system

Abstract

The system according to this embodiment aims to reduce resistance to meeting people online and to help people find compatible partners while protecting their privacy. [Solution] The system according to the embodiment comprises an analysis unit, a notification unit, a disclosure unit, a settings unit, and a protection unit. The analysis unit analyzes past interactions and measures compatible people. The notification unit sends a notification when the compatibility match rate is high according to the analysis unit. The disclosure unit discloses information when the other party also gives permission according to the notification unit. The settings unit configures settings to avoid matching with friends according to the disclosure unit. The protection unit protects privacy according to the settings unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are a sense of resistance to online encounters and concerns about privacy protection, leaving room for improvement.

[0005] The system according to the embodiment aims to reduce the sense of resistance to online encounters, protect privacy, and find compatible people.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a notification unit, a disclosure unit, a settings unit, and a protection unit. The analysis unit analyzes past interactions and measures compatible individuals. The notification unit sends a notification when the compatibility rate is high according to the analysis unit. The disclosure unit discloses information when the other party also gives permission according to the notification unit. The settings unit configures settings to avoid matching with friends according to the disclosure unit. The protection unit protects privacy according to the settings unit. [Effects of the Invention]

[0007] The system according to this embodiment reduces resistance to meeting people online and allows users to find compatible partners while protecting their privacy. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The dating support system according to an embodiment of the present invention is a system that uses an AI agent to analyze the personality of people who are hesitant to meet people online and find them a compatible partner. This dating support system first works in conjunction with a messaging app to analyze past interactions and determine who is compatible. Next, if the compatibility rate is high, a notification is sent, and the information is made public if the other party also gives permission. Furthermore, it is possible to set the system to avoid matching with friends, thus protecting privacy. The AI ​​agent analyzes the interactions in the messaging app and not only finds compatible people but also provides advice on interactions, supporting the improvement of dating skills. For example, the dating support system includes an analysis unit that analyzes past interactions and determines who is compatible. The analysis unit includes AI processing. Next, it includes a notification unit that sends a notification if the compatibility rate is high. The notification unit may also include AI processing. Furthermore, it includes a disclosure unit that makes the information public if the other party also gives permission. The disclosure unit may also include AI processing. It also includes a setting unit that allows setting the system to avoid matching with friends. The setting unit may also include AI processing. Finally, it includes a protection unit that protects privacy. The protection unit may also include AI processing. This allows the dating support system to help people who are hesitant about meeting people online, by having an AI agent analyze their personality and find a compatible partner.

[0029] The dating support system according to this embodiment comprises an analysis unit, a notification unit, a publication unit, a settings unit, and a protection unit. The analysis unit analyzes past interactions and measures compatible individuals. Past interactions include, but are not limited to, the content, frequency, and duration of messages. For example, the analysis unit analyzes the content of messages to measure compatible individuals. The analysis unit can also analyze the frequency of messages to measure compatible individuals. Furthermore, the analysis unit can analyze the duration of messages to measure compatible individuals. For example, the analysis unit analyzes the content of messages using natural language processing technology to measure compatible individuals. The notification unit sends a notification when the compatibility match rate is high as determined by the analysis unit. For example, the notification unit sends a push notification when the compatibility match rate is high. Furthermore, the notification unit can also send an email notification when the compatibility match rate is high. Furthermore, the notification unit can also send an SMS notification when the compatibility match rate is high. For example, the notification unit sends a push notification to inform the user when the compatibility match rate is high. The Public section makes information public only if the other party also gives permission via the Notification section. For example, the Public section makes profile information public if the other party also gives permission. The Public section can also make contact information public if the other party also gives permission. Furthermore, the Public section can also make information about hobbies and interests public if the other party also gives permission. For example, the Public section makes profile information public and notifies the user if the other party also gives permission. The Settings section allows the Public section to configure settings to avoid matching with friends. For example, the Settings section manages the friend list and configures settings to avoid matching with friends. Furthermore, the Settings section can also set exclusion criteria for matching based on the friend list. Furthermore, the Settings section can update the friend list and adjust the exclusion criteria for matching. For example, the Settings section manages the friend list and configures settings to avoid matching with friends. The Protection section protects privacy via the Settings section. For example, the Protection section protects privacy by encrypting data. Furthermore, the Protection section can also protect privacy by implementing access control. Furthermore, the Protection section can protect privacy by setting a privacy policy. For example, the Protection section protects privacy by encrypting data.As a result, the dating support system according to this embodiment allows an AI agent to analyze the personality of people who are hesitant to meet people online and find a compatible partner for them.

[0030] The analysis unit analyzes past interactions to determine compatibility. Past interactions include, but are not limited to, message content, frequency, and duration. For example, the analysis unit can analyze message content to determine compatibility. It can also analyze message frequency to determine compatibility. Furthermore, it can analyze message duration to determine compatibility. For example, the analysis unit uses natural language processing technology to analyze message content and determine compatibility. Specifically, it uses natural language processing technology to perform sentiment analysis and topic modeling of messages to understand users' interests, concerns, and emotional tendencies. This allows for a quantitative evaluation of commonalities and compatibility between users. Furthermore, message frequency analysis analyzes the frequency and response time of interactions to measure the activity of communication and the degree of interest in the other party. For example, users who frequently exchange messages are likely to be judged as compatible. Also, in message duration analysis, if the interaction continues for a long period of time, it may be judged as compatible. These analysis results are comprehensively evaluated to identify compatible users. The analysis department uses an AI agent to analyze this data in real time and propose the most suitable partner to each user. By learning from past data and understanding user preferences and tendencies, the AI ​​agent can perform more accurate compatibility measurements. As a result, the analysis department can propose the most suitable partner to each user, increasing the success rate of finding a match.

[0031] The notification unit sends notifications when the compatibility rate is high, as determined by the analysis unit. For example, the notification unit sends push notifications when the compatibility rate is high. The notification unit can also send email notifications when the compatibility rate is high. Furthermore, the notification unit can also send SMS notifications when the compatibility rate is high. For example, the notification unit sends a push notification to inform the user when the compatibility rate is high. Specifically, the notification unit sends a real-time notification to the user's device informing them that a compatible partner has been found. Push notifications utilize the smartphone's notification function to instantly convey information to the user. Email notifications send detailed information to the user's registered email address, providing the profile and contact information of the compatible partner. SMS notifications send a short message to the user's mobile phone number, concisely informing them that a compatible partner has been found. This allows the notification unit to quickly and reliably convey information to the user, ensuring they don't miss out on opportunities to meet someone. Furthermore, the notification unit can customize the frequency and method of notifications according to the user's notification settings. For example, if a user prefers push notifications, push notifications will be prioritized, with email and SMS notifications used as supplementary methods. Furthermore, the notification system can collect user feedback and continuously improve notification content and timing. This allows the notification system to provide users with the most optimal notification method, increasing the success rate of matches.

[0032] The Public section discloses information only if the other party also grants permission via the Notification section. For example, the Public section discloses profile information only if the other party grants permission. The Public section can also disclose contact information only if the other party grants permission. Furthermore, the Public section can also disclose information about hobbies and interests only if the other party grants permission. For example, the Public section discloses profile information and notifies the user if the other party also grants permission. Specifically, the Public section securely manages the user's profile information and only discloses it if the other party's permission is obtained. Profile information includes name, age, occupation, hobbies, and interests. This allows users to learn basic information about others and engage in deeper communication. In addition, contact information is only disclosed if the other party grants permission, providing contact methods such as phone numbers and email addresses. This allows users to contact each other directly and expands opportunities for encounters. Furthermore, disclosing information about hobbies and interests makes it easier for users with common hobbies and interests to meet. The Public section securely manages this information, providing opportunities for encounters while protecting user privacy. The Public section can customize the scope and content of the information disclosed according to the user's settings. For example, it is possible to set up the system to disclose only specific information, or to disclose information only to specific users. This allows the system to maximize opportunities for encounters while protecting user privacy.

[0033] The settings section allows users to configure settings to avoid matching with friends. For example, the settings section manages the friend list and configures settings to avoid matching with friends. The settings section can also set exclusion criteria based on the friend list. Furthermore, the settings section can update the friend list and adjust the exclusion criteria. Specifically, the settings section filters matches to avoid matching with friends based on the friend list previously registered by the user. The friend list contains information about friends and acquaintances the user wants to avoid matching with. This allows users to enjoy meeting people with peace of mind while protecting their privacy. The settings section provides a friend list management function, allowing users to add, delete, and update the list. The settings section can also set exclusion criteria based on the friend list and automatically exclude users who meet specific conditions. For example, it's possible to exclude users who work or attend school in the same workplace or school, or users who live in a specific area. Furthermore, the settings section continuously adjusts the exclusion criteria based on user feedback to achieve more accurate filtering. This allows the settings unit to provide an environment where users can enjoy meeting others with peace of mind while protecting their privacy.

[0034] The protection unit protects privacy through its settings. For example, the protection unit protects privacy by encrypting data. It can also protect privacy by implementing access control. Furthermore, the protection unit can protect privacy by setting a privacy policy. For example, the protection unit protects privacy by encrypting data. Specifically, the protection unit encrypts users' personal information and message content to prevent unauthorized access and data breaches. The encryption technology uses the latest encryption algorithms to maintain a high level of security. Access control also strictly manages access rights to user data, ensuring that only authorized users can access the data. This protects user privacy and prevents unauthorized access and data breaches. Furthermore, the protection unit sets a privacy policy, clearly defining guidelines regarding the handling of user data. The privacy policy includes detailed information about data collection, use, storage, and sharing, providing transparency to users. This allows users to understand how their data is handled and use the service with confidence. The protection unit maintains a consistently high level of security by conducting regular security audits, detecting system vulnerabilities, and taking necessary measures. This allows the protection unit to safeguard user privacy and provide a secure environment for encounters.

[0035] The dating support system according to this embodiment includes an advice unit that provides advice on interactions. The advice unit provides advice on interactions. Advice on interactions includes, but is not limited to, advice on how to write messages and the timing of messages. For example, the advice unit can advise on how to write messages. The advice unit can also advise on the timing of sending messages. Furthermore, the advice unit can also advise on the content of messages. For example, the advice unit can give specific advice on how to write messages and inform the user. In this way, the advice unit can help the AI ​​agent provide advice on interactions and improve their dating skills.

[0036] The dating support system according to this embodiment includes a support unit that assists in improving romantic abilities. The support unit assists in improving romantic abilities. Assisting in improving romantic abilities includes, but is not limited to, communication skills training and date plan suggestions. For example, the support unit provides communication skills training. The support unit can also suggest date plans. Furthermore, the support unit can also provide advice on romance. For example, the support unit provides specific communication skills training and informs the user. This allows the support unit to assist the AI ​​agent in improving romantic abilities.

[0037] The analysis unit can include not only the content of past interactions but also the frequency and time of those interactions in its analysis. For example, the analysis unit can analyze the times of day when a user frequently interacts and prioritize analyzing people who are compatible with that user during those times. Furthermore, if a user frequently interacts with a specific person, the analysis unit can focus its analysis on compatibility with that person. In addition, if a user often interacts at night, the analysis unit can focus its analysis on nighttime interactions. For example, the analysis unit can analyze the times of day when a user frequently interacts and prioritize analyzing people who are compatible with that user during those times. By including the frequency and time of interactions in the analysis, a more accurate compatibility measurement can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0038] The analysis unit can perform more accurate compatibility measurements by incorporating the user's hobbies and interests into the analysis. For example, the analysis unit can analyze keywords related to the user's hobbies and prioritize matching with people who share those hobbies. The analysis unit can also analyze interactions related to the user's interests and prioritize matching with people who share those interests. Furthermore, if the user frequently interacts with others regarding a particular hobby, the analysis unit can focus its analysis on compatibility related to that hobby. For example, the analysis unit can analyze keywords related to the user's hobbies and prioritize matching with people who share those hobbies. By incorporating the user's hobbies and interests into the analysis, more accurate compatibility measurements can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0039] The analysis unit can also include interactions from other social networking services (SNS) and messaging apps in its analysis. For example, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. Furthermore, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. For example, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. This allows for a broader analysis by including interactions from other social networking services (SNS) and messaging apps in the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0040] The analysis unit can prioritize analyzing compatible people nearby, taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing compatible people nearby based on the user's current location. The analysis unit can also prioritize analyzing compatible people near frequently visited locations based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and prioritize analyzing compatible people nearby. For example, the analysis unit prioritizes analyzing compatible people nearby based on the user's current location. This allows for the prioritization of analysis of compatible people nearby by taking into account the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0041] The notification unit can customize notification content and deliver notifications in the format best suited to the user. For example, if the user prefers visual information, the notification unit can send notifications that include images or videos. It can also send detailed text notifications if the user prefers text information. Furthermore, if the user prefers audio information, the notification unit can send notifications that include audio messages. This allows for customized notification content, enabling notifications to be delivered in the format best suited to the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0042] The notification unit can adjust the frequency of notifications based on the user's response. For example, if the user responds quickly to a notification, the notification unit will increase the frequency of notifications. Conversely, if the user responds slowly to a notification, the notification unit can decrease the frequency of notifications. Furthermore, if the user ignores a notification, the notification unit can significantly reduce the frequency of notifications. For example, if the notification unit responds quickly to a notification, the notification unit will increase the frequency of notifications. By adjusting the frequency of notifications based on the user's response, notifications can be delivered at a more appropriate frequency. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0043] The notification unit can provide notifications through multiple channels, such as email and SMS. For example, if the user prefers email, the notification unit will send a notification via email. It can also send a notification via SMS if the user prefers SMS. Furthermore, if the user prefers in-app notifications, the notification unit can send a notification within the app. For example, if the user prefers email, the notification unit will send a notification via email. By providing notifications through multiple channels, notifications can be delivered in the way that is most suitable for the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0044] The notification unit can include in the notification content the reasons for compatibility and details of the analysis results. For example, the notification unit can explain in detail why the notification content is compatible. The notification unit can also include details of the analysis results in the notification content. Furthermore, the notification unit can list the points that make the notification content compatible. For example, the notification unit can explain in detail why the notification content is compatible. By including in the notification content the reasons for compatibility and details of the analysis results, notifications can be made easier for users to understand. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0045] The public access section can allow users to select the scope of information they choose to make public. For example, the public access section can provide an interface that allows users to select the scope of information they choose to make public. The public access section can also provide users with the option to make only specific information public. Furthermore, the public access section can allow users to finely control the scope of information they choose to make public. For example, the public access section can provide an interface that allows users to select the scope of information they choose to make public. This protects user privacy by allowing users to choose the scope of information they choose to make public. Some or all of the processing described above in the public access section may be performed using AI, for example, or not using AI.

[0046] The public information section can periodically update the content of the public information to provide the latest information. For example, the public information section can periodically update the public information to provide the latest information. The public information section can also allow users to set when to update the public information. Furthermore, the public information section can save the update history of the public information and allow users to review it. For example, the public information section can periodically update the public information to provide the latest information. This ensures that the content of the public information is always up-to-date by periodically updating it. Some or all of the above processing in the public information section may be performed using AI, for example, or not using AI.

[0047] The public section may include information about the user's hobbies and interests in the public information. For example, the public section may include information about the user's hobbies in the public information. The public section may also include information about the user's interests in the public information. Furthermore, the public section may include information that the user is likely to be interested in. For example, the public section may include information about the user's hobbies in the public information. By including information about the user's hobbies and interests in the public information, more interesting information is provided. Some or all of the above processing in the public section may be performed using AI, for example, or not using AI.

[0048] The publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. For example, the publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. The publishing unit can also prioritize the publication of information that users frequently view. Furthermore, based on the user's viewing history, the publishing unit can include information that is likely to be of interest in the content of the next publication. For example, the publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. In this way, by analyzing the viewing history of published information, the content of the next publication can reflect the user's interests. Some or all of the above processing in the publishing unit may be performed using AI, for example, or without using AI.

[0049] The settings unit can suggest settings based on the user's past behavior history. For example, the settings unit can suggest the optimal settings based on the user's past behavior history. The settings unit can also suggest settings by referring to the user's past setting changes. Furthermore, the settings unit can analyze the user's behavior patterns and suggest the optimal settings. For example, the settings unit can suggest the optimal settings based on the user's past behavior history. This allows for more appropriate settings to be made by suggesting settings based on the user's past behavior history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0050] The settings unit can save a history of setting changes and allow the user to review it at any time. For example, the settings unit can save a history of setting changes and allow the user to review it. The settings unit can also display a list of setting changes the user has made in the past. Furthermore, the settings unit can provide reference information for when the user makes further setting changes based on the history of setting changes. For example, the settings unit can save a history of setting changes and allow the user to review it. This allows the user to review past setting changes by saving the history of setting changes. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0051] The settings unit can add options related to protecting user privacy to the settings. For example, the settings unit can add options related to protecting user privacy to the settings. The settings unit can also allow users to fine-tune their privacy settings. Furthermore, the settings unit can display a list of privacy setting options for the user to select from. For example, the settings unit can add options related to protecting user privacy to the settings. By adding privacy settings options to the settings, user privacy is further protected. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0052] The settings unit can synchronize settings with other devices and apps. For example, the settings unit can synchronize settings with other devices so that users can use the same settings on any device. The settings unit can also synchronize settings with other apps so that users can use consistent settings. Furthermore, the settings unit can allow users to check the synchronization status of the settings. For example, the settings unit can synchronize settings with other devices so that users can use the same settings on any device. This allows users to use consistent settings on any device by synchronizing settings with other devices and apps. Some or all of the above processes in the settings unit may be performed using AI, for example, or not using AI.

[0053] The protection unit can periodically update security measures for privacy protection. For example, the protection unit periodically updates security measures for privacy protection to provide the latest protection. The protection unit can also allow users to check the status of security measure updates. Furthermore, the protection unit can store a history of security measure updates and allow users to review it. For example, the protection unit periodically updates security measures for privacy protection to provide the latest protection. This ensures that the latest protection is always provided by periodically updating security measures for privacy protection. Some or all of the above processing in the protection unit may be performed using AI, for example, or without using AI.

[0054] The protection unit can collect user feedback regarding privacy protection and incorporate it into the system. For example, the protection unit can collect user feedback regarding privacy protection and incorporate it into the system. The protection unit can also provide an interface in which users can provide feedback. Furthermore, the protection unit can improve privacy protection settings based on the feedback. For example, the protection unit can collect user feedback regarding privacy protection and incorporate it into the system. This allows for more appropriate privacy protection by improving privacy protection settings based on user feedback. Some or all of the above processing in the protection unit may be performed using AI, for example, or without using AI.

[0055] The protection unit can add notification functions for privacy protection and inform users of their protection status. For example, the protection unit can add a function to notify users of the privacy protection status. The protection unit can also provide an interface that allows users to check their privacy protection status. Furthermore, the protection unit can also notify users if there are any changes to their privacy protection. For example, the protection unit can add a function to notify users of the privacy protection status. This allows users to understand their own privacy protection status by notifying them of their privacy protection status. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI.

[0056] The protection unit can synchronize privacy settings with other apps and services. For example, the protection unit can synchronize privacy settings with other apps to ensure users receive consistent protection. The protection unit can also synchronize privacy settings with other services to ensure users receive consistent protection. Furthermore, the protection unit can allow users to check the status of their privacy settings synchronization. For example, the protection unit synchronizes privacy settings with other apps to ensure users receive consistent protection. This ensures that users receive consistent privacy protection by synchronizing privacy settings with other apps and services. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI.

[0057] The advice unit can customize the advice based on the user's past interaction history. For example, the advice unit provides optimal advice based on the user's past interaction history. The advice unit can also customize the advice by referring to advice the user has received in the past. Furthermore, the advice unit can analyze the user's interaction patterns and provide optimal advice. For example, the advice unit provides optimal advice based on the user's past interaction history. By customizing the advice based on the user's past interaction history, more appropriate advice is provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI.

[0058] The advice unit can provide advice based on the success stories of other users. For example, the advice unit can provide optimal advice based on the success stories of other users. The advice unit can also customize the advice by referring to the methods that other users have used to succeed. Furthermore, the advice unit can analyze the success stories of other users and provide optimal advice. For example, the advice unit can provide optimal advice based on the success stories of other users. By providing advice based on the success stories of other users, more effective advice can be provided. Some or all of the above processes in the advice unit may be performed using AI, for example, or without using AI.

[0059] The support unit can customize the support content based on the user's past behavioral history. For example, the support unit can provide the optimal support content based on the user's past behavioral history. The support unit can also customize the support content by referring to the support the user has received in the past. Furthermore, the support unit can analyze the user's behavioral patterns and provide the optimal support content. For example, the support unit can provide the optimal support content based on the user's past behavioral history. By customizing the support content based on the user's past behavioral history, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0060] The support department can provide support based on the success stories of other users. For example, the support department can provide optimal support based on the success stories of other users. The support department can also customize support by referring to the methods that other users have successfully used. Furthermore, the support department can analyze the success stories of other users and provide optimal support. For example, the support department can provide optimal support based on the success stories of other users. By providing support based on the success stories of other users, more effective support can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or without using AI.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] A dating support system can include a suggestion section that proposes events and activities based on the user's hobbies and interests. For example, the suggestion section might suggest events that the user is interested in. It can also suggest activities related to the user's hobbies. Furthermore, based on the user's interests, the suggestion section can suggest participation in specific groups or communities. For instance, it might suggest events that the user is interested in and notify them. This expands the user's opportunities for meeting new people by suggesting events and activities based on their hobbies and interests.

[0063] The dating support system can include a health management unit that monitors the user's health status and adjusts the timing of encounters based on that status. For example, the health management unit proactively suggests encounter timings when the user is in good health. It can also delay encounter timings when the user is unwell. Furthermore, the health management unit can provide health-related advice based on the user's health status. By adjusting encounter timings based on the user's health status, the system can provide more appropriate opportunities for encounters.

[0064] A dating support system can include an experience analysis unit that analyzes a user's past romantic experiences and provides advice based on those experiences. For example, the experience analysis unit can analyze a user's past romantic experiences and provide advice based on successful experiences. It can also suggest areas for improvement based on unsuccessful experiences. Furthermore, the experience analysis unit can analyze a user's romantic patterns and provide optimal advice. For example, it can analyze a user's past romantic experiences and provide advice based on successful experiences. This allows for more effective dating support by providing advice based on the user's past romantic experiences.

[0065] A dating support system can include a behavioral analysis unit that analyzes a user's past behavioral history and suggests the optimal timing for a meeting based on that history. For example, the behavioral analysis unit can analyze when a user had a high success rate in past meetings and suggest that timing. It can also analyze a user's behavioral patterns and suggest the optimal timing for a meeting. Furthermore, the behavioral analysis unit can customize the timing of meetings based on the user's past behavioral history. For example, it can analyze when a user had a high success rate in past meetings and suggest that timing. This allows for more effective dating opportunities by suggesting the optimal timing based on the user's past behavioral history.

[0066] A dating support system can include a group suggestion unit that proposes groups and communities with shared interests based on the user's hobbies and interests. For example, the group suggestion unit could suggest groups related to the user's hobbies. It could also suggest participation in specific communities based on the user's interests. Furthermore, it could suggest groups and communities that the user might be interested in. For instance, it could suggest groups related to the user's hobbies and notify the user. This expands the user's opportunities for meeting new people by suggesting groups and communities based on their hobbies and interests.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The analysis unit analyzes past interactions to determine compatible individuals. Past interactions include message content, frequency, and duration. The analysis unit can analyze message content using natural language processing technology to determine compatible individuals. It can also analyze message frequency and duration. Step 2: The notification unit sends a notification if the compatibility match rate is high as determined by the analysis unit. The notification unit can send push notifications, email notifications, SMS notifications, etc. For example, it sends a push notification to inform the user if the compatibility match rate is high. Step 3: The Public section will disclose information only if the other party also grants permission via the Notification section. The Public section can disclose profile information, contact information, hobbies and interests, etc. For example, it will disclose profile information and notify the user if the other party also grants permission. Step 4: The settings section allows you to configure settings to avoid matching with friends via the public section. The settings section manages your friend list and allows you to configure settings to avoid matching with friends. You can also set and update exclusion criteria for matching based on your friend list. Step 5: The protection unit protects privacy through the settings unit. The protection unit can protect privacy by encrypting data, controlling access, and setting privacy policies. For example, it can protect privacy by encrypting data.

[0069] (Example of form 2) The dating support system according to an embodiment of the present invention is a system that uses an AI agent to analyze the personality of people who are hesitant to meet people online and find them a compatible partner. This dating support system first works in conjunction with a messaging app to analyze past interactions and determine who is compatible. Next, if the compatibility rate is high, a notification is sent, and the information is made public if the other party also gives permission. Furthermore, it is possible to set the system to avoid matching with friends, thus protecting privacy. The AI ​​agent analyzes the interactions in the messaging app and not only finds compatible people but also provides advice on interactions, supporting the improvement of dating skills. For example, the dating support system includes an analysis unit that analyzes past interactions and determines who is compatible. The analysis unit includes AI processing. Next, it includes a notification unit that sends a notification if the compatibility rate is high. The notification unit may also include AI processing. Furthermore, it includes a disclosure unit that makes the information public if the other party also gives permission. The disclosure unit may also include AI processing. It also includes a setting unit that allows setting the system to avoid matching with friends. The setting unit may also include AI processing. Finally, it includes a protection unit that protects privacy. The protection unit may also include AI processing. This allows the dating support system to help people who are hesitant about meeting people online, by having an AI agent analyze their personality and find a compatible partner.

[0070] The dating support system according to this embodiment comprises an analysis unit, a notification unit, a publication unit, a settings unit, and a protection unit. The analysis unit analyzes past interactions and measures compatible individuals. Past interactions include, but are not limited to, the content, frequency, and duration of messages. For example, the analysis unit analyzes the content of messages to measure compatible individuals. The analysis unit can also analyze the frequency of messages to measure compatible individuals. Furthermore, the analysis unit can analyze the duration of messages to measure compatible individuals. For example, the analysis unit analyzes the content of messages using natural language processing technology to measure compatible individuals. The notification unit sends a notification when the compatibility match rate is high as determined by the analysis unit. For example, the notification unit sends a push notification when the compatibility match rate is high. Furthermore, the notification unit can also send an email notification when the compatibility match rate is high. Furthermore, the notification unit can also send an SMS notification when the compatibility match rate is high. For example, the notification unit sends a push notification to inform the user when the compatibility match rate is high. The Public section makes information public only if the other party also gives permission via the Notification section. For example, the Public section makes profile information public if the other party also gives permission. The Public section can also make contact information public if the other party also gives permission. Furthermore, the Public section can also make information about hobbies and interests public if the other party also gives permission. For example, the Public section makes profile information public and notifies the user if the other party also gives permission. The Settings section allows the Public section to configure settings to avoid matching with friends. For example, the Settings section manages the friend list and configures settings to avoid matching with friends. Furthermore, the Settings section can also set exclusion criteria for matching based on the friend list. Furthermore, the Settings section can update the friend list and adjust the exclusion criteria for matching. For example, the Settings section manages the friend list and configures settings to avoid matching with friends. The Protection section protects privacy via the Settings section. For example, the Protection section protects privacy by encrypting data. Furthermore, the Protection section can also protect privacy by implementing access control. Furthermore, the Protection section can protect privacy by setting a privacy policy. For example, the Protection section protects privacy by encrypting data.As a result, the dating support system according to this embodiment allows an AI agent to analyze the personality of people who are hesitant to meet people online and find a compatible partner for them.

[0071] The analysis unit analyzes past interactions to determine compatibility. Past interactions include, but are not limited to, message content, frequency, and duration. For example, the analysis unit can analyze message content to determine compatibility. It can also analyze message frequency to determine compatibility. Furthermore, it can analyze message duration to determine compatibility. For example, the analysis unit uses natural language processing technology to analyze message content and determine compatibility. Specifically, it uses natural language processing technology to perform sentiment analysis and topic modeling of messages to understand users' interests, concerns, and emotional tendencies. This allows for a quantitative evaluation of commonalities and compatibility between users. Furthermore, message frequency analysis analyzes the frequency and response time of interactions to measure the activity of communication and the degree of interest in the other party. For example, users who frequently exchange messages are likely to be judged as compatible. Also, in message duration analysis, if the interaction continues for a long period of time, it may be judged as compatible. These analysis results are comprehensively evaluated to identify compatible users. The analysis department uses an AI agent to analyze this data in real time and propose the most suitable partner to each user. By learning from past data and understanding user preferences and tendencies, the AI ​​agent can perform more accurate compatibility measurements. As a result, the analysis department can propose the most suitable partner to each user, increasing the success rate of finding a match.

[0072] The notification unit sends notifications when the compatibility rate is high, as determined by the analysis unit. For example, the notification unit sends push notifications when the compatibility rate is high. The notification unit can also send email notifications when the compatibility rate is high. Furthermore, the notification unit can also send SMS notifications when the compatibility rate is high. For example, the notification unit sends a push notification to inform the user when the compatibility rate is high. Specifically, the notification unit sends a real-time notification to the user's device informing them that a compatible partner has been found. Push notifications utilize the smartphone's notification function to instantly convey information to the user. Email notifications send detailed information to the user's registered email address, providing the profile and contact information of the compatible partner. SMS notifications send a short message to the user's mobile phone number, concisely informing them that a compatible partner has been found. This allows the notification unit to quickly and reliably convey information to the user, ensuring they don't miss out on opportunities to meet someone. Furthermore, the notification unit can customize the frequency and method of notifications according to the user's notification settings. For example, if a user prefers push notifications, push notifications will be prioritized, with email and SMS notifications used as supplementary methods. Furthermore, the notification system can collect user feedback and continuously improve notification content and timing. This allows the notification system to provide users with the most optimal notification method, increasing the success rate of matches.

[0073] The Public section discloses information only if the other party also grants permission via the Notification section. For example, the Public section discloses profile information only if the other party grants permission. The Public section can also disclose contact information only if the other party grants permission. Furthermore, the Public section can also disclose information about hobbies and interests only if the other party grants permission. For example, the Public section discloses profile information and notifies the user if the other party also grants permission. Specifically, the Public section securely manages the user's profile information and only discloses it if the other party's permission is obtained. Profile information includes name, age, occupation, hobbies, and interests. This allows users to learn basic information about others and engage in deeper communication. In addition, contact information is only disclosed if the other party grants permission, providing contact methods such as phone numbers and email addresses. This allows users to contact each other directly and expands opportunities for encounters. Furthermore, disclosing information about hobbies and interests makes it easier for users with common hobbies and interests to meet. The Public section securely manages this information, providing opportunities for encounters while protecting user privacy. The Public section can customize the scope and content of the information disclosed according to the user's settings. For example, it is possible to set up the system to disclose only specific information, or to disclose information only to specific users. This allows the system to maximize opportunities for encounters while protecting user privacy.

[0074] The settings section allows users to configure settings to avoid matching with friends. For example, the settings section manages the friend list and configures settings to avoid matching with friends. The settings section can also set exclusion criteria based on the friend list. Furthermore, the settings section can update the friend list and adjust the exclusion criteria. Specifically, the settings section filters matches to avoid matching with friends based on the friend list previously registered by the user. The friend list contains information about friends and acquaintances the user wants to avoid matching with. This allows users to enjoy meeting people with peace of mind while protecting their privacy. The settings section provides a friend list management function, allowing users to add, delete, and update the list. The settings section can also set exclusion criteria based on the friend list and automatically exclude users who meet specific conditions. For example, it's possible to exclude users who work or attend school in the same workplace or school, or users who live in a specific area. Furthermore, the settings section continuously adjusts the exclusion criteria based on user feedback to achieve more accurate filtering. This allows the settings unit to provide an environment where users can enjoy meeting others with peace of mind while protecting their privacy.

[0075] The protection unit protects privacy through its settings. For example, the protection unit protects privacy by encrypting data. It can also protect privacy by implementing access control. Furthermore, the protection unit can protect privacy by setting a privacy policy. For example, the protection unit protects privacy by encrypting data. Specifically, the protection unit encrypts users' personal information and message content to prevent unauthorized access and data breaches. The encryption technology uses the latest encryption algorithms to maintain a high level of security. Access control also strictly manages access rights to user data, ensuring that only authorized users can access the data. This protects user privacy and prevents unauthorized access and data breaches. Furthermore, the protection unit sets a privacy policy, clearly defining guidelines regarding the handling of user data. The privacy policy includes detailed information about data collection, use, storage, and sharing, providing transparency to users. This allows users to understand how their data is handled and use the service with confidence. The protection unit maintains a consistently high level of security by conducting regular security audits, detecting system vulnerabilities, and taking necessary measures. This allows the protection unit to safeguard user privacy and provide a secure environment for encounters.

[0076] The dating support system according to this embodiment includes an advice unit that provides advice on interactions. The advice unit provides advice on interactions. Advice on interactions includes, but is not limited to, advice on how to write messages and the timing of messages. For example, the advice unit can advise on how to write messages. The advice unit can also advise on the timing of sending messages. Furthermore, the advice unit can also advise on the content of messages. For example, the advice unit can give specific advice on how to write messages and inform the user. In this way, the advice unit can help the AI ​​agent provide advice on interactions and improve their dating skills.

[0077] The dating support system according to this embodiment includes a support unit that assists in improving romantic abilities. The support unit assists in improving romantic abilities. Assisting in improving romantic abilities includes, but is not limited to, communication skills training and date plan suggestions. For example, the support unit provides communication skills training. The support unit can also suggest date plans. Furthermore, the support unit can also provide advice on romance. For example, the support unit provides specific communication skills training and informs the user. This allows the support unit to assist the AI ​​agent in improving romantic abilities.

[0078] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can delay the analysis until the user is relaxed. Alternatively, if the user is relaxed, the analysis unit can perform the analysis immediately and provide the results quickly. Furthermore, if the user is in a hurry, the analysis unit can prioritize the analysis and provide the results promptly. For example, if the user is stressed, the analysis unit can delay the analysis until the user is relaxed. By adjusting the timing of the analysis according to the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can include not only the content of past interactions but also the frequency and time of those interactions in its analysis. For example, the analysis unit can analyze the times of day when a user frequently interacts and prioritize analyzing people who are compatible with that user during those times. Furthermore, if a user frequently interacts with a specific person, the analysis unit can focus its analysis on compatibility with that person. In addition, if a user often interacts at night, the analysis unit can focus its analysis on nighttime interactions. For example, the analysis unit can analyze the times of day when a user frequently interacts and prioritize analyzing people who are compatible with that user during those times. By including the frequency and time of interactions in the analysis, a more accurate compatibility measurement can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0080] The analysis unit can perform more accurate compatibility measurements by incorporating the user's hobbies and interests into the analysis. For example, the analysis unit can analyze keywords related to the user's hobbies and prioritize matching with people who share those hobbies. The analysis unit can also analyze interactions related to the user's interests and prioritize matching with people who share those interests. Furthermore, if the user frequently interacts with others regarding a particular hobby, the analysis unit can focus its analysis on compatibility related to that hobby. For example, the analysis unit can analyze keywords related to the user's hobbies and prioritize matching with people who share those hobbies. By incorporating the user's hobbies and interests into the analysis, more accurate compatibility measurements can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0081] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize providing detailed analysis results. It can also prioritize providing concise analysis results if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can prioritize providing visually easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit will prioritize providing detailed analysis results. This allows for more appropriate analysis results to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can also include interactions from other social networking services (SNS) and messaging apps in its analysis. For example, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. Furthermore, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. For example, the analysis unit can analyze interactions a user has had on SNS to determine who they are compatible with. This allows for a broader analysis by including interactions from other social networking services (SNS) and messaging apps in the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0083] The analysis unit can prioritize analyzing compatible people nearby, taking into account the user's geographical location information. For example, the analysis unit prioritizes analyzing compatible people nearby based on the user's current location. The analysis unit can also prioritize analyzing compatible people near frequently visited locations based on the user's past location information. Furthermore, the analysis unit can update the user's location information in real time and prioritize analyzing compatible people nearby. For example, the analysis unit prioritizes analyzing compatible people nearby based on the user's current location. This allows for the prioritization of analysis of compatible people nearby by taking into account the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0084] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will send a notification immediately. Alternatively, if the user is stressed, the notification unit can delay the notification until the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can prioritize sending the notification. For example, if the user is relaxed, the notification unit will send a notification immediately. This allows for more appropriate timing of notifications by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The notification unit can customize notification content and deliver notifications in the format best suited to the user. For example, if the user prefers visual information, the notification unit can send notifications that include images or videos. It can also send detailed text notifications if the user prefers text information. Furthermore, if the user prefers audio information, the notification unit can send notifications that include audio messages. This allows for customized notification content, enabling notifications to be delivered in the format best suited to the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0086] The notification unit can adjust the frequency of notifications based on the user's response. For example, if the user responds quickly to a notification, the notification unit will increase the frequency of notifications. Conversely, if the user responds slowly to a notification, the notification unit can decrease the frequency of notifications. Furthermore, if the user ignores a notification, the notification unit can significantly reduce the frequency of notifications. For example, if the notification unit responds quickly to a notification, the notification unit will increase the frequency of notifications. By adjusting the frequency of notifications based on the user's response, notifications can be delivered at a more appropriate frequency. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0087] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is relaxed, the notification unit will prioritize important notifications. It can also prioritize less important notifications if the user is stressed. Furthermore, it can prioritize urgent notifications if the user is in a hurry. For example, if the notification unit is relaxed, it will prioritize important notifications. This allows for more appropriate notifications by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The notification unit can provide notifications through multiple channels, such as email and SMS. For example, if the user prefers email, the notification unit will send a notification via email. It can also send a notification via SMS if the user prefers SMS. Furthermore, if the user prefers in-app notifications, the notification unit can send a notification within the app. For example, if the user prefers email, the notification unit will send a notification via email. By providing notifications through multiple channels, notifications can be delivered in the way that is most suitable for the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI.

[0089] The notification unit can include in the notification content the reasons for compatibility and details of the analysis results. For example, the notification unit can explain in detail why the notification content is compatible. The notification unit can also include details of the analysis results in the notification content. Furthermore, the notification unit can list the points that make the notification content compatible. For example, the notification unit can explain in detail why the notification content is compatible. By including in the notification content the reasons for compatibility and details of the analysis results, notifications can be made easier for users to understand. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI.

[0090] The publishing unit can estimate the user's emotions and adjust the timing of information disclosure based on the estimated emotions. For example, if the user is relaxed, the publishing unit will immediately disclose the information. If the user is stressed, the publishing unit can delay disclosure until the user is relaxed. Furthermore, if the user is in a hurry, the publishing unit can prioritize disclosure. For example, if the publishing unit is relaxed, the information will be immediately disclosed. This allows for information to be disclosed at a more appropriate time by adjusting the timing of disclosure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The public access section can allow users to select the scope of information they choose to make public. For example, the public access section can provide an interface that allows users to select the scope of information they choose to make public. The public access section can also provide users with the option to make only specific information public. Furthermore, the public access section can allow users to finely control the scope of information they choose to make public. For example, the public access section can provide an interface that allows users to select the scope of information they choose to make public. This protects user privacy by allowing users to choose the scope of information they choose to make public. Some or all of the processing described above in the public access section may be performed using AI, for example, or not using AI.

[0092] The public information section can periodically update the content of the public information to provide the latest information. For example, the public information section can periodically update the public information to provide the latest information. The public information section can also allow users to set when to update the public information. Furthermore, the public information section can save the update history of the public information and allow users to review it. For example, the public information section can periodically update the public information to provide the latest information. This ensures that the content of the public information is always up-to-date by periodically updating it. Some or all of the above processing in the public information section may be performed using AI, for example, or not using AI.

[0093] The public service can estimate the user's emotions and prioritize the information it publishes based on those emotions. For example, if the user is relaxed, the public service will prioritize publishing important information. Conversely, if the user is stressed, it can prioritize publishing less important information. Furthermore, if the user is in a hurry, it can prioritize publishing urgent information. For instance, if the public service is relaxed, it will prioritize publishing important information. This ensures that more relevant information is published by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The public section may include information about the user's hobbies and interests in the public information. For example, the public section may include information about the user's hobbies in the public information. The public section may also include information about the user's interests in the public information. Furthermore, the public section may include information that the user is likely to be interested in. For example, the public section may include information about the user's hobbies in the public information. By including information about the user's hobbies and interests in the public information, more interesting information is provided. Some or all of the above processing in the public section may be performed using AI, for example, or not using AI.

[0095] The publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. For example, the publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. The publishing unit can also prioritize the publication of information that users frequently view. Furthermore, based on the user's viewing history, the publishing unit can include information that is likely to be of interest in the content of the next publication. For example, the publishing unit can analyze the viewing history of published information and reflect it in the content of the next publication. In this way, by analyzing the viewing history of published information, the content of the next publication can reflect the user's interests. Some or all of the above processing in the publishing unit may be performed using AI, for example, or without using AI.

[0096] The settings unit can estimate the user's emotions and adjust the timing of setting changes based on the estimated emotions. For example, if the user is relaxed, the settings unit can change the settings immediately. Alternatively, if the user is stressed, the settings unit can delay the setting change until the user is relaxed. Furthermore, if the user is in a hurry, the settings unit can prioritize the setting change. For example, if the user is relaxed, the settings unit can change the settings immediately. This allows for setting changes to be made at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The settings unit can suggest settings based on the user's past behavior history. For example, the settings unit can suggest the optimal settings based on the user's past behavior history. The settings unit can also suggest settings by referring to the user's past setting changes. Furthermore, the settings unit can analyze the user's behavior patterns and suggest the optimal settings. For example, the settings unit can suggest the optimal settings based on the user's past behavior history. This allows for more appropriate settings to be made by suggesting settings based on the user's past behavior history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0098] The settings unit can save a history of setting changes and allow the user to review it at any time. For example, the settings unit can save a history of setting changes and allow the user to review it. The settings unit can also display a list of setting changes the user has made in the past. Furthermore, the settings unit can provide reference information for when the user makes further setting changes based on the history of setting changes. For example, the settings unit can save a history of setting changes and allow the user to review it. This allows the user to review past setting changes by saving the history of setting changes. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI.

[0099] The settings unit can estimate the user's emotions and prioritize settings based on those emotions. For example, if the user is relaxed, the settings unit will prioritize suggesting important settings. If the user is stressed, the settings unit can prioritize suggesting less important settings. Furthermore, if the user is in a hurry, the settings unit can prioritize suggesting urgent settings. For example, if the user is relaxed, the settings unit will prioritize suggesting important settings. This allows for more appropriate settings by prioritizing settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The settings unit can add options related to protecting user privacy to the settings. For example, the settings unit can add options related to protecting user privacy to the settings. The settings unit can also allow users to fine-tune their privacy settings. Furthermore, the settings unit can display a list of privacy setting options for the user to select from. For example, the settings unit can add options related to protecting user privacy to the settings. By adding privacy settings options to the settings, user privacy is further protected. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI.

[0101] The settings unit can synchronize settings with other devices and apps. For example, the settings unit can synchronize settings with other devices so that users can use the same settings on any device. The settings unit can also synchronize settings with other apps so that users can use consistent settings. Furthermore, the settings unit can allow users to check the synchronization status of the settings. For example, the settings unit can synchronize settings with other devices so that users can use the same settings on any device. This allows users to use consistent settings on any device by synchronizing settings with other devices and apps. Some or all of the above processes in the settings unit may be performed using AI, for example, or not using AI.

[0102] The protection unit can estimate the user's emotions and adjust the level of privacy protection based on those emotions. For example, if the user is relaxed, the protection unit provides standard privacy protection. It can also provide enhanced privacy protection if the user is stressed. Furthermore, if the user is in a hurry, the protection unit can quickly adjust the level of privacy protection. For example, if the user is relaxed, the protection unit provides standard privacy protection. This allows for more appropriate privacy protection by adjusting the level of privacy protection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The protection unit can periodically update security measures for privacy protection. For example, the protection unit periodically updates security measures for privacy protection to provide the latest protection. The protection unit can also allow users to check the status of security measure updates. Furthermore, the protection unit can store a history of security measure updates and allow users to review it. For example, the protection unit periodically updates security measures for privacy protection to provide the latest protection. This ensures that the latest protection is always provided by periodically updating security measures for privacy protection. Some or all of the above processing in the protection unit may be performed using AI, for example, or without using AI.

[0104] The protection unit can collect user feedback regarding privacy protection and incorporate it into the system. For example, the protection unit can collect user feedback regarding privacy protection and incorporate it into the system. The protection unit can also provide an interface in which users can provide feedback. Furthermore, the protection unit can improve privacy protection settings based on the feedback. For example, the protection unit can collect user feedback regarding privacy protection and incorporate it into the system. This allows for more appropriate privacy protection by improving privacy protection settings based on user feedback. Some or all of the above processing in the protection unit may be performed using AI, for example, or without using AI.

[0105] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated emotions. For example, if the user is relaxed, the protection unit will prioritize standard privacy protection. It can also prioritize enhanced privacy protection if the user is stressed. Furthermore, if the user is in a hurry, the protection unit can prioritize rapid privacy protection. For example, if the user is relaxed, the protection unit will prioritize standard privacy protection. This allows for more appropriate privacy protection by prioritizing privacy protection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The protection unit can add notification functions for privacy protection and inform users of their protection status. For example, the protection unit can add a function to notify users of the privacy protection status. The protection unit can also provide an interface that allows users to check their privacy protection status. Furthermore, the protection unit can also notify users if there are any changes to their privacy protection. For example, the protection unit can add a function to notify users of the privacy protection status. This allows users to understand their own privacy protection status by notifying them of their privacy protection status. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI.

[0107] The protection unit can synchronize privacy settings with other apps and services. For example, the protection unit can synchronize privacy settings with other apps to ensure users receive consistent protection. The protection unit can also synchronize privacy settings with other services to ensure users receive consistent protection. Furthermore, the protection unit can allow users to check the status of their privacy settings synchronization. For example, the protection unit synchronizes privacy settings with other apps to ensure users receive consistent protection. This ensures that users receive consistent privacy protection by synchronizing privacy settings with other apps and services. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI.

[0108] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is relaxed, the advice unit will provide detailed advice. It can also provide concise advice if the user is stressed. Furthermore, if the user is in a hurry, the advice unit can provide quick advice. For example, if the user is relaxed, the advice unit will provide detailed advice. By adjusting the content of the advice according to the user's emotions, more appropriate advice is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The advice unit can customize the advice based on the user's past interaction history. For example, the advice unit provides optimal advice based on the user's past interaction history. The advice unit can also customize the advice by referring to advice the user has received in the past. Furthermore, the advice unit can analyze the user's interaction patterns and provide optimal advice. For example, the advice unit provides optimal advice based on the user's past interaction history. By customizing the advice based on the user's past interaction history, more appropriate advice is provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI.

[0110] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is relaxed, the advice unit will prioritize important advice. It can also prioritize less important advice if the user is stressed. Furthermore, if the user is in a hurry, it can prioritize urgent advice. For example, if the advice unit is relaxed, it will prioritize important advice. This allows for more appropriate advice to be provided by prioritizing advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The advice unit can provide advice based on the success stories of other users. For example, the advice unit can provide optimal advice based on the success stories of other users. The advice unit can also customize the advice by referring to the methods that other users have used to succeed. Furthermore, the advice unit can analyze the success stories of other users and provide optimal advice. For example, the advice unit can provide optimal advice based on the success stories of other users. By providing advice based on the success stories of other users, more effective advice can be provided. Some or all of the above processes in the advice unit may be performed using AI, for example, or without using AI.

[0112] The support unit can estimate the user's emotions and adjust the support content based on the estimated emotions. For example, if the user is relaxed, the support unit can provide detailed support. It can also provide concise support if the user is stressed. Furthermore, if the user is in a hurry, the support unit can provide support quickly. For example, if the user is relaxed, the support unit can provide detailed support. By adjusting the support content according to the user's emotions, more appropriate support is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The support unit can customize the support content based on the user's past behavioral history. For example, the support unit can provide the optimal support content based on the user's past behavioral history. The support unit can also customize the support content by referring to the support the user has received in the past. Furthermore, the support unit can analyze the user's behavioral patterns and provide the optimal support content. For example, the support unit can provide the optimal support content based on the user's past behavioral history. By customizing the support content based on the user's past behavioral history, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI.

[0114] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is relaxed, the support unit will prioritize important support. Conversely, if the user is stressed, the support unit can prioritize less important support. Furthermore, if the user is in a hurry, the support unit can prioritize urgent support. For example, if the user is relaxed, the support unit will prioritize important support. This allows for more appropriate support to be provided by prioritizing support according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The support department can provide support based on the success stories of other users. For example, the support department can provide optimal support based on the success stories of other users. The support department can also customize support by referring to the methods that other users have successfully used. Furthermore, the support department can analyze the success stories of other users and provide optimal support. For example, the support department can provide optimal support based on the success stories of other users. By providing support based on the success stories of other users, more effective support can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or without using AI.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] A dating support system can include a suggestion section that proposes events and activities based on the user's hobbies and interests. For example, the suggestion section might suggest events that the user is interested in. It can also suggest activities related to the user's hobbies. Furthermore, based on the user's interests, the suggestion section can suggest participation in specific groups or communities. For instance, it might suggest events that the user is interested in and notify them. This expands the user's opportunities for meeting new people by suggesting events and activities based on their hobbies and interests.

[0118] The dating support system can include a health management unit that monitors the user's health status and adjusts the timing of encounters based on that status. For example, the health management unit proactively suggests encounter timings when the user is in good health. It can also delay encounter timings when the user is unwell. Furthermore, the health management unit can provide health-related advice based on the user's health status. By adjusting encounter timings based on the user's health status, the system can provide more appropriate opportunities for encounters.

[0119] A dating support system can include an experience analysis unit that analyzes a user's past romantic experiences and provides advice based on those experiences. For example, the experience analysis unit can analyze a user's past romantic experiences and provide advice based on successful experiences. It can also suggest areas for improvement based on unsuccessful experiences. Furthermore, the experience analysis unit can analyze a user's romantic patterns and provide optimal advice. For example, it can analyze a user's past romantic experiences and provide advice based on successful experiences. This allows for more effective dating support by providing advice based on the user's past romantic experiences.

[0120] The dating support system may include an emotion adjustment unit that estimates the user's emotions and adjusts the timing of encounters based on those emotions. For example, if the user is relaxed, the emotion adjustment unit will proactively suggest an encounter. Conversely, if the user is stressed, the emotion adjustment unit can also delay the encounter. Furthermore, if the user is in a hurry, the emotion adjustment unit can prioritize suggesting an encounter. For example, if the emotion adjustment unit is relaxed, it will proactively suggest an encounter. In this way, by adjusting the timing of encounters based on the user's emotions, it is possible to provide more appropriate opportunities for encounters.

[0121] The dating support system may include an emotional advice unit that estimates the user's emotions and adjusts the content of the advice based on those emotions. For example, if the user is relaxed, the emotional advice unit provides detailed advice. It can also provide concise advice if the user is stressed. Furthermore, if the user is in a hurry, the emotional advice unit can provide quick advice. For example, if the emotional advice unit is relaxed, it provides detailed advice. This allows for more appropriate advice to be provided by adjusting the content of the advice based on the user's emotions.

[0122] The dating support system may include an emotion notification unit that estimates the user's emotions and adjusts the content of notifications based on those emotions. For example, the emotion notification unit provides detailed notifications when the user is relaxed. It can also provide concise notifications when the user is stressed. Furthermore, it can provide quick notifications when the user is in a hurry. For instance, the emotion notification unit provides detailed notifications when the user is relaxed. This allows for more appropriate notifications to be provided by adjusting the content of notifications based on the user's emotions.

[0123] The dating support system may include an emotion protection unit that estimates the user's emotions and adjusts the level of privacy protection based on those emotions. For example, the emotion protection unit provides standard privacy protection when the user is relaxed. It can also provide enhanced privacy protection when the user is stressed. Furthermore, the emotion protection unit can quickly adjust privacy protection when the user is in a hurry. For instance, the emotion protection unit provides standard privacy protection when the user is relaxed. This allows for more appropriate privacy protection by adjusting the level of privacy protection based on the user's emotions.

[0124] The dating support system may include an emotional support unit that estimates the user's emotions and adjusts the support content based on those emotions. For example, if the user is relaxed, the emotional support unit can provide detailed support. It can also provide concise support if the user is stressed. Furthermore, if the user is in a hurry, the emotional support unit can provide support quickly. For instance, if the emotional support unit is relaxed, it provides detailed support. This allows for more appropriate support to be provided by adjusting the support content based on the user's emotions.

[0125] A dating support system can include a behavioral analysis unit that analyzes a user's past behavioral history and suggests the optimal timing for a meeting based on that history. For example, the behavioral analysis unit can analyze when a user had a high success rate in past meetings and suggest that timing. It can also analyze a user's behavioral patterns and suggest the optimal timing for a meeting. Furthermore, the behavioral analysis unit can customize the timing of meetings based on the user's past behavioral history. For example, it can analyze when a user had a high success rate in past meetings and suggest that timing. This allows for more effective dating opportunities by suggesting the optimal timing based on the user's past behavioral history.

[0126] A dating support system can include a group suggestion unit that proposes groups and communities with shared interests based on the user's hobbies and interests. For example, the group suggestion unit could suggest groups related to the user's hobbies. It could also suggest participation in specific communities based on the user's interests. Furthermore, it could suggest groups and communities that the user might be interested in. For instance, it could suggest groups related to the user's hobbies and notify the user. This expands the user's opportunities for meeting new people by suggesting groups and communities based on their hobbies and interests.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The analysis unit analyzes past interactions to determine compatible individuals. Past interactions include message content, frequency, and duration. The analysis unit can analyze message content using natural language processing technology to determine compatible individuals. It can also analyze message frequency and duration. Step 2: The notification unit sends a notification if the compatibility match rate is high as determined by the analysis unit. The notification unit can send push notifications, email notifications, SMS notifications, etc. For example, it sends a push notification to inform the user if the compatibility match rate is high. Step 3: The Public section will disclose information only if the other party also grants permission via the Notification section. The Public section can disclose profile information, contact information, hobbies and interests, etc. For example, it will disclose profile information and notify the user if the other party also grants permission. Step 4: The settings section allows you to configure settings to avoid matching with friends via the public section. The settings section manages your friend list and allows you to configure settings to avoid matching with friends. You can also set and update exclusion criteria for matching based on your friend list. Step 5: The protection unit protects privacy through the settings unit. The protection unit can protect privacy by encrypting data, controlling access, and setting privacy policies. For example, it can protect privacy by encrypting data.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, notification unit, disclosure unit, setting unit, protection unit, advice unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes past interactions to determine compatible partners. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12 and sends a notification when the compatibility match rate is high. The disclosure unit is implemented by the control unit 46A of the smart device 14 and discloses information only if the other party also gives permission. The setting unit is implemented by the identification processing unit 290 of the data processing unit 12 and sets the user to avoid matching with friends. The protection unit is implemented by the control unit 46A of the smart device 14 and protects privacy. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides advice on interactions. The support unit is implemented by the control unit 46A of the smart device 14 and assists in improving dating skills. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, notification unit, disclosure unit, setting unit, protection unit, advice unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which analyzes past interactions and measures compatible people. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when the compatibility match rate is high. The disclosure unit is implemented by the control unit 46A of the smart glasses 214, which discloses information if the other party also gives permission. The setting unit is implemented by the identification processing unit 290 of the data processing unit 12, which sets the user to avoid matching with friends. The protection unit is implemented by the control unit 46A of the smart glasses 214, which protects privacy. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides advice on interactions. The support unit is implemented by the control unit 46A of the smart glasses 214, which helps improve dating skills. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the analysis unit, notification unit, disclosure unit, setting unit, protection unit, advice unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which analyzes past interactions and measures compatible individuals. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when the compatibility match rate is high. The disclosure unit is implemented by the control unit 46A of the headset terminal 314, which discloses information if the other party also gives permission. The setting unit is implemented by the identification processing unit 290 of the data processing unit 12, which sets the user to avoid matching with friends. The protection unit is implemented by the control unit 46A of the headset terminal 314, which protects privacy. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides advice on interactions. The support unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and supports the improvement of romantic ability. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In 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.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 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.

[0181] Each of the multiple elements described above, including the analysis unit, notification unit, disclosure unit, setting unit, protection unit, advice unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which analyzes past interactions and measures compatible partners. The notification unit is implemented by the identification processing unit 290 of the data processing unit 12, which sends a notification when the compatibility match rate is high. The disclosure unit is implemented by the control unit 46A of the robot 414, which discloses information only if the other party also gives permission. The setting unit is implemented by the identification processing unit 290 of the data processing unit 12, which sets the system to avoid matching with friends. The protection unit is implemented by the control unit 46A of the robot 414, which protects privacy. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides advice on interactions. The support unit is implemented by the control unit 46A of the robot 414, which assists in improving romantic abilities. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) An analysis unit analyzes past interactions to determine who is compatible with whom, The aforementioned analysis unit sends a notification when the compatibility match rate is high, The disclosure unit, which discloses information when the other party also gives permission as per the aforementioned notification unit, The aforementioned public access section includes a setting section that configures settings to avoid matching with friends, The setting unit includes a protection unit that protects privacy. A system characterized by the following features. (Note 2) An advice department is provided to offer guidance on communication. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a support department to help improve your romantic skills. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, The analysis will include not only the content of past interactions, but also the frequency and timing of those interactions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, By analyzing users' hobbies and interests, we can perform more accurate compatibility measurements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We will also include conversations from other social media and messaging apps in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system prioritizes analyzing users based on their geographical location to identify compatible individuals in their vicinity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned notification unit, Customize notification content and deliver notifications in the format that best suits the user. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification unit, Adjust notification frequency based on user response. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, We offer notification methods via multiple channels, such as email and SMS. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, The notification should include details about why the match is a good fit and the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned public section is, We estimate user sentiment and adjust the timing of information release based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned public section is, Allow users to choose the scope of information to be made public. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned public section is, We regularly update the content of publicly available information to provide the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned public section is, It estimates user sentiment and prioritizes publicly available information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned public section is, Include information about users' hobbies and interests in publicly available information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned public section is, We will analyze the viewing history of publicly available information and reflect it in the content of future publications. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned setting unit is, It estimates the user's emotions and adjusts the timing of setting changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned setting unit is, The system suggests settings based on the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned setting unit is, Save a history of settings changes so that users can review them at any time. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned setting unit is, It estimates the user's emotions and determines the priority of settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned setting unit is, Add user privacy protection options to the settings. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned setting unit is, Sync settings with other devices and apps. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned protective part is It estimates the user's emotions and adjusts the level of privacy protection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned protective part is We regularly update our security measures to protect privacy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned protective part is Collect user feedback regarding privacy protection and incorporate it into the system. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned protective part is It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned protective part is A notification feature has been added to inform users of their privacy protection status. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned protective part is Link your privacy settings with other apps and services. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, Customize the advice based on the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice section, We provide advice based on the success stories of other users. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned support unit, Customize the support provided based on the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned support unit, It estimates the user's emotions and determines the priority of support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned support unit, We provide support based on the success stories of other users. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. An analysis unit analyzes past interactions to determine who is compatible with whom, The aforementioned analysis unit sends a notification when the compatibility match rate is high, The disclosure unit, which discloses information when the other party also gives permission as per the aforementioned notification unit, The aforementioned public access section includes a setting section that configures settings to avoid matching with friends, The setting unit includes a protection unit that protects privacy. A system characterized by the following features.

2. An advice department is provided to offer guidance on communication. The system according to feature 1.

3. We have a support department to help improve your romantic skills. The system according to feature 1.

4. The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of the analysis based on the estimated user emotions. The system according to feature 1.

5. The aforementioned analysis unit, The analysis will include not only the content of past interactions, but also the frequency and timing of those interactions. The system according to feature 1.

6. The aforementioned analysis unit, By analyzing users' hobbies and interests, we can perform more accurate compatibility measurements. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, We will also include conversations from other social media and messaging apps in the analysis. The system according to feature 1.

9. The aforementioned analysis unit, The system prioritizes analyzing users based on their geographical location to identify compatible individuals in their vicinity. The system according to feature 1.

10. The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system according to feature 1.

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