System

Abstract

To provide a system for providing services even for future users by analyzing generic information by combining it with medical data or family structure data.SOLUTION: A system includes a collection section, an analysis section, a providing section, and a protection section. The collection section collects generic information. The analysis section analyzes generic information collected by the collection section by combining it with medical data or family structure data. The providing section provides an analysis section obtained by the analysis section. The protection section protects a user's generic information.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it has not adequately combined genetic information with medical and family structure data for analysis and provision of services to future users.

[0005] The system according to the embodiment aims to analyze genetic information in combination with medical data and family structure data, and to provide services to future users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit collects genetic information. The analysis unit analyzes the genetic information collected by the collection unit in combination with medical data or family structure data. The provision unit provides the analysis results obtained by the analysis unit. The protection unit protects the user's genetic information. Effect of the Invention

[0007] The system according to the embodiment can analyze genetic information in combination with medical data and family structure data, and can provide services to future users as well. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A genetic information utilization system according to an embodiment of the present invention is a system that collects genetic information, combines it with medical data and family structure data, analyzes it, and provides services such as health management advice, marriage partner selection, and blood relationship confirmation to users. In this system, a user undergoes a genetic test and collects the genetic information. Next, the collected genetic information is combined with medical data and family structure data and analyzed. Based on the analysis results, services such as health management advice, marriage partner selection, and blood relationship confirmation are provided to the user. In addition, these data can be used by future descendants, and services can be provided to future users as well. For example, a user undergoes a genetic test. At this time, the user provides a sample such as saliva or blood using a dedicated kit. The provided sample is sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed and the user's genetic data is generated. Next, the generated genetic data is combined with the medical data and family structure data for analysis. For example, the user's health risk can be evaluated by combining the user's genetic data with data such as past medical history, lifestyle habits, and family health conditions. In addition, optimal health management advice can be provided to the user based on the genetic data. In addition, a service that supports the selection of a marriage partner based on the genetic data is also provided. For example, it is possible to recommend a compatible partner based on the genetic data. It is also possible to confirm blood ties, and family relationships can be confirmed based on the genetic data. Finally, these data can also be used by future descendants, and services can be provided to future users as well. For example, if a user's descendants undergo genetic testing, more accurate analysis can be performed by combining the data with past data. This makes it possible to provide future users with services such as health management, marriage partner selection, and confirmation of blood ties. As a result, the genetic information utilization system can provide comprehensive services by collecting, analyzing, and providing the user's genetic information.

[0029] The genetic information utilization system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit collects genetic information of a user. The genetic information of the user includes, for example, DNA sequence data, genetic marker information, and the like, but is not limited to such examples. The collection unit collects samples such as saliva and blood using, for example, a dedicated kit. The collected samples are sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed, and the genetic data of the user is generated. The analysis unit analyzes the genetic information collected by the collection unit in combination with medical data and family structure data. The medical data includes, for example, electronic medical record information, diagnosis results, treatment history, and the like, but is not limited to such examples. The family structure data includes, for example, family trees, family health history, and the like, but is not limited to such examples. The analysis is performed, for example, using statistical analysis or machine learning algorithms. The provision unit provides the analysis results obtained by the analysis unit. The provision is performed, for example, in a report format, dashboard display, or other methods. The protection unit protects the genetic information of the user. The protection is performed, for example, by data encryption, access control, or other methods. As a result, the genetic information utilization system according to the embodiment can provide comprehensive services by collecting, analyzing, providing, and protecting users' genetic information.

[0030] The collection unit collects the user's genetic information. The user's genetic information includes, for example, DNA sequence data, genetic marker information, and the like, but is not limited to such examples. The collection unit collects samples such as saliva and blood using, for example, a dedicated kit. The dedicated kit includes a tube for collecting saliva and a lancet for collecting blood, so that the user can easily provide the sample. The collected sample is sent to a lab for analyzing the genetic information. The lab first checks the quality of the sample and performs appropriate processing, and then obtains DNA sequence data using next-generation sequencing (NGS) technology. NGS technology can analyze large amounts of genetic data at high speed and high accuracy, making it possible to analyze the user's genetic information in detail. The analyzed genetic data is stored in a dedicated database and used for subsequent analysis. In order to protect the user's privacy, the collection unit takes strict security measures in all processes from sample collection to data storage. For example, the sample is given an anonymous ID and is managed so as not to be directly linked to the user's personal information. In addition, temperature control is performed during transportation of the sample to prevent deterioration of quality. This enables the collection unit to safely and efficiently collect the user's genetic information and provide high-quality data for analysis.

[0031] The analysis unit combines the genetic information collected by the collection unit with medical data and family structure data to analyze it. The medical data includes, for example, electronic medical record information, diagnosis results, and treatment history, but is not limited to such examples. The family structure data includes, for example, family trees, family health history, and the like, but is not limited to such examples. The analysis is performed, for example, using statistical analysis and machine learning algorithms. Specifically, by integrating the genetic information and the medical data, it is possible to evaluate what disease risk a specific genetic mutation is associated with. For example, it is possible to analyze whether a specific genetic marker increases the risk of cardiovascular disease. In addition, it is possible to clarify how genetic disease risk is inherited within a family by using the family structure data. Machine learning algorithms are used to extract patterns from large amounts of data and build predictive models. For example, it is possible to build a model that predicts the risk of developing a specific disease by combining the genetic information and the lifestyle data. Furthermore, the analysis unit can also make suggestions for personalized medicine based on the user's genetic information. For example, it is possible to predict responsiveness to a specific drug and propose an optimal treatment. As a result, the analysis unit can analyze the user's genetic information from multiple angles and provide useful information for personalized medicine and health management.

[0032] The providing unit provides the analysis results obtained by the analyzing unit. The results are provided, for example, in a report format or a dashboard display. In the report format, health risks based on the user's genetic information, recommended lifestyle habits, preventive measures, and the like are described in detail. The report is created in a visually easy-to-understand manner using charts and graphs so that even users without specialized medical knowledge can easily understand it. In the dashboard display, the user can check the analysis results in real time through a web browser or a dedicated app. The dashboard displays the user's health condition, risk assessment, recommended action plans, and the like, allowing the user to efficiently manage their own health. In addition, the providing unit also provides a function that allows the user to share the analysis results with a medical professional. For example, the user can send the genetic information report to the doctor in charge and receive advice from the doctor. In addition, the providing unit can collect user feedback and use it to improve the service. For example, feedback such as how the user felt about the provided information and what additional information they would like is collected and reflected in the creation of the next report and improvements to the dashboard. This allows the providing unit to provide valuable information to the user and support health management.

[0033] The protection unit protects the genetic information of the user. The protection is performed, for example, by methods such as data encryption and access control. Data encryption is a technology for preventing a third party from reading the genetic information when it is stored or communicated. For example, the genetic information is encrypted using a powerful encryption algorithm such as AES (Advanced Encryption Standard). Access control is a mechanism for restricting the users and systems that can access the genetic information. For example, the user's authentication information is used to allow only users with legitimate authority to access the genetic information. In addition, the access log can be recorded to detect and track unauthorized access. In addition, the protection unit also takes measures for backing up and recovering data. For example, data is backed up periodically to prepare for the unlikely event of data loss. In addition, recovery procedures are in place to quickly recover data in the event of a disaster or system failure. This allows the protection unit to safely protect the genetic information of the user and ensure privacy. In addition, the protection unit also takes measures to comply with the latest laws, regulations, and guidelines regarding data protection and maintain compliance. For example, the protection department will strive to protect user data in accordance with regulations such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act). This will enable the protection department to gain the trust of users and improve the safety and reliability of the entire system.

[0034] The providing unit may include an advising unit that provides health management advice. The advising unit, for example, evaluates the health risk of the user and provides health management advice. The health management advice includes, for example, but is not limited to, dietary guidance, exercise plans, suggestions for improving lifestyle habits, and the like. For example, the advising unit evaluates the health risk of the user by combining the genetic data of the user with data such as past medical history, lifestyle habits, and family health conditions. In addition, the advising unit can provide optimal health management advice to the user. As a result, the provision of health management advice can assist the user in maintaining their health.

[0035] The providing unit may include a selecting unit that supports the selection of a marriage partner. The selecting unit, for example, recommends a compatible partner based on the genetic data of the user. The selection of a marriage partner may include, but is not limited to, genetic compatibility, personality diagnosis results, and the like. For example, the selecting unit compares the genetic data of the user with the genetic data of a compatible partner and recommends a compatible partner. The selecting unit may also recommend a compatible partner based on the personality diagnosis results of the user. This can support the user in choosing a partner by supporting the selection of a marriage partner.

[0036] The providing unit may include a confirmation unit that confirms blood ties. The confirmation unit confirms family relationships based on, for example, the genetic data of the user. Confirmation of blood ties includes, for example, DNA parent-child testing, checking of family trees, and the like, but is not limited to such examples. For example, the confirmation unit compares the genetic data of the user with the genetic data of family members to confirm blood ties. The confirmation unit can also check family trees to confirm family relationships. In this way, confirmation of blood ties can be supported in confirming the user's family relationships.

[0037] The collection unit can collect the genetic information of the user using a dedicated kit. The dedicated kit includes, but is not limited to, for example, a saliva collection kit and a blood collection kit. In the collection unit, for example, a user provides a sample such as saliva or blood using the dedicated kit. The provided sample is sent to a lab for analyzing the genetic information. In the lab, the genetic information is analyzed and the user's genetic data is generated. As a result, the collection of genetic information can be efficiently performed by using the dedicated kit.

[0038] The protection unit can encrypt the genetic information of the user to protect privacy. The encryption includes, for example, AES encryption, RSA encryption, and the like, but is not limited to such examples. For example, the protection unit encrypts the genetic information of the user using AES encryption. The protection unit can also encrypt the genetic information of the user using RSA encryption. In this way, the privacy of the user can be protected by encrypting the genetic information.

[0039] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can refer to the user's past medical history and recommend collection of a blood sample. The collection unit can also recommend collection of a saliva sample based on the user's past health check results. The collection unit can also select the optimal collection method taking into account the user's past allergy information. In this way, the optimal collection method can be selected by analyzing past health data.

[0040] The collection unit can perform filtering based on the user's lifestyle and environmental factors when collecting genetic information. The collection unit filters the collected data, for example, taking into account the user's smoking habits. The collection unit can also preferentially collect specific genetic information based on the user's diet. The collection unit can also collect genetic information related to environmental factors, taking into account the user's living environment. As a result, more accurate genetic information can be collected by filtering based on lifestyle and environmental factors.

[0041] When collecting genetic information, the collection unit can preferentially collect highly relevant information by taking into account the geographical location information of the user. For example, if the user lives at high altitude, the collection unit preferentially collects genetic information related to high altitude. Furthermore, if the user lives in an urban area, the collection unit can also preferentially collect genetic information related to an urban environment. Furthermore, if the user lives at the seaside, the collection unit can also preferentially collect genetic information related to a marine environment. In this way, by taking into account the geographical location information, highly relevant genetic information can be preferentially collected.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting genetic information. For example, if the user frequently posts about health, the collection unit preferentially collects health-related genetic information. In addition, if the user frequently posts about sports, the collection unit can also preferentially collect genetic information related to athletic ability. In addition, if the user frequently posts about food, the collection unit can also preferentially collect genetic information related to diet. In this way, by analyzing social media activities, related genetic information can be preferentially collected.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. For example, the analysis unit performs a detailed analysis of important genetic information. The analysis unit can also perform a brief analysis of less important genetic information. The analysis unit can also perform a detailed analysis when the user's health risk is high. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the genetic information.

[0044] The analysis unit can apply different analysis algorithms depending on the category of genetic information during analysis. For example, the analysis unit applies a health risk assessment algorithm to health-related genetic information. The analysis unit can also apply a compatibility assessment algorithm to genetic information related to selecting a marriage partner. The analysis unit can also apply a blood relationship assessment algorithm to genetic information related to confirming blood relationships. In this way, by applying different analysis algorithms depending on the category of genetic information, more accurate analysis is possible.

[0045] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the genetic information. For example, the analysis unit prioritizes the analysis of the most recently submitted genetic information. The analysis unit can also postpone the analysis of genetic information that has been submitted a long time ago. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of the analysis based on the time of submission, the latest information can be analyzed with priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during analysis. For example, the analysis unit prioritizes analysis of highly relevant genetic information. The analysis unit can also postpone analysis of less relevant genetic information. The analysis unit can also adjust the analysis schedule based on the relevance. Thus, by adjusting the order of analysis based on the relevance, important information can be prioritized in analysis.

[0047] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides detailed information for important analysis results. The providing unit can also provide concise information for analysis results that are not so important. The providing unit can also provide detailed information when the user's health risk is high. In this way, important information can be provided in detail by adjusting the level of detail of the provision based on the importance of the analysis result.

[0048] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the information. For example, the providing unit applies a health management advice algorithm to health-related analysis results. The providing unit can also apply a partner recommendation algorithm to analysis results related to marriage partner selection. The providing unit can also apply a blood relationship confirmation algorithm to analysis results related to confirmation of blood relationships. In this way, more appropriate information can be provided by applying different providing algorithms depending on the category of the analysis results.

[0049] The providing unit can determine the priority of provision based on the time of submission of the analysis results at the time of provision. For example, the providing unit provides the most recently submitted analysis results preferentially. The providing unit can also postpone analysis results that were submitted earlier. The providing unit can also adjust the provision schedule based on the time of submission. In this way, by determining the priority of provision based on the time of submission, the latest information can be provided preferentially.

[0050] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also postpone analysis results with low relevance. The providing unit can also adjust the schedule of provision based on relevance. In this way, important information can be provided preferentially by adjusting the order of provision based on relevance.

[0051] At the time of protection, the protection unit can select the optimal protection method by referring to the user's past data protection history. For example, the protection unit proposes the optimal protection method based on the protection method previously selected by the user. The protection unit can also analyze the user's past data protection history and select the most effective protection method. The protection unit can also customize the protection method by referring to the user's past data protection history. In this way, the optimal protection method can be selected by referring to the past data protection history.

[0052] During protection, the protection unit can customize the protection measures based on the user's current privacy settings. For example, the protection unit applies enhanced protection measures when the user's privacy settings are high. The protection unit can also apply standard protection measures when the user's privacy settings are low. The protection unit can also customize the protection measures based on the user's privacy settings. This allows for more appropriate protection to be provided by customizing the protection measures based on the current privacy settings.

[0053] During protection, the protection unit can select an optimal protection method by taking into consideration the geographical location information of the user. For example, if the user lives in an urban area, the protection unit applies a protection method suitable for an urban environment. Also, if the user lives in a highland area, the protection unit can apply a protection method suitable for a highland environment. Also, if the user lives by the sea, the protection unit can apply a protection method suitable for a marine environment. In this way, the optimal protection method can be selected by taking into consideration the geographical location information.

[0054] During protection, the protection unit can analyze the user's social media activity and suggest protection measures. For example, if the user frequently uses social media, the protection unit can suggest enhanced protection measures. In addition, if the user discloses a lot of personal information on social media, the protection unit can also suggest measures to strengthen privacy protection. In addition, the protection unit can analyze the user's social media activity and suggest optimal protection measures. In this way, optimal protection measures can be suggested by analyzing social media activities.

[0055] When giving advice, the advice unit can provide optimal advice by referring to the user's past health data. The advice unit can provide health management advice based on the user's past medical history, for example. The advice unit can also provide appropriate advice by referring to the user's past health checkup results. The advice unit can also provide optimal advice based on the user's past lifestyle habit data. In this way, optimal advice can be provided by referring to past health data.

[0056] When giving advice, the advice unit can customize the contents of the advice based on the user's current lifestyle. For example, if the user has a healthy lifestyle, the advice unit can provide advice for maintaining the lifestyle. Also, if the user has an unhealthy lifestyle, the advice unit can provide advice for improving the lifestyle. Also, the advice unit can provide specific advice based on the user's current lifestyle. In this way, by customizing the contents of the advice based on the user's current lifestyle, more appropriate advice can be provided.

[0057] When providing advice, the advice unit can provide optimal advice by taking into account the geographical location information of the user. For example, if the user lives in an urban area, the advice unit can provide advice suitable for an urban environment. Also, if the user lives in a highland area, the advice unit can provide advice suitable for a highland environment. Also, if the user lives by the sea, the advice unit can provide advice suitable for a marine environment. In this way, optimal advice can be provided by taking into account the geographical location information.

[0058] When giving advice, the advice unit can analyze the user's social media activity and suggest the content of the advice. For example, if the user frequently posts about health, the advice unit can provide health management advice. Furthermore, if the user frequently posts about sports, the advice unit can also provide advice about exercise. Furthermore, if the user frequently posts about food, the advice unit can also provide advice about diet. In this way, optimal advice can be suggested by analyzing social media activity.

[0059] At the time of selection, the selection unit can provide an optimal selection method by referring to the user's past partner selection history. For example, the selection unit recommends an optimal partner based on the user's past partner selection history. The selection unit can also analyze the user's past selection history and select the most compatible partner. The selection unit can also customize the selection method by referring to the user's past selection history. In this way, the optimal selection method can be provided by referring to the past partner selection history.

[0060] The selection unit can customize the selection means based on the user's current living situation at the time of selection. For example, when the user is busy, the selection unit provides a simple means for making a quick selection. The selection unit can also provide a detailed selection means when the user is relaxed. The selection unit can also customize the selection means based on the user's current living situation. In this way, by customizing the selection means based on the user's current living situation, a more appropriate selection result can be provided.

[0061] The selection unit can provide an optimal selection method by taking into consideration the geographical location information of the user at the time of selection. For example, if the user lives in an urban area, the selection unit can provide a selection method suitable for an urban environment. Also, if the user lives in a highland area, the selection unit can provide a selection method suitable for a highland environment. Also, if the user lives by the sea, the selection unit can provide a selection method suitable for a marine environment. In this way, the optimal selection method can be provided by taking into consideration the geographical location information.

[0062] At the time of selection, the selection unit can analyze the social media activity of the user and suggest a selection means. For example, if the user frequently uses social media, the selection unit can suggest a selection means based on the social media activity. Also, if the user has many friends on social media, the selection unit can suggest a selection means taking into consideration the friendships. Also, the selection unit can analyze the social media activity of the user and suggest an optimal selection means. In this way, the optimal selection means can be suggested by analyzing the social media activity.

[0063] At the time of confirmation, the confirmation unit can provide an optimal confirmation method by referring to the user's past family composition data. For example, the confirmation unit can suggest an optimal confirmation method based on the user's past family composition data. The confirmation unit can also analyze the user's past family composition data and select the most effective confirmation method. The confirmation unit can also customize the confirmation method by referring to the user's past family composition data. In this way, the optimal confirmation method can be provided by referring to the past family composition data.

[0064] The confirmation unit can customize the confirmation means based on the user's current living situation at the time of confirmation. For example, when the user is busy, the confirmation unit provides a simple means for quick confirmation. Also, when the user is relaxed, the confirmation unit can provide a detailed confirmation means. Also, the confirmation unit can customize the confirmation means based on the user's current living situation. In this way, by customizing the confirmation means based on the current living situation, more appropriate confirmation results can be provided.

[0065] The confirmation unit can provide an optimal confirmation method by taking into consideration the geographical location information of the user at the time of confirmation. For example, if the user lives in an urban area, the confirmation unit can provide a confirmation method suitable for an urban environment. Also, if the user lives in a highland area, the confirmation unit can provide a confirmation method suitable for a highland environment. Also, if the user lives on the seashore, the confirmation unit can provide a confirmation method suitable for a marine environment. In this way, the optimal confirmation method can be provided by taking into consideration the geographical location information.

[0066] At the time of confirmation, the confirmation unit can analyze the user's social media activity and suggest a means of confirmation. For example, if the user frequently uses social media, the confirmation unit can suggest a means of confirmation based on the social media activity. Also, if the user has many friends on social media, the confirmation unit can suggest a means of confirmation that takes into account the friendships. Also, the confirmation unit can analyze the user's social media activity and suggest the optimal means of confirmation. In this way, the optimal means of confirmation can be suggested by analyzing social media activity.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] When collecting the user's genetic information, the collection unit can monitor the user's lifestyle data in real time and dynamically adjust the type of genetic information to be collected. For example, when the user is exercising, genetic information related to exercise is preferentially collected. Also, when the user is eating, genetic information related to eating can also be collected. Furthermore, when the user is sleeping, genetic information related to sleep can also be collected. In this way, it is possible to collect optimal genetic information according to the user's lifestyle.

[0069] When supporting the selection of a marriage partner, the selection unit can analyze the user's past partner selection history and provide an optimal selection method. For example, a compatible partner is recommended based on the characteristics of partners selected by the user in the past. The selection method can also be customized by referring to the user's past selection history. Furthermore, the selection history of the user can be analyzed to suggest the most effective selection method. In this way, the optimal selection method can be provided by referring to the past partner selection history.

[0070] When collecting the user's genetic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives at high altitude, genetic information related to high altitude can be collected preferentially. Also, if the user lives in an urban area, genetic information related to an urban environment can be collected preferentially. Furthermore, if the user lives by the sea, genetic information related to a marine environment can be collected preferentially. In this way, by taking into account the geographical location information, highly relevant genetic information can be collected preferentially.

[0071] When analyzing the genetic information of a user, the analysis unit can apply different analysis algorithms depending on the category of the genetic information. For example, a health risk assessment algorithm can be applied to health-related genetic information. A compatibility assessment algorithm can also be applied to genetic information related to selecting a marriage partner. Furthermore, a blood relationship assessment algorithm can be applied to genetic information related to confirming blood relationships. In this way, by applying different analysis algorithms depending on the category of genetic information, more accurate analysis can be performed.

[0072] When collecting the user's genetic information, the collection unit can analyze the user's social media activities and collect related information. For example, if the user frequently posts about health, health-related genetic information can be preferentially collected. In addition, if the user frequently posts about sports, genetic information related to athletic ability can also be preferentially collected. Furthermore, if the user frequently posts about food, genetic information related to diet can also be preferentially collected. In this way, by analyzing social media activities, related genetic information can be preferentially collected.

[0073] The process flow of the first embodiment will be briefly described below.

[0074] Step 1: The collection unit collects the user's genetic information. The user's genetic information includes, for example, DNA sequence data, genetic marker information, etc. The collection unit collects samples such as saliva and blood using a dedicated kit, and these samples are sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed and the user's genetic data is generated. Step 2: The analysis unit combines the genetic information collected by the collection unit with medical data and family structure data and analyzes them. The medical data includes electronic medical records, diagnosis results, treatment history, etc., and the family structure data includes family trees and family health history, etc. The analysis is performed using statistical analysis and machine learning algorithms. Step 3: The providing unit provides the analysis results obtained by the analyzing unit in the form of a report, a dashboard display, or the like. Step 4: The protection unit protects the user's genetic information by methods such as data encryption and access control.

[0075] (Example 2) A genetic information utilization system according to an embodiment of the present invention is a system that collects genetic information, combines it with medical data and family structure data, analyzes it, and provides services such as health management advice, marriage partner selection, and blood relationship confirmation to users. In this system, a user undergoes a genetic test and collects the genetic information. Next, the collected genetic information is combined with medical data and family structure data and analyzed. Based on the analysis results, services such as health management advice, marriage partner selection, and blood relationship confirmation are provided to the user. In addition, these data can be used by future descendants, and services can be provided to future users as well. For example, a user undergoes a genetic test. At this time, the user provides a sample such as saliva or blood using a dedicated kit. The provided sample is sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed and the user's genetic data is generated. Next, the generated genetic data is combined with the medical data and family structure data for analysis. For example, the user's health risk can be evaluated by combining the user's genetic data with data such as past medical history, lifestyle habits, and family health conditions. In addition, optimal health management advice can be provided to the user based on the genetic data. In addition, a service that supports the selection of a marriage partner based on the genetic data is also provided. For example, it is possible to recommend a compatible partner based on the genetic data. It is also possible to confirm blood ties, and family relationships can be confirmed based on the genetic data. Finally, these data can also be used by future descendants, and services can be provided to future users as well. For example, if a user's descendants undergo genetic testing, more accurate analysis can be performed by combining the data with past data. This makes it possible to provide future users with services such as health management, marriage partner selection, and confirmation of blood ties. As a result, the genetic information utilization system can provide comprehensive services by collecting, analyzing, and providing the user's genetic information.

[0076] The genetic information utilization system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit collects genetic information of a user. The genetic information of the user includes, for example, DNA sequence data, genetic marker information, and the like, but is not limited to such examples. The collection unit collects samples such as saliva and blood using, for example, a dedicated kit. The collected samples are sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed, and the genetic data of the user is generated. The analysis unit analyzes the genetic information collected by the collection unit in combination with medical data and family structure data. The medical data includes, for example, electronic medical record information, diagnosis results, treatment history, and the like, but is not limited to such examples. The family structure data includes, for example, family trees, family health history, and the like, but is not limited to such examples. The analysis is performed, for example, using statistical analysis or machine learning algorithms. The provision unit provides the analysis results obtained by the analysis unit. The provision is performed, for example, in a report format, dashboard display, or other methods. The protection unit protects the genetic information of the user. The protection is performed, for example, by data encryption, access control, or other methods. As a result, the genetic information utilization system according to the embodiment can provide comprehensive services by collecting, analyzing, providing, and protecting users' genetic information.

[0077] The collection unit collects the user's genetic information. The user's genetic information includes, for example, DNA sequence data, genetic marker information, and the like, but is not limited to such examples. The collection unit collects samples such as saliva and blood using, for example, a dedicated kit. The dedicated kit includes a tube for collecting saliva and a lancet for collecting blood, so that the user can easily provide the sample. The collected sample is sent to a lab for analyzing the genetic information. The lab first checks the quality of the sample and performs appropriate processing, and then obtains DNA sequence data using next-generation sequencing (NGS) technology. NGS technology can analyze large amounts of genetic data at high speed and high accuracy, making it possible to analyze the user's genetic information in detail. The analyzed genetic data is stored in a dedicated database and used for subsequent analysis. In order to protect the user's privacy, the collection unit takes strict security measures in all processes from sample collection to data storage. For example, the sample is given an anonymous ID and is managed so as not to be directly linked to the user's personal information. In addition, temperature control is performed during transportation of the sample to prevent deterioration of quality. This enables the collection unit to safely and efficiently collect the user's genetic information and provide high-quality data for analysis.

[0078] The analysis unit combines the genetic information collected by the collection unit with medical data and family structure data to analyze it. The medical data includes, for example, electronic medical record information, diagnosis results, and treatment history, but is not limited to such examples. The family structure data includes, for example, family trees, family health history, and the like, but is not limited to such examples. The analysis is performed, for example, using statistical analysis and machine learning algorithms. Specifically, by integrating the genetic information and the medical data, it is possible to evaluate what disease risk a specific genetic mutation is associated with. For example, it is possible to analyze whether a specific genetic marker increases the risk of cardiovascular disease. In addition, it is possible to clarify how genetic disease risk is inherited within a family by using the family structure data. Machine learning algorithms are used to extract patterns from large amounts of data and build predictive models. For example, it is possible to build a model that predicts the risk of developing a specific disease by combining the genetic information and the lifestyle data. Furthermore, the analysis unit can also make suggestions for personalized medicine based on the user's genetic information. For example, it is possible to predict responsiveness to a specific drug and propose an optimal treatment. As a result, the analysis unit can analyze the user's genetic information from multiple angles and provide useful information for personalized medicine and health management.

[0079] The providing unit provides the analysis results obtained by the analyzing unit. The results are provided, for example, in a report format or a dashboard display. In the report format, health risks based on the user's genetic information, recommended lifestyle habits, preventive measures, and the like are described in detail. The report is created in a visually easy-to-understand manner using charts and graphs so that even users without specialized medical knowledge can easily understand it. In the dashboard display, the user can check the analysis results in real time through a web browser or a dedicated app. The dashboard displays the user's health condition, risk assessment, recommended action plans, and the like, allowing the user to efficiently manage their own health. In addition, the providing unit also provides a function that allows the user to share the analysis results with a medical professional. For example, the user can send the genetic information report to the doctor in charge and receive advice from the doctor. In addition, the providing unit can collect user feedback and use it to improve the service. For example, feedback such as how the user felt about the provided information and what additional information they would like is collected and reflected in the creation of the next report and improvements to the dashboard. This allows the providing unit to provide valuable information to the user and support health management.

[0080] The protection unit protects the genetic information of the user. The protection is performed, for example, by methods such as data encryption and access control. Data encryption is a technology for preventing a third party from reading the genetic information when it is stored or communicated. For example, the genetic information is encrypted using a powerful encryption algorithm such as AES (Advanced Encryption Standard). Access control is a mechanism for restricting the users and systems that can access the genetic information. For example, the user's authentication information is used to allow only users with legitimate authority to access the genetic information. In addition, the access log can be recorded to detect and track unauthorized access. In addition, the protection unit also takes measures for backing up and recovering data. For example, data is backed up periodically to prepare for the unlikely event of data loss. In addition, recovery procedures are in place to quickly recover data in the event of a disaster or system failure. This allows the protection unit to safely protect the genetic information of the user and ensure privacy. In addition, the protection unit also takes measures to comply with the latest laws, regulations, and guidelines regarding data protection and maintain compliance. For example, the protection department will strive to protect user data in accordance with regulations such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act). This will enable the protection department to gain the trust of users and improve the safety and reliability of the entire system.

[0081] The providing unit may include an advising unit that provides health management advice. The advising unit, for example, evaluates the health risk of the user and provides health management advice. The health management advice includes, for example, but is not limited to, dietary guidance, exercise plans, suggestions for improving lifestyle habits, and the like. For example, the advising unit evaluates the health risk of the user by combining the genetic data of the user with data such as past medical history, lifestyle habits, and family health conditions. In addition, the advising unit can provide optimal health management advice to the user. As a result, the provision of health management advice can assist the user in maintaining their health.

[0082] The providing unit may include a selecting unit that supports the selection of a marriage partner. The selecting unit, for example, recommends a compatible partner based on the genetic data of the user. The selection of a marriage partner may include, but is not limited to, genetic compatibility, personality diagnosis results, and the like. For example, the selecting unit compares the genetic data of the user with the genetic data of a compatible partner and recommends a compatible partner. The selecting unit may also recommend a compatible partner based on the personality diagnosis results of the user. This can support the user in choosing a partner by supporting the selection of a marriage partner.

[0083] The providing unit may include a confirmation unit that confirms blood ties. The confirmation unit confirms family relationships based on, for example, the genetic data of the user. Confirmation of blood ties includes, for example, DNA parent-child testing, checking of family trees, and the like, but is not limited to such examples. For example, the confirmation unit compares the genetic data of the user with the genetic data of family members to confirm blood ties. The confirmation unit can also check family trees to confirm family relationships. In this way, confirmation of blood ties can be supported in confirming the user's family relationships.

[0084] The collection unit can collect the genetic information of the user using a dedicated kit. The dedicated kit includes, but is not limited to, for example, a saliva collection kit and a blood collection kit. In the collection unit, for example, a user provides a sample such as saliva or blood using the dedicated kit. The provided sample is sent to a lab for analyzing the genetic information. In the lab, the genetic information is analyzed and the user's genetic data is generated. As a result, the collection of genetic information can be efficiently performed by using the dedicated kit.

[0085] The protection unit can encrypt the genetic information of the user to protect privacy. The encryption includes, for example, AES encryption, RSA encryption, and the like, but is not limited to such examples. For example, the protection unit encrypts the genetic information of the user using AES encryption. The protection unit can also encrypt the genetic information of the user using RSA encryption. In this way, the privacy of the user can be protected by encrypting the genetic information.

[0086] The collection unit can estimate the user's emotion and adjust the timing of collecting genetic information based on the estimated emotion of the user. For example, when the user is relaxed, the collection unit immediately collects genetic information. Also, when the user is stressed, the collection unit can delay the timing of collection and wait until the user relaxes. Also, when the user is in a hurry, the collection unit can provide a simple kit for quickly collecting. This allows genetic information to be collected at a more appropriate timing by adjusting the timing of collection according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit can refer to the user's past medical history and recommend collection of a blood sample. The collection unit can also recommend collection of a saliva sample based on the user's past health check results. The collection unit can also select the optimal collection method taking into account the user's past allergy information. In this way, the optimal collection method can be selected by analyzing past health data.

[0088] The collection unit can perform filtering based on the user's lifestyle and environmental factors when collecting genetic information. The collection unit filters the collected data, for example, taking into account the user's smoking habits. The collection unit can also preferentially collect specific genetic information based on the user's diet. The collection unit can also collect genetic information related to environmental factors, taking into account the user's living environment. As a result, more accurate genetic information can be collected by filtering based on lifestyle and environmental factors.

[0089] The collection unit can estimate the user's emotion and determine the priority of the genetic information to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit collects all genetic information evenly. In addition, when the user is stressed, the collection unit can also collect important genetic information with priority. In addition, when the user is in a hurry, the collection unit can collect only the most important genetic information. In this way, by determining the priority of the genetic information according to the user's emotion, important information can be collected with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] When collecting genetic information, the collection unit can preferentially collect highly relevant information by taking into account the geographical location information of the user. For example, if the user lives at high altitude, the collection unit preferentially collects genetic information related to high altitude. Furthermore, if the user lives in an urban area, the collection unit can also preferentially collect genetic information related to an urban environment. Furthermore, if the user lives at the seaside, the collection unit can also preferentially collect genetic information related to a marine environment. In this way, by taking into account the geographical location information, highly relevant genetic information can be preferentially collected.

[0091] The collection unit can analyze the user's social media activities and collect related information when collecting genetic information. For example, if the user frequently posts about health, the collection unit preferentially collects health-related genetic information. In addition, if the user frequently posts about sports, the collection unit can also preferentially collect genetic information related to athletic ability. In addition, if the user frequently posts about food, the collection unit can also preferentially collect genetic information related to diet. In this way, by analyzing social media activities, related genetic information can be preferentially collected.

[0092] The analysis unit can estimate the user's emotion and adjust the way of expressing the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. In addition, when the user is stressed, the analysis unit can also provide a concise and to-the-point analysis result. In addition, when the user is in a hurry, the analysis unit can also provide an analysis result that highlights only the most important information. In this way, by adjusting the way of expressing the analysis according to the user's emotion, more appropriate analysis results can be provided. The estimation of emotion is realized using an emotion estimation function using, 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.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. For example, the analysis unit performs a detailed analysis of important genetic information. The analysis unit can also perform a brief analysis of less important genetic information. The analysis unit can also perform a detailed analysis when the user's health risk is high. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the genetic information.

[0094] The analysis unit can apply different analysis algorithms depending on the category of genetic information during analysis. For example, the analysis unit applies a health risk assessment algorithm to health-related genetic information. The analysis unit can also apply a compatibility assessment algorithm to genetic information related to selecting a marriage partner. The analysis unit can also apply a blood relationship assessment algorithm to genetic information related to confirming blood relationships. In this way, by applying different analysis algorithms depending on the category of genetic information, more accurate analysis is possible.

[0095] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit provides a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise and to-the-point analysis result when the user is stressed. The analysis unit can also provide an analysis result that highlights only the most important information when the user is in a hurry. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0096] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of the genetic information. For example, the analysis unit prioritizes the analysis of the most recently submitted genetic information. The analysis unit can also postpone the analysis of genetic information that has been submitted a long time ago. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the priority of the analysis based on the time of submission, the latest information can be analyzed with priority.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during analysis. For example, the analysis unit prioritizes analysis of highly relevant genetic information. The analysis unit can also postpone analysis of less relevant genetic information. The analysis unit can also adjust the analysis schedule based on the relevance. Thus, by adjusting the order of analysis based on the relevance, important information can be prioritized in analysis.

[0098] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed information. Also, when the user is stressed, the providing unit can provide concise, to-the-point information. Also, when the user is in a hurry, the providing unit can provide only the most important information with emphasis. In this way, by adjusting the presentation method according to the user's emotion, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0099] The providing unit can adjust the level of detail of the provision based on the importance of the analysis result at the time of provision. For example, the providing unit provides detailed information for important analysis results. The providing unit can also provide concise information for analysis results that are not so important. The providing unit can also provide detailed information when the user's health risk is high. In this way, important information can be provided in detail by adjusting the level of detail of the provision based on the importance of the analysis result.

[0100] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the information. For example, the providing unit applies a health management advice algorithm to health-related analysis results. The providing unit can also apply a partner recommendation algorithm to analysis results related to marriage partner selection. The providing unit can also apply a blood relationship confirmation algorithm to analysis results related to confirmation of blood relationships. In this way, more appropriate information can be provided by applying different providing algorithms depending on the category of the analysis results.

[0101] The providing unit can estimate the user's emotion and adjust the length of the information provided based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed information. Also, when the user is stressed, the providing unit can provide concise information that focuses on the main points. Also, when the user is in a hurry, the providing unit can provide only the most important information with emphasis. This allows more appropriate information to be provided by adjusting the length of the information provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0102] The providing unit can determine the priority of provision based on the time of submission of the analysis results at the time of provision. For example, the providing unit provides the most recently submitted analysis results preferentially. The providing unit can also postpone analysis results that were submitted earlier. The providing unit can also adjust the provision schedule based on the time of submission. In this way, by determining the priority of provision based on the time of submission, the latest information can be provided preferentially.

[0103] The providing unit can adjust the order of provision based on the relevance of the analysis results at the time of provision. For example, the providing unit provides analysis results with high relevance preferentially. The providing unit can also postpone analysis results with low relevance. The providing unit can also adjust the schedule of provision based on relevance. In this way, important information can be provided preferentially by adjusting the order of provision based on relevance.

[0104] The protection unit can estimate the user's emotion and adjust the protection method based on the estimated user's emotion. For example, when the user is relaxed, the protection unit applies a standard protection method. Also, when the user is stressed, the protection unit can apply an enhanced protection method. Also, when the user is in a hurry, the protection unit can apply a simplified protection method for quickly providing protection. In this way, by adjusting the protection method according to the user's emotion, more appropriate protection can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] At the time of protection, the protection unit can select the optimal protection method by referring to the user's past data protection history. For example, the protection unit proposes the optimal protection method based on the protection method previously selected by the user. The protection unit can also analyze the user's past data protection history and select the most effective protection method. The protection unit can also customize the protection method by referring to the user's past data protection history. In this way, the optimal protection method can be selected by referring to the past data protection history.

[0106] During protection, the protection unit can customize the protection measures based on the user's current privacy settings. For example, the protection unit applies enhanced protection measures when the user's privacy settings are high. The protection unit can also apply standard protection measures when the user's privacy settings are low. The protection unit can also customize the protection measures based on the user's privacy settings. This allows for more appropriate protection to be provided by customizing the protection measures based on the current privacy settings.

[0107] The protection unit can estimate the user's emotion and determine the priority of protection based on the estimated user's emotion. For example, when the user is relaxed, the protection unit performs standard protection. In addition, when the user is stressed, the protection unit can also prioritize protection of important data. In addition, when the user is in a hurry, the protection unit can quickly protect the most important data. In this way, important data can be prioritized by determining the priority of protection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] During protection, the protection unit can select an optimal protection method by taking into consideration the geographical location information of the user. For example, if the user lives in an urban area, the protection unit applies a protection method suitable for an urban environment. Also, if the user lives in a highland area, the protection unit can apply a protection method suitable for a highland environment. Also, if the user lives by the sea, the protection unit can apply a protection method suitable for a marine environment. In this way, the optimal protection method can be selected by taking into consideration the geographical location information.

[0109] During protection, the protection unit can analyze the user's social media activity and suggest protection measures. For example, if the user frequently uses social media, the protection unit can suggest enhanced protection measures. In addition, if the user discloses a lot of personal information on social media, the protection unit can also suggest measures to strengthen privacy protection. In addition, the protection unit can analyze the user's social media activity and suggest optimal protection measures. In this way, optimal protection measures can be suggested by analyzing social media activities.

[0110] The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. For example, when the user is relaxed, the advice unit provides detailed advice. Also, when the user is stressed, the advice unit can provide concise advice that focuses on the main points. Also, when the user is in a hurry, the advice unit can provide advice that emphasizes only the most important information. In this way, by adjusting the way of expressing advice according to the user's emotion, more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0111] When giving advice, the advice unit can provide optimal advice by referring to the user's past health data. The advice unit can provide health management advice based on the user's past medical history, for example. The advice unit can also provide appropriate advice by referring to the user's past health checkup results. The advice unit can also provide optimal advice based on the user's past lifestyle habit data. In this way, optimal advice can be provided by referring to past health data.

[0112] When giving advice, the advice unit can customize the contents of the advice based on the user's current lifestyle. For example, if the user has a healthy lifestyle, the advice unit can provide advice for maintaining the lifestyle. Also, if the user has an unhealthy lifestyle, the advice unit can provide advice for improving the lifestyle. Also, the advice unit can provide specific advice based on the user's current lifestyle. In this way, by customizing the contents of the advice based on the user's current lifestyle, more appropriate advice can be provided.

[0113] The advice unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. For example, when the user is relaxed, the advice unit provides all advice equally. Also, when the user is stressed, the advice unit can provide important advice with priority. Also, when the user is in a hurry, the advice unit can provide only the most important advice. In this way, by determining the priority of advice according to the user's emotion, important advice can be provided with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] When providing advice, the advice unit can provide optimal advice by taking into account the geographical location information of the user. For example, if the user lives in an urban area, the advice unit can provide advice suitable for an urban environment. Also, if the user lives in a highland area, the advice unit can provide advice suitable for a highland environment. Also, if the user lives by the sea, the advice unit can provide advice suitable for a marine environment. In this way, optimal advice can be provided by taking into account the geographical location information.

[0115] When giving advice, the advice unit can analyze the user's social media activity and suggest the content of the advice. For example, if the user frequently posts about health, the advice unit can provide health management advice. Furthermore, if the user frequently posts about sports, the advice unit can also provide advice about exercise. Furthermore, if the user frequently posts about food, the advice unit can also provide advice about diet. In this way, optimal advice can be suggested by analyzing social media activity.

[0116] The selection unit can estimate the user's emotion and adjust the selection expression method based on the estimated user's emotion. For example, when the user is relaxed, the selection unit provides a detailed selection result. In addition, when the user is stressed, the selection unit can provide a concise and to-the-point selection result. In addition, when the user is in a hurry, the selection unit can provide a selection result that highlights only the most important information. In this way, by adjusting the selection expression method according to the user's emotion, a more appropriate selection result can be provided. The emotion estimation is realized using an emotion estimation function using, 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.

[0117] At the time of selection, the selection unit can provide an optimal selection method by referring to the user's past partner selection history. For example, the selection unit recommends an optimal partner based on the user's past partner selection history. The selection unit can also analyze the user's past selection history and select the most compatible partner. The selection unit can also customize the selection method by referring to the user's past selection history. In this way, the optimal selection method can be provided by referring to the past partner selection history.

[0118] The selection unit can customize the selection means based on the user's current living situation at the time of selection. For example, when the user is busy, the selection unit provides a simple means for making a quick selection. The selection unit can also provide a detailed selection means when the user is relaxed. The selection unit can also customize the selection means based on the user's current living situation. In this way, by customizing the selection means based on the user's current living situation, a more appropriate selection result can be provided.

[0119] The selection unit can estimate the user's emotion and determine the selection priority based on the estimated user's emotion. For example, when the user is relaxed, the selection unit provides all selection results equally. Also, when the user is stressed, the selection unit can provide important selection results preferentially. Also, when the user is in a hurry, the selection unit can provide only the most important selection results. In this way, by determining the selection priority according to the user's emotion, important selection results can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0120] The selection unit can provide an optimal selection method by taking into consideration the geographical location information of the user at the time of selection. For example, if the user lives in an urban area, the selection unit can provide a selection method suitable for an urban environment. Also, if the user lives in a highland area, the selection unit can provide a selection method suitable for a highland environment. Also, if the user lives by the sea, the selection unit can provide a selection method suitable for a marine environment. In this way, the optimal selection method can be provided by taking into consideration the geographical location information.

[0121] At the time of selection, the selection unit can analyze the social media activity of the user and suggest a selection means. For example, if the user frequently uses social media, the selection unit can suggest a selection means based on the social media activity. Also, if the user has many friends on social media, the selection unit can suggest a selection means taking into consideration the friendships. Also, the selection unit can analyze the social media activity of the user and suggest an optimal selection means. In this way, the optimal selection means can be suggested by analyzing the social media activity.

[0122] The confirmation unit can estimate the user's emotion and adjust the confirmation expression method based on the estimated user's emotion. For example, when the user is relaxed, the confirmation unit provides a detailed confirmation result. In addition, when the user is stressed, the confirmation unit can also provide a brief confirmation result that focuses on the main points. In addition, when the user is in a hurry, the confirmation unit can also provide a confirmation result that highlights only the most important information. In this way, by adjusting the confirmation expression method according to the user's emotion, a more appropriate confirmation result can be provided. The emotion estimation is realized using an emotion estimation function using, 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.

[0123] At the time of confirmation, the confirmation unit can provide an optimal confirmation method by referring to the user's past family composition data. For example, the confirmation unit can suggest an optimal confirmation method based on the user's past family composition data. The confirmation unit can also analyze the user's past family composition data and select the most effective confirmation method. The confirmation unit can also customize the confirmation method by referring to the user's past family composition data. In this way, the optimal confirmation method can be provided by referring to the past family composition data.

[0124] The confirmation unit can customize the confirmation means based on the user's current living situation at the time of confirmation. For example, when the user is busy, the confirmation unit provides a simple means for quick confirmation. Also, when the user is relaxed, the confirmation unit can provide a detailed confirmation means. Also, the confirmation unit can customize the confirmation means based on the user's current living situation. In this way, by customizing the confirmation means based on the current living situation, more appropriate confirmation results can be provided.

[0125] The confirmation unit can estimate the user's emotion and determine the priority of confirmation based on the estimated user's emotion. For example, when the user is relaxed, the confirmation unit provides all confirmation results equally. Also, when the user is stressed, the confirmation unit can provide important confirmation results preferentially. Also, when the user is in a hurry, the confirmation unit can provide only the most important confirmation results. In this way, by determining the priority of confirmation according to the user's emotion, important confirmation results can be provided preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0126] The confirmation unit can provide an optimal confirmation method by taking into consideration the geographical location information of the user at the time of confirmation. For example, if the user lives in an urban area, the confirmation unit can provide a confirmation method suitable for an urban environment. Also, if the user lives in a highland area, the confirmation unit can provide a confirmation method suitable for a highland environment. Also, if the user lives on the seashore, the confirmation unit can provide a confirmation method suitable for a marine environment. In this way, the optimal confirmation method can be provided by taking into consideration the geographical location information.

[0127] At the time of confirmation, the confirmation unit can analyze the user's social media activity and suggest a means of confirmation. For example, if the user frequently uses social media, the confirmation unit can suggest a means of confirmation based on the social media activity. Also, if the user has many friends on social media, the confirmation unit can suggest a means of confirmation that takes into account the friendships. Also, the confirmation unit can analyze the user's social media activity and suggest the optimal means of confirmation. In this way, the optimal means of confirmation can be suggested by analyzing social media activity.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] When collecting the user's genetic information, the collection unit can monitor the user's lifestyle data in real time and dynamically adjust the type of genetic information to be collected. For example, when the user is exercising, genetic information related to exercise is preferentially collected. Also, when the user is eating, genetic information related to eating can also be collected. Furthermore, when the user is sleeping, genetic information related to sleep can also be collected. In this way, it is possible to collect optimal genetic information according to the user's lifestyle.

[0130] When providing health management advice to the user, the providing unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, advice to relax can be provided preferentially. Also, if the user is relaxed, specific advice to maintain health can be provided. Furthermore, if the user is in a hurry, concise advice that gets to the point can be provided. In this way, optimal health management advice can be provided according to the user's emotions.

[0131] When supporting the selection of a marriage partner, the selection unit can analyze the user's past partner selection history and provide an optimal selection method. For example, a compatible partner is recommended based on the characteristics of partners selected by the user in the past. The selection method can also be customized by referring to the user's past selection history. Furthermore, the selection history of the user can be analyzed to suggest the most effective selection method. In this way, the optimal selection method can be provided by referring to the past partner selection history.

[0132] When confirming blood ties, the confirmation unit can estimate the user's emotions and adjust the confirmation expression method based on the estimated emotions. For example, if the user is relaxed, a detailed confirmation result can be provided. Also, if the user is stressed, a concise confirmation result that focuses on the main points can be provided. Furthermore, if the user is in a hurry, a confirmation result that highlights only the most important information can be provided. In this way, by adjusting the confirmation expression method according to the user's emotions, more appropriate confirmation results can be provided.

[0133] When collecting the user's genetic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives at high altitude, genetic information related to high altitude can be collected preferentially. Also, if the user lives in an urban area, genetic information related to an urban environment can be collected preferentially. Furthermore, if the user lives by the sea, genetic information related to a marine environment can be collected preferentially. In this way, by taking into account the geographical location information, highly relevant genetic information can be collected preferentially.

[0134] When protecting the genetic information of a user, the protection unit can estimate the user's emotions and adjust the protection method based on the estimated emotions. For example, when the user is relaxed, a standard protection method can be applied. Also, when the user is stressed, an enhanced protection method can be applied. Furthermore, when the user is in a hurry, a simplified protection method for quickly providing protection can be applied. In this way, by adjusting the protection method according to the user's emotions, more appropriate protection can be provided.

[0135] When analyzing the genetic information of a user, the analysis unit can apply different analysis algorithms depending on the category of the genetic information. For example, a health risk assessment algorithm can be applied to health-related genetic information. A compatibility assessment algorithm can also be applied to genetic information related to selecting a marriage partner. Furthermore, a blood relationship assessment algorithm can be applied to genetic information related to confirming blood relationships. In this way, by applying different analysis algorithms depending on the category of genetic information, more accurate analysis can be performed.

[0136] The providing unit can estimate the user's emotion and adjust the manner of presentation based on the estimated emotion. For example, when the user is relaxed, detailed information can be provided. When the user is stressed, concise information that focuses on the main points can be provided. Furthermore, when the user is in a hurry, only the most important information can be provided with emphasis. In this way, by adjusting the manner of presentation according to the user's emotion, more appropriate information can be provided.

[0137] When collecting the user's genetic information, the collection unit can analyze the user's social media activities and collect related information. For example, if the user frequently posts about health, health-related genetic information can be preferentially collected. In addition, if the user frequently posts about sports, genetic information related to athletic ability can also be preferentially collected. Furthermore, if the user frequently posts about food, genetic information related to diet can also be preferentially collected. In this way, by analyzing social media activities, related genetic information can be preferentially collected.

[0138] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided that focus on the main points. Furthermore, if the user is in a hurry, analysis results that highlight only the most important information can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0139] The process flow of the second embodiment will be briefly described below.

[0140] Step 1: The collection unit collects the user's genetic information. The user's genetic information includes, for example, DNA sequence data, genetic marker information, etc. The collection unit collects samples such as saliva and blood using a dedicated kit, and these samples are sent to a laboratory for analyzing the genetic information. In the laboratory, the genetic information is analyzed and the user's genetic data is generated. Step 2: The analysis unit combines the genetic information collected by the collection unit with medical data and family structure data and analyzes them. The medical data includes electronic medical records, diagnosis results, treatment history, etc., and the family structure data includes family trees and family health history, etc. The analysis is performed using statistical analysis and machine learning algorithms. Step 3: The providing unit provides the analysis results obtained by the analyzing unit in the form of a report, a dashboard display, or the like. Step 4: The protection unit protects the user's genetic information by methods such as data encryption and access control.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0143] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision 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 collection unit is realized by the control unit 46A of the smart device 14 and collects the user's genetic information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and combines the collected genetic information with medical data and family structure data to analyze it. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the analysis result to the user. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's genetic information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0146] 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0152] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] 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.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0155] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0159] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, and protection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects the user's genetic information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and combines the collected genetic information with medical data and family composition data for analysis. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the analysis result to the user. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's genetic information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0161] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0162] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] 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.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0168] 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.

[0169] 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.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0171] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0173] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0175] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision 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 collection unit is realized by the control unit 46A of the headset type terminal 314 and collects the user's genetic information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and combines the collected genetic information with medical data and family structure data for analysis. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the analysis result to the user. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's genetic information. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0180] 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.

[0181] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0182] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0183] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0184] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0185] 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.

[0186] 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.

[0187] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0188] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0189] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0190] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0191] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0192] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0193] Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision 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 collection unit is realized by the control unit 46A of the robot 414, and collects the genetic information of the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and combines the collected genetic information with medical data and family structure data to analyze it. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the analysis result to the user. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the genetic information of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0194] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0195] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0196] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0197] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0198] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0199] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0200] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0201] 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.

[0202] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0203] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0204] 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.

[0205] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0206] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0207] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0208] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0209] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0210] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0211] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0212] (Appendix 1) A collection unit that collects genetic information; an analysis unit that analyzes the genetic information collected by the collection unit in combination with medical data or family structure data; a providing unit that provides an analysis result obtained by the analysis unit; Equipped with a protection unit that protects the user's genetic information A system characterized by: (Appendix 2) The providing unit is Equipping the department with advice on health management 2. The system of claim 1, (Appendix 3) The providing unit is Establish a selection department to assist in the selection of marriage partners 2. The system of claim 1, (Appendix 4) The providing unit is Equipped with a verification section to verify blood ties 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collecting the user's genetic information using a special kit 2. The system of claim 1, (Appendix 6) The protective part is Encrypting users' genetic information to protect privacy 2. The system of claim 1, (Appendix 7) The collecting unit includes: The system estimates the user's emotions and adjusts the timing of collection of genetic information based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past health data and select the appropriate collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting genetic information, it will be filtered based on the user's lifestyle and environmental factors. 2. The system of claim 1, (Appendix 10) The collecting unit includes: The user's emotions are estimated, and the priority of genetic information to be collected is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting genetic information, the system prioritizes collection of information that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting genetic information, we analyze users' social media activity to collect related information. 2. The system of claim 1, (Appendix 13) The analysis unit includes: Infer user sentiment and adjust analytics presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit includes: During analysis, adjust the level of analysis detail based on the importance of genetic information 2. The system of claim 1, (Appendix 15) The analysis unit includes: During analysis, different analysis algorithms are applied depending on the category of genetic information. 2. The system of claim 1, (Appendix 16) The analysis unit includes: Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit includes: During analysis, prioritize analysis based on when genetic information was submitted 2. The system of claim 1, (Appendix 18) The analysis unit includes: During analysis, the order of analysis is adjusted based on the relevance of the genetic information. 2. The system of claim 1, (Appendix 19) The providing unit is To estimate a user's emotion and adjust a presentation method of the offering based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The providing unit is At the time of delivery, adjust the level of detail of the delivery based on the importance of the analysis results 2. The system of claim 1, (Appendix 21) The providing unit is When serving, different serving algorithms are applied depending on the category of analysis results. 2. The system of claim 1, (Appendix 22) The providing unit is Inferring user emotion and adjusting the length of the offering based on the estimated user emotion 2. The system of claim 1, (Appendix 23) The providing unit is At the time of delivery, delivery will be prioritized based on the timeliness of the analysis results. 2. The system of claim 1, (Appendix 24) The providing unit is At the time of serving, adjust the order of serving based on the relevance of the analysis results 2. The system of claim 1, (Appendix 25) The protective part is To estimate a user's emotions and adjust a protection method based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The protective part is When protecting data, the system selects the appropriate protection method by referring to the user's past data protection history. 2. The system of claim 1, (Appendix 27) The protective part is When protecting, customize protection measures based on the user's current privacy settings 2. The system of claim 1, (Appendix 28) The protective part is To estimate a user's emotion and determine a protection priority based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The protective part is When protecting, the appropriate protection method is selected taking into account the user's geographic location information 2. The system of claim 1, (Appendix 30) The protective part is When protecting, it analyzes the user's social media activity and suggests protection measures 2. The system of claim 1, (Appendix 31) The advice unit, Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 3. The system of claim 2, (Appendix 32) The advice unit, When providing advice, the system refers to the user's past health data to provide optimal advice. 3. The system of claim 2, (Appendix 33) The advice unit, When providing advice, customize the advice content based on the user's current lifestyle habits 3. The system of claim 2, (Appendix 34) The advice unit, Estimate the user's emotions and prioritize advice based on the estimated user emotions. 3. The system of claim 2, (Appendix 35) The advice unit, When providing advice, the system takes into account the user's geographic location to provide appropriate advice. 3. The system of claim 2, (Appendix 36) The advice unit, When providing advice, the system analyzes the user's social media activity to suggest the content of the advice. 3. The system of claim 2, (Appendix 37) The selection unit is Inferring user emotion and adjusting the way selections are expressed based on the estimated user emotion 4. The system of claim 3. (Appendix 38) The selection unit is When selecting a partner, the system provides the optimal selection method by referring to the user's past partner selection history. 4. The system of claim 3. (Appendix 39) The selection unit is At the time of selection, customize the selection method based on the user's current life situation 4. The system of claim 3. (Appendix 40) The selection unit is Estimate user emotions and determine selection priorities based on the estimated user emotions 4. The system of claim 3. (Appendix 41) The selection unit is When selecting, provide an appropriate selection method taking into account the user's geographic location information. 4. The system of claim 3. (Appendix 42) The selection unit is When selecting, analyze users' social media activity to suggest selection methods 4. The system of claim 3. (Appendix 43) The confirmation unit is Inferring a user's emotions and adjusting the way confirmation is expressed based on the estimated user's emotions 5. The system of claim 4. (Appendix 44) The confirmation unit is When verifying, the system provides the optimal verification method by referencing the user's past family composition data. 5. The system of claim 4. (Appendix 45) The confirmation unit is During verification, customize the verification experience based on the user's current life situation 5. The system of claim 4. (Appendix 46) The confirmation unit is Estimate user's emotions and prioritize confirmations based on the estimated user's emotions 5. The system of claim 4. (Appendix 47) The confirmation unit is When verifying, provide appropriate verification methods taking into account the user's geographic location 5. The system of claim 4. (Appendix 48) The confirmation unit is At the time of verification, the system analyzes the user's social media activity to suggest verification methods 5. The system of claim 4. [Explanation of symbols]

[0213] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects genetic information; an analysis unit that analyzes the genetic information collected by the collection unit in combination with medical data or family structure data; a providing unit that provides an analysis result obtained by the analysis unit; Equipped with a protection unit that protects the user's genetic information A system characterized by:

2. The providing unit is Equipping the department with advice on health management 2. The system of claim 1 .

3. The providing unit is Establish a selection department to assist in the selection of marriage partners 2. The system of claim 1 .

4. The providing unit is Equipped with a verification section to verify blood ties 2. The system of claim 1 .

5. The collecting unit includes: Collecting the user's genetic information using a special kit 2. The system of claim 1 .

6. The protective part is Encrypting users' genetic information to protect privacy 2. The system of claim 1 .

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

2. The system of claim 1 .

8. The collecting unit includes: Analyze the user's past health data and select the appropriate collection method 2. The system of claim 1 .

9. The collecting unit includes: When collecting genetic information, it will be filtered based on the user's lifestyle and environmental factors.

2. The system of claim 1 .

10. The collecting unit includes: The user's emotions are estimated, and the priority of genetic information to be collected is determined based on the estimated user's emotions.

2. The system of claim 1 .

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