System

Abstract

To provide a system according to an embodiment, the system intending to match marriage partners on the basis of biological compatibility.SOLUTION: A system according to an embodiment includes: a provision part; an analysis part; a determination part; a matching part; and a notification part. The provision part provides gene information. The analysis part analyzes the gene information provided by the provision part. The determination part determines compatibility on the basis of the gene information analyzed by the analysis part. The matching part matches partners on the basis of the compatibility determined by the determination part. The notification part notifies the result matched by the matching part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has focused on matching based on appearance and economic status, and there has been a problem in that matching that takes biological compatibility into account is not adequate.

[0005] The system according to the embodiment aims to match marriage partners based on biological compatibility. [Means for solving the problem]

[0006] The system according to the embodiment includes a providing unit, an analyzing unit, a determining unit, a matching unit, and a notifying unit. The providing unit provides genetic information. The analyzing unit analyzes the genetic information provided by the providing unit. The determining unit determines compatibility based on the genetic information analyzed by the analyzing unit. The matching unit matches a partner based on the compatibility determined by the determining unit. The notifying unit notifies the result of matching by the matching unit. Effect of the Invention

[0007] The system according to the embodiment can match marriage partners based on biological compatibility. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The matching system according to the embodiment of the present invention is a system for people who seek true happiness in married life based on biological compatibility, eliminating appearance, economic power, and momentary excitement as much as possible. This system judges the compatibility between men and women at the genetic level and matches marriage partners. In addition, the accuracy of matching can be improved by accumulating data. This is expected to eliminate the risk of divorce and contribute to increasing the number of marriages and birth rates. For example, a user registers with the app and provides genetic information. The genetic information is analyzed by a specialized institution to clarify the biological characteristics of the user. For example, immune system genes and pheromone receptor genes are analyzed. This information is stored in the app's database. Next, the app matches a compatible partner based on the registered user's genetic information. Specifically, the app selects the optimal partner taking into consideration gene complementarity and immune system diversity. For example, by matching partners with different immune system genes, the possibility of future children having stronger immune systems increases. Furthermore, the app notifies the user of the matching results. The user can exchange messages with the notified partner and actually meet them. At this time, the app supports anonymous message exchange to protect the user's privacy. The app also collects user feedback and accumulates data to improve the accuracy of matching. For example, by collecting and analyzing information such as the relationship status after matching and the success or failure of marriage, more accurate matching will be possible in the future. This app will realize marriages that pursue true happiness, not influenced by appearance or economic power, and reduce the risk of divorce. It is expected that this will contribute to an increase in the number of marriages and birth rates. This allows the matching system to judge compatibility based on the user's genetic information and match the most suitable partner.

[0029] The matching system according to the embodiment includes a providing unit, an analyzing unit, a determining unit, a matching unit, and a notifying unit. The providing unit provides genetic information of a user. The genetic information of the user includes, for example, immune system genes and pheromone receptor genes, but is not limited to such examples. The providing unit provides, for example, an interface for the user to provide genetic information. The analyzing unit analyzes the genetic information provided by the providing unit. The analyzing unit uses, for example, an algorithm for analyzing the genetic information. The determining unit determines compatibility based on the genetic information analyzed by the analyzing unit. The determining unit determines compatibility by taking into account, for example, gene complementarity and immune system diversity. The matching unit matches a partner based on the compatibility determined by the determining unit. The matching unit uses, for example, an algorithm for selecting a partner with good compatibility. The notifying unit notifies the result of matching by the matching unit. The notifying unit provides, for example, an interface for notifying the user of the matching result. As a result, the matching system according to the embodiment can determine compatibility based on the genetic information of the user and match the optimal partner. For example, the providing unit provides an interface for a user to provide genetic information. The analyzing unit uses an algorithm for analyzing the genetic information provided by the providing unit. The judging unit uses an algorithm for judging compatibility based on the genetic information analyzed by the analyzing unit. The matching unit uses an algorithm for matching a partner based on the compatibility judged by the judging unit. The notifying unit provides an interface for notifying the results of matching by the matching unit. In this way, the matching system according to the embodiment can judge compatibility based on the user's genetic information and match the most suitable partner.

[0030] The providing unit provides the genetic information of the user. The genetic information of the user includes, for example, genes of the immune system and genes of pheromone receptors, but is not limited to such examples. The providing unit provides, for example, an interface for the user to provide the genetic information. Specifically, the providing unit provides a web interface or a mobile application for the user to input the genetic information. This interface is designed to allow the user to input his / her own genetic information safely and easily, and uses encryption technology to protect the user's privacy. The user can, for example, collect a saliva sample, extract the genetic information using a dedicated kit, and input the information into the interface. The providing unit has a database for centrally managing the input genetic information and transmitting it to the analysis unit. In addition, the providing unit displays guidelines and precautions for the user when providing the genetic information, and supports the user to provide accurate information. In this way, the providing unit can efficiently and safely collect the user's genetic information and improve the reliability of the entire system. In addition, the providing unit displays a consent form and a privacy policy when the user provides the genetic information, and supports legal procedures for protecting the user's rights. This enables the provider to properly manage the user's genetic information and ensure the transparency and reliability of the entire system.

[0031] The analysis unit analyzes the genetic information provided by the provision unit. The analysis unit uses, for example, an algorithm for analyzing the genetic information. Specifically, the analysis unit uses bioinformatics technology for analyzing gene sequences and identifying functional characteristics of genes. For example, the analysis unit performs alignment and mutation analysis of gene sequences to create a detailed profile of the user's genetic information. Based on these profiles, the analysis unit evaluates the diversity of the user's immune system and the characteristics of the pheromone receptors. Furthermore, the analysis unit uses a machine learning algorithm to analyze the pattern of the genetic information and extracts features for identifying a compatible partner. For example, a scoring system that takes into account gene complementarity and immune system diversity is constructed to evaluate the user's genetic information. The analysis unit stores these analysis results in a database and makes them accessible to the judgment unit. In addition, in order to improve the accuracy of the analysis results, the analysis unit periodically adjusts the parameters of the algorithm and incorporates the latest research results. This allows the analysis unit to analyze the user's genetic information with high accuracy and improve the performance of the entire system. Furthermore, the analysis unit anonymizes the user's genetic information and takes measures to protect privacy. This enables the analysis unit to safely analyze the user's genetic information, ensuring the reliability of the entire system.

[0032] The judgment unit judges the compatibility based on the genetic information analyzed by the analysis unit. The judgment unit judges the compatibility by considering, for example, gene complementarity and immune system diversity. Specifically, the judgment unit uses an algorithm to evaluate the gene complementarity between users based on the genetic profile provided by the analysis unit. For example, a scoring system for evaluating the complementarity of alleles at a specific gene locus and the diversity of the immune system is constructed, and the compatibility of the user is quantified. The judgment unit sets a standard for identifying a compatible partner based on these scores, and evaluates the genetic information of the user. Furthermore, the judgment unit uses a machine learning algorithm to learn past matching data and success cases, and improves the accuracy of the compatibility judgment. For example, based on past matching results, the characteristics of a compatible combination are extracted and reflected in the compatibility judgment of a new user. The judgment unit stores these results in a database, and makes them accessible to the matching unit. In addition, the judgment unit feeds back the results of the compatibility judgment to the user, and supports the user in understanding the compatibility based on his or her own genetic information. As a result, the judgment unit can perform a highly accurate compatibility judgment based on the user's genetic information, and improve the performance of the entire system. Furthermore, the determination unit takes measures to protect the privacy of the user and anonymizes the results of the compatibility determination, thereby enabling the determination unit to safely manage the genetic information of the user and ensure the reliability of the entire system.

[0033] The matching unit matches a partner based on the compatibility determined by the determination unit. The matching unit uses, for example, an algorithm for selecting a partner with good compatibility. Specifically, the matching unit evaluates the compatibility between users based on the compatibility score provided by the determination unit and selects the most suitable partner. For example, an algorithm that preferentially matches users with high compatibility scores is used to realize optimal matching based on the genetic information of the users. The matching unit stores these matching results in a database and makes them accessible to the notification unit. In addition, the matching unit has a filtering function for performing matching that takes into account the user's wishes and conditions. For example, the user can input conditions such as desired age, region, and hobbies, and perform matching based on the conditions. Furthermore, the matching unit has a learning function for improving the accuracy of the algorithm based on past matching results and user feedback. As a result, the matching unit can realize highly accurate matching based on the user's genetic information and improve the performance of the entire system. Furthermore, the matching unit takes measures to protect the user's privacy and manages the matching results in anonymized form. As a result, the matching unit can safely manage the user's genetic information and ensure the reliability of the entire system.

[0034] The notification unit notifies the result of matching by the matching unit. The notification unit provides, for example, an interface for notifying the user of the matching result. Specifically, the notification unit provides a web interface or a mobile application that allows the user to check the matching result. This interface is designed to allow the user to easily check the matching result, and uses encryption technology to protect the user's privacy. The notification unit updates the matching result in real time, allowing the user to check the latest information. In addition, the notification unit has a function that allows the user to provide feedback on the matching result, and supports improvements to the system that reflect the user's opinions. For example, it is possible to evaluate whether the user is satisfied with the matching result, and adjust the algorithm based on the result. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only a web interface or a mobile application, but also email, SMS, push notification, etc. in combination. As a result, the notification unit can quickly and reliably provide the matching result to the user, and improve the reliability of the entire system. Furthermore, the notification unit takes measures to protect the user's privacy and manages the matching result by anonymizing it. As a result, the notification unit can safely manage the user's genetic information and ensure the reliability of the entire system.

[0035] The matching system includes a collection unit that collects user feedback. The collection unit, for example, provides an interface for collecting user feedback. The collection unit, for example, collects feedback such as user satisfaction and specific opinions and suggestions. The collection unit can collect feedback, for example, in the form of a questionnaire. The collection unit can also provide an automated system for collecting user feedback. For example, the collection unit automatically collects user feedback and stores it in a database. In this way, the collection unit can improve the accuracy of matching by collecting user feedback. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user feedback to AI and cause the AI ​​to analyze the feedback.

[0036] The matching system includes a storage unit that stores collected data and improves the accuracy of matching. The storage unit, for example, provides a database for storing collected data. The storage unit stores data, for example, taking into account the form of feedback and the structure of the database. The storage unit, for example, analyzes collected data and provides an algorithm for enabling more accurate matching in the future. For example, the storage unit improves a matching algorithm based on collected data. As a result, the storage unit stores collected data, enabling more accurate matching in the future. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input collected data to AI and have AI execute data analysis.

[0037] The matching system includes a support unit that supports anonymous message exchange. The support unit provides, for example, an interface for users to exchange messages anonymously. The support unit uses, for example, an anonymization technique for protecting personal information of users. The support unit provides, for example, a system for protecting privacy when users exchange messages anonymously. For example, the support unit anonymizes personal information of a user and exchanges messages. This allows the support unit to exchange messages with a partner while protecting the privacy of the user. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input personal information of a user to AI and cause AI to execute anonymization processing.

[0038] The providing unit can select an appropriate genetic information providing method based on the user's past health condition and lifestyle. The providing unit selects an optimal genetic information providing method, for example, by considering the user's past health condition and lifestyle. For example, the providing unit selects a genetic information providing method based on the result of a medical checkup the user has taken in the past. The providing unit can also adjust the timing of genetic information provision by considering the user's lifestyle (smoking, drinking, etc.). The providing unit can also select a genetic information providing method by considering the user's exercise habits. In this way, the providing unit can select an optimal genetic information providing method by considering the user's past health condition and lifestyle. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data to AI and cause the AI ​​to select a genetic information providing method.

[0039] The providing unit can perform filtering based on the user's current health condition and living environment when providing the genetic information. The providing unit filters the genetic information provision, for example, taking into account the user's current health condition and living environment. For example, the providing unit suspends the genetic information provision when the user is currently ill. The providing unit can also adjust the timing of the genetic information provision, taking into account the user's living environment (residence, workplace, etc.). The providing unit can also select a method of providing the genetic information based on the user's current health condition. As a result, the providing unit can provide more appropriate genetic information by filtering based on the user's current health condition and living environment. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data to AI and cause the AI ​​to perform filtering.

[0040] When providing genetic information, the providing unit can provide highly relevant information preferentially in consideration of the geographical location information of the user. The providing unit determines the priority order of providing genetic information, for example, in consideration of the geographical location information of the user. For example, the providing unit preferentially provides genetic information of the area where the user lives. In addition, when the user is traveling, the providing unit can also preferentially provide genetic information of the travel destination. In addition, when the user is planning to move, the providing unit can also preferentially provide genetic information of the new residence. In this way, the providing unit can provide highly relevant genetic information preferentially by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to provide highly relevant information.

[0041] The providing unit can analyze the social media activity of the user and provide related information when providing the genetic information. The providing unit uses, for example, an algorithm for analyzing the social media activity of the user. The providing unit can provide the related genetic information based on, for example, health information shared by the user on social media. The providing unit can also provide the related genetic information based on health-related accounts that the user follows on social media. The providing unit can also provide the related genetic information based on health-related groups that the user participates in on social media. In this way, the providing unit can provide the related genetic information by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media data of the user to AI and cause the AI ​​to provide the related information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the genetic information. For example, the analysis unit performs a detailed analysis of important genetic information. The analysis unit can also perform a concise analysis of general genetic information. The analysis unit can also perform a specialized analysis of specific genetic information. This allows the analysis unit to provide a more appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the genetic information to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of genetic information during analysis. The analysis unit applies different analysis algorithms depending on, for example, the category of genetic information. For example, the analysis unit applies a specific analysis algorithm to genetic information of the immune system. The analysis unit can also apply another analysis algorithm to genetic information of a pheromone receptor. The analysis unit can also apply an appropriate analysis algorithm to other genetic information. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic information to AI and cause AI to apply the analysis algorithm.

[0044] The analysis unit can determine the analysis priority based on the time of providing the genetic information during the analysis. The analysis unit determines the analysis priority based on, for example, the time of providing the genetic information. For example, the analysis unit preferentially analyzes the genetic information provided recently. The analysis unit can also postpone the genetic information provided in the past. The analysis unit can also adjust the order of analysis based on the time of provision. In this way, the analysis unit can provide more appropriate analysis results by determining the analysis priority based on the time of providing the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the genetic information to AI and cause the AI ​​to determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the genetic information. For example, the analysis unit preferentially analyzes genetic information with high relevance. The analysis unit can also postpone genetic information with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the genetic information. As a result, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic information to AI and cause AI to adjust the order of analysis.

[0046] The judgment unit can improve the accuracy of the compatibility judgment based on the interrelationship of the genetic information at the time of judgment. The judgment unit, for example, makes the compatibility judgment taking into consideration the interrelationship of the genetic information. For example, the judgment unit makes the compatibility judgment taking into consideration the interrelationship of the genetic information of the immune system. The judgment unit can also make the compatibility judgment taking into consideration the interrelationship of the genetic information of the pheromone receptor. The judgment unit can also make the compatibility judgment taking into consideration the interrelationship of other genetic information. Thereby, the judgment unit can improve the accuracy of the compatibility judgment by taking into consideration the interrelationship of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the compatibility judgment.

[0047] The judgment unit can make a compatibility judgment based on the attribute information of the genetic information provider at the time of judgment. The judgment unit makes a compatibility judgment, for example, taking into account the attribute information of the genetic information provider. For example, the judgment unit makes a compatibility judgment taking into account the age of the genetic information provider. The judgment unit can also make a compatibility judgment taking into account the gender of the genetic information provider. The judgment unit can also make a compatibility judgment taking into account the lifestyle of the genetic information provider. Thereby, the judgment unit can make a more appropriate compatibility judgment by taking into account the attribute information of the genetic information provider. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the attribute information of the genetic information provider to AI and cause the AI ​​to execute the compatibility judgment.

[0048] The judgment unit can make a compatibility judgment based on the geographical distribution of the genetic information at the time of judgment. The judgment unit makes a compatibility judgment by considering, for example, the geographical distribution of the genetic information. For example, the judgment unit makes a compatibility judgment based on the geographical distribution of the genetic information. The judgment unit can also make a compatibility judgment by preferentially considering geographically close genetic information. The judgment unit can also make a compatibility judgment by putting geographically distant genetic information on the back burner. Thereby, the judgment unit can make a more appropriate compatibility judgment by considering the geographical distribution of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the judgment unit can input the geographical distribution of the genetic information to the AI ​​and cause the AI ​​to execute the compatibility judgment.

[0049] The judgment unit can improve the accuracy of the compatibility judgment by referring to the related literature of the genetic information at the time of judgment. The judgment unit, for example, performs the compatibility judgment by referring to the related literature of the genetic information. For example, the judgment unit performs the compatibility judgment based on the related literature of the genetic information. The judgment unit can also improve the accuracy of the compatibility judgment by referring to the data of the related literature. The judgment unit can also adjust the compatibility judgment criteria by analyzing the related literature of the genetic information. Thereby, the judgment unit can improve the accuracy of the compatibility judgment by referring to the related literature of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the related literature of the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the compatibility judgment.

[0050] The matching unit can improve the accuracy of matching by considering the interrelationships of the genetic information during matching. The matching unit performs matching by considering, for example, the interrelationships of the genetic information. For example, the matching unit performs matching by considering the interrelationships of the genetic information of the immune system. The matching unit can also perform matching by considering the interrelationships of the genetic information of pheromone receptors. The matching unit can also perform matching by considering the interrelationships of other genetic information. In this way, the matching unit can improve the accuracy of matching by considering the interrelationships of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the genetic information to an AI and cause the AI ​​to execute the improvement of the matching accuracy.

[0051] The matching unit can perform matching while considering attribute information of the provider of genetic information at the time of matching. The matching unit performs matching while considering attribute information of the provider of genetic information, for example. For example, the matching unit performs matching while considering the age of the provider of genetic information. The matching unit can also perform matching while considering the gender of the provider of genetic information. The matching unit can also perform matching while considering the lifestyle of the provider of genetic information. Thereby, the matching unit can perform more appropriate matching by considering the attribute information of the provider of genetic information. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input attribute information of the provider of genetic information to AI and cause AI to perform matching.

[0052] The matching unit can perform matching while taking into consideration the geographical distribution of the genetic information at the time of matching. The matching unit performs matching while taking into consideration, for example, the geographical distribution of the genetic information. For example, the matching unit performs matching based on the geographical distribution of the genetic information. The matching unit can also perform matching by preferentially considering geographically close genetic information. The matching unit can also perform matching by putting geographically distant genetic information on hold for later. Thereby, the matching unit can perform more appropriate matching by taking into consideration the geographical distribution of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the geographical distribution of the genetic information to AI and cause AI to perform matching.

[0053] The matching unit can improve the accuracy of matching by referring to the related literature of the genetic information at the time of matching. The matching unit performs matching by referring to, for example, the related literature of the genetic information. For example, the matching unit performs matching based on the related literature of the genetic information. The matching unit can also improve the accuracy of matching by referring to the data of the related literature. The matching unit can also adjust the matching criteria by analyzing the related literature of the genetic information. Thereby, the matching unit can improve the accuracy of matching by referring to the related literature of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the related literature of the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the matching accuracy.

[0054] The notification unit can adjust the level of detail of the notification based on the importance of the matching result at the time of notification. The notification unit adjusts the level of detail of the notification based on, for example, the importance of the matching result. For example, the notification unit transmits a detailed notification for an important matching result. The notification unit can also transmit a concise notification for a general matching result. The notification unit can also transmit a specialized notification for a specific matching result. Thereby, the notification unit can provide a more appropriate notification by adjusting the level of detail of the notification based on the importance of the matching result. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance of the matching result to AI and cause AI to adjust the level of detail of the notification.

[0055] The notification unit can apply different notification algorithms depending on the category of the matching result at the time of notification. The notification unit applies different notification algorithms depending on, for example, the category of the matching result. For example, the notification unit applies a specific notification algorithm to the matching result based on genetic information of the immune system. The notification unit can also apply another notification algorithm to the matching result based on genetic information of the pheromone receptor. The notification unit can also apply an appropriate notification algorithm to the matching result based on other genetic information. In this way, the notification unit can provide a more appropriate notification by applying different notification algorithms depending on the category of the matching result. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the category of the matching result to AI and cause the AI ​​to apply the notification algorithm.

[0056] The notification unit can determine the priority of notifications based on the time of providing the matching results at the time of notification. The notification unit determines the priority of notifications based on, for example, the time of providing the matching results. For example, the notification unit gives priority to notifying recent matching results. The notification unit can also postpone past matching results. The notification unit can also adjust the order of notifications based on the time of provision. In this way, the notification unit can provide more appropriate notifications by determining the priority of notifications based on the time of providing the matching results. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the time of providing the matching results to an AI and cause the AI ​​to determine the priority of notifications.

[0057] The notification unit can adjust the order of notifications based on the relevance of the matching results at the time of notification. The notification unit adjusts the order of notifications based on, for example, the relevance of the matching results. For example, the notification unit gives priority to notifying matching results with high relevance. The notification unit can also postpone matching results with low relevance. The notification unit can also adjust the order of notifications based on the relevance of the matching results. As a result, the notification unit can provide more appropriate notifications by adjusting the order of notifications based on the relevance of the matching results. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the relevance of the matching results to AI and cause the AI ​​to adjust the order of notifications.

[0058] The collection unit can select the optimal collection method in consideration of the user's past relationship status when collecting feedback. The collection unit selects the optimal feedback collection method in consideration of, for example, the user's past relationship status. For example, the collection unit selects the feedback collection method based on information on a partner with whom the user has dated in the past. The collection unit can also adjust the timing of feedback collection in consideration of the user's past relationship status (such as the relationship period and frequency of dating). The collection unit can also select the feedback collection method in consideration of the user's past relationship pattern. In this way, the collection unit can select the optimal feedback collection method by considering the user's past relationship status. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past relationship status data to AI and cause the AI ​​to select the feedback collection method.

[0059] The collection unit can preferentially collect highly relevant feedback in consideration of the geographical location information of the user when collecting feedback. The collection unit, for example, performs feedback collection in consideration of the geographical location information of the user. For example, the collection unit preferentially collects feedback of the area where the user lives. In addition, when the user is traveling, the collection unit can also preferentially collect feedback of the travel destination. In addition, when the user is planning to move, the collection unit can also preferentially collect feedback of the new residence. In this way, the collection unit can preferentially collect highly relevant feedback by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant feedback.

[0060] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit optimizes the storage algorithm by referring to past storage data, for example. For example, the storage unit optimizes the storage algorithm based on past storage data. The storage unit can also adjust the storage algorithm based on the analysis results of the past storage data. The storage unit can also improve the storage algorithm based on the trends of the past storage data. This allows the storage unit to optimize the storage algorithm by referring to the past storage data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the past storage data to AI and cause AI to execute optimization of the storage algorithm.

[0061] The storage unit can weight the stored data based on the time when the feedback is provided during storage. The storage unit weights the stored data based on, for example, the time when the feedback is provided. For example, the storage unit prioritizes and stores recent feedback data. The storage unit can also accumulate past feedback data with less importance. The storage unit can also adjust the weighting of the stored data based on the time when the feedback is provided. This allows the storage unit to weight the stored data based on the time when the feedback is provided, thereby enabling more appropriate data storage. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the time when the feedback is provided to an AI and cause the AI ​​to perform weighting of the stored data.

[0062] The support unit can select the optimal support method by referring to the user's past message history when supporting message exchange. The support unit, for example, selects the optimal support method by referring to the user's past message history. For example, the support unit selects the optimal support method based on the user's past message history. The support unit can also adjust the support method based on the analysis result of the user's past message history. The support unit can also improve the support method based on the tendency of the user's past message history. Thereby, the support unit can select the optimal support method by referring to the user's past message history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's past message history into AI and cause the AI ​​to select the support method.

[0063] The support unit can select an appropriate support method based on the device information of the user when supporting message exchange. The support unit selects the optimal support method, for example, by taking into account the device information of the user. For example, when the user is using a smartphone, the support unit provides a support method that matches the screen size. In addition, when the user is using a tablet, the support unit can also provide a support method that is optimized for a large screen. In addition, when the user is using a smartwatch, the support unit can also provide a simple and highly visible support method. This allows the support unit to select the optimal support method by taking into account the device information of the user. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the device information of the user into AI and cause the AI ​​to select the support method.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The matching system can determine compatibility by taking into account the hobbies and interests of the user. For example, the determination unit analyzes information on hobbies and interests provided by the user and selects a compatible partner. The determination unit can also preferentially match a partner who has the same hobbies and interests as the user. Furthermore, the determination unit can provide common topics based on the user's hobbies and interests. This allows the matching system to match a more suitable partner by taking into account the user's hobbies and interests.

[0066] When storing collected data, the storage unit can evaluate the reliability of the data and preferentially store highly reliable data. For example, the storage unit evaluates the reliability of user feedback and preferentially stores highly reliable feedback. The storage unit can also weight the data based on the reliability of the data. Furthermore, the storage unit can filter out data with low reliability. In this way, the storage unit can realize more accurate matching by taking the reliability of the data into consideration.

[0067] The providing unit can adjust the method of providing the genetic information taking into account the health condition of the user. For example, the providing unit selects the method of providing the genetic information based on the results of a health checkup the user has undergone. The providing unit can also adjust the timing of providing the genetic information taking into account the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the providing unit can also select the method of providing the genetic information taking into account the user's exercise habits. In this way, the providing unit can provide more appropriate genetic information by taking into account the user's health condition.

[0068] When analyzing genetic information, the analysis unit can adjust the analysis method taking into account the user's living environment. For example, if the user lives in an urban area, the analysis unit performs the analysis taking into account environmental factors specific to urban areas. Also, if the user lives in a rural area, the analysis unit can also perform the analysis taking into account environmental factors specific to rural areas. Furthermore, the analysis unit can adjust the analysis method taking into account the user's work environment. In this way, the analysis unit can provide more appropriate analysis results by taking into account the user's living environment.

[0069] The matching unit can perform matching by taking into consideration the geographical location information of the user. For example, the matching unit performs matching by giving priority to genetic information of the area where the user lives. In addition, when the user is traveling, the matching unit can also perform matching by taking into consideration genetic information of the travel destination. Furthermore, when the user is planning to move, the matching unit can also perform matching by taking into consideration genetic information of the new residence. In this way, the matching unit can perform more appropriate matching by taking into consideration the geographical location information of the user.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The providing unit provides genetic information of a user. The genetic information of a user includes, but is not limited to, immune system genes and pheromone receptor genes. The providing unit provides an interface for the user to provide the genetic information. Step 2: The analysis unit analyzes the genetic information provided by the provision unit. The analysis unit uses an algorithm for analyzing the genetic information. Step 3: The determination unit determines compatibility based on the genetic information analyzed by the analysis unit. The determination unit determines compatibility taking into account gene complementarity, immune system diversity, and the like. Step 4: The matching unit matches a partner based on the compatibility determined by the determination unit. The matching unit uses an algorithm to select a partner with good compatibility. Step 5: The notification unit notifies the user of the matching result obtained by the matching unit. The notification unit provides an interface for notifying the user of the matching result.

[0072] (Example 2) The matching system according to the embodiment of the present invention is a system for people who seek true happiness in married life based on biological compatibility, eliminating appearance, economic power, and momentary excitement as much as possible. This system judges the compatibility between men and women at the genetic level and matches marriage partners. In addition, the accuracy of matching can be improved by accumulating data. This is expected to eliminate the risk of divorce and contribute to increasing the number of marriages and birth rates. For example, a user registers with the app and provides genetic information. The genetic information is analyzed by a specialized institution to clarify the biological characteristics of the user. For example, immune system genes and pheromone receptor genes are analyzed. This information is stored in the app's database. Next, the app matches a compatible partner based on the registered user's genetic information. Specifically, the app selects the optimal partner taking into consideration gene complementarity and immune system diversity. For example, by matching partners with different immune system genes, the possibility of future children having stronger immune systems increases. Furthermore, the app notifies the user of the matching results. The user can exchange messages with the notified partner and actually meet them. At this time, the app supports anonymous message exchange to protect the user's privacy. The app also collects user feedback and accumulates data to improve the accuracy of matching. For example, by collecting and analyzing information such as the relationship status after matching and the success or failure of marriage, more accurate matching will be possible in the future. This app will realize marriages that pursue true happiness, not influenced by appearance or economic power, and reduce the risk of divorce. It is expected that this will contribute to an increase in the number of marriages and birth rates. This allows the matching system to judge compatibility based on the user's genetic information and match the most suitable partner.

[0073] The matching system according to the embodiment includes a providing unit, an analyzing unit, a determining unit, a matching unit, and a notifying unit. The providing unit provides genetic information of a user. The genetic information of the user includes, for example, immune system genes and pheromone receptor genes, but is not limited to such examples. The providing unit provides, for example, an interface for the user to provide genetic information. The analyzing unit analyzes the genetic information provided by the providing unit. The analyzing unit uses, for example, an algorithm for analyzing the genetic information. The determining unit determines compatibility based on the genetic information analyzed by the analyzing unit. The determining unit determines compatibility by taking into account, for example, gene complementarity and immune system diversity. The matching unit matches a partner based on the compatibility determined by the determining unit. The matching unit uses, for example, an algorithm for selecting a partner with good compatibility. The notifying unit notifies the result of matching by the matching unit. The notifying unit provides, for example, an interface for notifying the user of the matching result. As a result, the matching system according to the embodiment can determine compatibility based on the genetic information of the user and match the optimal partner. For example, the providing unit provides an interface for a user to provide genetic information. The analyzing unit uses an algorithm for analyzing the genetic information provided by the providing unit. The judging unit uses an algorithm for judging compatibility based on the genetic information analyzed by the analyzing unit. The matching unit uses an algorithm for matching a partner based on the compatibility judged by the judging unit. The notifying unit provides an interface for notifying the results of matching by the matching unit. In this way, the matching system according to the embodiment can judge compatibility based on the user's genetic information and match the most suitable partner.

[0074] The providing unit provides the genetic information of the user. The genetic information of the user includes, for example, genes of the immune system and genes of pheromone receptors, but is not limited to such examples. The providing unit provides, for example, an interface for the user to provide the genetic information. Specifically, the providing unit provides a web interface or a mobile application for the user to input the genetic information. This interface is designed to allow the user to input his / her own genetic information safely and easily, and uses encryption technology to protect the user's privacy. The user can, for example, collect a saliva sample, extract the genetic information using a dedicated kit, and input the information into the interface. The providing unit has a database for centrally managing the input genetic information and transmitting it to the analysis unit. In addition, the providing unit displays guidelines and precautions for the user when providing the genetic information, and supports the user to provide accurate information. In this way, the providing unit can efficiently and safely collect the user's genetic information and improve the reliability of the entire system. In addition, the providing unit displays a consent form and a privacy policy when the user provides the genetic information, and supports legal procedures for protecting the user's rights. This enables the provider to properly manage the user's genetic information and ensure the transparency and reliability of the entire system.

[0075] The analysis unit analyzes the genetic information provided by the provision unit. The analysis unit uses, for example, an algorithm for analyzing the genetic information. Specifically, the analysis unit uses bioinformatics technology for analyzing gene sequences and identifying functional characteristics of genes. For example, the analysis unit performs alignment and mutation analysis of gene sequences to create a detailed profile of the user's genetic information. Based on these profiles, the analysis unit evaluates the diversity of the user's immune system and the characteristics of the pheromone receptors. Furthermore, the analysis unit uses a machine learning algorithm to analyze the pattern of the genetic information and extracts features for identifying a compatible partner. For example, a scoring system that takes into account gene complementarity and immune system diversity is constructed to evaluate the user's genetic information. The analysis unit stores these analysis results in a database and makes them accessible to the judgment unit. In addition, in order to improve the accuracy of the analysis results, the analysis unit periodically adjusts the parameters of the algorithm and incorporates the latest research results. This allows the analysis unit to analyze the user's genetic information with high accuracy and improve the performance of the entire system. Furthermore, the analysis unit anonymizes the user's genetic information and takes measures to protect privacy. This enables the analysis unit to safely analyze the user's genetic information, ensuring the reliability of the entire system.

[0076] The judgment unit judges the compatibility based on the genetic information analyzed by the analysis unit. The judgment unit judges the compatibility by considering, for example, gene complementarity and immune system diversity. Specifically, the judgment unit uses an algorithm to evaluate the gene complementarity between users based on the genetic profile provided by the analysis unit. For example, a scoring system for evaluating the complementarity of alleles at a specific gene locus and the diversity of the immune system is constructed, and the compatibility of the user is quantified. The judgment unit sets a standard for identifying a compatible partner based on these scores, and evaluates the genetic information of the user. Furthermore, the judgment unit uses a machine learning algorithm to learn past matching data and success cases, and improves the accuracy of the compatibility judgment. For example, based on past matching results, the characteristics of a compatible combination are extracted and reflected in the compatibility judgment of a new user. The judgment unit stores these results in a database, and makes them accessible to the matching unit. In addition, the judgment unit feeds back the results of the compatibility judgment to the user, and supports the user in understanding the compatibility based on his or her own genetic information. As a result, the judgment unit can perform a highly accurate compatibility judgment based on the user's genetic information, and improve the performance of the entire system. Furthermore, the determination unit takes measures to protect the privacy of the user and anonymizes the results of the compatibility determination, thereby enabling the determination unit to safely manage the genetic information of the user and ensure the reliability of the entire system.

[0077] The matching unit matches a partner based on the compatibility determined by the determination unit. The matching unit uses, for example, an algorithm for selecting a partner with good compatibility. Specifically, the matching unit evaluates the compatibility between users based on the compatibility score provided by the determination unit and selects the most suitable partner. For example, an algorithm that preferentially matches users with high compatibility scores is used to realize optimal matching based on the genetic information of the users. The matching unit stores these matching results in a database and makes them accessible to the notification unit. In addition, the matching unit has a filtering function for performing matching that takes into account the user's wishes and conditions. For example, the user can input conditions such as desired age, region, and hobbies, and perform matching based on the conditions. Furthermore, the matching unit has a learning function for improving the accuracy of the algorithm based on past matching results and user feedback. As a result, the matching unit can realize highly accurate matching based on the user's genetic information and improve the performance of the entire system. Furthermore, the matching unit takes measures to protect the user's privacy and manages the matching results in anonymized form. As a result, the matching unit can safely manage the user's genetic information and ensure the reliability of the entire system.

[0078] The notification unit notifies the result of matching by the matching unit. The notification unit provides, for example, an interface for notifying the user of the matching result. Specifically, the notification unit provides a web interface or a mobile application that allows the user to check the matching result. This interface is designed to allow the user to easily check the matching result, and uses encryption technology to protect the user's privacy. The notification unit updates the matching result in real time, allowing the user to check the latest information. In addition, the notification unit has a function that allows the user to provide feedback on the matching result, and supports improvements to the system that reflect the user's opinions. For example, it is possible to evaluate whether the user is satisfied with the matching result, and adjust the algorithm based on the result. Furthermore, the notification unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only a web interface or a mobile application, but also email, SMS, push notification, etc. in combination. As a result, the notification unit can quickly and reliably provide the matching result to the user, and improve the reliability of the entire system. Furthermore, the notification unit takes measures to protect the user's privacy and manages the matching result by anonymizing it. As a result, the notification unit can safely manage the user's genetic information and ensure the reliability of the entire system.

[0079] The matching system includes a collection unit that collects user feedback. The collection unit, for example, provides an interface for collecting user feedback. The collection unit, for example, collects feedback such as user satisfaction and specific opinions and suggestions. The collection unit can collect feedback, for example, in the form of a questionnaire. The collection unit can also provide an automated system for collecting user feedback. For example, the collection unit automatically collects user feedback and stores it in a database. In this way, the collection unit can improve the accuracy of matching by collecting user feedback. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user feedback to AI and cause the AI ​​to analyze the feedback.

[0080] The matching system includes a storage unit that stores collected data and improves the accuracy of matching. The storage unit, for example, provides a database for storing collected data. The storage unit stores data, for example, taking into account the form of feedback and the structure of the database. The storage unit, for example, analyzes collected data and provides an algorithm for enabling more accurate matching in the future. For example, the storage unit improves a matching algorithm based on collected data. As a result, the storage unit stores collected data, enabling more accurate matching in the future. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input collected data to AI and have AI execute data analysis.

[0081] The matching system includes a support unit that supports anonymous message exchange. The support unit provides, for example, an interface for users to exchange messages anonymously. The support unit uses, for example, an anonymization technique for protecting personal information of users. The support unit provides, for example, a system for protecting privacy when users exchange messages anonymously. For example, the support unit anonymizes personal information of a user and exchanges messages. This allows the support unit to exchange messages with a partner while protecting the privacy of the user. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input personal information of a user to AI and cause AI to execute anonymization processing.

[0082] The providing unit can estimate the user's emotion and adjust the timing of providing the genetic information based on the estimated user's emotion. The providing unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The providing unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit transmits a notification to prompt the user to provide the genetic information when the user is relaxed. In addition, the providing unit can postpone the provision of the genetic information when the user is stressed. In addition, the providing unit can quickly provide the genetic information when the user is excited. Thereby, the providing unit can provide the genetic information at a more appropriate timing by adjusting the timing of providing the genetic information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotion data to the AI ​​and have the AI ​​perform emotion estimation.

[0083] The providing unit can select an appropriate genetic information providing method based on the user's past health condition and lifestyle. The providing unit selects an optimal genetic information providing method, for example, by considering the user's past health condition and lifestyle. For example, the providing unit selects a genetic information providing method based on the result of a medical checkup the user has taken in the past. The providing unit can also adjust the timing of genetic information provision by considering the user's lifestyle (smoking, drinking, etc.). The providing unit can also select a genetic information providing method by considering the user's exercise habits. In this way, the providing unit can select an optimal genetic information providing method by considering the user's past health condition and lifestyle. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data to AI and cause the AI ​​to select a genetic information providing method.

[0084] The providing unit can perform filtering based on the user's current health condition and living environment when providing the genetic information. The providing unit filters the genetic information provision, for example, taking into account the user's current health condition and living environment. For example, the providing unit suspends the genetic information provision when the user is currently ill. The providing unit can also adjust the timing of the genetic information provision, taking into account the user's living environment (residence, workplace, etc.). The providing unit can also select a method of providing the genetic information based on the user's current health condition. As a result, the providing unit can provide more appropriate genetic information by filtering based on the user's current health condition and living environment. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health condition data to AI and cause the AI ​​to perform filtering.

[0085] The providing unit can estimate the user's emotion and determine the priority of the genetic information to be provided based on the estimated user's emotion. The providing unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The providing unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit provides detailed genetic information preferentially when the user is relaxed. In addition, the providing unit can also provide basic genetic information preferentially when the user is stressed. In addition, the providing unit can also provide important genetic information preferentially when the user is excited. In this way, the providing unit can provide more appropriate genetic information by determining the priority of the genetic information to be provided according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0086] When providing genetic information, the providing unit can provide highly relevant information preferentially in consideration of the geographical location information of the user. The providing unit determines the priority order of providing genetic information, for example, in consideration of the geographical location information of the user. For example, the providing unit preferentially provides genetic information of the area where the user lives. In addition, when the user is traveling, the providing unit can also preferentially provide genetic information of the travel destination. In addition, when the user is planning to move, the providing unit can also preferentially provide genetic information of the new residence. In this way, the providing unit can provide highly relevant genetic information preferentially by considering the geographical location information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to provide highly relevant information.

[0087] The providing unit can analyze the social media activity of the user and provide related information when providing the genetic information. The providing unit uses, for example, an algorithm for analyzing the social media activity of the user. The providing unit can provide the related genetic information based on, for example, health information shared by the user on social media. The providing unit can also provide the related genetic information based on health-related accounts that the user follows on social media. The providing unit can also provide the related genetic information based on health-related groups that the user participates in on social media. In this way, the providing unit can provide the related genetic information by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media data of the user to AI and cause the AI ​​to provide the related information.

[0088] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The analysis unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit provides a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is stressed. The analysis unit can also provide a visually attractive analysis result when the user is excited. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the genetic information. For example, the analysis unit performs a detailed analysis of important genetic information. The analysis unit can also perform a concise analysis of general genetic information. The analysis unit can also perform a specialized analysis of specific genetic information. This allows the analysis unit to provide a more appropriate analysis result by adjusting the level of detail of the analysis based on the importance of the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the genetic information to an AI and cause the AI ​​to adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of genetic information during analysis. The analysis unit applies different analysis algorithms depending on, for example, the category of genetic information. For example, the analysis unit applies a specific analysis algorithm to genetic information of the immune system. The analysis unit can also apply another analysis algorithm to genetic information of a pheromone receptor. The analysis unit can also apply an appropriate analysis algorithm to other genetic information. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of genetic information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic information to AI and cause AI to apply the analysis algorithm.

[0091] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The analysis unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit provides a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is stressed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows the analysis unit to provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0092] The analysis unit can determine the analysis priority based on the time of providing the genetic information during the analysis. The analysis unit determines the analysis priority based on, for example, the time of providing the genetic information. For example, the analysis unit preferentially analyzes the genetic information provided recently. The analysis unit can also postpone the genetic information provided in the past. The analysis unit can also adjust the order of analysis based on the time of provision. In this way, the analysis unit can provide more appropriate analysis results by determining the analysis priority based on the time of providing the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the genetic information to AI and cause the AI ​​to determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the genetic information. For example, the analysis unit preferentially analyzes genetic information with high relevance. The analysis unit can also postpone genetic information with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the genetic information. As a result, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of the genetic information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic information to AI and cause AI to adjust the order of analysis.

[0094] The judgment unit can estimate the user's emotion and adjust the compatibility judgment criteria based on the estimated user's emotion. The judgment unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The judgment unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the judgment unit performs a detailed compatibility judgment when the user is relaxed. In addition, the judgment unit can also perform a concise compatibility judgment when the user is stressed. In addition, the judgment unit can perform a visually attractive compatibility judgment when the user is excited. Thereby, the judgment unit can adjust the compatibility judgment criteria according to the user's emotion to perform a more appropriate compatibility judgment. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input a user's emotion data to the AI ​​and have the AI ​​perform emotion estimation.

[0095] The judgment unit can improve the accuracy of the compatibility judgment based on the interrelationship of the genetic information at the time of judgment. The judgment unit, for example, makes the compatibility judgment taking into consideration the interrelationship of the genetic information. For example, the judgment unit makes the compatibility judgment taking into consideration the interrelationship of the genetic information of the immune system. The judgment unit can also make the compatibility judgment taking into consideration the interrelationship of the genetic information of the pheromone receptor. The judgment unit can also make the compatibility judgment taking into consideration the interrelationship of other genetic information. Thereby, the judgment unit can improve the accuracy of the compatibility judgment by taking into consideration the interrelationship of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the compatibility judgment.

[0096] The judgment unit can make a compatibility judgment based on the attribute information of the genetic information provider at the time of judgment. The judgment unit makes a compatibility judgment, for example, taking into account the attribute information of the genetic information provider. For example, the judgment unit makes a compatibility judgment taking into account the age of the genetic information provider. The judgment unit can also make a compatibility judgment taking into account the gender of the genetic information provider. The judgment unit can also make a compatibility judgment taking into account the lifestyle of the genetic information provider. Thereby, the judgment unit can make a more appropriate compatibility judgment by taking into account the attribute information of the genetic information provider. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the attribute information of the genetic information provider to AI and cause the AI ​​to execute the compatibility judgment.

[0097] The judgment unit can estimate the user's emotion and adjust the order of displaying the compatibility judgment results based on the estimated user's emotion. The judgment unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The judgment unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the judgment unit preferentially displays detailed compatibility judgment results when the user is relaxed. In addition, the judgment unit can also preferentially display concise compatibility judgment results when the user is stressed. In addition, the judgment unit can also preferentially display visually attractive compatibility judgment results when the user is excited. In this way, the judgment unit can provide more appropriate compatibility judgment results by adjusting the order of displaying the compatibility judgment results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input a user's emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0098] The judgment unit can make a compatibility judgment based on the geographical distribution of the genetic information at the time of judgment. The judgment unit makes a compatibility judgment by considering, for example, the geographical distribution of the genetic information. For example, the judgment unit makes a compatibility judgment based on the geographical distribution of the genetic information. The judgment unit can also make a compatibility judgment by preferentially considering geographically close genetic information. The judgment unit can also make a compatibility judgment by putting geographically distant genetic information on the back burner. Thereby, the judgment unit can make a more appropriate compatibility judgment by considering the geographical distribution of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the judgment unit can input the geographical distribution of the genetic information to the AI ​​and cause the AI ​​to execute the compatibility judgment.

[0099] The judgment unit can improve the accuracy of the compatibility judgment by referring to the related literature of the genetic information at the time of judgment. The judgment unit, for example, performs the compatibility judgment by referring to the related literature of the genetic information. For example, the judgment unit performs the compatibility judgment based on the related literature of the genetic information. The judgment unit can also improve the accuracy of the compatibility judgment by referring to the data of the related literature. The judgment unit can also adjust the compatibility judgment criteria by analyzing the related literature of the genetic information. Thereby, the judgment unit can improve the accuracy of the compatibility judgment by referring to the related literature of the genetic information. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the related literature of the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the accuracy of the compatibility judgment.

[0100] The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. The matching unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The matching unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the matching unit applies detailed matching criteria when the user is relaxed. The matching unit can also apply concise matching criteria when the user is stressed. The matching unit can also apply visually appealing matching criteria when the user is excited. This allows the matching unit to perform more appropriate matching by adjusting the matching criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0101] The matching unit can improve the accuracy of matching by considering the interrelationships of the genetic information during matching. The matching unit performs matching by considering, for example, the interrelationships of the genetic information. For example, the matching unit performs matching by considering the interrelationships of the genetic information of the immune system. The matching unit can also perform matching by considering the interrelationships of the genetic information of pheromone receptors. The matching unit can also perform matching by considering the interrelationships of other genetic information. In this way, the matching unit can improve the accuracy of matching by considering the interrelationships of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed by using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the genetic information to an AI and cause the AI ​​to execute the improvement of the matching accuracy.

[0102] The matching unit can perform matching while considering attribute information of the genetic information provider at the time of matching. The matching unit performs matching while considering, for example, attribute information of the genetic information provider. For example, the matching unit performs matching while considering the age of the genetic information provider. The matching unit can also perform matching while considering the gender of the genetic information provider. The matching unit can also perform matching while considering the lifestyle of the genetic information provider. Thereby, the matching unit can perform more appropriate matching by considering the attribute information of the genetic information provider. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input attribute information of the genetic information provider to AI and cause AI to perform matching.

[0103] The matching unit can estimate the user's emotion and adjust the order of displaying the matching results based on the estimated user's emotion. The matching unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The matching unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the matching unit preferentially displays detailed matching results when the user is relaxed. In addition, the matching unit can also preferentially display concise matching results when the user is stressed. In addition, the matching unit can also preferentially display visually attractive matching results when the user is excited. In this way, the matching unit can provide more appropriate matching results by adjusting the order of displaying the matching results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input a user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0104] The matching unit can perform matching while taking into consideration the geographical distribution of the genetic information at the time of matching. The matching unit performs matching while taking into consideration, for example, the geographical distribution of the genetic information. For example, the matching unit performs matching based on the geographical distribution of the genetic information. The matching unit can also perform matching by preferentially considering geographically close genetic information. The matching unit can also perform matching by putting geographically distant genetic information on hold for later. Thereby, the matching unit can perform more appropriate matching by taking into consideration the geographical distribution of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the geographical distribution of the genetic information to AI and cause AI to perform matching.

[0105] The matching unit can improve the accuracy of matching by referring to the related literature of the genetic information at the time of matching. The matching unit performs matching by referring to, for example, the related literature of the genetic information. For example, the matching unit performs matching based on the related literature of the genetic information. The matching unit can also improve the accuracy of matching by referring to the data of the related literature. The matching unit can also adjust the matching criteria by analyzing the related literature of the genetic information. Thereby, the matching unit can improve the accuracy of matching by referring to the related literature of the genetic information. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI. For example, the matching unit can input the related literature of the genetic information to the AI ​​and cause the AI ​​to execute the improvement of the matching accuracy.

[0106] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. The notification unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The notification unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the notification unit transmits a detailed notification when the user is relaxed. The notification unit can also transmit a concise notification when the user is stressed. The notification unit can also transmit a visually attractive notification when the user is excited. This allows the notification unit to provide a more appropriate notification by adjusting the notification expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0107] The notification unit can adjust the level of detail of the notification based on the importance of the matching result at the time of notification. The notification unit adjusts the level of detail of the notification based on, for example, the importance of the matching result. For example, the notification unit transmits a detailed notification for an important matching result. The notification unit can also transmit a concise notification for a general matching result. The notification unit can also transmit a specialized notification for a specific matching result. Thereby, the notification unit can provide a more appropriate notification by adjusting the level of detail of the notification based on the importance of the matching result. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the importance of the matching result to AI and cause AI to adjust the level of detail of the notification.

[0108] The notification unit can apply different notification algorithms depending on the category of the matching result at the time of notification. The notification unit applies different notification algorithms depending on, for example, the category of the matching result. For example, the notification unit applies a specific notification algorithm to the matching result based on genetic information of the immune system. The notification unit can also apply another notification algorithm to the matching result based on genetic information of the pheromone receptor. The notification unit can also apply an appropriate notification algorithm to the matching result based on other genetic information. In this way, the notification unit can provide a more appropriate notification by applying different notification algorithms depending on the category of the matching result. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the category of the matching result to AI and cause the AI ​​to apply the notification algorithm.

[0109] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user's emotion. The notification unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The notification unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the notification unit transmits a detailed notification when the user is relaxed. The notification unit can also transmit a concise notification when the user is stressed. The notification unit can also transmit a visually appealing notification when the user is excited. This allows the notification unit to provide a more appropriate notification by adjusting the length of the notification according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0110] The notification unit can determine the priority of notifications based on the time of providing the matching results at the time of notification. The notification unit determines the priority of notifications based on, for example, the time of providing the matching results. For example, the notification unit gives priority to notifying recent matching results. The notification unit can also postpone past matching results. The notification unit can also adjust the order of notifications based on the time of provision. In this way, the notification unit can provide more appropriate notifications by determining the priority of notifications based on the time of providing the matching results. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the time of providing the matching results to an AI and cause the AI ​​to determine the priority of notifications.

[0111] The notification unit can adjust the order of notifications based on the relevance of the matching results at the time of notification. The notification unit adjusts the order of notifications based on, for example, the relevance of the matching results. For example, the notification unit gives priority to notifying matching results with high relevance. The notification unit can also postpone matching results with low relevance. The notification unit can also adjust the order of notifications based on the relevance of the matching results. As a result, the notification unit can provide more appropriate notifications by adjusting the order of notifications based on the relevance of the matching results. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the relevance of the matching results to AI and cause the AI ​​to adjust the order of notifications.

[0112] The collection unit can estimate the user's emotion and adjust the timing of feedback collection based on the estimated user's emotion. The collection unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The collection unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit transmits a notification to prompt feedback collection when the user is relaxed. In addition, the collection unit can postpone feedback collection when the user is stressed. In addition, the collection unit can quickly collect feedback when the user is excited. In this way, the collection unit can collect more appropriate feedback by adjusting the timing of feedback collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0113] The collection unit can select the optimal collection method in consideration of the user's past relationship status when collecting feedback. The collection unit selects the optimal feedback collection method in consideration of, for example, the user's past relationship status. For example, the collection unit selects the feedback collection method based on information on a partner with whom the user has dated in the past. The collection unit can also adjust the timing of feedback collection in consideration of the user's past relationship status (such as the relationship period and frequency of dating). The collection unit can also select the feedback collection method in consideration of the user's past relationship pattern. In this way, the collection unit can select the optimal feedback collection method by considering the user's past relationship status. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past relationship status data to AI and cause the AI ​​to select the feedback collection method.

[0114] The collection unit can estimate the user's emotion and determine the priority of the feedback to be collected based on the estimated user's emotion. The collection unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The collection unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit preferentially collects detailed feedback when the user is relaxed. In addition, the collection unit can also preferentially collect basic feedback when the user is stressed. In addition, the collection unit can also preferentially collect important feedback when the user is excited. In this way, the collection unit can collect more appropriate feedback by determining the priority of the feedback to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data into an AI and have the AI ​​perform emotion estimation.

[0115] The collection unit can preferentially collect highly relevant feedback in consideration of the geographical location information of the user when collecting feedback. The collection unit, for example, performs feedback collection in consideration of the geographical location information of the user. For example, the collection unit preferentially collects feedback of the area where the user lives. In addition, when the user is traveling, the collection unit can also preferentially collect feedback of the travel destination. In addition, when the user is planning to move, the collection unit can also preferentially collect feedback of the new residence. In this way, the collection unit can preferentially collect highly relevant feedback by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant feedback.

[0116] The storage unit can estimate the user's emotion and select stored data based on the estimated user's emotion. The storage unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The storage unit estimates the user's emotion using, for example, facial expression recognition, voice analysis, text analysis, and other techniques. For example, the storage unit preferentially stores detailed data when the user is relaxed. In addition, the storage unit can also preferentially store basic data when the user is stressed. In addition, the storage unit can also preferentially store important data when the user is excited. In this way, the storage unit can store more appropriate data by selecting stored data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0117] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit optimizes the storage algorithm by referring to past storage data, for example. For example, the storage unit optimizes the storage algorithm based on past storage data. The storage unit can also adjust the storage algorithm based on the analysis results of the past storage data. The storage unit can also improve the storage algorithm based on the trends of the past storage data. This allows the storage unit to optimize the storage algorithm by referring to the past storage data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the past storage data to AI and cause AI to execute optimization of the storage algorithm.

[0118] The storage unit can estimate the user's emotion and adjust the frequency of accumulation based on the estimated user's emotion. The storage unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The storage unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the storage unit frequently accumulates data when the user is relaxed. In addition, the storage unit can also reduce the frequency of data accumulation when the user is stressed. In addition, the storage unit can preferentially accumulate important data when the user is excited. This allows the storage unit to adjust the frequency of accumulation according to the user's emotion, thereby enabling more appropriate data accumulation. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to an AI and cause the AI ​​to execute emotion estimation.

[0119] The storage unit can weight the stored data based on the time when the feedback is provided during storage. The storage unit weights the stored data based on, for example, the time when the feedback is provided. For example, the storage unit prioritizes and stores recent feedback data. The storage unit can also accumulate past feedback data with less importance. The storage unit can also adjust the weighting of the stored data based on the time when the feedback is provided. This allows the storage unit to weight the stored data based on the time when the feedback is provided, thereby enabling more appropriate data storage. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the time when the feedback is provided to an AI and cause the AI ​​to perform weighting of the stored data.

[0120] The support unit can estimate the user's emotion and adjust the message exchange support method based on the estimated user's emotion. The support unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The support unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the support unit provides detailed support when the user is relaxed. The support unit can also provide concise support when the user is stressed. The support unit can also provide visually attractive support when the user is excited. This allows the support unit to provide more appropriate support by adjusting the message exchange support method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute emotion estimation.

[0121] The support unit can select the optimal support method by referring to the user's past message history when supporting message exchange. The support unit, for example, selects the optimal support method by referring to the user's past message history. For example, the support unit selects the optimal support method based on the user's past message history. The support unit can also adjust the support method based on the analysis result of the user's past message history. The support unit can also improve the support method based on the tendency of the user's past message history. Thereby, the support unit can select the optimal support method by referring to the user's past message history. A part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the user's past message history into AI and cause the AI ​​to select the support method.

[0122] The support unit can estimate the user's emotion and determine the priority of message exchange based on the estimated user's emotion. The support unit uses, for example, an emotion estimation algorithm for estimating the user's emotion. The support unit estimates the user's emotion using, for example, techniques such as facial expression recognition, voice analysis, and text analysis. For example, the support unit preferentially supports detailed message exchange when the user is relaxed. In addition, the support unit can also preferentially support concise message exchange when the user is stressed. In addition, the support unit can also preferentially support visually attractive message exchange when the user is excited. In this way, the support unit can support more appropriate message exchange by determining the priority of message exchange according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support department can input the user's emotional data into the AI ​​and have the AI ​​perform emotion estimation.

[0123] The support unit can select an appropriate support method based on the device information of the user when supporting message exchange. The support unit selects the optimal support method, for example, by taking into account the device information of the user. For example, when the user is using a smartphone, the support unit provides a support method that matches the screen size. In addition, when the user is using a tablet, the support unit can also provide a support method that is optimized for a large screen. In addition, when the user is using a smartwatch, the support unit can also provide a simple and highly visible support method. This allows the support unit to select the optimal support method by taking into account the device information of the user. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the device information of the user into AI and cause the AI ​​to select the support method.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The matching system can determine compatibility by taking into account the hobbies and interests of the user. For example, the determination unit analyzes information on hobbies and interests provided by the user and selects a compatible partner. The determination unit can also preferentially match a partner who has the same hobbies and interests as the user. Furthermore, the determination unit can provide common topics based on the user's hobbies and interests. This allows the matching system to match a more suitable partner by taking into account the user's hobbies and interests.

[0126] When collecting feedback from the user, the collection unit can estimate the user's emotion and adjust the feedback collection method based on the estimated emotion. For example, when the user is relaxed, the collection unit collects detailed feedback. Also, when the user is stressed, the collection unit can collect brief feedback. Furthermore, when the user is excited, the collection unit can preferentially collect important feedback. In this way, the collection unit can collect more appropriate feedback by adjusting the feedback collection method according to the user's emotion.

[0127] When storing collected data, the storage unit can evaluate the reliability of the data and preferentially store highly reliable data. For example, the storage unit evaluates the reliability of user feedback and preferentially stores highly reliable feedback. The storage unit can also weight the data based on the reliability of the data. Furthermore, the storage unit can filter out data with low reliability. In this way, the storage unit can realize more accurate matching by taking the reliability of the data into consideration.

[0128] When supporting the user's message exchange, the support unit can estimate the user's emotion and adjust the support content based on the estimated emotion. For example, the support unit can provide detailed support when the user is relaxed. Also, the support unit can provide concise support when the user is stressed. Furthermore, the support unit can provide visually attractive support when the user is excited. In this way, the support unit can provide more appropriate support by adjusting the support content according to the user's emotion.

[0129] The providing unit can adjust the method of providing the genetic information taking into account the health condition of the user. For example, the providing unit selects the method of providing the genetic information based on the results of a health checkup the user has undergone. The providing unit can also adjust the timing of providing the genetic information taking into account the user's lifestyle habits (smoking, drinking, etc.). Furthermore, the providing unit can also select the method of providing the genetic information taking into account the user's exercise habits. In this way, the providing unit can provide more appropriate genetic information by taking into account the user's health condition.

[0130] The providing unit can estimate the user's emotion and adjust the content of the genetic information to be provided based on the estimated emotion. For example, the providing unit can provide detailed genetic information when the user is relaxed. The providing unit can also provide basic genetic information when the user is stressed. Furthermore, the providing unit can also provide important genetic information when the user is excited. In this way, the providing unit can provide more appropriate genetic information by adjusting the content of the genetic information to be provided according to the user's emotion.

[0131] When analyzing genetic information, the analysis unit can adjust the analysis method taking into account the user's living environment. For example, if the user lives in an urban area, the analysis unit performs the analysis taking into account environmental factors specific to urban areas. Also, if the user lives in a rural area, the analysis unit can also perform the analysis taking into account environmental factors specific to rural areas. Furthermore, the analysis unit can adjust the analysis method taking into account the user's work environment. In this way, the analysis unit can provide more appropriate analysis results by taking into account the user's living environment.

[0132] The judgment unit can estimate the user's emotions and adjust the compatibility judgment criteria based on the estimated emotions. For example, the judgment unit performs a detailed compatibility judgment when the user is relaxed. Also, the judgment unit can perform a concise compatibility judgment when the user is stressed. Furthermore, the judgment unit can perform a visually attractive compatibility judgment when the user is excited. In this way, the judgment unit can perform a more appropriate compatibility judgment by adjusting the compatibility judgment criteria according to the user's emotions.

[0133] The notification unit can estimate the user's emotion and adjust the way the notification is expressed based on the estimated emotion. For example, the notification unit can send a detailed notification when the user is relaxed. The notification unit can also send a concise notification when the user is stressed. Furthermore, the notification unit can send a visually appealing notification when the user is excited. In this way, the notification unit can provide a more appropriate notification by adjusting the way the notification is expressed depending on the user's emotion.

[0134] The matching unit can perform matching by taking into consideration the geographical location information of the user. For example, the matching unit performs matching by giving priority to genetic information of the area where the user lives. In addition, when the user is traveling, the matching unit can also perform matching by taking into consideration genetic information of the travel destination. Furthermore, when the user is planning to move, the matching unit can also perform matching by taking into consideration genetic information of the new residence. In this way, the matching unit can perform more appropriate matching by taking into consideration the geographical location information of the user.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The providing unit provides genetic information of a user. The genetic information of a user includes, but is not limited to, immune system genes and pheromone receptor genes. The providing unit provides an interface for the user to provide the genetic information. Step 2: The analysis unit analyzes the genetic information provided by the provision unit. The analysis unit uses an algorithm for analyzing the genetic information. Step 3: The determination unit determines compatibility based on the genetic information analyzed by the analysis unit. The determination unit determines compatibility taking into account gene complementarity, immune system diversity, and the like. Step 4: The matching unit matches a partner based on the compatibility determined by the determination unit. The matching unit uses an algorithm to select a partner with good compatibility. Step 5: The notification unit notifies the user of the matching result obtained by the matching unit. The notification unit provides an interface for notifying the user of the matching result.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned providing unit, analyzing unit, determining unit, matching unit, notifying unit, collecting unit, storing unit, and supporting unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14, and provides an interface for the user to provide genetic information. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the provided genetic information. The determining unit is realized by the specific processing unit 290 of the data processing device 12, and determines compatibility based on the analyzed genetic information. The matching unit is realized by the specific processing unit 290 of the data processing device 12, and selects a compatible partner. The notifying unit is realized by the control unit 46A of the smart device 14, and notifies the user of the matching result. The collecting unit is realized by the control unit 46A of the smart device 14, and collects user feedback. The storing unit is realized by the specific processing unit 290 of the data processing device 12, and stores the collected data. The support unit is realized by the control unit 46A of the smart device 14, and supports anonymous message exchange. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned providing unit, analyzing unit, determining unit, matching unit, notifying unit, collecting unit, storing unit, and supporting unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for the user to provide genetic information. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the provided genetic information. The determining unit is realized by the specific processing unit 290 of the data processing device 12, and determines compatibility based on the analyzed genetic information. The matching unit is realized by the specific processing unit 290 of the data processing device 12, and selects a compatible partner. The notifying unit is realized by the control unit 46A of the smart glasses 214, and notifies the user of the matching result. The collecting unit is realized by the control unit 46A of the smart glasses 214, and collects user feedback. The storing unit is realized by the specific processing unit 290 of the data processing device 12, and stores the collected data. The support unit is realized by the control unit 46A of the smart glasses 214, and supports anonymous message exchange. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements including the above-mentioned providing unit, analyzing unit, determining unit, matching unit, notifying unit, collecting unit, storing unit, and supporting unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for the user to provide genetic information. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the provided genetic information. The determining unit is realized by the specific processing unit 290 of the data processing device 12, and determines compatibility based on the analyzed genetic information. The matching unit is realized by the specific processing unit 290 of the data processing device 12, and selects a compatible partner. The notifying unit is realized by the control unit 46A of the headset type terminal 314, and notifies the user of the matching result. The collecting unit is realized by the control unit 46A of the headset type terminal 314, and collects user feedback. The storing unit is realized by the specific processing unit 290 of the data processing device 12, and stores the collected data. The support unit is realized by the control unit 46A of the headset type terminal 314, and supports anonymous message exchange. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a 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.

[0175] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] Each of the multiple elements including the above-mentioned providing unit, analyzing unit, judging unit, matching unit, notifying unit, collecting unit, storing unit, and supporting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414, and provides an interface for the user to provide genetic information. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the provided genetic information. The judging unit is realized by the specific processing unit 290 of the data processing device 12, and judges compatibility based on the analyzed genetic information. The matching unit is realized by the specific processing unit 290 of the data processing device 12, and selects a compatible partner. The notifying unit is realized by the control unit 46A of the robot 414, and notifies the user of the matching result. The collecting unit is realized by the control unit 46A of the robot 414, and collects user feedback. The storing unit is realized by the specific processing unit 290 of the data processing device 12, and stores the collected data. The support unit is realized by the control unit 46A of the robot 414, and supports anonymous message exchange. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various modifications are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) A providing unit that provides genetic information; an analysis unit that analyzes the genetic information provided by the provision unit; A determination unit that determines compatibility based on the genetic information analyzed by the analysis unit; a matching unit that matches a partner based on the compatibility determined by the determination unit; A notification unit that notifies the result of matching by the matching unit. A system characterized by: (Appendix 2) A collection unit for collecting user feedback is provided. 2. The system of claim 1, (Appendix 3) Equipped with a storage unit that accumulates collected data and improves matching accuracy 3. The system of claim 2, (Appendix 4) Equipped with a support department that supports anonymous message exchange 2. The system of claim 1, (Appendix 5) The providing unit is The system estimates the user's emotions and adjusts the timing of providing genetic information based on the estimated user emotions. 2. The system of claim 1, (Appendix 6) The providing unit is Select the appropriate method of providing genetic information based on the user's past health condition and lifestyle habits 2. The system of claim 1, (Appendix 7) The providing unit is When providing genetic information, filtering is performed based on the user's current health condition and living environment. 2. The system of claim 1, (Appendix 8) The providing unit is The user's emotions are estimated, and the priority of the genetic information to be provided is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 9) The providing unit is When providing genetic information, the system takes into account the user's geographic location information and provides highly relevant information preferentially. 2. The system of claim 1, (Appendix 10) The providing unit is When providing genetic information, the user's social media activity will be analyzed to provide relevant information 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, the level of detail of the analysis is adjusted based on the importance of the genetic information. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of genetic information. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is At the time of analysis, the analysis will be prioritized based on the timing of genetic information provided. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the order of analysis is adjusted based on the relevance of gene information. 2. The system of claim 1, (Appendix 17) The determination unit is Estimate the user's emotions and adjust the compatibility criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The determination unit is When making a judgment, the accuracy of compatibility judgment is improved based on the correlation of genetic information. 2. The system of claim 1, (Appendix 19) The determination unit is When making a decision, compatibility is judged based on the attribute information of the genetic information provider. 2. The system of claim 1, (Appendix 20) The determination unit is Inferring the user's emotions and adjusting the order in which the compatibility judgment results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 21) The determination unit is When making a decision, compatibility will be judged based on the geographical distribution of genetic information. 2. The system of claim 1, (Appendix 22) The determination unit is When making a judgment, refer to related literature on genetic information to improve the accuracy of the compatibility judgment. 2. The system of claim 1, (Appendix 23) The matching unit is Estimate the user's emotions and adjust the matching criteria based on the estimated user's emotions. 2. The system of claim 1, (Appendix 24) The matching unit is When matching, the accuracy of matching is improved by taking into account the correlation of genetic information. 2. The system of claim 1, (Appendix 25) The matching unit is When matching, the attribute information of the genetic information provider is taken into consideration. 2. The system of claim 1, (Appendix 26) The matching unit is Inferring user emotions and adjusting the order in which matching results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The matching unit is When matching, the geographic distribution of genetic information is taken into account. 2. The system of claim 1, (Appendix 28) The matching unit is When matching, the accuracy of matching is improved by referring to related literature on genetic information. 2. The system of claim 1, (Appendix 29) The notification unit is Infer user emotions and adjust notification presentation based on the inferred user emotions 2. The system of claim 1, (Appendix 30) The notification unit is When notifying users, adjust the level of detail of notifications based on the importance of the matching results. 2. The system of claim 1, (Appendix 31) The notification unit is During notification, apply different notification algorithms depending on the category of the match result. 2. The system of claim 1, (Appendix 32) The notification unit is Infer user emotion and adjust notification length based on the estimated user emotion 2. The system of claim 1, (Appendix 33) The notification unit is During notifications, prioritize notifications based on when matching results are available 2. The system of claim 1, (Appendix 34) The notification unit is During notifications, adjust the order of notifications based on the relevance of the match results 2. The system of claim 1, (Appendix 35) The collecting unit includes: To estimate a user's emotion and adjust a timing of feedback collection based on the estimated user's emotion. 3. The system of claim 2, (Appendix 36) The collecting unit includes: When collecting feedback, choose the best collection method based on the user's past relationship status 3. The system of claim 2, (Appendix 37) The collecting unit includes: Estimate user sentiment and prioritize feedback to be collected based on the estimated user sentiment 3. The system of claim 2, (Appendix 38) The collecting unit includes: When collecting feedback, prioritize relevant feedback based on the user's geographic location 3. The system of claim 2, (Appendix 39) The storage unit is Estimate the user's emotions and select stored data based on the estimated user's emotions. 4. The system of claim 3. (Appendix 40) The storage unit is When storing, the storage algorithm is optimized by referencing past storage data. 4. The system of claim 3. (Appendix 41) The storage unit is A user's emotion is estimated, and the frequency of accumulation is adjusted based on the estimated user's emotion. 4. The system of claim 3. (Appendix 42) The storage unit is As it accumulates, it weights the accumulated data based on when the feedback was provided. 4. The system of claim 3. (Appendix 43) The support portion is To estimate a user's emotion and adjust a method of supporting a message exchange based on the estimated user's emotion. 5. The system of claim 4. (Appendix 44) The support portion is When providing support through message exchange, the most appropriate support method can be selected by referring to the user's past message history. 5. The system of claim 4. (Appendix 45) The support portion is To estimate a user's emotion and determine a priority of a message exchange based on the estimated user's emotion. 5. The system of claim 4. (Appendix 46) The support portion is When supporting a conversation, select the appropriate support method based on the user's device information 5. The system of claim 4. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A providing unit that provides genetic information; an analysis unit that analyzes the genetic information provided by the provision unit; A determination unit that determines compatibility based on the genetic information analyzed by the analysis unit; a matching unit that matches a partner based on the compatibility determined by the determination unit; A notification unit that notifies the result of matching by the matching unit. A system characterized by:

2. A collection unit for collecting user feedback is provided.

2. The system of claim 1 .

3. Equipped with a storage unit that accumulates collected data and improves matching accuracy 3. The system of claim 2.

4. Equipped with a support department that supports anonymous message exchange 2. The system of claim 1 .

5. The providing unit is The system estimates the user's emotions and adjusts the timing of providing genetic information based on the estimated user emotions.

2. The system of claim 1 .

6. The providing unit is Select the appropriate method of providing genetic information based on the user's past health condition and lifestyle habits 2. The system of claim 1 .

7. The providing unit is When providing genetic information, filtering is performed based on the user's current health condition and living environment.

2. The system of claim 1 .

8. The providing unit is The user's emotions are estimated, and the priority of the genetic information to be provided is determined based on the estimated user's emotions.

2. The system of claim 1 .

9. The providing unit is When providing genetic information, the system takes into account the user's geographic location information and provides highly relevant information preferentially.

2. The system of claim 1 .

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