system

Abstract

The system according to this embodiment aims to analyze user information and objectively find compatible partners. [Solution] The system according to the embodiment comprises a collection unit, a measurement unit, an alert unit, a proximity alert unit, and an agreement unit. The collection unit collects user information. The measurement unit analyzes the information collected by the collection unit and measures compatibility. The alert unit issues a matching alert based on the compatibility measured by the measurement unit. The proximity alert unit issues an alert when there is a highly compatible person of the opposite sex nearby. The agreement unit facilitates the disclosure of personal information and progress to an actual date based on mutual agreement.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, when finding a romantic partner or a marriage partner, there is often a problem of relying on subjective judgments, and it is difficult to find a compatible partner.

[0005] The system according to the embodiment aims to analyze user information and objectively find a compatible partner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a measurement unit, an alert unit, a proximity alert unit, and an agreement unit. The collection unit collects user information. The measurement unit analyzes the information collected by the collection unit and measures compatibility. The alert unit issues a matching alert based on the compatibility measured by the measurement unit. The proximity alert unit issues an alert when there is a highly compatible person of the opposite sex nearby. The agreement unit facilitates the disclosure of personal information and the development of an actual date based on mutual agreement. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user information and objectively find compatible partners. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The matching system according to an embodiment of the present invention is a system for men and women who do not have a romantic partner or someone they like, which links all information about the user (purchase and travel history, posted information, etc.) with a generating AI agent. When a user turns on a button in the app, the generating AI measures compatibility when the user passes by someone of the opposite sex they are interested in and notifies the user with a matching alert. An alert is also issued if there is an AI-matched person nearby. With mutual agreement, personal information is disclosed and the process can progress to an actual date. This mechanism allows users to choose a truly compatible soulmate. For example, when a user turns on a button in the app, they can notify the system of their detailed status. This information may include purchase history, travel history, and posted information. This information is input into the generating AI. Next, when the user passes by someone of the opposite sex they are interested in, the generating AI measures compatibility. The generating AI analyzes the user's information and the other person's information to measure compatibility. For example, compatibility can be measured based on information such as purchase history, travel history, and posted information. This allows users to choose a partner based on information they were not consciously aware of. If the compatibility measured by the generating AI is high, a matching alert is issued. For example, an alert is issued if you pass someone with a high compatibility rate. If the other person also gives permission, information can be shared with them. This allows users to choose a truly compatible soulmate. Furthermore, an alert is issued if there is an AI-matched person of the opposite sex nearby. For example, an alert is issued if there is an AI-matched person of the opposite sex nearby. With mutual agreement, personal information can be disclosed and the interaction can progress to an actual date. This allows users to choose a truly compatible soulmate. Unlike dating apps, for example, it has an objective AI-powered compatibility measurement that prevents users from being deceived by exaggerated photos and self-introductions, allowing users to choose a truly compatible soulmate. It is also possible to set it so that the other person is not displayed if they are a friend, protecting privacy. In this way, the matching system collects user information, measures compatibility, and issues matching alerts, allowing users to choose a truly compatible soulmate.

[0029] The matching system according to the embodiment comprises a collection unit, a measurement unit, an alert unit, a proximity alert unit, and an agreement unit. The collection unit collects user information. User information includes, but is not limited to, purchase history, travel history, and posted information. The collection unit collects, for example, the user's purchase history. For example, the collection unit can collect the type of product the user purchased and the date and time of purchase. The collection unit can also collect the user's travel history. For example, the collection unit can collect the places the user visited and the means of transportation. Furthermore, the collection unit can also collect user posted information. For example, the collection unit can collect the content and frequency of the user's posted information. The collection unit can also collect user likes. For example, the collection unit can collect the subjects and frequency of the user's likes. The measurement unit analyzes the information collected by the collection unit and measures compatibility. Compatibility is measured based on criteria such as personality assessment, common hobbies, and shared values, but is not limited to such examples. For example, the measurement unit performs a personality assessment of the user and the other person based on the collected information and measures their compatibility. The measurement unit can also measure compatibility based on shared hobbies. Furthermore, the measurement unit can measure compatibility based on the alignment of values. For example, the measurement unit evaluates the degree of alignment of the user and the other person's values ​​and measures their compatibility. The alert unit issues matching alerts based on the compatibility measured by the measurement unit. Matching alerts are issued based on, for example, the format of the notification or the conditions for the alert, but are not limited to these examples. For example, the alert unit issues a pop-up notification when the compatibility is high. The alert unit can also issue an audio alert when the compatibility is high. Furthermore, the alert unit can also notify with vibration when the compatibility is high. The proximity alert unit issues an alert when there is a highly compatible person of the opposite sex nearby. Proximity alerts are issued based on, for example, the range of distance or the accuracy of location information, but are not limited to these examples. For example, the proximity alert unit issues a notification when there is a highly compatible person of the opposite sex near the user. Furthermore, the proximity alert unit can also issue an alert based on the user's location information. Furthermore, the proximity alert unit can also issue an alert based on the accuracy of the user's location information.The agreement section allows for the disclosure of personal information and the progression to an actual date based on mutual consent. This agreement is based on, for example, methods for confirming consent and conditions for disclosing personal information, but is not limited to these examples. For instance, the agreement section confirms the consent of the user and the other party. The agreement section can also set conditions for disclosing personal information. Furthermore, the agreement section can provide a process for progressing to an actual date. This allows the matching system, according to the embodiment, to collect user information, measure compatibility, and issue matching alerts, enabling users to choose a truly compatible soulmate.

[0030] The data collection unit collects user information. User information includes, but is not limited to, purchase history, travel history, and posted information. For example, the data collection unit collects user purchase history. Specifically, it can collect detailed data such as the type of product purchased, the date and time of purchase, the place of purchase, and the frequency of purchase. This allows for an understanding of the user's purchasing trends and preferences. The data collection unit can also collect user travel history. For example, the data collection unit can collect data such as the places visited by the user, the means of transportation, travel time, and duration of stay. This allows for an understanding of the user's behavior patterns and living area. Furthermore, the data collection unit can also collect user posted information. For example, the data collection unit can collect data such as the content of the user's posted information, the frequency of posting, the time of posting, and the number of reactions and likes to the posts. This allows for an understanding of the user's interests and emotional tendencies. The data collection unit can also collect user likes. For example, the data collection unit can collect data such as the items that the user liked (posts, comments, photos, etc.), the frequency of likes, and the time of likes. This allows for an understanding of user preferences and interests. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the measurement and alerting units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall system performance.

[0031] The measurement unit analyzes the information collected by the collection unit to measure compatibility. Compatibility is measured based on criteria such as personality assessment, shared hobbies, and shared values, but is not limited to these examples. Specifically, the measurement unit performs personality assessments of the user and the other party based on the collected information and measures compatibility. Psychological tests and questionnaires can be used for personality assessment, and the user's personality traits and behavioral patterns are analyzed in detail. The measurement unit can also measure compatibility based on shared hobbies. For example, it identifies hobbies and activities that the users are interested in together and evaluates compatibility based on that. Furthermore, the measurement unit can also measure compatibility based on shared values. For example, it evaluates the degree of agreement between the user and the other party's values, beliefs, and lifestyles and measures compatibility based on that. The measurement unit combines these criteria to perform a comprehensive compatibility evaluation and provide the user with the best possible match. Furthermore, the measurement unit can use AI to analyze the collected data and improve the accuracy of compatibility measurement. The AI ​​utilizes past data and statistical information to predict the user's behavioral patterns and preferences, and performs a more accurate compatibility evaluation. Furthermore, the measurement unit can collect user feedback and continuously improve its compatibility measurement algorithm. This allows the measurement unit to provide users with highly accurate compatibility evaluations and achieve optimal matching.

[0032] The alert unit issues matching alerts based on compatibility measured by the measurement unit. Matching alerts are issued based on, for example, the format of the notification or the conditions for the alert, but are not limited to these examples. Specifically, the alert unit issues a pop-up notification when compatibility is high. The pop-up notification appears on the user's device and informs them that a highly compatible partner has been found. The alert unit can also issue an audio alert when compatibility is high. The audio alert is delivered audibly from the user's device to attract the user's attention. The alert unit can also notify with vibration when compatibility is high. The vibration notification causes the user's device to vibrate to convey the notification to the user. The alert unit can combine these notification methods to reliably deliver matching alerts to the user. Furthermore, the alert unit can customize the notification method and alert conditions according to the user's settings. For example, if the user does not want to receive notifications at certain times or locations, the alert unit can control notifications based on those conditions. The alert unit can also collect user feedback and continuously improve the notification method and alert conditions. This allows the alerting unit to reliably provide users with matching alerts at the appropriate time, thereby improving user convenience.

[0033] The proximity alert unit issues an alert when a highly compatible person of the opposite sex is nearby. Proximity alerts are issued based on factors such as distance range and location accuracy, but are not limited to these examples. Specifically, the proximity alert unit notifies the user when a highly compatible person of the opposite sex is nearby. The notification appears on the user's device, informing them that a highly compatible person is nearby. The proximity alert unit can also issue alerts based on the user's location. Location information is obtained using technologies such as GPS, Wi-Fi, and Bluetooth, allowing for precise location tracking. Furthermore, the proximity alert unit can issue alerts based on the accuracy of the user's location information. For example, higher location accuracy allows for more accurate alerts. By combining these technologies, the proximity alert unit can reliably deliver proximity alerts to the user. Additionally, the proximity alert unit can customize notification methods and alert conditions according to user settings. For example, if a user does not want to receive notifications in certain locations or time periods, the proximity alert unit can control notifications based on those conditions. The proximity alert unit can also collect user feedback to continuously improve notification methods and alert conditions. This allows the proximity alert unit to reliably provide users with proximity alerts at the appropriate time, thereby improving user convenience.

[0034] The Agreement Section allows for the disclosure of personal information and the progression to an actual date based on mutual agreement. Agreement is made based on, for example, methods for confirming agreement and conditions for disclosing personal information, but is not limited to these examples. Specifically, the Agreement Section confirms the agreement between the user and the other party. Confirmation of agreement is a process that confirms that the user and the other party have agreed to each other, and is done, for example, through the in-app messaging function or a dedicated agreement confirmation screen. The Agreement Section can also set conditions for disclosing personal information. These conditions allow the user to set what information to disclose to the other party, for example, they can select and disclose information such as name, contact information, and address. Furthermore, the Agreement Section can also provide a process for progressing to an actual date. For example, the Agreement Section can provide a function to adjust the date, time, and location of the date, supporting the user and the other party in smoothly realizing the date. The Agreement Section can also provide functions to ensure the safety of the date. For example, it can provide a function to verify the identity of the other party before the date or a function to share location information during the date. In this way, the Agreement Section can provide support to users so that they can enjoy the date with peace of mind. Furthermore, the Agreement Section can collect user feedback and continuously improve the agreement process and date support functions. This allows the agreement section to provide users with a high level of satisfaction and improve the overall reliability and usability of the system.

[0035] The data collection unit can collect information such as purchase history, travel history, posts, and likes. For example, the data collection unit can collect a user's purchase history. For example, the data collection unit can collect the type of product the user purchased and the date and time of purchase. The data collection unit can also collect a user's travel history. For example, the data collection unit can collect the places the user visited and the means of transportation. Furthermore, the data collection unit can also collect a user's posts. For example, the data collection unit can collect the content and frequency of a user's posts. The data collection unit can also collect a user's likes. For example, the data collection unit can collect the subjects and frequency of a user's likes. By collecting information such as a user's purchase history, travel history, posts, and likes, the accuracy of compatibility measurement is improved. Purchase history includes, but is not limited to, the type of product purchased and the date and time of purchase. Travel history includes, but is not limited to, the places visited and the means of transportation. Posting information includes, but is not limited to, the content and frequency of posts. "Likes" include, for example, the target and frequency of likes, but are not limited to such examples. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the user's purchase history into AI and have AI perform an analysis of the purchase history.

[0036] The measurement unit can analyze the information collected by the collection unit and measure compatibility. For example, the measurement unit can perform a personality assessment of the user and the other person based on the collected information and measure compatibility. For example, the measurement unit can evaluate the compatibility of the user and the other person based on the results of the personality assessment. The measurement unit can also measure compatibility based on common hobbies. For example, the measurement unit can evaluate the common hobbies of the user and the other person and measure compatibility. The measurement unit can also measure compatibility based on the alignment of values. For example, the measurement unit can evaluate the degree of alignment of the values ​​of the user and the other person and measure compatibility. In this way, by analyzing the collected information and measuring compatibility, the user can choose a partner based on information they were not consciously aware of. The analysis includes, but is not limited to, methods such as data mining and machine learning algorithms. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the collected information into a generative AI and have the generative AI perform the compatibility measurement.

[0037] The alert unit can issue a matching alert when compatibility is high. For example, the alert unit can issue a pop-up notification when compatibility is high. For example, the alert unit can display a pop-up notification to inform the user when compatibility is high. The alert unit can also issue an audio alert when compatibility is high. For example, the alert unit can notify the user by voice when compatibility is high. The alert unit can also notify the user by vibration when compatibility is high. For example, the alert unit can notify the user by vibration when compatibility is high. This ensures that users do not miss out on compatible partners by issuing matching alerts when compatibility is high. Criteria for high compatibility include, but are not limited to, compatibility scores and the number of common points. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the compatibility score into the AI ​​and have the AI ​​output the alert.

[0038] The proximity alert unit can issue an alert when a highly compatible person of the opposite sex is nearby. For example, the proximity alert unit will notify the user when a highly compatible person of the opposite sex is near them. For example, the proximity alert unit can also issue an alert based on the user's location information. Furthermore, the proximity alert unit can also issue an alert based on the accuracy of the user's location information. This ensures that the user does not miss the opportunity to meet a compatible person by issuing an alert when a highly compatible person of the opposite sex is nearby. The "nearby" range includes, but is not limited to, the range of distance and the accuracy of location information. Some or all of the processing described above in the proximity alert unit may be performed using, for example, AI, or not using AI. For example, the proximity alert unit can input the user's location information into AI and have AI execute the alert output.

[0039] The Agreement Unit allows for the disclosure of personal information and the progression to an actual date based on mutual agreement. The Agreement Unit, for example, confirms the agreement between the user and the other party. For example, the Agreement Unit confirms the agreement between the user and the other party and then discloses personal information. The Agreement Unit can also set conditions for the disclosure of personal information. For example, the Agreement Unit sets conditions for the disclosure of personal information based on the agreement between the user and the other party. Furthermore, the Agreement Unit can provide a process for progressing to an actual date. For example, the Agreement Unit provides a process for progressing to an actual date based on the agreement between the user and the other party. This allows users to meet compatible partners in person by disclosing personal information and progressing to an actual date based on mutual agreement. Disclosure of personal information includes, but is not limited to, names, contact information, and addresses. Some or all of the above-described processes in the Agreement Unit may be performed using, for example, AI, or not using AI. For example, the Agreement Unit can input the agreement between the user and the other party into AI and have AI perform the confirmation of the agreement.

[0040] The data collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the data collection unit can prioritize collecting the user's past purchase history. The data collection unit can also analyze the user's movement patterns and collect behavioral data at specific locations. The data collection unit can also analyze the user's social media activity and collect relevant posting information. This allows the data collection unit to select the optimal information collection method by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, places visited, products purchased, and browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI and have AI select the information collection method.

[0041] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can collect relevant purchase history based on the user's current living situation. The data collection unit can also prioritize the collection of specific posts based on the user's areas of interest. The data collection unit can also filter and collect travel history based on the user's living situation. This allows for the collection of more relevant information by filtering based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but are not limited to, hobbies, topics of interest, and accounts followed. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's lifestyle and areas of interest into the AI, and have the AI ​​perform information filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit can prioritize the collection of purchase history related to that location. The data collection unit can also prioritize the collection of relevant travel history based on the user's current location. The data collection unit can also prioritize the collection of relevant posted information by considering the user's geographical location information. By prioritizing the collection of highly relevant information by considering the user's geographical location information, more appropriate information can be collected. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of the information.

[0043] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, if the user uses a specific hashtag, the data collection unit can collect posts related to that hashtag. The data collection unit can also prioritize collecting posts from accounts that the user follows. The data collection unit can also analyze the user's "like" history and collect relevant posts. In this way, relevant information can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform the data collection.

[0044] The measurement unit can improve the accuracy of compatibility measurements by referring to the user's past compatibility data. For example, the measurement unit can improve the accuracy of measurements by referring to data of partners with whom the user has shown high compatibility in the past. For example, the measurement unit can improve the accuracy of measurements by referring to data of partners with whom the user has shown high compatibility in the past. The measurement unit can also improve the accuracy of measurements by analyzing the user's past compatibility data and finding specific patterns. For example, the measurement unit can improve the accuracy of measurements by analyzing the user's past compatibility data and finding specific patterns. The measurement unit can also adjust the compatibility measurement algorithm based on the user's past compatibility data. For example, the measurement unit adjusts the compatibility measurement algorithm based on the user's past compatibility data. This allows for improved accuracy of measurements by referring to the user's past compatibility data. Past compatibility data includes, but is not limited to, past matching results and compatibility score history. Some or all of the above-described processes in the measurement unit may be performed using, for example, generative AI, or without generative AI. For example, the measurement unit can input the user's past compatibility data into the generating AI, allowing the AI ​​to improve the accuracy of compatibility measurements.

[0045] The measurement unit can customize the measurement criteria based on the user's current lifestyle and areas of interest when measuring compatibility. For example, the measurement unit can customize the compatibility measurement criteria according to the user's current lifestyle. The measurement unit can also customize the compatibility measurement criteria based on the user's areas of interest. For example, the measurement unit can customize the compatibility measurement criteria based on the user's areas of interest. The measurement unit can also adjust the compatibility measurement algorithm according to the user's lifestyle. For example, the measurement unit adjusts the compatibility measurement algorithm according to the user's lifestyle. This makes it possible to perform more appropriate compatibility measurements by customizing the measurement criteria based on the user's current lifestyle and areas of interest. The measurement criteria include, but are not limited to, evaluation items and weighting. Some or all of the above processing in the measurement unit may be performed using, for example, generative AI, or without using generative AI. For example, the measurement unit can input the user's lifestyle and areas of interest into the generating AI, and have the generating AI customize the criteria for compatibility measurement.

[0046] The measurement unit can improve the accuracy of compatibility measurements by taking into account the user's geographical location information. For example, if the user is in a specific location, the measurement unit will prioritize using compatibility data related to that location. The measurement unit can also improve the accuracy of compatibility measurements based on the user's current location. The measurement unit can also adjust the compatibility measurement algorithm by taking into account the user's geographical location information. This allows for improved measurement accuracy by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the measurement unit can input the user's geographical location information into a generative AI and have the generative AI perform the improvement of compatibility measurement accuracy.

[0047] The measurement unit can analyze the user's social media activity and adjust the measurement criteria when measuring compatibility. For example, if the user uses a specific hashtag, the measurement unit will use compatibility data related to that hashtag. The measurement unit can also prioritize the use of data from accounts that the user follows. The measurement unit can also analyze the user's like history and adjust the compatibility measurement criteria. This allows the measurement criteria to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the user's social media activity into a generative AI and have the generative AI adjust the measurement criteria.

[0048] The alert unit can select the optimal display method by referring to the user's past alert history when displaying an alert. For example, the alert unit can refer to the alert history of users who have shown high compatibility in the past and select the optimal display method. The alert unit can also analyze the user's past alert history, find specific patterns, and select the optimal display method. The alert unit can also adjust the alert display method based on the user's past alert history. This allows the system to select the optimal alert display method by referring to the user's past alert history. Past alert history includes, but is not limited to, past notification content and alert frequency. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alerting unit can input the user's past alert history into the AI ​​and have the AI ​​select how to display the alerts.

[0049] The alert unit can customize the content of alerts based on the user's current living situation and areas of interest when displaying an alert. For example, the alert unit can customize the content of alerts according to the user's current living situation. For example, the alert unit can customize the content of alerts according to the user's current living situation. The alert unit can also customize the content of alerts based on the user's areas of interest. For example, the alert unit can customize the content of alerts based on the user's areas of interest. The alert unit can also adjust how alerts are displayed according to the user's living situation. For example, the alert unit adjusts how alerts are displayed according to the user's living situation. This allows for the provision of more relevant alerts by customizing the content of alerts based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert function can input the user's lifestyle and areas of interest into the AI, allowing the AI ​​to customize the content of the alerts.

[0050] The alert unit can display the most appropriate alert when displaying an alert, taking into account the user's geographical location. For example, if the user is in a specific location, the alert unit can prioritize displaying alerts related to that location. The alert unit can also prioritize displaying relevant alerts based on the user's current location. The alert unit can also adjust how alerts are displayed, taking into account the user's geographical location. This allows the system to display the most appropriate alert by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's geographical location information into AI and have AI adjust how alerts are displayed.

[0051] The alert unit can analyze the user's social media activity and adjust the content of the alert when displaying an alert. For example, if the user is using a specific hashtag, the alert unit can display an alert related to that hashtag. The alert unit can also display an alert related to a specific account if the user is following that account. The alert unit can also analyze the user's like history and adjust the content of the alert. This allows the alert content to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's social media activity into AI and have AI adjust the content of the alert.

[0052] The proximity alert unit can select the optimal display method by referring to the user's past proximity alert history when displaying a proximity alert. For example, the proximity alert unit can refer to the proximity alert history of users who have shown high compatibility in the past and select the optimal display method. The proximity alert unit can also analyze the user's past proximity alert history, find specific patterns, and select the optimal display method. The proximity alert unit can also adjust the display method of proximity alerts based on the user's past proximity alert history. For example, the proximity alert unit can adjust the display method of proximity alerts based on the user's past proximity alert history. This allows the optimal proximity alert display method to be selected by referring to the user's past proximity alert history. Past proximity alert history includes, but is not limited to, past notification content and alert frequency. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or without AI. For example, the proximity alert unit can input the user's past proximity alert history into the AI ​​and have the AI ​​select how to display the proximity alerts.

[0053] The proximity alert unit can customize the content of proximity alerts based on the user's current living situation and areas of interest when displaying them. For example, the proximity alert unit can customize the content of proximity alerts according to the user's current living situation. For example, the proximity alert unit can customize the content of proximity alerts according to the user's current living situation. The proximity alert unit can also customize the content of proximity alerts based on the user's areas of interest. For example, the proximity alert unit can customize the content of proximity alerts based on the user's areas of interest. The proximity alert unit can also adjust how proximity alerts are displayed according to the user's living situation. For example, the proximity alert unit adjusts how proximity alerts are displayed according to the user's living situation. By customizing the content of proximity alerts based on the user's current living situation and areas of interest, more relevant proximity alerts can be provided. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or not using AI. For example, the proximity alert unit can input the user's lifestyle and areas of interest into the AI, allowing the AI ​​to customize the content of proximity alerts.

[0054] The proximity alert unit can display the most appropriate proximity alert when displaying a proximity alert, taking into account the user's geographical location information. For example, if the user is in a specific location, the proximity alert unit will prioritize displaying proximity alerts related to that location. The proximity alert unit can also prioritize displaying relevant proximity alerts based on the user's current location. The proximity alert unit can also adjust how proximity alerts are displayed, taking into account the user's geographical location information. This allows for the display of the most appropriate proximity alert by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or without AI. For example, the proximity alert unit can input the user's geographical location information into the AI ​​and have the AI ​​adjust how proximity alerts are displayed.

[0055] The proximity alert unit can analyze the user's social media activity and adjust the content of proximity alerts when displaying them. For example, if the user is using a specific hashtag, the proximity alert unit can display proximity alerts related to that hashtag. The proximity alert unit can also display proximity alerts related to accounts that the user follows. The proximity alert unit can also analyze the user's like history and adjust the content of proximity alerts. This allows for adjustment of proximity alert content by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or not. For example, the proximity alert unit can input the user's social media activity into AI and have AI adjust the content of proximity alerts.

[0056] The agreement unit can select the optimal process by referring to the user's past agreement history during the agreement process. For example, the agreement unit can select the optimal process by referring to the agreement history of parties with whom the user has shown high compatibility in the past. The agreement unit can also analyze the user's past agreement history, find specific patterns, and select the optimal process. The agreement unit can also adjust the agreement process based on the user's past agreement history. This allows the optimal agreement process to be selected by referring to the user's past agreement history. Past agreement history includes, but is not limited to, past agreement content and agreement frequency. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not. For example, the agreement unit can input the user's past agreement history into AI and have AI perform the selection of the agreement process.

[0057] The agreement unit can customize the content of the agreement during the agreement process based on the user's current living situation and areas of interest. For example, the agreement unit can customize the content of the agreement according to the user's current living situation. The agreement unit can also customize the content of the agreement based on the user's areas of interest. For example, the agreement unit can customize the content of the agreement based on the user's areas of interest. The agreement unit can also adjust the agreement process according to the user's living situation. For example, the agreement unit adjusts the agreement process according to the user's living situation. This allows for a more relevant agreement to be provided by customizing the content of the agreement based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's living situation and areas of interest into AI and have the AI ​​perform the customization of the agreement.

[0058] The agreement unit can provide an optimal agreement process by taking into account the user's geographical location information during the agreement process. For example, if the user is in a specific location, the agreement unit can prioritize providing an agreement process related to that location. The agreement unit can also prioritize providing an agreement process related to the user's current location. The agreement unit can also adjust the agreement process by taking into account the user's geographical location information. This allows the agreement unit to provide an optimal agreement process by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's geographical location information into AI and have AI perform the adjustment of the agreement process.

[0059] The agreement unit can analyze the user's social media activity during the agreement process and adjust the content of the agreement. For example, if the user uses a specific hashtag, the agreement unit can provide an agreement related to that hashtag. For example, if the agreement unit uses a specific hashtag, the agreement unit can provide an agreement related to that hashtag. For example, if the agreement unit follows a specific account, the agreement unit can provide an agreement related to that account. For example, the agreement unit can analyze the user's like history and adjust the content of the agreement. For example, the agreement unit can analyze the user's like history and adjust the content of the agreement. This allows the content of the agreement to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's social media activity into AI and have AI perform the adjustment of the content of the agreement.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The matching system can analyze a user's past behavioral history and select the most suitable method of information collection. For example, it can prioritize collecting the user's past purchase history, which they have frequently used. It can also analyze the user's movement patterns and collect behavioral data at specific locations. Furthermore, it can analyze the user's social media activity and collect relevant tweets and likes. This allows the system to select the most suitable method of information collection by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, places visited, products purchased, and browsing history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the information collection method.

[0062] The matching system can filter information based on the user's current lifestyle and areas of interest. For example, it can collect relevant purchase history based on the user's current lifestyle. It can also prioritize the collection of specific tweets or likes based on the user's areas of interest. Furthermore, it can filter and collect travel history based on the user's lifestyle. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's lifestyle and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0063] The matching system can improve the accuracy of compatibility measurement by referring to the user's past compatibility data. For example, it can improve the accuracy of measurement by referring to data of people with whom the user has shown high compatibility in the past. It can also analyze the user's past compatibility data to find specific patterns and improve the accuracy of measurement. Furthermore, the compatibility measurement algorithm can be adjusted based on the user's past compatibility data. This allows for improved accuracy of measurement by referring to the user's past compatibility data. Past compatibility data includes, but is not limited to, past matching results and compatibility score history. Some or all of the above processing in the measurement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the measurement unit can input the user's past compatibility data into a generative AI and have the generative AI perform the improvement of compatibility measurement accuracy.

[0064] The matching system can collect information while considering the user's geographical location. For example, if a user is in a specific location, it can prioritize collecting purchase history related to that location. It can also prioritize collecting relevant travel history based on the user's current location. Furthermore, it can prioritize collecting relevant posts and likes while considering the user's geographical location. This allows for the collection of more appropriate information by prioritizing the collection of highly relevant information while considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of the information.

[0065] The matching system can analyze a user's social media activity and collect relevant information. For example, if a user uses a specific hashtag, it can collect posts related to that hashtag. It can also prioritize collecting posts from accounts that a user follows. Furthermore, it can analyze a user's "like" history and collect relevant posts. This allows the system to collect relevant information by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into an AI and have the AI ​​collect the information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user information. User information includes purchase history, travel history, posts, likes, etc. For example, the data collection unit collects the types of products the user has purchased, the date and time of purchase, places visited and means of transportation, the content and frequency of posts, and the subjects and frequency of likes. Step 2: The measurement unit analyzes the information collected by the collection unit and measures compatibility. Compatibility is measured based on criteria such as personality assessment, shared hobbies, and shared values. For example, the measurement unit performs personality assessments on the user and the other person, evaluates the degree of shared hobbies and shared values, and measures compatibility. Step 3: The alert unit issues a matching alert based on the compatibility measured by the measurement unit. The matching alert is issued based on the notification format and alert conditions. For example, if the compatibility is high, it will notify via a pop-up notification, audio alert, or vibration. Step 4: The proximity alert unit issues an alert when a highly compatible person of the opposite sex is nearby. Proximity alerts are issued based on the distance range and the accuracy of the location information. For example, it will send a notification when a highly compatible person of the opposite sex is near the user. Step 5: The agreement section progresses to the disclosure of personal information and an actual date based on mutual consent. The agreement is made based on the method of confirming the agreement and the conditions for disclosing personal information. For example, it provides a process for confirming the agreement of the user and the other party, setting the conditions for disclosing personal information, and progressing to an actual date.

[0068] (Example of form 2) The matching system according to an embodiment of the present invention is a system for men and women who do not have a romantic partner or someone they like, which links all information about the user (purchase and travel history, posted information, etc.) with a generating AI agent. When a user turns on a button in the app, the generating AI measures compatibility when the user passes by someone of the opposite sex they are interested in and notifies the user with a matching alert. An alert is also issued if there is an AI-matched person nearby. With mutual agreement, personal information is disclosed and the process can progress to an actual date. This mechanism allows users to choose a truly compatible soulmate. For example, when a user turns on a button in the app, they can notify the system of their detailed status. This information may include purchase history, travel history, and posted information. This information is input into the generating AI. Next, when the user passes by someone of the opposite sex they are interested in, the generating AI measures compatibility. The generating AI analyzes the user's information and the other person's information to measure compatibility. For example, compatibility can be measured based on information such as purchase history, travel history, and posted information. This allows users to choose a partner based on information they were not consciously aware of. If the compatibility measured by the generating AI is high, a matching alert is issued. For example, an alert is issued if you pass someone with a high compatibility rate. If the other person also gives permission, information can be shared with them. This allows users to choose a truly compatible soulmate. Furthermore, an alert is issued if there is an AI-matched person of the opposite sex nearby. For example, an alert is issued if there is an AI-matched person of the opposite sex nearby. With mutual agreement, personal information can be disclosed and the interaction can progress to an actual date. This allows users to choose a truly compatible soulmate. Unlike dating apps, for example, it has an objective AI-powered compatibility measurement that prevents users from being deceived by exaggerated photos and self-introductions, allowing users to choose a truly compatible soulmate. It is also possible to set it so that the other person is not displayed if they are a friend, protecting privacy. In this way, the matching system collects user information, measures compatibility, and issues matching alerts, allowing users to choose a truly compatible soulmate.

[0069] The matching system according to the embodiment comprises a collection unit, a measurement unit, an alert unit, a proximity alert unit, and an agreement unit. The collection unit collects user information. User information includes, but is not limited to, purchase history, travel history, and posted information. The collection unit collects, for example, the user's purchase history. For example, the collection unit can collect the type of product the user purchased and the date and time of purchase. The collection unit can also collect the user's travel history. For example, the collection unit can collect the places the user visited and the means of transportation. Furthermore, the collection unit can also collect the user's posts. For example, the collection unit can collect the content and frequency of the user's posts. The collection unit can also collect the user's likes. For example, the collection unit can collect the items the user liked and how often. The measurement unit analyzes the information collected by the collection unit and measures compatibility. Compatibility is measured based on criteria such as personality assessment, common hobbies, and shared values, but is not limited to such examples. For example, the measurement unit performs a personality assessment of the user and the other person based on the collected information and measures their compatibility. The measurement unit can also measure compatibility based on shared hobbies. Furthermore, the measurement unit can measure compatibility based on the alignment of values. For example, the measurement unit evaluates the degree of alignment of the user and the other person's values ​​and measures their compatibility. The alert unit issues matching alerts based on the compatibility measured by the measurement unit. Matching alerts are issued based on, for example, the format of the notification or the conditions for the alert, but are not limited to these examples. For example, the alert unit issues a pop-up notification when the compatibility is high. The alert unit can also issue an audio alert when the compatibility is high. Furthermore, the alert unit can also notify with vibration when the compatibility is high. The proximity alert unit issues an alert when there is a highly compatible person of the opposite sex nearby. Proximity alerts are issued based on, for example, the range of distance or the accuracy of location information, but are not limited to these examples. For example, the proximity alert unit issues a notification when there is a highly compatible person of the opposite sex near the user. Furthermore, the proximity alert unit can also issue an alert based on the user's location information. Furthermore, the proximity alert unit can also issue an alert based on the accuracy of the user's location information.The agreement section allows for the disclosure of personal information and the progression to an actual date based on mutual consent. This agreement is based on, for example, methods for confirming consent and conditions for disclosing personal information, but is not limited to these examples. For instance, the agreement section confirms the consent of the user and the other party. The agreement section can also set conditions for disclosing personal information. Furthermore, the agreement section can provide a process for progressing to an actual date. This allows the matching system, according to the embodiment, to collect user information, measure compatibility, and issue matching alerts, enabling users to choose a truly compatible soulmate.

[0070] The data collection unit collects user information. User information includes, but is not limited to, purchase history, travel history, and posted information. For example, the data collection unit collects user purchase history. Specifically, it can collect detailed data such as the type of product purchased, the date and time of purchase, the place of purchase, and the frequency of purchase. This allows for an understanding of the user's purchasing trends and preferences. The data collection unit can also collect user travel history. For example, it can collect data such as the places visited by the user, the means of transportation, travel time, and duration of stay. This allows for an understanding of the user's behavior patterns and living area. Furthermore, the data collection unit can also collect user posted information. For example, it can collect data such as the content of the user's posts, the frequency of posts, the time of posts, and the reactions to posts. This allows for an understanding of the user's interests and emotional tendencies. The data collection unit can also collect user "likes." For example, it can collect data such as the objects (posts, comments, photos, etc.) that the user liked, the frequency of likes, and the time of likes. This allows for an understanding of the user's preferences and interests. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the measurement and alerting units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The measurement unit analyzes the information collected by the collection unit to measure compatibility. Compatibility is measured based on criteria such as personality assessment, shared hobbies, and shared values, but is not limited to these examples. Specifically, the measurement unit performs personality assessments of the user and the other party based on the collected information and measures compatibility. Psychological tests and questionnaires can be used for personality assessment, and the user's personality traits and behavioral patterns are analyzed in detail. The measurement unit can also measure compatibility based on shared hobbies. For example, it identifies hobbies and activities that the users are interested in together and evaluates compatibility based on that. Furthermore, the measurement unit can also measure compatibility based on shared values. For example, it evaluates the degree of agreement between the user and the other party's values, beliefs, and lifestyles and measures compatibility based on that. The measurement unit combines these criteria to perform a comprehensive compatibility evaluation and provide the user with the best possible match. Furthermore, the measurement unit can use AI to analyze the collected data and improve the accuracy of compatibility measurement. The AI ​​utilizes past data and statistical information to predict the user's behavioral patterns and preferences, and performs a more accurate compatibility evaluation. Furthermore, the measurement unit can collect user feedback and continuously improve its compatibility measurement algorithm. This allows the measurement unit to provide users with highly accurate compatibility evaluations and achieve optimal matching.

[0072] The alert unit issues matching alerts based on compatibility measured by the measurement unit. Matching alerts are issued based on, for example, the format of the notification or the conditions for the alert, but are not limited to these examples. Specifically, the alert unit issues a pop-up notification when compatibility is high. The pop-up notification appears on the user's device and informs them that a highly compatible partner has been found. The alert unit can also issue an audio alert when compatibility is high. The audio alert is delivered audibly from the user's device to attract the user's attention. The alert unit can also notify with vibration when compatibility is high. The vibration notification causes the user's device to vibrate to convey the notification to the user. The alert unit can combine these notification methods to reliably deliver matching alerts to the user. Furthermore, the alert unit can customize the notification method and alert conditions according to the user's settings. For example, if the user does not want to receive notifications at certain times or locations, the alert unit can control notifications based on those conditions. The alert unit can also collect user feedback and continuously improve the notification method and alert conditions. This allows the alerting unit to reliably provide users with matching alerts at the appropriate time, thereby improving user convenience.

[0073] The proximity alert unit issues an alert when a highly compatible person of the opposite sex is nearby. Proximity alerts are issued based on, for example, the range of distance or the accuracy of location information, but are not limited to these examples. Specifically, the proximity alert unit issues a notification when a highly compatible person of the opposite sex is near the user. The notification is displayed on the user's device, informing them that a highly compatible person of the opposite sex is nearby. The proximity alert unit can also issue alerts based on the user's location information. Location information is obtained using technologies such as GPS, Wi-Fi, and Bluetooth®, allowing the user's precise location to be determined. Furthermore, the proximity alert unit can issue alerts based on the accuracy of the user's location information. For example, if the location information is highly accurate, a more accurate alert can be issued. The proximity alert unit combines these technologies to reliably deliver proximity alerts to the user. In addition, the proximity alert unit can customize the notification method and alert conditions according to the user's settings. For example, if a user does not want to receive notifications in certain locations or times, the proximity alert unit can control notifications based on those conditions. The proximity alert unit can also collect user feedback and continuously improve the notification method and alert conditions. This allows the proximity alert unit to reliably provide users with proximity alerts at the appropriate time, thereby improving user convenience.

[0074] The Agreement Section allows for the disclosure of personal information and the progression to an actual date based on mutual agreement. Agreement is made based on, for example, methods for confirming agreement and conditions for disclosing personal information, but is not limited to these examples. Specifically, the Agreement Section confirms the agreement between the user and the other party. Confirmation of agreement is a process that confirms that the user and the other party have agreed to each other, and is done, for example, through the in-app messaging function or a dedicated agreement confirmation screen. The Agreement Section can also set conditions for disclosing personal information. These conditions allow the user to set what information to disclose to the other party, for example, they can select and disclose information such as name, contact information, and address. Furthermore, the Agreement Section can also provide a process for progressing to an actual date. For example, the Agreement Section can provide a function to adjust the date, time, and location of the date, supporting the user and the other party in smoothly realizing the date. The Agreement Section can also provide functions to ensure the safety of the date. For example, it can provide a function to verify the identity of the other party before the date or a function to share location information during the date. In this way, the Agreement Section can provide support to users so that they can enjoy the date with peace of mind. Furthermore, the Agreement Section can collect user feedback and continuously improve the agreement process and date support functions. This allows the agreement section to provide users with a high level of satisfaction and improve the overall reliability and usability of the system.

[0075] The data collection unit can collect information such as purchase history, travel history, and posting information. For example, the data collection unit can collect a user's purchase history. For example, the data collection unit can collect the type of product the user purchased and the date and time of purchase. The data collection unit can also collect a user's travel history. For example, the data collection unit can collect the places the user visited and the means of transportation. Furthermore, the data collection unit can also collect a user's posting information. For example, the data collection unit can collect the content and frequency of a user's posts. The data collection unit can also collect a user's likes. For example, the data collection unit can collect the subjects and frequency of a user's likes. By collecting information such as a user's purchase history, travel history, and posting information, the accuracy of compatibility measurement is improved. Purchase history includes, but is not limited to, the type of product purchased and the date and time of purchase. Travel history includes, but is not limited to, the places visited and the means of transportation. Posting information includes, but is not limited to, the content and frequency of posts. Likes include, but are not limited to, the subjects and frequency of likes. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history into the AI ​​and have the AI ​​perform the analysis of the purchase history.

[0076] The measurement unit can analyze the information collected by the collection unit and measure compatibility. For example, the measurement unit can perform a personality assessment of the user and the other person based on the collected information and measure compatibility. For example, the measurement unit can evaluate the compatibility of the user and the other person based on the results of the personality assessment. The measurement unit can also measure compatibility based on common hobbies. For example, the measurement unit can evaluate the common hobbies of the user and the other person and measure compatibility. The measurement unit can also measure compatibility based on the alignment of values. For example, the measurement unit can evaluate the degree of alignment of the values ​​of the user and the other person and measure compatibility. In this way, by analyzing the collected information and measuring compatibility, the user can choose a partner based on information they were not consciously aware of. The analysis includes, but is not limited to, methods such as data mining and machine learning algorithms. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the collected information into a generative AI and have the generative AI perform the compatibility measurement.

[0077] The alert unit can issue a matching alert when compatibility is high. For example, the alert unit can issue a pop-up notification when compatibility is high. For example, the alert unit can display a pop-up notification to inform the user when compatibility is high. The alert unit can also issue an audio alert when compatibility is high. For example, the alert unit can notify the user by voice when compatibility is high. The alert unit can also notify the user by vibration when compatibility is high. For example, the alert unit can notify the user by vibration when compatibility is high. This ensures that users do not miss out on compatible partners by issuing matching alerts when compatibility is high. Criteria for high compatibility include, but are not limited to, compatibility scores and the number of common points. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the compatibility score into the AI ​​and have the AI ​​output the alert.

[0078] The proximity alert unit can issue an alert when a highly compatible person of the opposite sex is nearby. For example, the proximity alert unit will notify the user when a highly compatible person of the opposite sex is near them. For example, the proximity alert unit can also issue an alert based on the user's location information. Furthermore, the proximity alert unit can also issue an alert based on the accuracy of the user's location information. This ensures that the user does not miss the opportunity to meet a compatible person by issuing an alert when a highly compatible person of the opposite sex is nearby. The "nearby" range includes, but is not limited to, the range of distance and the accuracy of location information. Some or all of the processing described above in the proximity alert unit may be performed using, for example, AI, or not using AI. For example, the proximity alert unit can input the user's location information into AI and have AI execute the alert output.

[0079] The Agreement Unit allows for the disclosure of personal information and the progression to an actual date based on mutual agreement. The Agreement Unit, for example, confirms the agreement between the user and the other party. For example, the Agreement Unit confirms the agreement between the user and the other party and then discloses personal information. The Agreement Unit can also set conditions for the disclosure of personal information. For example, the Agreement Unit sets conditions for the disclosure of personal information based on the agreement between the user and the other party. Furthermore, the Agreement Unit can provide a process for progressing to an actual date. For example, the Agreement Unit provides a process for progressing to an actual date based on the agreement between the user and the other party. This allows users to meet compatible partners in person by disclosing personal information and progressing to an actual date based on mutual agreement. Disclosure of personal information includes, but is not limited to, names, contact information, and addresses. Some or all of the above-described processes in the Agreement Unit may be performed using, for example, AI, or not using AI. For example, the Agreement Unit can input the agreement between the user and the other party into AI and have AI perform the confirmation of the agreement.

[0080] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect only basic information such as purchase history and travel history. For example, if the user is stressed, the data collection unit can collect only basic information such as purchase history and travel history. The data collection unit can also collect detailed information such as posts and likes if the user is relaxed. For example, if the user is relaxed, the data collection unit can also collect detailed information such as posts and likes. The data collection unit can also collect real-time behavioral data if the user is excited. For example, if the user is excited, the data collection unit can also collect real-time behavioral data. This allows for the collection of more appropriate information by adjusting the type of information collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the types of information to be collected.

[0081] The data collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the data collection unit can prioritize collecting the user's past purchase history. The data collection unit can also analyze the user's movement patterns and collect behavioral data at specific locations. The data collection unit can also analyze the user's social media activity and collect relevant posts and likes. This allows the data collection unit to select the optimal information collection method by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, places visited, products purchased, and browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI and have AI select the information collection method.

[0082] The data collection unit can filter information based on the user's current living situation and areas of interest. For example, the data collection unit can collect relevant purchase history based on the user's current living situation. The data collection unit can also prioritize the collection of specific posts and likes based on the user's areas of interest. The data collection unit can also filter and collect travel history based on the user's living situation. This allows for the collection of more relevant information by filtering based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but are not limited to, hobbies, topics of interest, and accounts followed. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's lifestyle and areas of interest into the AI, and have the AI ​​perform information filtering.

[0083] 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 may prioritize collecting purchase history. For example, if the user is relaxed, the data collection unit may prioritize collecting posts and likes. For example, if the user is relaxed, the data collection unit may prioritize collecting posts and likes. For example, if the user is excited, the data collection unit may prioritize collecting real-time behavioral data. For example, if the user is excited, the data collection unit may prioritize collecting real-time behavioral data. This allows for the priority collection of more important information by determining the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the information.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is in a specific location, the data collection unit can prioritize the collection of purchase history related to that location. The data collection unit can also prioritize the collection of relevant travel history based on the user's current location. The data collection unit can also prioritize the collection of relevant posts and likes by considering the user's geographical location. For example, the data collection unit can prioritize the collection of relevant tweets and likes by considering the user's geographical location. This allows for the collection of more appropriate information by prioritizing the collection of highly relevant information by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of the information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information when gathering data. For example, if the user uses a specific hashtag, the data collection unit can collect posts related to that hashtag. The data collection unit can also prioritize collecting posts from accounts that the user follows. The data collection unit can also analyze the user's "like" history and collect relevant posts. In this way, relevant information can be collected by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's social media activity into AI and have the AI ​​perform the data collection.

[0086] The measurement unit can estimate the user's emotions and adjust the compatibility measurement algorithm based on the estimated user emotions. For example, if the user is relaxed, the generating AI can use an algorithm that performs a detailed compatibility measurement. For example, if the user is relaxed, the generating AI can use an algorithm that performs a detailed compatibility measurement. For example, if the user is in a hurry, the generating AI can use an algorithm that performs a simplified compatibility measurement. For example, if the user is excited, the generating AI can use an algorithm that takes emotional fluctuations into account. For example, if the user is excited, the generating AI can use an algorithm that takes emotional fluctuations into account. By adjusting the compatibility measurement algorithm based on the user's emotions, more accurate compatibility measurement becomes possible. The compatibility measurement algorithm includes, but is not limited to, the model used and parameter settings. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the measurement unit can input user emotion data into a generative AI and have the generative AI adjust the compatibility measurement algorithm.

[0087] The measurement unit can improve the accuracy of compatibility measurements by referring to the user's past compatibility data. For example, the measurement unit can improve the accuracy of measurements by referring to data of partners with whom the user has shown high compatibility in the past. For example, the measurement unit can improve the accuracy of measurements by referring to data of partners with whom the user has shown high compatibility in the past. The measurement unit can also improve the accuracy of measurements by analyzing the user's past compatibility data and finding specific patterns. For example, the measurement unit can improve the accuracy of measurements by analyzing the user's past compatibility data and finding specific patterns. The measurement unit can also adjust the compatibility measurement algorithm based on the user's past compatibility data. For example, the measurement unit adjusts the compatibility measurement algorithm based on the user's past compatibility data. This allows for improved accuracy of measurements by referring to the user's past compatibility data. Past compatibility data includes, but is not limited to, past matching results and compatibility score history. Some or all of the above-described processes in the measurement unit may be performed using, for example, generative AI, or without generative AI. For example, the measurement unit can input the user's past compatibility data into a generating AI, allowing the AI ​​to improve the accuracy of compatibility measurements.

[0088] The measurement unit can customize the measurement criteria based on the user's current lifestyle and areas of interest when measuring compatibility. For example, the measurement unit can customize the compatibility measurement criteria according to the user's current lifestyle. The measurement unit can also customize the compatibility measurement criteria based on the user's areas of interest. For example, the measurement unit can customize the compatibility measurement criteria based on the user's areas of interest. The measurement unit can also adjust the compatibility measurement algorithm according to the user's lifestyle. For example, the measurement unit adjusts the compatibility measurement algorithm according to the user's lifestyle. This makes it possible to perform more appropriate compatibility measurements by customizing the measurement criteria based on the user's current lifestyle and areas of interest. The measurement criteria include, but are not limited to, evaluation items and weighting. Some or all of the above processing in the measurement unit may be performed using, for example, generative AI, or without generative AI. For example, the measurement unit can input the user's lifestyle and areas of interest into the generating AI, and have the generating AI customize the criteria for compatibility measurement.

[0089] The measurement unit can estimate the user's emotions and adjust the method of displaying the compatibility measurement results based on the estimated user emotions. For example, if the user is nervous, the measurement unit can provide a simple and highly visible display method. For example, if the user is nervous, the measurement unit can provide a simple and highly visible display method. The measurement unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the measurement unit can provide a display method that includes detailed information. The measurement unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the measurement unit is in a hurry, the measurement unit can provide a display method that gets to the point. By adjusting the method of displaying the compatibility measurement results based on the user's emotions, a more appropriate display becomes possible. Methods of displaying the results include, but are not limited to, graph displays, text displays, and notification formats. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI adjust the method of displaying the results.

[0090] The measurement unit can improve the accuracy of compatibility measurements by taking into account the user's geographical location information. For example, if the user is in a specific location, the measurement unit will prioritize using compatibility data related to that location. The measurement unit can also improve the accuracy of compatibility measurements based on the user's current location. The measurement unit can also adjust the compatibility measurement algorithm by taking into account the user's geographical location information. This allows for improved measurement accuracy by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or without a generative AI. For example, the measurement unit can input the user's geographical location information into a generative AI and have the generative AI perform the improvement of compatibility measurement accuracy.

[0091] The measurement unit can analyze the user's social media activity and adjust the measurement criteria when measuring compatibility. For example, if the user uses a specific hashtag, the measurement unit will use compatibility data related to that hashtag. The measurement unit can also prioritize the use of data from accounts that the user follows. The measurement unit can also analyze the user's like history and adjust the compatibility measurement criteria. This allows the measurement criteria to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the measurement unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the measurement unit can input the user's social media activity into a generative AI and have the generative AI adjust the measurement criteria.

[0092] The alert unit can estimate the user's emotions and adjust the way alerts are displayed based on those emotions. For example, if the user is nervous, the alert unit can provide a simple and highly visible display method. For example, if the user is nervous, the alert unit can provide a simple and highly visible display method. The alert unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the alert unit can provide a display method that includes detailed information. The alert unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the user is in a hurry, the alert unit can provide a display method that gets to the point. By adjusting the way alerts are displayed based on the user's emotions, more appropriate alerts can be displayed. The methods of displaying alerts include, but are not limited to, pop-up notifications, audio alerts, and vibrations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into the AI ​​and have the AI ​​adjust how the alert is displayed.

[0093] The alert unit can select the optimal display method by referring to the user's past alert history when displaying an alert. For example, the alert unit can refer to the alert history of users who have shown high compatibility in the past and select the optimal display method. The alert unit can also analyze the user's past alert history, find specific patterns, and select the optimal display method. The alert unit can also adjust the alert display method based on the user's past alert history. This allows the system to select the optimal alert display method by referring to the user's past alert history. Past alert history includes, but is not limited to, past notification content and alert frequency. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alerting unit can input the user's past alert history into the AI ​​and have the AI ​​select how to display the alerts.

[0094] The alert unit can customize the content of alerts based on the user's current living situation and areas of interest when displaying an alert. For example, the alert unit can customize the content of alerts according to the user's current living situation. For example, the alert unit can customize the content of alerts according to the user's current living situation. The alert unit can also customize the content of alerts based on the user's areas of interest. For example, the alert unit can customize the content of alerts based on the user's areas of interest. The alert unit can also adjust how alerts are displayed according to the user's living situation. For example, the alert unit adjusts how alerts are displayed according to the user's living situation. This allows for the provision of more relevant alerts by customizing the content of alerts based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert function can input the user's lifestyle and areas of interest into the AI, allowing the AI ​​to customize the content of the alerts.

[0095] The alerting unit can estimate the user's emotions and prioritize alerts based on those emotions. For example, if the user is stressed, the alerting unit will prioritize important alerts. For example, if the user is stressed, the alerting unit will prioritize important alerts. For example, if the user is relaxed, the alerting unit will prioritize alerts containing detailed information. For example, if the user is relaxed, the alerting unit will prioritize alerts containing detailed information. For example, if the user is in a hurry, the alerting unit will prioritize alerts that get to the point. For example, if the user is in a hurry, the alerting unit will prioritize alerts that get to the point. In this way, by prioritizing alerts based on the user's emotions, more important alerts can be prioritized. Prioritization includes, but is not limited to, importance scoring and urgency assessment. Emotion estimation is achieved using emotion estimation functions, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of alerts.

[0096] The alert unit can display the most appropriate alert when displaying an alert, taking into account the user's geographical location. For example, if the user is in a specific location, the alert unit can prioritize displaying alerts related to that location. The alert unit can also prioritize displaying relevant alerts based on the user's current location. The alert unit can also adjust how alerts are displayed, taking into account the user's geographical location. This allows the system to display the most appropriate alert by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's geographical location information into AI and have AI adjust how alerts are displayed.

[0097] The alert unit can analyze the user's social media activity and adjust the content of the alert when displaying an alert. For example, if the user is using a specific hashtag, the alert unit can display an alert related to that hashtag. The alert unit can also display an alert related to a specific account if the user is following that account. The alert unit can also analyze the user's like history and adjust the content of the alert. This allows the alert content to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's social media activity into AI and have AI adjust the content of the alert.

[0098] The proximity alert unit can estimate the user's emotions and adjust the display method of proximity alerts based on the estimated emotions. For example, if the user is nervous, the proximity alert unit can provide a simple and highly visible display method. For example, if the user is nervous, the proximity alert unit can provide a simple and highly visible display method. For example, if the user is relaxed, the proximity alert unit can provide a display method that includes detailed information. For example, if the user is relaxed, the proximity alert unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the proximity alert unit can provide a display method that gets to the point. For example, if the user is in a hurry, the proximity alert unit can provide a display method that gets to the point. By adjusting the display method of proximity alerts based on the user's emotions, more appropriate proximity alerts can be displayed. Display methods for proximity alerts include, but are not limited to, pop-up notifications, audio alerts, and vibrations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proximity alert unit may be performed using AI, for example, or without AI. For example, the proximity alert unit can input user emotion data into the AI ​​and have the AI ​​adjust how proximity alerts are displayed.

[0099] The proximity alert unit can select the optimal display method by referring to the user's past proximity alert history when displaying a proximity alert. For example, the proximity alert unit can refer to the proximity alert history of users who have shown high compatibility in the past and select the optimal display method. The proximity alert unit can also analyze the user's past proximity alert history, find specific patterns, and select the optimal display method. The proximity alert unit can also adjust the display method of proximity alerts based on the user's past proximity alert history. For example, the proximity alert unit can adjust the display method of proximity alerts based on the user's past proximity alert history. This allows the optimal proximity alert display method to be selected by referring to the user's past proximity alert history. Past proximity alert history includes, but is not limited to, past notification content and alert frequency. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or without AI. For example, the proximity alert unit can input the user's past proximity alert history into the AI ​​and have the AI ​​select how to display the proximity alerts.

[0100] The proximity alert unit can customize the content of proximity alerts based on the user's current living situation and areas of interest when displaying them. For example, the proximity alert unit can customize the content of proximity alerts according to the user's current living situation. For example, the proximity alert unit can customize the content of proximity alerts according to the user's current living situation. The proximity alert unit can also customize the content of proximity alerts based on the user's areas of interest. For example, the proximity alert unit can customize the content of proximity alerts based on the user's areas of interest. The proximity alert unit can also adjust how proximity alerts are displayed according to the user's living situation. For example, the proximity alert unit adjusts how proximity alerts are displayed according to the user's living situation. By customizing the content of proximity alerts based on the user's current living situation and areas of interest, more relevant proximity alerts can be provided. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or not using AI. For example, the proximity alert unit can input the user's lifestyle and areas of interest into the AI, allowing the AI ​​to customize the content of proximity alerts.

[0101] The proximity alert unit can estimate the user's emotions and prioritize proximity alerts based on those emotions. For example, if the user is stressed, the proximity alert unit will prioritize displaying important proximity alerts. Similarly, if the user is relaxed, the proximity alert unit can prioritize displaying proximity alerts containing detailed information. Furthermore, if the user is in a hurry, the proximity alert unit can prioritize displaying proximity alerts that are concise and to the point. This allows for prioritizing more important proximity alerts based on the user's emotions. Prioritization includes, but is not limited to, importance scoring and urgency assessment. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the proximity alert unit may be performed using AI, or not using AI. For example, the proximity alert unit may input user sentiment data into the AI ​​and have the AI ​​determine the priority of proximity alerts.

[0102] The proximity alert unit can display the most appropriate proximity alert when displaying a proximity alert, taking into account the user's geographical location information. For example, if the user is in a specific location, the proximity alert unit will prioritize displaying proximity alerts related to that location. The proximity alert unit can also prioritize displaying relevant proximity alerts based on the user's current location. The proximity alert unit can also adjust how proximity alerts are displayed, taking into account the user's geographical location information. This allows for the display of the most appropriate proximity alert by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or without AI. For example, the proximity alert unit can input the user's geographical location information into the AI ​​and have the AI ​​adjust how proximity alerts are displayed.

[0103] The proximity alert unit can analyze the user's social media activity and adjust the content of proximity alerts when displaying them. For example, if the user is using a specific hashtag, the proximity alert unit can display proximity alerts related to that hashtag. The proximity alert unit can also display proximity alerts related to accounts that the user follows. The proximity alert unit can also analyze the user's like history and adjust the content of proximity alerts. This allows for adjustment of proximity alert content by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the proximity alert unit may be performed using, for example, AI, or not. For example, the proximity alert unit can input the user's social media activity into AI and have AI adjust the content of proximity alerts.

[0104] The agreement unit can estimate the user's emotions and adjust the agreement process based on the estimated emotions. For example, if the user is tense, the agreement unit can provide a simple and easy-to-understand agreement process. For example, if the user is tense, the agreement unit can provide a simple and easy-to-understand agreement process. For example, if the user is relaxed, the agreement unit can provide an agreement process that includes detailed information. For example, if the user is relaxed, the agreement unit can provide an agreement process that includes detailed information. For example, if the user is in a hurry, the agreement unit can provide an agreement process that gets straight to the point. For example, if the user is in a hurry, the agreement unit can provide an agreement process that gets straight to the point. This allows for a more appropriate agreement process to be provided by adjusting the agreement process based on the user's emotions. The agreement process may include, but is not limited to, methods for confirming agreement and details of the steps. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the agreement unit may be performed using AI, for example, or without AI. For example, the agreement unit may input user emotion data into the AI ​​and have the AI ​​adjust the agreement process.

[0105] The agreement unit can select the optimal process by referring to the user's past agreement history during the agreement process. For example, the agreement unit can select the optimal process by referring to the agreement history of parties with whom the user has shown high compatibility in the past. The agreement unit can also analyze the user's past agreement history, find specific patterns, and select the optimal process. The agreement unit can also adjust the agreement process based on the user's past agreement history. This allows the optimal agreement process to be selected by referring to the user's past agreement history. Past agreement history includes, but is not limited to, past agreement content and agreement frequency. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not. For example, the agreement unit can input the user's past agreement history into AI and have AI perform the selection of the agreement process.

[0106] The agreement unit can customize the content of the agreement during the agreement process based on the user's current living situation and areas of interest. For example, the agreement unit can customize the content of the agreement according to the user's current living situation. The agreement unit can also customize the content of the agreement based on the user's areas of interest. For example, the agreement unit can customize the content of the agreement based on the user's areas of interest. The agreement unit can also adjust the agreement process according to the user's living situation. For example, the agreement unit adjusts the agreement process according to the user's living situation. This allows for a more relevant agreement to be provided by customizing the content of the agreement based on the user's current living situation and areas of interest. Current living situation includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's living situation and areas of interest into AI and have the AI ​​perform the customization of the agreement.

[0107] The consensus section can estimate the user's emotions and determine the priority of agreements based on the estimated emotions. For example, if the user is tense, the consensus section will prioritize displaying important agreements. For example, if the user is tense, the consensus section will prioritize displaying important agreements. For example, if the user is relaxed, the consensus section will prioritize displaying agreements that contain detailed information. For example, if the user is relaxed, the consensus section will prioritize displaying agreements that contain detailed information. For example, if the user is in a hurry, the consensus section will prioritize displaying agreements that get straight to the point. For example, if the user is in a hurry, the consensus section will prioritize displaying agreements that get straight to the point. In this way, by determining the priority of agreements based on the user's emotions, more important agreements can be prioritized. Prioritization includes, but is not limited to, importance scoring and urgency assessment. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the agreement unit may be performed using AI, for example, or without AI. For example, the agreement unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of agreements.

[0108] The agreement unit can provide an optimal agreement process by taking into account the user's geographical location information during the agreement process. For example, if the user is in a specific location, the agreement unit can prioritize providing an agreement process related to that location. The agreement unit can also prioritize providing an agreement process related to the user's current location. The agreement unit can also adjust the agreement process by taking into account the user's geographical location information. This allows the agreement unit to provide an optimal agreement process by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's geographical location information into AI and have AI perform the adjustment of the agreement process.

[0109] The agreement unit can analyze the user's social media activity during the agreement process and adjust the content of the agreement. For example, if the user uses a specific hashtag, the agreement unit can provide an agreement related to that hashtag. For example, if the agreement unit uses a specific hashtag, the agreement unit can provide an agreement related to that hashtag. For example, if the agreement unit follows a specific account, the agreement unit can provide an agreement related to that account. For example, the agreement unit can analyze the user's like history and adjust the content of the agreement. For example, the agreement unit can analyze the user's like history and adjust the content of the agreement. This allows the content of the agreement to be adjusted by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the above processing in the agreement unit may be performed using, for example, AI, or not using AI. For example, the agreement unit can input the user's social media activity into AI and have AI perform the adjustment of the content of the agreement.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The matching system can estimate the user's emotions and adjust the compatibility measurement criteria based on those emotions. For example, if the user is relaxed, detailed compatibility measurement criteria can be used. If the user is in a hurry, simplified compatibility measurement criteria can be used. Furthermore, if the user is excited, compatibility measurement criteria that take emotional fluctuations into account can be used. This allows for more accurate compatibility measurement by adjusting the compatibility measurement criteria based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI or not. For example, the measurement unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the compatibility measurement criteria.

[0112] The matching system can analyze a user's past behavioral history and select the optimal method of information collection. For example, it can prioritize collecting the user's past purchase history, which they have frequently used. It can also analyze the user's movement patterns and collect behavioral data at specific locations. Furthermore, it can analyze the user's social media activity and collect relevant posts and likes. This allows the system to select the optimal method of information collection by analyzing the user's past behavioral history. Past behavioral history includes, but is not limited to, places visited, products purchased, and browsing history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past behavioral history into AI and have the AI ​​select the information collection method.

[0113] The matching system can estimate the user's emotions and adjust the type of information collected based on those emotions. For example, if the user is stressed, only basic information such as purchase history and travel history can be collected. If the user is relaxed, more detailed information such as posts and likes can also be collected. Furthermore, if the user is excited, real-time behavioral data can also be collected. This allows for the collection of more appropriate information by adjusting the type of information collected based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into an AI and have the AI ​​adjust the type of information to be collected.

[0114] The matching system can filter information based on the user's current lifestyle and areas of interest. For example, it can collect relevant purchase history based on the user's current lifestyle. It can also prioritize the collection of specific posts and likes based on the user's areas of interest. Furthermore, it can filter and collect travel history based on the user's lifestyle. This allows for the collection of more relevant information by filtering based on the user's current lifestyle and areas of interest. Current lifestyle includes, but is not limited to, occupation, residence, and family structure. Areas of interest include, but is not limited to, hobbies, topics of interest, and accounts followed. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's lifestyle and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0115] The matching system can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the alert display method based on the user's emotions, more appropriate alerts can be displayed. Alert display methods include, but are not limited to, pop-up notifications, audio alerts, and vibrations. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into AI and have the AI ​​adjust how alerts are displayed.

[0116] The matching system can improve the accuracy of compatibility measurement by referring to the user's past compatibility data. For example, it can improve the accuracy of measurement by referring to data of people with whom the user has shown high compatibility in the past. It can also analyze the user's past compatibility data to find specific patterns and improve the accuracy of measurement. Furthermore, the compatibility measurement algorithm can be adjusted based on the user's past compatibility data. This allows for improved accuracy of measurement by referring to the user's past compatibility data. Past compatibility data includes, but is not limited to, past matching results and compatibility score history. Some or all of the above processing in the measurement unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the measurement unit can input the user's past compatibility data into a generative AI and have the generative AI perform the improvement of compatibility measurement accuracy.

[0117] The matching system can estimate the user's emotions and adjust the agreement process based on those emotions. For example, if the user is tense, it can provide a simple and easy-to-understand agreement process. If the user is relaxed, it can provide an agreement process that includes more detailed information. Furthermore, if the user is in a hurry, it can provide an agreement process that gets straight to the point. By adjusting the agreement process based on the user's emotions, a more appropriate agreement process can be provided. The agreement process may include, but is not limited to, methods for confirming agreement and details of the steps. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the agreement unit may be performed using AI or not. For example, the agreement unit can input user emotion data into AI and have the AI ​​perform the adjustment of the agreement process.

[0118] The matching system can collect information while considering the user's geographical location. For example, if a user is in a specific location, it can prioritize collecting purchase history related to that location. It can also prioritize collecting relevant travel history based on the user's current location. Furthermore, it can prioritize collecting relevant posts and likes while considering the user's geographical location. This allows for the collection of more appropriate information by prioritizing the collection of highly relevant information while considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into the AI ​​and have the AI ​​determine the priority of the information.

[0119] The matching system can estimate the user's emotions and adjust the display method of proximity alerts based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the display method of proximity alerts based on the user's emotions, more appropriate proximity alerts can be displayed. Display methods for proximity alerts include, but are not limited to, pop-up notifications, audio alerts, and vibrations. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the proximity alert unit may be performed using AI or not. For example, the proximity alert unit can input user emotion data into AI and have the AI ​​perform the adjustment of the proximity alert display method.

[0120] The matching system can analyze a user's social media activity and collect relevant information. For example, if a user uses a specific hashtag, it can collect posts related to that hashtag. It can also prioritize collecting posts from accounts that a user follows. Furthermore, it can analyze a user's "like" history and collect relevant posts. This allows the system to collect relevant information by analyzing a user's social media activity. Social media activity includes, but is not limited to, posts, likes, and shares. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity into an AI and have the AI ​​collect the information.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects user information. User information includes purchase history, travel history, posts, likes, etc. For example, the data collection unit collects the types of products the user has purchased, the date and time of purchase, places visited and means of transportation, the content and frequency of posts, and the subjects and frequency of likes. Step 2: The measurement unit analyzes the information collected by the collection unit and measures compatibility. Compatibility is measured based on criteria such as personality assessment, shared hobbies, and shared values. For example, the measurement unit performs personality assessments on the user and the other person, evaluates the degree of shared hobbies and shared values, and measures compatibility. Step 3: The alert unit issues a matching alert based on the compatibility measured by the measurement unit. The matching alert is issued based on the notification format and alert conditions. For example, if the compatibility is high, it will notify via a pop-up notification, audio alert, or vibration. Step 4: The proximity alert unit issues an alert when a highly compatible person of the opposite sex is nearby. Proximity alerts are issued based on the distance range and the accuracy of the location information. For example, it will send a notification when a highly compatible person of the opposite sex is near the user. Step 5: The agreement section progresses to the disclosure of personal information and an actual date based on mutual consent. The agreement is made based on the method of confirming the agreement and the conditions for disclosing personal information. For example, it provides a process for confirming the agreement of the user and the other party, setting the conditions for disclosing personal information, and progressing to an actual date.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, measurement unit, alert unit, proximity alert unit, and agreement unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information such as the user's purchase history, travel history, tweets, and likes. The measurement unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected information to measure compatibility. The alert unit is implemented by the control unit 46A of the smart device 14 and notifies the user with a pop-up notification, voice alert, or vibration when compatibility is high. The proximity alert unit is implemented by the control unit 46A of the smart device 14 and issues a notification when a highly compatible person of the opposite sex is nearby the user. The agreement unit is implemented by the identification processing unit 290 of the data processing device 12 and provides a process for disclosing personal information and progressing to an actual date based on mutual agreement. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, measurement unit, alert unit, proximity alert unit, and agreement unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information such as the user's purchase history, travel history, tweets, and likes. The measurement unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected information to measure compatibility. The alert unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user with a pop-up notification, voice alert, or vibration when compatibility is high. The proximity alert unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user when there is a highly compatible person of the opposite sex nearby. The agreement unit is implemented by the identification processing unit 290 of the data processing device 12 and provides a process for disclosing personal information and progressing to an actual date based on mutual agreement. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, measurement unit, alert unit, proximity alert unit, and agreement 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 information such as the user's purchase history, travel history, tweets, and likes. The measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to measure compatibility. The alert unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user with a pop-up notification, voice alert, or vibration when compatibility is high. The proximity alert unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user when there is a highly compatible person of the opposite sex nearby. The agreement unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a process for disclosing personal information and progressing to an actual date based on mutual agreement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, measurement unit, alert unit, proximity alert unit, and agreement 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 information such as the user's purchase history, movement history, tweets, and likes. The measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to measure compatibility. The alert unit is implemented by the control unit 46A of the robot 414 and notifies the user with a pop-up notification, voice alert, or vibration when compatibility is high. The proximity alert unit is implemented by the control unit 46A of the robot 414 and issues a notification when a highly compatible person of the opposite sex is nearby the user. The agreement unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides a process for disclosing personal information and progressing to an actual date based on mutual agreement. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection unit that collects user information, A measurement unit analyzes the information collected by the aforementioned collection unit and measures compatibility, An alert unit that issues a matching alert based on the compatibility measured by the aforementioned measurement unit, A proximity alert unit that issues an alert when there is a highly compatible person of the opposite sex nearby, It includes a section for mutual agreement to disclose personal information and to proceed to an actual date. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects information such as purchase history, travel history, posts, and likes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned measuring unit is The data collected by the data collection unit is analyzed to measure compatibility. The system described in Appendix 1, characterized by the features described herein. (Note 4) The alert unit is, A matching alert will be issued if the compatibility is high. The system described in Appendix 1, characterized by the features described herein. (Note 5) The proximity alert unit is An alert will be sent if there is a highly compatible person of the opposite sex nearby. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned agreement section, With mutual consent, personal information is disclosed and the relationship progresses to an actual date. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected 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 method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 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 information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the compatibility measurement algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned measuring unit is During compatibility testing, the system improves accuracy by referencing the user's past compatibility data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned measuring unit is During compatibility testing, the measurement criteria are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned measuring unit is Adjusting how we estimate user emotions and display compatibility test results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned measuring unit is To improve the accuracy of compatibility testing, the system takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned measuring unit is During compatibility testing, we analyze users' social media activity to adjust the measurement criteria. The system described in Appendix 1, characterized by the features described herein. (Note 19) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The alert unit is, When displaying an alert, the system will refer to the user's past alert history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The alert unit is, When displaying an alert, the content of the alert will be customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The alert unit is, When displaying alerts, the system takes the user's geographical location into consideration to display the most appropriate alert. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, When displaying an alert, the system analyzes the user's social media activity and adjusts the content of the alert accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The proximity alert unit is It estimates the user's emotions and adjusts how proximity alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The proximity alert unit is When displaying proximity alerts, the system refers to the user's past proximity alert history to select the optimal display method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The proximity alert unit is When proximity alerts are displayed, the content of the alerts is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The proximity alert unit is It estimates the user's emotions and prioritizes proximity alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The proximity alert unit is When displaying proximity alerts, the system will consider the user's geographical location to display the most appropriate proximity alert. The system described in Appendix 1, characterized by the features described herein. (Note 30) The proximity alert unit is When proximity alerts are displayed, the system analyzes the user's social media activity and adjusts the content of the proximity alerts accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned agreement section, It estimates user emotions and adjusts the consensus process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned agreement section, During the agreement process, the system selects the optimal process by referring to the user's past agreement history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned agreement section, During the agreement process, the content of the agreement is customized based on the user's current life circumstances and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned agreement section, It estimates user sentiment and determines consensus priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned agreement section, During the agreement process, we provide an optimal agreement process that takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned agreement section, During the agreement process, we analyze users' social media activity and adjust the content of the agreement accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, A measurement unit analyzes the information collected by the aforementioned collection unit and measures compatibility, An alert unit that issues a matching alert based on the compatibility measured by the aforementioned measurement unit, A proximity alert unit that issues an alert when there is a highly compatible person of the opposite sex nearby, It includes a section for mutual agreement to disclose personal information and to proceed to an actual date. A system characterized by the following features.

2. The aforementioned collection unit is Collects information such as purchase history, travel history, and posted information. The system according to feature 1.

3. The aforementioned measuring unit is The information collected by the aforementioned collection unit is analyzed to measure compatibility. The system according to feature 1.

4. The alert unit is, A matching alert will be issued if the compatibility is high. The system according to feature 1.

5. The proximity alert unit is An alert will be sent if there is a highly compatible person of the opposite sex nearby. The system according to feature 1.

6. The aforementioned agreement section, With mutual consent, personal information is disclosed and the relationship progresses to an actual date. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected 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 method for collecting information. The system according to feature 1.

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

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 according to feature 1.

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