system

Abstract

The system according to the embodiment aims to identify and match an ideal partner by utilizing the genetic information of the user. [Solution] A system according to an embodiment includes an input unit, an analysis unit, an identification unit, a matching unit, and a protection unit. The input unit inputs a user's genetic information. The analysis unit analyzes the genetic information input by the input unit. The identification unit identifies an ideal partner based on the genetic information analyzed by the analysis unit. The matching unit performs matching with the partner identified by the identification unit. The protection unit protects the privacy of the genetic information.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately utilize a user's genetic information to identify and match ideal partners, and there is room for improvement.

[0005] The system according to the embodiment aims to identify and match an ideal partner by utilizing the genetic information of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, an identification unit, a matching unit, and a protection unit. The input unit inputs a user's genetic information. The analysis unit analyzes the genetic information input by the input unit. The identification unit identifies an ideal partner based on the genetic information analyzed by the analysis unit. The matching unit performs matching with the partner identified by the identification unit. The protection unit protects the privacy of the genetic information. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the user's genetic information to identify an ideal partner and perform matching. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, a pen or a finger) to receive user input by 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A matchmaking app according to an embodiment of the present invention is a system that uses AI to help users meet their ideal partner at the genetic level. This system allows users to input their genetic information, and AI analyzes that information to identify and facilitate a matchmaking experience. Specifically, users acquire their genetic information using a genetic testing kit and input it into the app. AI then analyzes the input genetic information and identifies their ideal partner at the genetic level. The app then provides users with the opportunity to meet the identified partner. This system allows users to meet their ideal partner based on their genetic information. For example, it can identify their ideal partner by taking into account genetic compatibility and health risks. The app matches users with the identified partner and notifies them. For example, it can send messages or suggest dates within the app. In this way, users can meet their ideal partner based on their genetic information. For example, meeting a partner with good genetic compatibility increases the chances of having healthy children. Furthermore, because the system is based on genetic information, compatibility with the partner can be expected. This allows users to meet their ideal partner and enjoy a happy married life. This allows dating apps to match users with their ideal partner based on their genetic information.

[0029] A matchmaking app according to an embodiment includes an input unit, an analysis unit, an identification unit, a matching unit, and a protection unit. The input unit inputs genetic information of a user. The genetic information of the user includes, for example, a saliva sample or a blood sample, but is not limited to these examples. The input unit acquires the genetic information of the user using, for example, a genetic testing kit. The genetic testing kit is for collecting, for example, a saliva sample and analyzing the genetic information. The analysis unit analyzes the genetic information input by the input unit. The analysis unit identifies an ideal partner, for example, taking into account genetic compatibility and health risks. The analysis unit can analyze the genetic information using AI and identify the ideal partner. The identification unit identifies the ideal partner based on the genetic information analyzed by the analysis unit. The identification unit identifies the ideal partner, for example, taking into account genetic compatibility and health risks. The identification unit can identify the ideal partner using AI. The matching unit matches the partner identified by the identification unit. The matching unit can, for example, send messages within the app or suggest dates. The matching unit can perform matching using AI. The protection unit protects the privacy of the genetic information. The protection unit protects the privacy of the genetic information using, for example, data encryption, access control, anonymization technology, etc. The protection unit can perform privacy protection using AI. As a result, the matchmaking app according to the embodiment allows a user to meet their ideal partner based on the user's genetic information. For example, the output unit notifies the user of the matching result. Notification methods include, for example, in-app notification, email, SMS, etc., but are not limited to these examples. The output unit can perform notification using AI.

[0030] The input unit can acquire the user's genetic information using a genetic testing kit. Examples of genetic testing kits include, but are not limited to, saliva samples and blood samples. The input unit uses, for example, a genetic testing kit for collecting a saliva sample and analyzing the genetic information. The input unit can also use a genetic testing kit for collecting a blood sample and analyzing the genetic information. For example, a genetic testing kit for collecting a saliva sample is designed to be easily used at home by a user. The user collects a saliva sample and sends the sample according to instructions enclosed with the genetic testing kit. The genetic testing kit analyzes the genetic information and provides data for inputting the results into the app. This allows the user's genetic information to be accurately acquired. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the genetic information acquired by the genetic testing kit into AI and obtain the analysis results.

[0031] The analysis unit can identify an ideal partner by taking into account genetic compatibility and health risks. The analysis unit, for example, calculates the degree of genetic identity to evaluate genetic compatibility. For example, the analysis unit compares the user's genetic information with the genetic information of an ideal partner and calculates the degree of identity. The analysis unit can also consider genetic mutations, medical history, lifestyle habits, etc. to evaluate health risks. For example, the analysis unit evaluates whether a specific genetic mutation is present based on the user's genetic information. The analysis unit can also evaluate health risks based on the user's medical history and lifestyle habits. This makes it possible to identify an ideal partner by taking into account genetic compatibility and health risks. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input genetic information into AI and have the AI ​​evaluate compatibility and health risks.

[0032] The identification unit can notify the user of information about the identified other party. The identification unit can notify the user of information such as the name, age, hobbies, and contact information of the identified other party. Notification methods include, but are not limited to, in-app notifications, email, and SMS. The identification unit can notify the user of information about the identified other party using AI. For example, the identification unit can input the information about the identified other party into AI and generate notification content. This allows the information about the identified other party to be notified to the user. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using AI or without using AI. For example, the identification unit can input the information about the identified other party into AI and generate notification content.

[0033] The matching unit can send messages within the app or make date suggestions. The matching unit can, for example, send text messages within the app. For example, the matching unit provides an interface for the user to send messages to the specified partner. The matching unit can also send image and voice messages. For example, the matching unit provides an interface for the user to send images and voice messages to the specified partner. The matching unit can also make date suggestions. For example, the matching unit provides an interface for the user to suggest a date location, date, time, and activity to the specified partner. This makes it possible to send messages within the app or make date suggestions. Some or all of the above-described processing in the matching unit may be performed using, or without, AI, for example. For example, the matching unit can input the user's message or date suggestions into AI to generate optimal suggestions.

[0034] The protection unit can provide a mechanism for protecting the privacy of genetic information. The protection unit protects the genetic information using, for example, data encryption technology. For example, the protection unit encrypts the genetic information to prevent third parties from accessing it. The protection unit can also protect the genetic information using access control technology. For example, the protection unit limits access to the genetic information to specific users or devices. The protection unit can also protect the genetic information using anonymization technology. For example, the protection unit deletes information that can identify an individual from the genetic information and anonymizes it. This makes it possible to protect the privacy of the genetic information. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can cause AI to perform encryption, access control, and anonymization of the genetic information.

[0035] The input unit can analyze the user's past genetic information input history and select the optimal input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the input unit preferentially suggests voice input. The input unit can also analyze the time period during which the user has previously input data and suggest the optimal input timing. For example, if the user has previously input data at night, the input unit can send a notification prompting the user to input data at night. The input unit can also recommend a specific input method based on the user's past input history. For example, if the user has previously preferred text input, the input unit can recommend text input. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input history into AI and select the optimal input method.

[0036] When inputting genetic information, the input unit can filter the genetic information based on the user's current health condition and lifestyle habits. For example, when the user inputs the results of a health checkup, the input unit filters the genetic information based on that information. For example, the input unit analyzes the user's health checkup results and preferentially inputs specific genetic information. The input unit can also select genetic information to input taking into account the user's lifestyle habits (smoking, drinking, etc.). For example, if the user is a smoker, the input unit preferentially inputs genetic information related to smoking. The input unit can also preferentially input related genetic information based on the user's exercise habits. For example, if the user exercises regularly, the input unit preferentially inputs genetic information related to exercise. This makes it possible to filter genetic information based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's health checkup results and lifestyle habit data into AI and perform filtering.

[0037] When inputting genetic information, the input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the input unit prioritizes inputting genetic information related to that area. For example, if the user lives in a specific area, the input unit prioritizes inputting genetic information related to that area. Furthermore, if the user is traveling, the input unit can also input genetic information related to the area of ​​the travel destination. For example, if the user is traveling, the input unit inputs genetic information related to the area of ​​the travel destination. Furthermore, if the user is planning to move, the input unit can also prioritize inputting genetic information related to the new area. For example, if the user is planning to move, the input unit prioritizes inputting genetic information related to the new area. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI and prioritize inputting highly relevant information.

[0038] When inputting genetic information, the input unit can analyze the user's social media activity and input related information. The input unit, for example, inputs genetic information based on health information shared by the user on social media. For example, the input unit inputs genetic information based on health information shared by the user on social media. The input unit can also analyze the user's social media posts and input related genetic information. For example, the input unit can analyze the user's social media posts and input related genetic information. The input unit can also input genetic information taking into account the user's social media friendships. For example, the input unit inputs genetic information taking into account the user's social media friendships. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to AI and input related information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. The analysis unit, for example, performs a detailed analysis on important genetic information. For example, the analysis unit performs a detailed analysis on genetic information related to health risks. The analysis unit can also perform a brief analysis on genetic information of low importance. For example, the analysis unit performs a brief analysis on basic genetic information. The analysis unit can also perform a detailed analysis on genetic information of high interest to the user. For example, the analysis unit performs a detailed analysis on genetic information in which the user is interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the genetic information. Some or all of the above-described 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 importance data of the genetic information into AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit applies a specific analysis algorithm to genetic information related to health risks. For example, the analysis unit applies a specific analysis algorithm to genetic information related to health risks. The analysis unit can also apply a different analysis algorithm to information related to genetic compatibility. For example, the analysis unit applies a different analysis algorithm to information related to genetic compatibility. The analysis unit can also apply different analysis algorithms depending on the user's interests. For example, the analysis unit applies different analysis algorithms depending on the user's interests. This makes it possible to apply different analysis algorithms depending on the gene category. 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 category data of genetic information into AI and apply different analysis algorithms.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the genetic information. For example, the analysis unit prioritizes analysis of recently submitted genetic information. For example, the analysis unit prioritizes analysis of recently submitted genetic information. The analysis unit can also postpone genetic information that was submitted earlier. For example, the analysis unit postpones genetic information that was submitted earlier. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit adjusts the order of analysis based on the submission date. This makes it possible to determine the priority of analysis based on the submission date of the genetic information. Some or all of the above-described 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 data on the submission date of the genetic information into AI to determine the priority of analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic information. The analysis unit, for example, prioritizes analysis of highly relevant genetic information. For example, the analysis unit prioritizes analysis of highly relevant genetic information. The analysis unit can also postpone analysis of less relevant genetic information. For example, the analysis unit postpones analysis of less relevant genetic information. The analysis unit can also adjust the order of analysis based on the relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the genetic information. Some or all of the above-described 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 relevance data of the genetic information into AI and adjust the order of analysis.

[0043] The identification unit can improve the accuracy of identification by taking into account the interrelationships of the genetic information during identification. The identification unit, for example, analyzes the interrelationships of the genetic information and improves the accuracy of identification. For example, the identification unit analyzes the interrelationships of the genetic information and improves the accuracy of identification. The identification unit can also preferentially identify genetic information with strong interrelationships. For example, the identification unit preferentially identifies genetic information with strong interrelationships. The identification unit can also improve the accuracy of identification based on the interrelationships. For example, the identification unit improves the accuracy of identification based on the interrelationships. This makes it possible to improve the accuracy of identification by taking into account the interrelationships of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelation data of the genetic information into AI to improve the accuracy of identification.

[0044] The identification unit can perform identification by taking into consideration attribute information of the submitter of the genetic information. The identification unit performs identification by taking into consideration, for example, the age and gender of the submitter. For example, the identification unit performs identification by taking into consideration the age and gender of the submitter. The identification unit can also perform identification by taking into consideration the lifestyle of the submitter. For example, the identification unit performs identification by taking into consideration the lifestyle of the submitter. The identification unit can also perform identification by taking into consideration the health condition of the submitter. For example, the identification unit performs identification by taking into consideration the health condition of the submitter. In this way, identification can be performed by taking into consideration the attribute information of the submitter of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input attribute information data of the submitter into AI and perform identification.

[0045] The identification unit can perform identification taking into consideration the geographical distribution of the genetic information during identification. For example, the identification unit prioritizes identifying geographically close genetic information. For example, the identification unit prioritizes identifying geographically close genetic information. The identification unit can also improve the accuracy of identification based on the geographical distribution. For example, the identification unit improves the accuracy of identification based on the geographical distribution. The identification unit can also adjust the order of identification taking into consideration the geographical distribution. For example, the identification unit adjusts the order of identification taking into consideration the geographical distribution. This allows identification to be performed taking into consideration the geographical distribution of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical distribution data of the genetic information into AI and perform identification.

[0046] During identification, the identification unit can improve the accuracy of identification by referring to related literature of the genetic information. The identification unit, for example, improves the accuracy of identification by referring to related literature. For example, the identification unit improves the accuracy of identification by referring to related literature. The identification unit can also adjust the identification criteria based on related literature. For example, the identification unit adjusts the identification criteria based on related literature. The identification unit can also complement the identification results by referring to related literature. For example, the identification unit complements the identification results by referring to related literature. This makes it possible to improve the accuracy of identification by referring to related literature of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input related literature data into AI to improve the accuracy of identification.

[0047] During matching, the matching unit can analyze the user's past matching history and select the optimal matching method. The matching unit, for example, analyzes the user's past matching history and proposes the optimal matching method. For example, the matching unit analyzes the user's past matching history and proposes the optimal matching method. The matching unit can also prioritize proposing matching methods that have been successful in the past. For example, the matching unit prioritizes proposing matching methods that have been successful in the past. The matching unit can also suggest avoiding matching methods that have failed in the past. For example, the matching unit suggests avoiding matching methods that have failed in the past. This makes it possible to select the optimal matching method based on the user's past matching history. Some or all of the above-described 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 the user's past matching history data into AI to select the optimal matching method.

[0048] The matching unit can customize the matching means based on the user's current living situation during matching. For example, when the user is busy, the matching unit provides a simple matching means. For example, the matching unit provides a simple matching means when the user is busy. The matching unit can also provide a detailed matching means when the user is relaxed. For example, the matching unit provides a detailed matching means when the user is relaxed. The matching unit can also customize the matching means according to the user's living situation. For example, the matching unit customizes the matching means according to the user's living situation. This makes it possible to customize the matching means based on the user's current living situation. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's living situation data into AI to customize the matching means.

[0049] The matching unit can select the optimal matching method during matching by taking into account the user's geographical location information. The matching unit, for example, proposes the optimal matching method based on the user's geographical location information. For example, the matching unit proposes the optimal matching method based on the user's geographical location information. The matching unit can also prioritize matching with geographically close partners. For example, the matching unit prioritizes matching with geographically close partners. The matching unit can also adjust the order of matching by taking into account the geographical location information. For example, the matching unit adjusts the order of matching by taking into account the geographical location information. This makes it possible to select the optimal matching method based on the user's geographical location information. Some or all of the above-described 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 the user's geographical location information data into AI and select the optimal matching method.

[0050] The matching unit can analyze the user's social media activity and suggest a matching means when matching. The matching unit, for example, analyzes the user's social media activity and suggests an optimal matching means. For example, the matching unit analyzes the user's social media activity and suggests an optimal matching means. The matching unit can also perform matching based on common hobbies and interests on social media. For example, the matching unit performs matching based on common hobbies and interests on social media. The matching unit can also perform matching taking into account friendships on social media. For example, the matching unit performs matching taking into account friendships on social media. This makes it possible to suggest an optimal matching means based on the user's social media activity. Some or all of the above-described 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 the user's social media activity data into AI and suggest an optimal matching means.

[0051] During privacy protection, the protection unit can select an optimal protection method by referring to the user's past privacy protection history. For example, the protection unit can refer to the user's past privacy protection history and suggest an optimal protection method. For example, the protection unit can refer to the user's past privacy protection history and suggest an optimal protection method. The protection unit can also preferentially suggest privacy protection methods that have been successful in the past. For example, the protection unit can preferentially suggest privacy protection methods that have been successful in the past. The protection unit can also suggest privacy protection methods that have failed in the past to be avoided. For example, the protection unit suggests privacy protection methods that have failed in the past to be avoided. This makes it possible to select an optimal protection method based on the user's past privacy protection history. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the user's past privacy protection history data into AI to select an optimal protection method.

[0052] During privacy protection, the protection unit can select an optimal protection method by taking into account the user's device information. The protection unit, for example, proposes an optimal privacy protection method based on the user's device information. For example, the protection unit proposes an optimal privacy protection method based on the user's device information. The protection unit can also select a privacy protection method by taking into account the security settings of the device. For example, the protection unit selects a privacy protection method by taking into account the security settings of the device. The protection unit can also select a privacy protection method by taking into account the device usage status. For example, the protection unit selects a privacy protection method by taking into account the device usage status. This makes it possible to select an optimal privacy protection method based on the user's device information. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's device information data into AI to select an optimal privacy protection method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The input unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has previously preferred voice input, the input unit will preferentially suggest voice input. Also, if the user has previously input during a specific time period, the input unit can send a notification prompting input during that time period. This makes it possible to suggest the optimal input method based on the user's past input history.

[0055] The matching unit can analyze the user's past matching history and suggest the optimal matching method. For example, it can preferentially suggest matching methods that the user has been successful in the past. It can also suggest methods that have failed in the past, avoiding such methods. In this way, it is possible to suggest the optimal matching method based on the user's past matching history.

[0056] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. For example, it can perform a detailed analysis on important genetic information and a brief analysis on less important genetic information. It can also perform a detailed analysis on genetic information that is of great interest to the user. This allows the level of detail of the analysis to be adjusted based on the importance of the genetic information.

[0057] The identification unit can improve the accuracy of identification by taking into account the interrelationships between genetic information. For example, it can preferentially identify genetic information with strong interrelationships. It can also improve the accuracy of identification based on the interrelationships. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between genetic information.

[0058] The input unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, genetic information related to that area can be prioritized. Also, if the user is traveling, genetic information related to the area of ​​the user's travel destination can be input. This allows highly relevant information to be prioritized based on the user's geographical location information.

[0059] The protection unit can select an optimal privacy protection method by taking into account the user's device information. For example, the protection unit can suggest an optimal privacy protection method based on the user's device information. The protection unit can also select a privacy protection method by taking into account the security settings of the device. This allows the optimal privacy protection method to be selected based on the user's device information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input unit inputs the user's genetic information. The user's genetic information includes, for example, a saliva sample or a blood sample. The input unit acquires the user's genetic information using a genetic testing kit. The genetic testing kit is used to collect a saliva sample and analyze the genetic information. Step 2: The analysis unit analyzes the genetic information input by the input unit. The analysis unit identifies an ideal partner by taking into account genetic compatibility and health risks. The analysis unit can analyze the genetic information using AI and identify an ideal partner. Step 3: The identification unit identifies an ideal partner based on the genetic information analyzed by the analysis unit. The identification unit identifies an ideal partner by taking into account genetic compatibility and health risks. The identification unit can use AI to identify an ideal partner. Step 4: The matching unit matches the person identified by the identification unit. The matching unit sends messages within the app and suggests dates. The matching unit can use AI to perform matching. Step 5: The protection unit protects the privacy of the genetic information. The protection unit protects the privacy of the genetic information using data encryption, access control, anonymization technology, etc. The protection unit can use AI to protect privacy.

[0062] (Example 2) A matchmaking app according to an embodiment of the present invention is a system that uses AI to help users meet their ideal partner at the genetic level. This system allows users to input their genetic information, and AI analyzes that information to identify and facilitate a matchmaking experience. Specifically, users acquire their genetic information using a genetic testing kit and input it into the app. AI then analyzes the input genetic information and identifies their ideal partner at the genetic level. The app then provides users with the opportunity to meet the identified partner. This system allows users to meet their ideal partner based on their genetic information. For example, it can identify their ideal partner by taking into account genetic compatibility and health risks. The app matches users with the identified partner and notifies them. For example, it can send messages or suggest dates within the app. In this way, users can meet their ideal partner based on their genetic information. For example, meeting a partner with good genetic compatibility increases the chances of having healthy children. Furthermore, because the system is based on genetic information, compatibility with the partner can be expected. This allows users to meet their ideal partner and enjoy a happy married life. This allows dating apps to match users with their ideal partner based on their genetic information.

[0063] A matchmaking app according to an embodiment includes an input unit, an analysis unit, an identification unit, a matching unit, and a protection unit. The input unit inputs genetic information of a user. The genetic information of the user includes, for example, a saliva sample or a blood sample, but is not limited to these examples. The input unit acquires the genetic information of the user using, for example, a genetic testing kit. The genetic testing kit is for collecting, for example, a saliva sample and analyzing the genetic information. The analysis unit analyzes the genetic information input by the input unit. The analysis unit identifies an ideal partner, for example, taking into account genetic compatibility and health risks. The analysis unit can analyze the genetic information using AI and identify the ideal partner. The identification unit identifies the ideal partner based on the genetic information analyzed by the analysis unit. The identification unit identifies the ideal partner, for example, taking into account genetic compatibility and health risks. The identification unit can identify the ideal partner using AI. The matching unit matches the partner identified by the identification unit. The matching unit can, for example, send messages within the app or suggest dates. The matching unit can perform matching using AI. The protection unit protects the privacy of the genetic information. The protection unit protects the privacy of the genetic information using, for example, data encryption, access control, anonymization technology, etc. The protection unit can perform privacy protection using AI. As a result, the matchmaking app according to the embodiment allows a user to meet their ideal partner based on the user's genetic information. For example, the output unit notifies the user of the matching result. Notification methods include, for example, in-app notification, email, SMS, etc., but are not limited to these examples. The output unit can perform notification using AI.

[0064] The input unit can acquire the user's genetic information using a genetic testing kit. Examples of genetic testing kits include, but are not limited to, saliva samples and blood samples. The input unit uses, for example, a genetic testing kit for collecting a saliva sample and analyzing the genetic information. The input unit can also use a genetic testing kit for collecting a blood sample and analyzing the genetic information. For example, a genetic testing kit for collecting a saliva sample is designed to be easily used at home by a user. The user collects a saliva sample and sends the sample according to instructions enclosed with the genetic testing kit. The genetic testing kit analyzes the genetic information and provides data for inputting the results into the app. This allows the user's genetic information to be accurately acquired. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the genetic information acquired by the genetic testing kit into AI and obtain the analysis results.

[0065] The analysis unit can identify an ideal partner by taking into account genetic compatibility and health risks. The analysis unit, for example, calculates the degree of genetic identity to evaluate genetic compatibility. For example, the analysis unit compares the user's genetic information with the genetic information of an ideal partner and calculates the degree of identity. The analysis unit can also consider genetic mutations, medical history, lifestyle habits, etc. to evaluate health risks. For example, the analysis unit evaluates whether a specific genetic mutation is present based on the user's genetic information. The analysis unit can also evaluate health risks based on the user's medical history and lifestyle habits. This makes it possible to identify an ideal partner by taking into account genetic compatibility and health risks. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input genetic information into AI and have the AI ​​evaluate compatibility and health risks.

[0066] The identification unit can notify the user of information about the identified other party. The identification unit can notify the user of information such as the name, age, hobbies, and contact information of the identified other party. Notification methods include, but are not limited to, in-app notifications, email, and SMS. The identification unit can notify the user of information about the identified other party using AI. For example, the identification unit can input the information about the identified other party into AI and generate notification content. This allows the information about the identified other party to be notified to the user. Some or all of the above-mentioned processing in the identification unit can be performed, for example, using AI or without using AI. For example, the identification unit can input the information about the identified other party into AI and generate notification content.

[0067] The matching unit can send messages within the app or make date suggestions. The matching unit can, for example, send text messages within the app. For example, the matching unit provides an interface for the user to send messages to the specified partner. The matching unit can also send image and voice messages. For example, the matching unit provides an interface for the user to send images and voice messages to the specified partner. The matching unit can also make date suggestions. For example, the matching unit provides an interface for the user to suggest a date location, date, time, and activity to the specified partner. This makes it possible to send messages within the app or make date suggestions. Some or all of the above-described processing in the matching unit may be performed using, or without, AI, for example. For example, the matching unit can input the user's message or date suggestions into AI to generate optimal suggestions.

[0068] The protection unit can provide a mechanism for protecting the privacy of genetic information. The protection unit protects the genetic information using, for example, data encryption technology. For example, the protection unit encrypts the genetic information to prevent third parties from accessing it. The protection unit can also protect the genetic information using access control technology. For example, the protection unit limits access to the genetic information to specific users or devices. The protection unit can also protect the genetic information using anonymization technology. For example, the protection unit deletes information that can identify an individual from the genetic information and anonymizes it. This makes it possible to protect the privacy of the genetic information. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can cause AI to perform encryption, access control, and anonymization of the genetic information.

[0069] The input unit can estimate the user's emotion and adjust the timing of inputting genetic information based on the estimated user's emotion. For example, the input unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on changes in facial expression. The input unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the input unit analyzes the tone and speed of the voice and calculates an emotion score. The input unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the input unit calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the timing of inputting genetic information according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit can input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The input unit can analyze the user's past genetic information input history and select the optimal input method. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the input unit preferentially suggests voice input. The input unit can also analyze the time period during which the user has previously input data and suggest the optimal input timing. For example, if the user has previously input data at night, the input unit can send a notification prompting the user to input data at night. The input unit can also recommend a specific input method based on the user's past input history. For example, if the user has previously preferred text input, the input unit can recommend text input. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's past input history into AI and select the optimal input method.

[0071] When inputting genetic information, the input unit can filter the genetic information based on the user's current health condition and lifestyle habits. For example, when the user inputs the results of a health checkup, the input unit filters the genetic information based on that information. For example, the input unit analyzes the user's health checkup results and preferentially inputs specific genetic information. The input unit can also select genetic information to input taking into account the user's lifestyle habits (smoking, drinking, etc.). For example, if the user is a smoker, the input unit preferentially inputs genetic information related to smoking. The input unit can also preferentially input related genetic information based on the user's exercise habits. For example, if the user exercises regularly, the input unit preferentially inputs genetic information related to exercise. This makes it possible to filter genetic information based on the user's health condition and lifestyle habits. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the user's health checkup results and lifestyle habit data into AI and perform filtering.

[0072] The input unit can estimate the user's emotions and determine the priority of genetic information to be input based on the estimated user's emotions. For example, when the user is relaxed, the input unit prioritizes input of important genetic information. For example, when the user is relaxed, the input unit prioritizes input of genetic information related to health risks. Furthermore, when the user is feeling stressed, the input unit can start inputting simple genetic information. For example, when the user is feeling stressed, the input unit prioritizes input of basic genetic information. Furthermore, when the user is concentrating, the input unit can prioritize input of detailed genetic information. For example, when the user is concentrating, the input unit prioritizes input of detailed genetic information. This makes it possible to determine the priority of genetic information to be input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed, for example, using AI or without AI. For example, the input unit can input the user's emotional data into the generation AI and determine the priority of the genetic information to be input.

[0073] When inputting genetic information, the input unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the input unit prioritizes inputting genetic information related to that area. For example, if the user lives in a specific area, the input unit prioritizes inputting genetic information related to that area. Furthermore, if the user is traveling, the input unit can also input genetic information related to the area of ​​the travel destination. For example, if the user is traveling, the input unit inputs genetic information related to the area of ​​the travel destination. Furthermore, if the user is planning to move, the input unit can also prioritize inputting genetic information related to the new area. For example, if the user is planning to move, the input unit prioritizes inputting genetic information related to the new area. This makes it possible to prioritize inputting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's geographical location information to AI and prioritize inputting highly relevant information.

[0074] When inputting genetic information, the input unit can analyze the user's social media activity and input related information. The input unit, for example, inputs genetic information based on health information shared by the user on social media. For example, the input unit inputs genetic information based on health information shared by the user on social media. The input unit can also analyze the user's social media posts and input related genetic information. For example, the input unit can analyze the user's social media posts and input related genetic information. The input unit can also input genetic information taking into account the user's social media friendships. For example, the input unit inputs genetic information taking into account the user's social media friendships. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input the user's social media activity data to AI and input related information.

[0075] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can provide detailed analysis results when the user is relaxed, for example. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. For example, the analysis unit can provide concise analysis results when the user is stressed. The analysis unit can also provide detailed analysis results when the user is concentrating. For example, the analysis unit can provide detailed analysis results when the user is concentrating. This makes it possible to adjust the way the analysis is presented depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the way the analysis is presented.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. The analysis unit, for example, performs a detailed analysis on important genetic information. For example, the analysis unit performs a detailed analysis on genetic information related to health risks. The analysis unit can also perform a brief analysis on genetic information of low importance. For example, the analysis unit performs a brief analysis on basic genetic information. The analysis unit can also perform a detailed analysis on genetic information of high interest to the user. For example, the analysis unit performs a detailed analysis on genetic information in which the user is interested. This makes it possible to adjust the level of detail of the analysis based on the importance of the genetic information. Some or all of the above-described 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 importance data of the genetic information into AI to adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit applies a specific analysis algorithm to genetic information related to health risks. For example, the analysis unit applies a specific analysis algorithm to genetic information related to health risks. The analysis unit can also apply a different analysis algorithm to information related to genetic compatibility. For example, the analysis unit applies a different analysis algorithm to information related to genetic compatibility. The analysis unit can also apply different analysis algorithms depending on the user's interests. For example, the analysis unit applies different analysis algorithms depending on the user's interests. This makes it possible to apply different analysis algorithms depending on the gene category. 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 category data of genetic information into AI and apply different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can provide detailed analysis results when the user is relaxed, for example. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. For example, the analysis unit can provide concise analysis results when the user is stressed. The analysis unit can also provide detailed analysis results when the user is concentrating. For example, the analysis unit can provide detailed analysis results when the user is concentrating. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and adjust the length of the analysis.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the genetic information. For example, the analysis unit prioritizes analysis of recently submitted genetic information. For example, the analysis unit prioritizes analysis of recently submitted genetic information. The analysis unit can also postpone genetic information that was submitted earlier. For example, the analysis unit postpones genetic information that was submitted earlier. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit adjusts the order of analysis based on the submission date. This makes it possible to determine the priority of analysis based on the submission date of the genetic information. Some or all of the above-described 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 data on the submission date of the genetic information into AI to determine the priority of analysis.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic information. The analysis unit, for example, prioritizes analysis of highly relevant genetic information. For example, the analysis unit prioritizes analysis of highly relevant genetic information. The analysis unit can also postpone analysis of less relevant genetic information. For example, the analysis unit postpones analysis of less relevant genetic information. The analysis unit can also adjust the order of analysis based on the relevance. For example, the analysis unit adjusts the order of analysis based on the relevance. This makes it possible to adjust the order of analysis based on the relevance of the genetic information. Some or all of the above-described 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 relevance data of the genetic information into AI and adjust the order of analysis.

[0081] The identification unit can estimate the user's emotions and adjust the specific criteria based on the estimated user's emotions. For example, the identification unit applies detailed specific criteria when the user is relaxed. For example, the identification unit applies detailed specific criteria when the user is relaxed. The identification unit can also apply concise specific criteria when the user is stressed. For example, the identification unit applies concise specific criteria when the user is stressed. The identification unit can also apply detailed specific criteria when the user is concentrating. For example, the identification unit applies detailed specific criteria when the user is concentrating. This allows the specific criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using AI, for example, or without AI. For example, the identification unit can input the user's emotion data into the generation AI and adjust the specific criteria.

[0082] The identification unit can improve the accuracy of identification by taking into account the interrelationships of the genetic information during identification. The identification unit, for example, analyzes the interrelationships of the genetic information and improves the accuracy of identification. For example, the identification unit analyzes the interrelationships of the genetic information and improves the accuracy of identification. The identification unit can also preferentially identify genetic information with strong interrelationships. For example, the identification unit preferentially identifies genetic information with strong interrelationships. The identification unit can also improve the accuracy of identification based on the interrelationships. For example, the identification unit improves the accuracy of identification based on the interrelationships. This makes it possible to improve the accuracy of identification by taking into account the interrelationships of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelation data of the genetic information into AI to improve the accuracy of identification.

[0083] The identification unit can perform identification by taking into consideration attribute information of the submitter of the genetic information. The identification unit performs identification by taking into consideration, for example, the age and gender of the submitter. For example, the identification unit performs identification by taking into consideration the age and gender of the submitter. The identification unit can also perform identification by taking into consideration the lifestyle of the submitter. For example, the identification unit performs identification by taking into consideration the lifestyle of the submitter. The identification unit can also perform identification by taking into consideration the health condition of the submitter. For example, the identification unit performs identification by taking into consideration the health condition of the submitter. In this way, identification can be performed by taking into consideration the attribute information of the submitter of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input attribute information data of the submitter into AI and perform identification.

[0084] The identification unit can estimate the user's emotions and adjust the order in which specific results are displayed based on the estimated user's emotions. For example, when the user is relaxed, the identification unit prioritizes displaying detailed results. For example, when the user is relaxed, the identification unit prioritizes displaying detailed results. The identification unit can also prioritize displaying concise results when the user is stressed. For example, when the user is stressed, the identification unit prioritizes displaying concise results. The identification unit can also prioritize displaying detailed results when the user is concentrating. For example, when the user is concentrating, the identification unit prioritizes displaying detailed results. This makes it possible to adjust the order in which specific results are displayed according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit may be performed using an AI, or may be performed without using an AI. For example, the identification unit can input user emotion data into the generation AI and adjust the order in which specific results are displayed.

[0085] The identification unit can perform identification taking into consideration the geographical distribution of the genetic information during identification. For example, the identification unit prioritizes identifying geographically close genetic information. For example, the identification unit prioritizes identifying geographically close genetic information. The identification unit can also improve the accuracy of identification based on the geographical distribution. For example, the identification unit improves the accuracy of identification based on the geographical distribution. The identification unit can also adjust the order of identification taking into consideration the geographical distribution. For example, the identification unit adjusts the order of identification taking into consideration the geographical distribution. This allows identification to be performed taking into consideration the geographical distribution of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input geographical distribution data of the genetic information into AI and perform identification.

[0086] During identification, the identification unit can improve the accuracy of identification by referring to related literature of the genetic information. The identification unit, for example, improves the accuracy of identification by referring to related literature. For example, the identification unit improves the accuracy of identification by referring to related literature. The identification unit can also adjust the identification criteria based on related literature. For example, the identification unit adjusts the identification criteria based on related literature. The identification unit can also complement the identification results by referring to related literature. For example, the identification unit complements the identification results by referring to related literature. This makes it possible to improve the accuracy of identification by referring to related literature of the genetic information. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input related literature data into AI to improve the accuracy of identification.

[0087] The matching unit can estimate the user's emotion and adjust the matching method based on the estimated user's emotion. For example, the matching unit provides a detailed matching method when the user is relaxed. For example, the matching unit provides a detailed matching method when the user is relaxed. The matching unit can also provide a simple matching method when the user is stressed. For example, the matching unit provides a simple matching method when the user is stressed. The matching unit can also provide a detailed matching method when the user is concentrating. For example, the matching unit provides a detailed matching method when the user is concentrating. This makes it possible to adjust the matching method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without AI. For example, the matching unit can input the user's emotion data into the generation AI and adjust the matching method.

[0088] During matching, the matching unit can analyze the user's past matching history and select the optimal matching method. The matching unit, for example, analyzes the user's past matching history and proposes the optimal matching method. For example, the matching unit analyzes the user's past matching history and proposes the optimal matching method. The matching unit can also prioritize proposing matching methods that have been successful in the past. For example, the matching unit prioritizes proposing matching methods that have been successful in the past. The matching unit can also suggest avoiding matching methods that have failed in the past. For example, the matching unit suggests avoiding matching methods that have failed in the past. This makes it possible to select the optimal matching method based on the user's past matching history. Some or all of the above-described 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 the user's past matching history data into AI to select the optimal matching method.

[0089] The matching unit can customize the matching means based on the user's current living situation during matching. For example, when the user is busy, the matching unit provides a simple matching means. For example, the matching unit provides a simple matching means when the user is busy. The matching unit can also provide a detailed matching means when the user is relaxed. For example, the matching unit provides a detailed matching means when the user is relaxed. The matching unit can also customize the matching means according to the user's living situation. For example, the matching unit customizes the matching means according to the user's living situation. This makes it possible to customize the matching means based on the user's current living situation. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's living situation data into AI to customize the matching means.

[0090] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated user's emotions. For example, when the user is relaxed, the matching unit prioritizes detailed matching. For example, when the user is relaxed, the matching unit prioritizes detailed matching. The matching unit can also prioritize concise matching when the user is stressed. For example, when the user is stressed, the matching unit prioritizes concise matching. The matching unit can also prioritize detailed matching when the user is concentrating. For example, when the user is concentrating, the matching unit prioritizes detailed matching. This makes it possible to determine matching priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit may be performed using an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and determine matching priorities.

[0091] The matching unit can select the optimal matching method during matching by taking into account the user's geographical location information. The matching unit, for example, proposes the optimal matching method based on the user's geographical location information. For example, the matching unit proposes the optimal matching method based on the user's geographical location information. The matching unit can also prioritize matching with geographically close partners. For example, the matching unit prioritizes matching with geographically close partners. The matching unit can also adjust the order of matching by taking into account the geographical location information. For example, the matching unit adjusts the order of matching by taking into account the geographical location information. This makes it possible to select the optimal matching method based on the user's geographical location information. Some or all of the above-described 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 the user's geographical location information data into AI and select the optimal matching method.

[0092] The matching unit can analyze the user's social media activity and suggest a matching means when matching. The matching unit, for example, analyzes the user's social media activity and suggests an optimal matching means. For example, the matching unit analyzes the user's social media activity and suggests an optimal matching means. The matching unit can also perform matching based on common hobbies and interests on social media. For example, the matching unit performs matching based on common hobbies and interests on social media. The matching unit can also perform matching taking into account friendships on social media. For example, the matching unit performs matching taking into account friendships on social media. This makes it possible to suggest an optimal matching means based on the user's social media activity. Some or all of the above-described 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 the user's social media activity data into AI and suggest an optimal matching means.

[0093] The protection unit can estimate the user's emotion and adjust the privacy protection method based on the estimated user's emotion. For example, the protection unit provides a detailed privacy protection method when the user is relaxed. For example, the protection unit provides a detailed privacy protection method when the user is relaxed. The protection unit can also provide a concise privacy protection method when the user is stressed. For example, the protection unit provides a concise privacy protection method when the user is stressed. The protection unit can also provide a detailed privacy protection method when the user is concentrating. For example, the protection unit provides a detailed privacy protection method when the user is concentrating. This allows the privacy protection method to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without AI. For example, the protection unit can input the user's emotion data into the generation AI and adjust the privacy protection method.

[0094] During privacy protection, the protection unit can select an optimal protection method by referring to the user's past privacy protection history. For example, the protection unit can refer to the user's past privacy protection history and suggest an optimal protection method. For example, the protection unit can refer to the user's past privacy protection history and suggest an optimal protection method. The protection unit can also preferentially suggest privacy protection methods that have been successful in the past. For example, the protection unit can preferentially suggest privacy protection methods that have been successful in the past. The protection unit can also suggest privacy protection methods that have failed in the past to be avoided. For example, the protection unit suggests privacy protection methods that have failed in the past to be avoided. This makes it possible to select an optimal protection method based on the user's past privacy protection history. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the user's past privacy protection history data into AI to select an optimal protection method.

[0095] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user's emotions. For example, when the user is relaxed, the protection unit prioritizes detailed privacy protection. For example, when the user is relaxed, the protection unit prioritizes detailed privacy protection. The protection unit can also prioritize concise privacy protection when the user is stressed. For example, when the user is stressed, the protection unit prioritizes concise privacy protection. The protection unit can also prioritize detailed privacy protection when the user is concentrating. For example, when the user is concentrating, the protection unit prioritizes detailed privacy protection. This makes it possible to determine the priority of privacy protection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit can be performed, for example, using AI or without AI. For example, the protection unit can input user emotional data into the generation AI and determine the priority of privacy protection.

[0096] During privacy protection, the protection unit can select an optimal protection method by taking into account the user's device information. The protection unit, for example, proposes an optimal privacy protection method based on the user's device information. For example, the protection unit proposes an optimal privacy protection method based on the user's device information. The protection unit can also select a privacy protection method by taking into account the security settings of the device. For example, the protection unit selects a privacy protection method by taking into account the security settings of the device. The protection unit can also select a privacy protection method by taking into account the device usage status. For example, the protection unit selects a privacy protection method by taking into account the device usage status. This makes it possible to select an optimal privacy protection method based on the user's device information. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's device information data into AI to select an optimal privacy protection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, matching unit, and protection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs the user's genetic information using the reception device 38 of the smart device 14. The analysis unit analyzes the genetic information using the identification processing unit 290 of the data processing device 12. The identification unit identifies an ideal partner using the identification processing unit 290 of the data processing device 12. The matching unit performs matching with the partner identified by the control unit 46A of the smart device 14. The protection unit protects the privacy of the genetic information using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, matching unit, and protection unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs the user's genetic information using the microphone 238 of the smart glasses 214. The analysis unit analyzes the genetic information using the identification processing unit 290 of the data processing device 12. The identification unit identifies an ideal partner using the identification processing unit 290 of the data processing device 12. The matching unit performs matching with the partner identified by the control unit 46A of the smart glasses 214. The protection unit protects the privacy of the genetic information using the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, matching unit, and protection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the input unit inputs the user's genetic information using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the genetic information by the identification processing unit 290 of the data processing device 12. The identification unit identifies an ideal partner by the identification processing unit 290 of the data processing device 12. The matching unit performs matching with the partner identified by the control unit 46A of the headset type terminal 314. The protection unit protects the privacy of the genetic information by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, identification unit, matching unit, and protection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs the user's genetic information using the microphone 238 of the robot 414. The analysis unit analyzes the genetic information by the identification processing unit 290 of the data processing device 12. The identification unit identifies an ideal partner by the identification processing unit 290 of the data processing device 12. The matching unit performs matching with the partner identified by the control unit 46A of the robot 414. The protection unit protects the privacy of the genetic information by the identification processing unit 290 of the data processing device 12.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. Conversely, if the user is stressed, the analysis unit can perform a brief analysis. Furthermore, if the user is concentrating, the analysis unit can perform a more in-depth analysis of specific genetic information. This allows the accuracy of the analysis to be adjusted according to the user's emotions.

[0099] The input unit can analyze the user's past input history and suggest the optimal input method. For example, if the user has previously preferred voice input, the input unit will preferentially suggest voice input. Also, if the user has previously input during a specific time period, the input unit can send a notification prompting input during that time period. This makes it possible to suggest the optimal input method based on the user's past input history.

[0100] The determination unit can estimate the user's emotion and adjust the specific criteria based on the estimated emotion. For example, if the user is relaxed, the determination unit can apply detailed specific criteria. Conversely, if the user is stressed, the determination unit can apply simple specific criteria. Also, if the user is concentrating, the determination unit can apply the specific criteria more strictly. In this way, the specific criteria can be adjusted according to the user's emotion.

[0101] The matching unit can analyze the user's past matching history and suggest the optimal matching method. For example, it can preferentially suggest matching methods that the user has been successful in the past. It can also suggest methods that have failed in the past, avoiding such methods. In this way, it is possible to suggest the optimal matching method based on the user's past matching history.

[0102] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated emotions. For example, if the user is relaxed, the protection unit can provide a detailed privacy protection method. Conversely, if the user is stressed, the protection unit can provide a simple privacy protection method. Also, if the user is concentrating, the protection unit can apply a specific privacy protection method more strictly. In this way, the privacy protection method can be adjusted according to the user's emotions.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information. For example, it can perform a detailed analysis on important genetic information and a brief analysis on less important genetic information. It can also perform a detailed analysis on genetic information that is of great interest to the user. This allows the level of detail of the analysis to be adjusted based on the importance of the genetic information.

[0104] The identification unit can improve the accuracy of identification by taking into account the interrelationships between genetic information. For example, it can preferentially identify genetic information with strong interrelationships. It can also improve the accuracy of identification based on the interrelationships. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between genetic information.

[0105] The matching unit can estimate the user's emotions and determine the priority of matching based on the estimated emotions. For example, if the user is relaxed, detailed matching can be prioritized. Conversely, if the user is stressed, simple matching can be prioritized. Furthermore, if the user is concentrating, detailed matching can be prioritized. In this way, the priority of matching can be determined according to the user's emotions.

[0106] The input unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, if the user lives in a specific area, genetic information related to that area can be prioritized. Also, if the user is traveling, genetic information related to the area of ​​the user's travel destination can be input. This allows highly relevant information to be prioritized based on the user's geographical location information.

[0107] The protection unit can select an optimal privacy protection method by taking into account the user's device information. For example, the protection unit can suggest an optimal privacy protection method based on the user's device information. The protection unit can also select a privacy protection method by taking into account the security settings of the device. This allows the optimal privacy protection method to be selected based on the user's device information.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The input unit inputs the user's genetic information. The user's genetic information includes, for example, a saliva sample or a blood sample. The input unit acquires the user's genetic information using a genetic testing kit. The genetic testing kit is used to collect a saliva sample and analyze the genetic information. Step 2: The analysis unit analyzes the genetic information input by the input unit. The analysis unit identifies an ideal partner by taking into account genetic compatibility and health risks. The analysis unit can analyze the genetic information using AI and identify an ideal partner. Step 3: The identification unit identifies an ideal partner based on the genetic information analyzed by the analysis unit. The identification unit identifies an ideal partner by taking into account genetic compatibility and health risks. The identification unit can use AI to identify an ideal partner. Step 4: The matching unit matches the person identified by the identification unit. The matching unit sends messages within the app and suggests dates. The matching unit can use AI to perform matching. Step 5: The protection unit protects the privacy of the genetic information. The protection unit protects the privacy of the genetic information using data encryption, access control, anonymization technology, etc. The protection unit can use AI to protect privacy.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0111] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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, RAM 30, and 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.

[0117] 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.

[0118] 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 audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] 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 carried out in a secure state.

[0121] Fig. 4 shows an example of the 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0127] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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, RAM 30, and 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.

[0133] 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.

[0134] 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 audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] 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 carried out in a secure state.

[0137] 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.

[0138] 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.

[0139] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0141] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0143] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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, RAM 30, and 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.

[0149] 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.

[0150] 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 audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] 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 carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0154] 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.

[0155] 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.

[0156] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0158] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] 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 voice indicating a user input regarding 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.

[0160] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] 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.

[0171] 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 USB (Universal Serial Bus) 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.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0176] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 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. an input unit for inputting the user's genetic information; an analysis unit that analyzes the genetic information input by the input unit; a specifying unit that specifies an ideal partner based on the genetic information analyzed by the analyzing unit; a matching unit that performs matching with the other party identified by the identification unit; a protection unit for protecting the privacy of genetic information. A system characterized by:

2. The input unit Obtaining the user's genetic information using a genetic testing kit 2. The system of claim 1.

3. The analysis unit Identify your ideal partner by considering genetic compatibility and health risks 2. The system of claim 1.

4. The identification unit Notify the user of the identified person's information 2. The system of claim 1.

5. The matching unit Send messages and date suggestions within the app 2. The system of claim 1.

6. The protective part is Providing mechanisms for protecting the privacy of genetic information 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the timing of genetic information input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past genetic information input history and select the optimal input method 2. The system of claim 1.

9. The input unit When entering genetic information, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1.

10. The input unit Estimate the user's emotions and prioritize the genetic information to be input based on the estimated user emotions.

2. The system of claim 1.

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