make more effective proposals by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the proposal function may be performed using AI or not. For example, the proposal function can input the importance of the recipient into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0099] The proposal unit can apply different proposal algorithms depending on the recipient's category when making a proposal. For example, if the recipient is a friend, the proposal unit will apply a proposal algorithm for friends. If the recipient is a business partner, the proposal unit can apply a proposal algorithm for business. If the recipient is a family member, the proposal unit can apply a proposal algorithm for family. This allows the proposal unit to make more appropriate proposals by applying different proposal algorithms depending on the recipient's category. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the recipient's category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed explanations. If the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's emotions into an AI, which can then adjust the length of its suggestions.

[0101] The proposal unit can determine the priority of proposals based on the recipient's profile information when making a proposal. For example, the proposal unit can prioritize proposals of high importance based on the recipient's profile information. The proposal unit can determine the priority of proposals by considering the recipient's profile information. The proposal unit can make the most appropriate proposal based on the recipient's profile information. As a result, the proposal unit can make more effective proposals by determining the priority of proposals based on the recipient's profile information. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the recipient's profile information into AI, and the AI ​​can determine the priority of proposals.

[0102] The proposal unit can adjust the order of proposals based on the relevance of the recipient. For example, the proposal unit can present high-priority proposals first based on the relevance of the recipient. The proposal unit can adjust the order of proposals considering the relevance of the recipient. The proposal unit can determine the optimal order of proposals based on the relevance of the recipient. This allows the proposal unit to make more effective proposals by adjusting the order of proposals based on the relevance of the recipient. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the relevance of the recipient into the AI, and the AI ​​can adjust the order of proposals.

[0103] The dialogue unit can estimate the user's emotions and adjust the way the dialogue proceeds based on the estimated emotions. For example, if the user is tense, the dialogue unit can engage in conversation to help them relax. If the user is relaxed, the dialogue unit can engage in more detailed conversation. If the user is in a hurry, the dialogue unit can proceed with the conversation quickly. In this way, the dialogue unit can provide a more appropriate dialogue by adjusting the way the dialogue proceeds based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's emotions into the AI, and the AI ​​can adjust the way the dialogue proceeds.

[0104] The dialogue unit can select the most appropriate question during a conversation by referring to the user's past conversation history. For example, the dialogue unit can select relevant questions based on the user's past conversation history. The dialogue unit can select the most effective question from the user's conversation history. The dialogue unit can analyze the user's past conversation history and suggest the most appropriate question. In this way, the dialogue unit can select the most appropriate question by referring to the user's past conversation history. Some or all of the above processes in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's conversation history into AI, and the AI ​​can select the most appropriate question.

[0105] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. For example, the dialogue unit can provide relevant dialogue content based on the user's current situation. The dialogue unit can customize the content of the dialogue considering the user's situation. The dialogue unit can provide optimal dialogue content according to the user's current situation. As a result, the dialogue unit can enable more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's current situation into the AI, and the AI ​​can customize the content of the dialogue.

[0106] The dialogue unit can estimate the user's emotions and determine the priority of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit will prioritize high-priority dialogues. If the user is relaxed, the dialogue unit will prioritize detailed dialogues. If the user is in a hurry, the dialogue unit will proceed with the dialogue quickly. In this way, the dialogue unit can prioritize important dialogues by determining the priority of the dialogue based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's emotions into an AI, and the AI ​​can determine the priority of the dialogue.

[0107] The dialogue unit can select the most appropriate dialogue content during a conversation, taking into account the user's geographical location. For example, the dialogue unit can provide relevant dialogue content based on the user's current location. The dialogue unit can select dialogue content considering the user's geographical location. The dialogue unit can provide the most appropriate dialogue content based on the user's location. As a result, the dialogue unit can provide highly relevant dialogue content by considering the user's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, or not. For example, the dialogue unit can input the user's geographical location information into the AI, and the AI ​​can select the dialogue content.

[0108] The dialogue unit can analyze the user's social media activity during a conversation and suggest dialogue content. For example, the dialogue unit can analyze the user's social media activity and provide relevant dialogue content. The dialogue unit can suggest dialogue content based on the user's social media posts. The dialogue unit can provide optimal dialogue content by referring to the user's social media activity history. In this way, the dialogue unit can provide relevant dialogue content by analyzing the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the user's social media activity into AI, and the AI ​​can suggest dialogue content.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple analysis method. If the user is relaxed, the analysis unit can provide a detailed analysis method. If the user is in a hurry, the analysis unit can perform a rapid analysis. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 or not. For example, the analysis unit can input the user's emotions into the AI, which can then adjust the analysis method.

[0110] The analysis unit can select the optimal analysis method by referring to the user's past behavior history during analysis. For example, the analysis unit can select the optimal analysis method based on the user's past behavior history. The analysis unit can select the most effective analysis method from the user's behavior history. The analysis unit can analyze the user's past behavior history and propose the optimal analysis method. In this way, the analysis unit can select the optimal analysis method by referring to the user's past behavior history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's behavior history into AI, and the AI ​​can select the optimal analysis method.

[0111] The analysis unit can customize the means of analysis based on the user's current situation during the analysis. For example, the analysis unit can provide relevant analysis means based on the user's current situation. The analysis unit can customize the means of analysis considering the user's situation. The analysis unit can provide the optimal analysis means according to the user's current situation. This allows the analysis unit to perform a more appropriate analysis by customizing the means of analysis based on the user's current situation. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's current situation into the AI, and the AI ​​can customize the means of analysis.

[0112] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize high-priority analyses. If the user is relaxed, the analysis unit will prioritize detailed analyses. If the user is in a hurry, the analysis unit can proceed with the analysis quickly. In this way, the analysis unit can prioritize important analyses by determining the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 or not. For example, the analysis unit can input the user's emotions into an AI, and the AI ​​can determine the priority of the analysis.

[0113] The analysis unit can select the optimal analysis method while considering the user's geographical location information during analysis. For example, the analysis unit can provide relevant analysis methods based on the user's current location. The analysis unit can select an analysis method while considering the user's geographical location information. The analysis unit can provide the optimal analysis method based on the user's location information. In this way, the analysis unit can provide highly relevant analysis methods by considering the user's geographical location information. 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 the user's geographical location information into AI, and the AI ​​can select an analysis method.

[0114] The analysis unit can analyze a user's social media activity and propose analytical methods during the analysis process. For example, the analysis unit can analyze a user's social media activity and provide relevant analytical methods. The analysis unit can propose analytical methods based on the content of a user's social media posts. The analysis unit can provide the most suitable analytical methods by referring to a user's social media activity history. In this way, the analysis unit can provide relevant analytical methods by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input a user's social media activity into an AI, which can then propose analytical methods.

[0115] The generation unit can estimate the user's emotions and adjust the method of generating the date plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a relaxed date plan. If the user is in a hurry, the generation unit can generate a date plan that can be enjoyed in a short amount of time. If the user is excited, the generation unit can generate an active date plan. In this way, the generation unit can provide a more appropriate date plan by adjusting the method of generating the date plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's emotions into the AI, and the AI ​​can adjust the method of generating the date plan.

[0116] The generation unit can select the optimal date plan by referring to the other party's past dating history during the generation process. For example, the generation unit can select the optimal date plan based on the other party's past dating history. The generation unit can select the most effective date plan from the other party's dating history. The generation unit can analyze the other party's past dating history and propose the optimal date plan. In this way, the generation unit can select the optimal date plan by referring to the other party's past dating history. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's dating history into AI, and the AI ​​can select the optimal date plan.

[0117] The generation unit can customize the date plan based on the other person's current hobbies and interests during the generation process. For example, the generation unit can provide relevant date plans based on the other person's current hobbies. The generation unit can customize the date plan considering the other person's interests. The generation unit can provide the optimal date plan according to the other person's current hobbies and interests. In this way, the generation unit can provide a more appropriate date plan by customizing it based on the other person's current hobbies and interests. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into the AI, and the AI ​​can customize the date plan.

[0118] The generation unit can estimate the user's emotions and prioritize date plans based on those emotions. For example, if the user is stressed, the generation unit can prioritize relaxing date plans. If the user is relaxed, the generation unit can prioritize active date plans. If the user is in a hurry, the generation unit can prioritize date plans that can be enjoyed in a short amount of time. In this way, the generation unit can prioritize important date plans by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's emotions into an AI, which can then determine the priority of date plans.

[0119] The generation unit can select the optimal date plan by considering the other party's geographical location information during generation. For example, the generation unit provides relevant date plans based on the other party's current location. The generation unit can select a date plan by considering the other party's geographical location information. The generation unit can provide the optimal date plan based on the other party's location information. In this way, the generation unit can provide highly relevant date plans by considering the other party's geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's geographical location information into the AI, and the AI ​​can select a date plan.

[0120] The generation unit can analyze the other party's social media activity and propose a date plan during the generation process. For example, the generation unit can analyze the other party's social media activity and provide a relevant date plan. The generation unit can propose a date plan based on the content of the other party's social media posts. The generation unit can provide the optimal date plan by referring to the other party's social media activity history. In this way, the generation unit can provide a relevant date plan by analyzing the other party's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other party's social media activity into AI, and the AI ​​can propose a date plan.

[0121] The tracking unit can estimate the user's emotions and adjust the progress tracking method based on the estimated user emotions. For example, if the user is stressed, the tracking unit can provide a simple tracking method. If the user is relaxed, the tracking unit can provide a detailed tracking method. If the user is in a hurry, the tracking unit can perform tracking quickly. This allows the tracking unit to provide more appropriate progress management by adjusting the progress tracking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 tracking unit may be performed using AI or not. For example, the tracking unit can input the user's emotions into the AI, which can then adjust the progress tracking method.

[0122] The tracking unit can select the optimal tracking method by referring to the user's past progress history during tracking. For example, the tracking unit can select the optimal tracking method based on the user's past progress history. The tracking unit can select the most effective tracking method from the user's progress history. The tracking unit can analyze the user's past progress history and propose the optimal tracking method. Thus, the tracking unit can select the optimal tracking method by referring to the user's past progress history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's progress history into AI, and the AI ​​can select the optimal tracking method.

[0123] The tracking unit can customize the means of tracking based on the user's current situation during tracking. For example, the tracking unit can provide relevant tracking means based on the user's current situation. The tracking unit can customize the means of tracking considering the user's situation. The tracking unit can provide the optimal tracking means according to the user's current situation. This enables more appropriate tracking by customizing the means of tracking based on the user's current situation. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's current situation into the AI, and the AI ​​can customize the means of tracking.

[0124] The tracking unit can estimate the user's emotions and prioritize progress based on the estimated emotions. For example, if the user is stressed, the tracking unit will prioritize tracking high-priority progress. If the user is relaxed, the tracking unit can prioritize tracking detailed progress. If the user is in a hurry, the tracking unit can quickly track progress. This allows the tracking unit to prioritize tracking important progress by prioritizing progress based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 tracking unit may be performed using AI or not. For example, the tracking unit can input the user's emotions into an AI, which can then determine the priority of progress.

[0125] The tracking unit can select the optimal tracking method while considering the user's geographical location information. For example, the tracking unit provides a relevant tracking method based on the user's current location. The tracking unit can select a tracking method considering the user's geographical location information. The tracking unit can provide the optimal tracking method based on the user's location information. In this way, the tracking unit can provide a highly relevant tracking method by considering the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's geographical location information into the AI, and the AI ​​can select a tracking method.

[0126] The tracking unit can analyze the user's social media activity during tracking and propose tracking methods. For example, the tracking unit can analyze the user's social media activity and provide relevant tracking methods. The tracking unit can propose tracking methods based on the content of the user's social media posts. The tracking unit can provide the optimal tracking method by referring to the user's social media activity history. In this way, the tracking unit can provide relevant tracking methods by analyzing the user's social media activity. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's social media activity into AI, and the AI ​​can propose tracking methods.

[0127] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide a simple feedback method. If the user is relaxed, the feedback unit can provide a detailed feedback method. If the user is in a hurry, the feedback unit can provide quick feedback. This allows the feedback unit to provide more appropriate feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback unit may be performed using AI or not. For example, the feedback unit can input the user's emotions into the AI, and the AI ​​can adjust the feedback method.

[0128] The feedback unit can select the optimal feedback method by referring to the user's past feedback history when providing feedback. For example, the feedback unit can select the optimal feedback method based on the user's past feedback history. The feedback unit can select the most effective feedback method from the user's feedback history. The feedback unit can analyze the user's past feedback history and propose the optimal feedback method. In this way, the feedback unit can select the optimal feedback method by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's feedback history into AI, and the AI ​​can select the optimal feedback method.

[0129] The feedback unit can customize the means of feedback based on the user's current situation when providing feedback. For example, the feedback unit provides relevant feedback means based on the user's current situation. The feedback unit can customize the means of feedback considering the user's situation. The feedback unit can customize a date plan based on the hobbies and interests of the most suitable person to provide feedback to, according to the user's current situation. The generation unit provides relevant date plans based on the other person's current hobbies. The generation unit can customize the date plan considering the other person's interests. The generation unit can provide the most suitable date plan according to the other person's current hobbies and interests. As a result, the generation unit can provide a more appropriate date plan by customizing the date plan based on the other person's current hobbies and interests. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the other person's hobbies and interests into the AI, and the AI ​​can customize the date plan.

[0130] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize high-importance feedback. If the user is relaxed, the feedback unit will prioritize detailed feedback. If the user is in a hurry, the feedback unit will provide quick feedback. In this way, the feedback unit can prioritize important feedback by prioritizing it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 feedback unit may be performed using AI or not. For example, the feedback unit can input the user's emotions into an AI, which can then determine the priority of the feedback.

[0131] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. For example, the feedback unit can provide relevant feedback methods based on the user's current location. The feedback unit can select a feedback method considering the user's geographical location information. The feedback unit can provide the optimal feedback method based on the user's location information. As a result, the feedback unit can provide highly relevant feedback methods by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into AI, and the AI ​​can select a feedback method.

[0132] The feedback unit can analyze the user's social media activity and suggest methods of feedback when providing feedback. For example, the feedback unit can analyze the user's social media activity and provide relevant feedback methods. The feedback unit can suggest methods of feedback based on the content of the user's social media posts. The feedback unit can provide the most suitable feedback method by referring to the user's social media activity history. In this way, the feedback unit can provide relevant feedback methods by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity into AI, and the AI ​​can suggest methods of feedback.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The matchmaking agent system can also include a health management department that monitors the user's health status. This department collects user health data and provides advice to reduce stress and fatigue during matchmaking activities. For example, it can monitor the user's sleep patterns and exercise levels and recommend appropriate rest and exercise. It can also analyze the user's diet and suggest nutritionally balanced meals. Furthermore, it can provide advice on relaxation methods and stress management to support the user's mental health. This allows users to engage in matchmaking activities while maintaining a healthy lifestyle.

[0135] The matchmaking agent system can also include a fashion advice department that advises users on their fashion style. This department proposes optimal fashion styles based on the user's body type and preferences. For example, it can suggest clothing and color combinations that suit the user's body type. It can also suggest outfits appropriate for the user's date situation. Furthermore, it can suggest fashion items that fit the user's budget. This allows users to approach dates with confidence.

[0136] The matchmaking agent system can also include a communication training department to further enhance users' communication skills. This department provides training to improve users' conversational skills and expressive abilities. For example, it can teach users how to ask effective questions and how to draw out information from others. It can also instruct users on the use of facial expressions and gestures. Furthermore, it can provide training to improve users' self-introduction and presentation skills. This allows users to communicate with others with confidence.

[0137] The matchmaking support agent system can also include a hobby discovery section to help users deepen their hobbies and interests. This section suggests new hobbies and activities based on the user's interests. For example, it can suggest events and workshops related to areas of interest. It can also provide online courses and learning materials to help users acquire new skills. Furthermore, it can introduce users to communities and groups where they can enjoy shared hobbies with others. This allows users to grow personally through new hobbies and become more attractive in their matchmaking efforts.

[0138] The matchmaking agent system can also include a date timing adjustment unit that estimates the user's emotions and adjusts the timing of dates based on those emotions. The date timing adjustment unit analyzes the user's emotional state and suggests the optimal timing for a date. For example, if the user is relaxed, the date timing adjustment unit can move the date forward. Conversely, if the user is stressed, it can delay the date to allow time for relaxation. Furthermore, if the user is in a hurry, the date timing adjustment unit can suggest a date plan that can be enjoyed in a short amount of time. This allows the user to enjoy a date at the optimal time.

[0139] The matchmaking agent system can also include a date location suggestion unit that estimates the user's emotions and suggests date locations based on those emotions. The date location suggestion unit analyzes the user's emotional state and proposes the most suitable date location. For example, if the user is relaxed, it can suggest a quiet and calming place. If the user is excited, it can suggest a place where they can enjoy active activities. Furthermore, if the user is stressed, it can suggest a relaxing spa or relaxation facility. This allows the user to choose the most suitable date location according to their emotions.

[0140] The matchmaking agent system can also include a dating activity suggestion unit that estimates the user's emotions and proposes dating activities based on those emotions. The dating activity suggestion unit analyzes the user's emotional state and proposes the most suitable dating activity. For example, if the user is relaxed, the unit can suggest a leisurely activity. If the user is excited, it can suggest an active activity. Furthermore, if the user is stressed, the unit can suggest a relaxing activity. This allows the user to enjoy the most suitable dating activity according to their emotions.

[0141] The matchmaking agent system can also include a date feedback unit that estimates the user's emotions and provides date feedback based on those emotions. The date feedback unit analyzes the user's emotional state and provides optimal feedback. For example, if the user is relaxed, the date feedback unit can provide detailed feedback. If the user is stressed, it can provide simple and easy-to-understand feedback. Furthermore, if the user is in a hurry, the date feedback unit can provide quick feedback. This ensures that users receive optimal feedback tailored to their emotions.

[0142] The matchmaking agent system can also include a date evaluation unit that estimates the user's emotions and evaluates the date based on those emotions. The date evaluation unit analyzes the user's emotional state and provides an optimal evaluation. For example, if the user is relaxed, the evaluation unit can provide a detailed evaluation. If the user is stressed, it can provide a simpler evaluation. Furthermore, if the user is in a hurry, the evaluation unit can provide a quick evaluation. This ensures that the user receives an optimal evaluation tailored to their emotions.

[0143] The matchmaking agent system can also include a date preparation support unit that estimates the user's emotions and supports date preparations based on those emotions. The date preparation support unit analyzes the user's emotional state and suggests the optimal preparation method. For example, if the user is relaxed, the date preparation support unit can provide a detailed preparation list. If the user is stressed, it can provide a simple and easy-to-understand preparation list. Furthermore, if the user is in a hurry, the date preparation support unit can suggest ways to prepare quickly. This allows the user to make the optimal preparation according to their emotions.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The data collection unit collects the user's values ​​and hobbies. For example, the data collection unit analyzes the user's values ​​and hobbies based on the profile information and past behavioral history registered by the user. The data collection unit can use AI to analyze the user's values ​​and hobbies in detail. Step 2: The suggestion unit proposes the most suitable partner based on the information collected by the collection unit. For example, the suggestion unit uses AI to select the most suitable partner based on the collected information and proposes it to the user. The suggestion unit can use AI to propose partners that match the user's values ​​and hobbies with high accuracy. Step 3: The dialogue unit deepens its self-understanding through dialogue with the user. For example, the dialogue unit uses AI to ask questions to the user and analyzes the user's personality and values ​​based on their answers. The dialogue unit can use AI to conduct dialogues that deepen the user's self-understanding. Step 4: The analysis unit analyzes the results obtained by the dialogue unit. For example, the analysis unit uses AI to analyze the dialogue results and analyze the user's personality and values ​​in detail. The analysis unit can use AI to analyze the dialogue results with high accuracy. Step 5: The generation unit automatically generates a date plan tailored to the other person's hobbies. For example, the generation unit uses AI to suggest the optimal date plan based on the other person's hobbies and interests. Using AI, the generation unit can generate a date plan that will please the other person with high accuracy. Step 6: The tracking unit tracks the progress of the matchmaking process. For example, the tracking unit uses AI to analyze the impressions of the date and provide advice for the next date. The tracking unit can use AI to track the progress of the matchmaking process in detail. Step 7: The feedback unit suggests improvements based on the progress tracked by the tracking unit. For example, the feedback unit uses AI to analyze progress and suggest improvements to the user. The feedback unit can use AI to provide effective feedback based on the progress of the matchmaking process.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, proposal unit, dialogue unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's values ​​and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the collected information. The dialogue unit is implemented by the control unit 46A of the smart device 14 and deepens self-understanding through dialogue with the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the results of the dialogue. The generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates a date plan tailored to the partner's hobbies. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the progress of the matchmaking activity. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements based on the progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, proposal unit, dialogue unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's values ​​and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the collected information. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and deepens self-understanding through dialogue with the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the results of the dialogue. The generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates a date plan tailored to the other person's hobbies. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the progress of the matchmaking activity. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which proposes improvements based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, proposal unit, dialogue unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's values ​​and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the collected information. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and deepens self-understanding through dialogue with the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the results of the dialogue. The generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates a date plan tailored to the other person's hobbies. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the progress of the matchmaking activity. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which proposes improvements based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] Each of the multiple elements described above, including the collection unit, proposal unit, dialogue unit, analysis unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's values ​​and hobbies. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable partner based on the collected information. The dialogue unit is implemented by the control unit 46A of the robot 414 and deepens self-understanding through dialogue with the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the results of the dialogue. The generation unit is implemented by the control unit 46A of the robot 414 and automatically generates a date plan tailored to the partner's hobbies. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and tracks the progress of the matchmaking activity. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements based on the progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] (Note 1) The collection department collects users' values ​​and hobbies, A proposal unit proposes an appropriate partner based on the information collected by the aforementioned collection unit, A dialogue unit that promotes self-understanding through interaction with the user, An analysis unit that analyzes the results obtained by the aforementioned dialogue unit, A generation unit that automatically generates a date plan tailored to the other person's hobbies, A tracking department that monitors the progress of matchmaking, The system includes a feedback unit that proposes improvements based on the progress tracked by the tracking unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user profile information and past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose the most suitable partner based on the information we collect. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, We ask users questions and analyze their personality and values ​​based on their answers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Suggest a suitable date plan based on the other person's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is We analyze your impressions of the date and provide advice for your next date. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting profile information and behavioral history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting profile information and activity history, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting profile information and behavioral history, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting profile information and behavioral history, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, a different proposal algorithm is applied depending on the recipient's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the recipient's profile information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the conversation progresses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned dialogue unit, During the conversation, the system selects the most appropriate question by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned dialogue unit, During an interaction, the content of the conversation is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned dialogue unit, It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned dialogue unit, During the conversation, the system selects the most appropriate dialogue content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned dialogue unit, During conversations, the system analyzes the user's social media activity and suggests conversation topics. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During analysis, the analysis methods are customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, we will analyze users' social media activity and propose analytical methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is It estimates the user's emotions and adjusts the method of generating date plans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, the system selects the optimal date plan by referring to the other person's past dating history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating a date plan, it customizes the plan based on the other person's current hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is It estimates the user's emotions and prioritizes date plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is During generation, the system selects the optimal date plan by considering the other person's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned generation unit, When generating a date plan, it analyzes the other person's social media activity and suggests a date plan. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned tracking unit is We estimate the user's emotions and adjust how progress is tracked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned tracking unit is During tracking, the system selects the optimal tracking method by referring to the user's past progress history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned tracking unit is During tracking, the tracking method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned tracking unit is It estimates the user's emotions and prioritizes progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The tracking unit selects an optimal tracking method considering the user's geographical location information during tracking. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 42) The tracking unit analyzes the user's social media activities during tracking and proposes means of tracking. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 43) [[ID=十六]]The feedback unit estimates the user's emotion and adjusts the feedback method based on the estimated user's emotion. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 44) The feedback unit selects an optimal feedback method by referring to the user's past feedback history during feedback. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 45) The feedback unit customizes the feedback means based on the user's current situation during feedback. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 46) The feedback unit estimates the user's emotion and determines the feedback priority based on the estimated user's emotion. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 47) The feedback unit selects an optimal feedback method considering the user's geographical location information during feedback. The system according to Supplementary Note 1, characterized in that. (Supplementary Note 48) The feedback unit analyzes the user's social media activities during feedback and proposes means of feedback. The system according to appended note 1, characterized in that...

Explanation of symbols

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department collects users' values ​​and hobbies, A proposal unit proposes an appropriate partner based on the information collected by the aforementioned collection unit, A dialogue unit that promotes self-understanding through interaction with the user, An analysis unit that analyzes the results obtained by the aforementioned dialogue unit, A generation unit that automatically generates a date plan tailored to the other person's hobbies, A tracking department that monitors the progress of matchmaking, The system includes a feedback unit that proposes improvements based on the progress tracked by the tracking unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user profile information and past behavioral history. The system according to feature 1.

3. The aforementioned proposal section is, We propose the most suitable partner based on the information we collect. The system according to feature 1.

4. The aforementioned dialogue unit, We ask users questions and analyze their personality and values ​​based on their answers. The system according to feature 1.

5. The generating unit is Suggest a suitable date plan based on the other person's hobbies and interests. The system according to feature 1.

6. The aforementioned tracking unit is We analyze your impressions of the date and provide advice for your next date. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting profile information and behavioral history based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

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