system

Abstract

The system according to the embodiment aims to assess the risk of genetic diseases, grasp the progress of the diseases, and propose appropriate treatments and preventive measures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects a patient's genetic sequence data, clinical symptoms, treatment history, and other related information. The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of genetic diseases. The proposal unit proposes treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. The support unit provides the content proposed by the proposal unit to medical professionals.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately assess the risk of genetic diseases or grasp their progression, and there is room for improvement.

[0005] The system according to the embodiment aims to assess the risk of genetic diseases, grasp the progress of the diseases, and propose appropriate treatments and preventive measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects a patient's genetic sequence data, clinical symptoms, treatment history, and other related information. The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of genetic diseases. The proposal unit proposes treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. The support unit provides the content proposed by the proposal unit to medical professionals. [Effects of the Invention]

[0007] The system according to the embodiment can assess the risk of genetic diseases, grasp the progress of the diseases, and propose appropriate treatments and preventive measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0028] (Example 1) A genetic disease management system according to an embodiment of the present invention collects a patient's gene sequence data, clinical symptoms, treatment history, and other related information, and uses AI to analyze this information to assess the risk and progression of genetic diseases and recommend appropriate treatments and preventive measures. This system collects information such as a patient's gene sequence data, clinical symptoms, and treatment history, and uses AI to analyze this information to assess the risk and progression of genetic diseases. Based on the analysis results, the system recommends appropriate treatments and preventive measures for the patient. This allows patients to optimally manage their genetic diseases. Medical professionals can also use the AI's advice based on the patient's information to determine treatment plans. This is expected to improve the quality of genetic disease treatment and improve the patient's health. For example, information such as a patient's gene sequence data, clinical symptoms, and treatment history is collected. This information is input into a platform. AI then analyzes this information to assess the risk and progression of genetic diseases. For example, it determines whether a specific gene mutation increases the risk of developing the disease. Based on the analysis results, the AI ​​recommends appropriate treatments and preventive measures for the patient. For example, it advises whether a specific treatment is effective or whether lifestyle changes are necessary. This allows patients to optimally manage their genetic diseases. This platform is also useful for medical professionals. Medical professionals can decide on treatment plans based on the patient's information and the advice provided by AI. This is expected to improve the quality of treatment for genetic diseases and the patient's health. As a result, the genetic disease management system will be able to efficiently manage genetic diseases by collecting, analyzing, proposing, and providing support for information on patients' genetic diseases.

[0029] A genetic disease management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects a patient's genetic sequence data, clinical symptoms, treatment history, and other related information. For example, the collection unit can use technologies such as whole genome sequencing or exome sequencing to acquire the patient's genetic sequence data. The collection unit can also acquire data from an electronic medical record system or a wearable device to record the patient's clinical symptoms. The collection unit can also acquire information such as past treatment methods, treatment durations, and treatment results from an electronic medical record or a medical database to collect the patient's treatment history. For example, the collection unit acquires the patient's genetic sequence data using whole genome sequencing technology. The collection unit can also acquire the patient's clinical symptoms from a wearable device. The collection unit can also acquire the patient's treatment history from an electronic medical record system. The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of a genetic disease. For example, the analysis unit can use AI to analyze genetic mutations and evaluate whether a specific genetic mutation increases the risk of developing a disease. The analysis unit can also use AI to analyze clinical symptoms and evaluate the progression of the disease. Furthermore, the analysis unit can also use AI to analyze treatment history and evaluate how past treatments affect the current disease. For example, the analysis unit can use AI to analyze gene mutations and evaluate whether a specific gene mutation increases the risk of developing the disease. Furthermore, the analysis unit can also use AI to analyze clinical symptoms and evaluate the progression of the disease. Furthermore, the analysis unit can also use AI to analyze treatment history and evaluate how past treatments affect the current disease. The suggestion unit proposes appropriate treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose optimal treatments based on the analysis results. Furthermore, the suggestion unit can use AI to propose lifestyle improvements based on the analysis results. Furthermore, the suggestion unit can use AI to propose preventive measures such as vaccinations based on the analysis results.For example, the suggestion unit may use AI to suggest an optimal treatment based on the analysis results. The suggestion unit may also use AI to suggest lifestyle improvements based on the analysis results. The suggestion unit may also use AI to suggest preventive measures such as vaccinations based on the analysis results. The support unit provides the content suggested by the suggestion unit to a healthcare professional. The support unit may provide the content suggested by the suggestion unit to a healthcare professional, for example, via email or an online platform. The support unit may also provide the content suggested to a healthcare professional in person. The support unit may also create a customized report to provide the content suggested to a healthcare professional. For example, the support unit may provide the content suggested to a healthcare professional via email. The support unit may also provide the content suggested to a healthcare professional through an online platform. The support unit may also provide the content suggested to a healthcare professional in person. As a result, the genetic disease management system according to the embodiment can efficiently manage genetic diseases by collecting, analyzing, suggesting, and providing support for information about a patient's genetic disease.

[0030] The collection unit can analyze the patient's past medical history and select an information collection method. For example, the collection unit can select the most effective information collection method from the patient's past medical history. The collection unit can also prioritize specific tests and questions based on the patient's past treatment history. The collection unit can also analyze the patient's past medical history and identify points to pay attention to when collecting information. For example, the collection unit can select the most effective information collection method from the patient's past medical history. The collection unit can also prioritize specific tests and questions based on the patient's past treatment history. The collection unit can also analyze the patient's past medical history and identify points to pay attention to when collecting information. This allows information to be collected efficiently by selecting the optimal information collection method based on the patient's past medical history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the patient's past medical history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0031] The collection unit can perform filtering based on the patient's current living situation and environment when collecting information. For example, the collection unit can collect only highly relevant information by taking into account the patient's current living situation. The collection unit can also prioritize collecting specific information based on the patient's environment. The collection unit can also filter unnecessary information based on the patient's living situation and environment. For example, the collection unit can collect only highly relevant information by taking into account the patient's current living situation. The collection unit can also prioritize collecting specific information based on the patient's environment. The collection unit can also filter unnecessary information based on the patient's living situation and environment. In this way, highly relevant information can be collected by filtering information based on the patient's living situation and environment. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input patient living situation data to the generation AI and cause the generation AI to filter highly relevant information.

[0032] When collecting information, the collection unit can prioritize collecting highly relevant information based on the patient's geographical location information. For example, the collection unit can prioritize collecting information about region-specific diseases based on the patient's geographical location information. The collection unit can also collect information about optimal medical institutions taking into account the patient's geographical location information. The collection unit can also prioritize collecting information related to environmental factors based on the patient's geographical location information. For example, the collection unit prioritizes collecting information about region-specific diseases based on the patient's geographical location information. The collection unit can also collect information about optimal medical institutions taking into account the patient's geographical location information. The collection unit can also prioritize collecting information related to environmental factors based on the patient's geographical location information. In this way, information about region-specific diseases can be prioritized by taking into account the patient's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the patient's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0033] When collecting information, the collection unit can analyze the patient's social media activity and collect related information. For example, the collection unit can analyze the patient's social media activity and collect related information from health-related posts. The collection unit can also collect information related to interests and concerns based on the patient's social media activity. The collection unit can also analyze the patient's social media activity and collect information related to lifestyle habits. For example, the collection unit can analyze the patient's social media activity and collect related information from health-related posts. The collection unit can also collect information related to interests and concerns based on the patient's social media activity. The collection unit can also analyze the patient's social media activity and collect information related to lifestyle habits. In this way, health-related information can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data to the generation AI and cause the generation AI to collect related information.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic mutation. For example, if there is an important genetic mutation, the analysis unit can perform a detailed analysis. Furthermore, if there is a genetic mutation of low importance, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the genetic mutation. For example, if there is an important genetic mutation, the analysis unit can perform a detailed analysis. Furthermore, if there is a genetic mutation of low importance, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the genetic mutation. In this way, the level of detail of the analysis can be adjusted according to the importance of the genetic mutation, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic mutation data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the genetic disease. For example, the analysis unit can apply an optimal analysis algorithm for a specific genetic disease. The analysis unit can also use different analysis methods depending on the category of the genetic disease. Furthermore, the analysis unit can select an analysis algorithm based on the characteristics of the genetic disease. For example, the analysis unit applies an optimal analysis algorithm for a specific genetic disease. The analysis unit can also use different analysis methods depending on the category of the genetic disease. The analysis unit can also select an analysis algorithm based on the characteristics of the genetic disease. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the genetic disease. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic disease data to a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time when the genetic data was acquired. The analysis unit can, for example, prioritize analyzing the most recent genetic data. The analysis unit can also analyze the most recent data while referring to past genetic data. The analysis unit can also determine the order of analysis based on the time when the genetic data was acquired. For example, the analysis unit prioritizes analyzing the most recent genetic data. The analysis unit can also analyze the most recent data while referring to past genetic data. The analysis unit can also determine the order of analysis based on the time when the genetic data was acquired. In this way, by determining the priority of analysis based on the time when the genetic data was acquired, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic data to a generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit can prioritize analysis of highly relevant genetic data. The analysis unit can also postpone analysis of less relevant genetic data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit prioritizes analysis of highly relevant genetic data. The analysis unit can also postpone analysis of less relevant genetic data. The analysis unit can also adjust the order of analysis based on the relevance of the genetic data. In this way, adjusting the order of analysis based on the relevance of the genetic data enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic data to a generation AI and cause the generation AI to adjust the order of analysis.

[0038] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment when making a proposal. For example, the suggestion unit can make a detailed proposal for an important treatment. The suggestion unit can also make a simplified proposal for a less important treatment. The suggestion unit can also adjust the depth of the proposal based on the importance of the treatment. For example, the suggestion unit can make a detailed proposal for an important treatment. The suggestion unit can also make a simplified proposal for a less important treatment. The suggestion unit can also adjust the depth of the proposal based on the importance of the treatment. In this way, by adjusting the level of detail of the proposal based on the importance of the treatment, it is possible to make a proposal efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input treatment data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the treatment. For example, the proposal unit can apply an optimal proposal algorithm for a specific treatment. The proposal unit can also use different proposal methods depending on the category of the treatment. Furthermore, the proposal unit can select a proposal algorithm based on the characteristics of the treatment. For example, the proposal unit applies an optimal proposal algorithm for a specific treatment. The proposal unit can also use different proposal methods depending on the category of the treatment. The proposal unit can also select a proposal algorithm based on the characteristics of the treatment. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the category of the treatment. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input treatment data to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0040] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the treatment method was submitted. The proposal unit can, for example, prioritize the most recent treatment method. The proposal unit can also propose the most recent treatment method while referring to past treatment methods. The proposal unit can also determine the order of proposals based on the time when the treatment method was submitted. For example, the proposal unit prioritizes the most recent treatment method. The proposal unit can also propose the most recent treatment method while referring to past treatment methods. The proposal unit can also determine the order of proposals based on the time when the treatment method was submitted. In this way, by determining the priority of proposals based on the time when the treatment method was submitted, the most recent treatment method can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input treatment method data to a generation AI and cause the generation AI to determine the priority of proposals.

[0041] The suggestion unit can adjust the order of suggestions based on the relevance of the treatments when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant treatments. The suggestion unit can also postpone less relevant treatments. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatments. For example, the suggestion unit prioritizes suggesting highly relevant treatments. The suggestion unit can also postpone less relevant treatments. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatments. In this way, suggestions can be made efficiently by adjusting the order of suggestions based on the relevance of the treatments. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input treatment data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0042] When providing support, the support unit can select the optimal support method by referring to the medical professional's past treatment history. For example, the support unit can select the optimal support method based on the medical professional's past treatment history. The support unit can also refer to the medical professional's past treatment history and provide a specific support method preferentially. The support unit can also analyze the medical professional's past treatment history and identify points that require attention when providing support. For example, the support unit selects the optimal support method based on the medical professional's past treatment history. The support unit can also refer to the medical professional's past treatment history and provide a specific support method preferentially. The support unit can also analyze the medical professional's past treatment history and identify points that require attention when providing support. This allows for efficient support by selecting the optimal support method based on the medical professional's past treatment history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the medical professional's treatment history data into the generation AI and cause the generation AI to select the optimal support method.

[0043] The support unit can customize the means of support based on the medical professional's specialty when providing support. The support unit can, for example, provide the optimal means of support based on the medical professional's specialty. The support unit can also preferentially provide a specific means of support in consideration of the medical professional's specialty. The support unit can also customize the means of support based on the medical professional's specialty. For example, the support unit can provide the optimal means of support based on the medical professional's specialty. The support unit can also preferentially provide a specific means of support in consideration of the medical professional's specialty. The support unit can also customize the means of support based on the medical professional's specialty. In this way, specialized support can be provided by customizing the means of support based on the medical professional's specialty. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input data on the medical professional's specialty into the generation AI and cause the generation AI to customize the means of support.

[0044] When providing support, the support unit can select the optimal support method by taking into account the geographical location information of the medical worker. For example, the support unit can provide a support method for a region-specific disease based on the geographical location information of the medical worker. The support unit can also provide information on optimal medical institutions by taking into account the geographical location information of the medical worker. Furthermore, the support unit can provide a support method related to environmental factors based on the geographical location information of the medical worker. For example, the support unit can provide a support method for a region-specific disease based on the geographical location information of the medical worker. The support unit can also provide information on optimal medical institutions by taking into account the geographical location information of the medical worker. The support unit can also provide a support method related to environmental factors based on the geographical location information of the medical worker. In this way, a support method for a region-specific disease can be provided by taking into account the geographical location information of the medical worker. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the geographical location data of the medical worker to the generation AI and cause the generation AI to select the optimal support method.

[0045] During support, the support unit can analyze the social media activity of the medical professional and suggest support measures. For example, the support unit can analyze the social media activity of the medical professional and suggest support measures related to expertise. The support unit can also suggest support measures related to interests and concerns based on the social media activity of the medical professional. Furthermore, the support unit can analyze the social media activity of the medical professional and suggest support measures based on the latest medical information. For example, the support unit can analyze the social media activity of the medical professional and suggest support measures related to expertise. The support unit can also suggest support measures related to interests and concerns based on the social media activity of the medical professional. The support unit can also analyze the social media activity of the medical professional and suggest support measures based on the latest medical information. In this way, relevant support measures can be suggested by analyzing the social media activity of the medical professional. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the social media data of the medical professional into the generation AI and cause the generation AI to suggest support measures.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] In addition to a patient's genetic sequence data, the collection unit can also collect data on the patient's diet and exercise habits. For example, the collection unit can obtain data through an application in which the patient records their daily diet. The collection unit can also obtain exercise data from a fitness tracker used by the patient. Furthermore, the collection unit can analyze the patient's diet and exercise data to evaluate how it affects the risk and progression of genetic diseases. This enables more comprehensive genetic disease management based on the patient's lifestyle.

[0048] The collection unit can collect not only the patient's past medical history but also the family's medical history. For example, the collection unit can collect the genetic diseases and treatment history that the patient's family has experienced in the past. The collection unit can also more accurately assess the risk of genetic diseases based on the family's medical history. Furthermore, the collection unit can analyze the family's medical history and suggest preventive measures for genetic diseases. This enables comprehensive genetic disease management that takes into account the medical history of the entire family.

[0049] The collection department can utilize local medical resources based on the patient's geographic location information. For example, the collection department can collect information on medical institutions and specialists in the patient's area and provide it to the patient. The collection department can also utilize local medical resources to propose appropriate treatments and preventive measures for the patient. Furthermore, the collection department can customize the patient's treatment plan based on local medical resources. This enables genetic disease management that makes maximum use of local medical resources.

[0050] The collection unit can analyze not only a patient's social media activities but also their activities in online communities. For example, the collection unit can analyze the content posted on online forums and support groups in which the patient participates and collect relevant information. The collection unit can also evaluate the patient's lifestyle and health status based on the health information the patient shares in online communities. Furthermore, the collection unit can infer the patient's emotions and psychological state through their activities in online communities. This makes it possible to collect comprehensive information from a patient's online activities.

[0051] During analysis, the analysis unit can take into account the importance of gene mutations as well as the patient's lifestyle and environmental factors. For example, the analysis unit can evaluate the impact of the patient's diet and exercise habits on gene mutations. The analysis unit can also analyze how the patient's living environment and work environment affect the risk of genetic diseases. Furthermore, the analysis unit can suggest preventive measures for genetic diseases based on the patient's lifestyle and environmental factors. This enables comprehensive analysis that takes into account not only gene mutations but also lifestyle and environmental factors.

[0052] During analysis, the analysis unit can apply different analysis algorithms depending on the patient's age and gender, in addition to the genetic disease category. For example, the analysis unit can perform analysis on young patients taking into account genetic mutations specific to the growth period. The analysis unit can also perform analysis on elderly patients taking into account genetic mutations associated with aging. Furthermore, the analysis unit can evaluate the risk of specific genetic diseases depending on gender. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the patient's age and gender.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection department collects the patient's genetic sequence data, clinical symptoms, treatment history, and other relevant information. For example, it obtains genetic sequence data using whole genome sequencing or exome sequencing technology, records clinical symptoms from electronic medical record systems or wearable devices, and obtains information such as past treatment methods, treatment duration, and treatment results from electronic medical records and medical databases. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of genetic diseases. For example, it uses AI to analyze gene mutations to evaluate whether specific gene mutations increase the risk of developing the disease, analyzes clinical symptoms to evaluate the progression of the disease, and analyzes treatment history to evaluate how past treatments affect the current disease. Step 3: The proposal unit proposes appropriate treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. For example, using AI, the unit proposes optimal treatments based on the analysis results, suggests lifestyle improvements, and suggests preventive measures such as vaccinations. Step 4: The Support Department provides the recommendations made by the Proposal Department to the healthcare professionals, for example, by email, through an online platform, in person, or by creating a customized report.

[0055] (Example 2) A genetic disease management system according to an embodiment of the present invention collects a patient's gene sequence data, clinical symptoms, treatment history, and other related information, and uses AI to analyze this information to assess the risk and progression of genetic diseases and recommend appropriate treatments and preventive measures. This system collects information such as a patient's gene sequence data, clinical symptoms, and treatment history, and uses AI to analyze this information to assess the risk and progression of genetic diseases. Based on the analysis results, the system recommends appropriate treatments and preventive measures for the patient. This allows patients to optimally manage their genetic diseases. Medical professionals can also use the AI's advice based on the patient's information to determine treatment plans. This is expected to improve the quality of genetic disease treatment and improve the patient's health. For example, information such as a patient's gene sequence data, clinical symptoms, and treatment history is collected. This information is input into a platform. AI then analyzes this information to assess the risk and progression of genetic diseases. For example, it determines whether a specific gene mutation increases the risk of developing the disease. Based on the analysis results, the AI ​​recommends appropriate treatments and preventive measures for the patient. For example, it advises whether a specific treatment is effective or whether lifestyle changes are necessary. This allows patients to optimally manage their genetic diseases. This platform is also useful for medical professionals. Medical professionals can decide on treatment plans based on the patient's information and the advice provided by AI. This is expected to improve the quality of treatment for genetic diseases and the patient's health. As a result, the genetic disease management system will be able to efficiently manage genetic diseases by collecting, analyzing, proposing, and providing support for information on patients' genetic diseases.

[0056] A genetic disease management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a support unit. The collection unit collects a patient's genetic sequence data, clinical symptoms, treatment history, and other related information. For example, the collection unit can use technologies such as whole genome sequencing or exome sequencing to acquire the patient's genetic sequence data. The collection unit can also acquire data from an electronic medical record system or a wearable device to record the patient's clinical symptoms. The collection unit can also acquire information such as past treatment methods, treatment durations, and treatment results from an electronic medical record or a medical database to collect the patient's treatment history. For example, the collection unit acquires the patient's genetic sequence data using whole genome sequencing technology. The collection unit can also acquire the patient's clinical symptoms from a wearable device. The collection unit can also acquire the patient's treatment history from an electronic medical record system. The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of a genetic disease. For example, the analysis unit can use AI to analyze genetic mutations and evaluate whether a specific genetic mutation increases the risk of developing a disease. The analysis unit can also use AI to analyze clinical symptoms and evaluate the progression of the disease. Furthermore, the analysis unit can also use AI to analyze treatment history and evaluate how past treatments affect the current disease. For example, the analysis unit can use AI to analyze gene mutations and evaluate whether a specific gene mutation increases the risk of developing the disease. Furthermore, the analysis unit can also use AI to analyze clinical symptoms and evaluate the progression of the disease. Furthermore, the analysis unit can also use AI to analyze treatment history and evaluate how past treatments affect the current disease. The suggestion unit proposes appropriate treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. For example, the suggestion unit can use AI to propose optimal treatments based on the analysis results. Furthermore, the suggestion unit can use AI to propose lifestyle improvements based on the analysis results. Furthermore, the suggestion unit can use AI to propose preventive measures such as vaccinations based on the analysis results.For example, the suggestion unit may use AI to suggest an optimal treatment based on the analysis results. The suggestion unit may also use AI to suggest lifestyle improvements based on the analysis results. The suggestion unit may also use AI to suggest preventive measures such as vaccinations based on the analysis results. The support unit provides the content suggested by the suggestion unit to a healthcare professional. The support unit may provide the content suggested by the suggestion unit to a healthcare professional, for example, via email or an online platform. The support unit may also provide the content suggested to a healthcare professional in person. The support unit may also create a customized report to provide the content suggested to a healthcare professional. For example, the support unit may provide the content suggested to a healthcare professional via email. The support unit may also provide the content suggested to a healthcare professional through an online platform. The support unit may also provide the content suggested to a healthcare professional in person. As a result, the genetic disease management system according to the embodiment can efficiently manage genetic diseases by collecting, analyzing, suggesting, and providing support for information about a patient's genetic disease.

[0057] The collection unit can estimate the patient's emotions and adjust the timing of information collection based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can delay information collection until the patient is relaxed. Furthermore, if the patient is relaxed, the collection unit can immediately start information collection. Furthermore, if the patient is feeling anxious, the collection unit can adjust the timing of information collection to provide a sense of security. For example, if the patient is feeling stressed, the collection unit can delay information collection until the patient is relaxed. Furthermore, if the patient is feeling relaxed, the collection unit can immediately start information collection. Furthermore, if the patient is feeling anxious, the collection unit can adjust the timing of information collection to provide a sense of security. Thus, by adjusting the timing of information collection according to the patient's emotions, information can be collected at a more appropriate time. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of a patient into the generation AI and have the generation AI perform emotion estimation.

[0058] The collection unit can analyze the patient's past medical history and select an information collection method. For example, the collection unit can select the most effective information collection method from the patient's past medical history. The collection unit can also prioritize specific tests and questions based on the patient's past treatment history. The collection unit can also analyze the patient's past medical history and identify points to pay attention to when collecting information. For example, the collection unit can select the most effective information collection method from the patient's past medical history. The collection unit can also prioritize specific tests and questions based on the patient's past treatment history. The collection unit can also analyze the patient's past medical history and identify points to pay attention to when collecting information. This allows information to be collected efficiently by selecting the optimal information collection method based on the patient's past medical history. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the patient's past medical history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0059] The collection unit can perform filtering based on the patient's current living situation and environment when collecting information. For example, the collection unit can collect only highly relevant information by taking into account the patient's current living situation. The collection unit can also prioritize collecting specific information based on the patient's environment. The collection unit can also filter unnecessary information based on the patient's living situation and environment. For example, the collection unit can collect only highly relevant information by taking into account the patient's current living situation. The collection unit can also prioritize collecting specific information based on the patient's environment. The collection unit can also filter unnecessary information based on the patient's living situation and environment. In this way, highly relevant information can be collected by filtering information based on the patient's living situation and environment. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input patient living situation data to the generation AI and cause the generation AI to filter highly relevant information.

[0060] The collection unit can estimate the patient's emotions and determine the priority of information to be collected based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can prioritize collecting important information. Furthermore, if the patient is relaxed, the collection unit can also collect detailed information. Furthermore, if the patient is feeling anxious, the collection unit can prioritize collecting information necessary to provide a sense of security. For example, if the patient is feeling stressed, the collection unit prioritizes collecting important information. Furthermore, if the patient is relaxed, the collection unit can also collect detailed information. Furthermore, if the patient is feeling anxious, the collection unit can prioritize collecting information necessary to provide a sense of security. Thus, by determining the priority of information according to the patient's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data of a patient into the generation AI and have the generation AI perform emotion estimation.

[0061] When collecting information, the collection unit can prioritize collecting highly relevant information based on the patient's geographical location information. For example, the collection unit can prioritize collecting information about region-specific diseases based on the patient's geographical location information. The collection unit can also collect information about optimal medical institutions taking into account the patient's geographical location information. The collection unit can also prioritize collecting information related to environmental factors based on the patient's geographical location information. For example, the collection unit prioritizes collecting information about region-specific diseases based on the patient's geographical location information. The collection unit can also collect information about optimal medical institutions taking into account the patient's geographical location information. The collection unit can also prioritize collecting information related to environmental factors based on the patient's geographical location information. In this way, information about region-specific diseases can be prioritized by taking into account the patient's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0062] When collecting information, the collection unit can analyze the patient's social media activity and collect related information. For example, the collection unit can analyze the patient's social media activity and collect related information from health-related posts. The collection unit can also collect information related to interests and concerns based on the patient's social media activity. The collection unit can also analyze the patient's social media activity and collect information related to lifestyle habits. For example, the collection unit can analyze the patient's social media activity and collect related information from health-related posts. The collection unit can also collect information related to interests and concerns based on the patient's social media activity. The collection unit can also analyze the patient's social media activity and collect information related to lifestyle habits. In this way, health-related information can be collected by analyzing the patient's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the patient's social media data to the generation AI and cause the generation AI to collect related information.

[0063] The analysis unit can estimate the patient's emotions and adjust the presentation method of the analysis based on the estimated patient's emotions. For example, if the patient is feeling stressed, the analysis unit can provide simple and easy-to-understand analysis results. Furthermore, if the patient is feeling relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is feeling anxious, the analysis unit can adjust the analysis results to provide a sense of security. For example, if the patient is feeling stressed, the analysis unit can provide simple and easy-to-understand analysis results. Furthermore, if the patient is feeling relaxed, the analysis unit can also adjust the analysis results to provide a sense of security. By adjusting the presentation method of the analysis according to the patient's emotions, it is possible to provide an analysis result that is easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the genetic mutation. For example, if there is an important genetic mutation, the analysis unit can perform a detailed analysis. Furthermore, if there is a genetic mutation of low importance, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the genetic mutation. For example, if there is an important genetic mutation, the analysis unit can perform a detailed analysis. Furthermore, if there is a genetic mutation of low importance, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the genetic mutation. In this way, the level of detail of the analysis can be adjusted according to the importance of the genetic mutation, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic mutation data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the genetic disease. For example, the analysis unit can apply an optimal analysis algorithm for a specific genetic disease. The analysis unit can also use different analysis methods depending on the category of the genetic disease. Furthermore, the analysis unit can select an analysis algorithm based on the characteristics of the genetic disease. For example, the analysis unit applies an optimal analysis algorithm for a specific genetic disease. The analysis unit can also use different analysis methods depending on the category of the genetic disease. The analysis unit can also select an analysis algorithm based on the characteristics of the genetic disease. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the genetic disease. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic disease data to a generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0066] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. For example, if the patient is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is anxious, the analysis unit can adjust the length of the analysis result to provide a sense of security. For example, if the patient is stressed, the analysis unit can provide a short and concise analysis result. Furthermore, if the patient is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the patient is anxious, the analysis unit can adjust the length of the analysis result to provide a sense of security. Thus, by adjusting the length of the analysis according to the patient's emotions, an analysis result of an appropriate length for the patient can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the time when the genetic data was acquired. The analysis unit can, for example, prioritize analyzing the most recent genetic data. The analysis unit can also analyze the most recent data while referring to past genetic data. The analysis unit can also determine the order of analysis based on the time when the genetic data was acquired. For example, the analysis unit prioritizes analyzing the most recent genetic data. The analysis unit can also analyze the most recent data while referring to past genetic data. The analysis unit can also determine the order of analysis based on the time when the genetic data was acquired. In this way, by determining the priority of analysis based on the time when the genetic data was acquired, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic data to a generation AI and have the generation AI determine the analysis priority.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit can prioritize analysis of highly relevant genetic data. The analysis unit can also postpone analysis of less relevant genetic data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit prioritizes analysis of highly relevant genetic data. The analysis unit can also postpone analysis of less relevant genetic data. The analysis unit can also adjust the order of analysis based on the relevance of the genetic data. In this way, adjusting the order of analysis based on the relevance of the genetic data enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input genetic data to a generation AI and cause the generation AI to adjust the order of analysis.

[0069] The suggestion unit can estimate the patient's emotions and adjust the way the suggestions are expressed based on the estimated patient's emotions. For example, if the patient is feeling stressed, the suggestion unit can provide simple and easy-to-understand suggestions. Furthermore, if the patient is feeling relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the patient is feeling anxious, the suggestion unit can adjust the way the suggestions are expressed to provide a sense of security. For example, if the patient is feeling stressed, the suggestion unit can provide simple and easy-to-understand suggestions. Furthermore, if the patient is feeling relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the patient is feeling anxious, the suggestion unit can adjust the way the suggestions are expressed to provide a sense of security. By adjusting the way the suggestions are expressed based on the patient's emotions, the suggestion unit can provide suggestions that are easy for the patient to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0070] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment when making a proposal. For example, the suggestion unit can make a detailed proposal for an important treatment. The suggestion unit can also make a simplified proposal for a less important treatment. The suggestion unit can also adjust the depth of the proposal based on the importance of the treatment. For example, the suggestion unit can make a detailed proposal for an important treatment. The suggestion unit can also make a simplified proposal for a less important treatment. The suggestion unit can also adjust the depth of the proposal based on the importance of the treatment. In this way, by adjusting the level of detail of the proposal based on the importance of the treatment, it is possible to make a proposal efficiently. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input treatment data to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0071] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the treatment. For example, the proposal unit can apply an optimal proposal algorithm for a specific treatment. The proposal unit can also use different proposal methods depending on the category of the treatment. Furthermore, the proposal unit can select a proposal algorithm based on the characteristics of the treatment. For example, the proposal unit applies an optimal proposal algorithm for a specific treatment. The proposal unit can also use different proposal methods depending on the category of the treatment. The proposal unit can also select a proposal algorithm based on the characteristics of the treatment. This improves the accuracy of the proposal by applying the optimal proposal algorithm depending on the category of the treatment. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input treatment data to the generation AI and cause the generation AI to apply the optimal proposal algorithm.

[0072] The suggestion unit can estimate the patient's emotions and adjust the length of the suggestions based on the estimated patient's emotions. For example, if the patient is stressed, the suggestion unit can provide short and concise suggestions. Furthermore, if the patient is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the patient is anxious, the suggestion unit can adjust the length of the suggestions to provide a sense of security. For example, if the patient is stressed, the suggestion unit can provide short and concise suggestions. Furthermore, if the patient is relaxed, the suggestion unit can adjust the length of the suggestions to provide a sense of security. By adjusting the length of the suggestions according to the patient's emotions, it is possible to provide suggestions of an appropriate length for the patient. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0073] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the treatment method was submitted. The proposal unit can, for example, prioritize the most recent treatment method. The proposal unit can also propose the most recent treatment method while referring to past treatment methods. The proposal unit can also determine the order of proposals based on the time when the treatment method was submitted. For example, the proposal unit prioritizes the most recent treatment method. The proposal unit can also propose the most recent treatment method while referring to past treatment methods. The proposal unit can also determine the order of proposals based on the time when the treatment method was submitted. In this way, by determining the priority of proposals based on the time when the treatment method was submitted, the most recent treatment method can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input treatment method data to a generation AI and cause the generation AI to determine the priority of proposals.

[0074] The suggestion unit can adjust the order of suggestions based on the relevance of the treatments when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant treatments. The suggestion unit can also postpone less relevant treatments. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatments. For example, the suggestion unit prioritizes suggesting highly relevant treatments. The suggestion unit can also postpone less relevant treatments. The suggestion unit can also adjust the order of suggestions based on the relevance of the treatments. In this way, suggestions can be made efficiently by adjusting the order of suggestions based on the relevance of the treatments. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input treatment data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0075] The support unit can estimate the patient's emotions and adjust the support method based on the estimated patient's emotions. For example, if the patient is feeling stressed, the support unit can provide a support method that helps the patient relax. Furthermore, if the patient is relaxed, the support unit can provide a detailed support method. Furthermore, if the patient is feeling anxious, the support unit can adjust the support method to give the patient a sense of security. For example, if the patient is feeling stressed, the support unit can provide a support method that helps the patient relax. Furthermore, if the patient is feeling relaxed, the support unit can provide a detailed support method. Furthermore, if the patient is feeling anxious, the support unit can adjust the support method to give the patient a sense of security. In this way, by adjusting the support method according to the patient's emotions, appropriate support can be provided to the patient. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0076] When providing support, the support unit can select the optimal support method by referring to the medical professional's past treatment history. For example, the support unit can select the optimal support method based on the medical professional's past treatment history. The support unit can also refer to the medical professional's past treatment history and provide a specific support method preferentially. The support unit can also analyze the medical professional's past treatment history and identify points that require attention when providing support. For example, the support unit selects the optimal support method based on the medical professional's past treatment history. The support unit can also refer to the medical professional's past treatment history and provide a specific support method preferentially. The support unit can also analyze the medical professional's past treatment history and identify points that require attention when providing support. This allows for efficient support by selecting the optimal support method based on the medical professional's past treatment history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the medical professional's treatment history data into the generation AI and cause the generation AI to select the optimal support method.

[0077] The support unit can customize the means of support based on the medical professional's specialty when providing support. The support unit can, for example, provide the optimal means of support based on the medical professional's specialty. The support unit can also preferentially provide a specific means of support in consideration of the medical professional's specialty. The support unit can also customize the means of support based on the medical professional's specialty. For example, the support unit can provide the optimal means of support based on the medical professional's specialty. The support unit can also preferentially provide a specific means of support in consideration of the medical professional's specialty. The support unit can also customize the means of support based on the medical professional's specialty. In this way, specialized support can be provided by customizing the means of support based on the medical professional's specialty. Some or all of the above-described processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input data on the medical professional's specialty into the generation AI and cause the generation AI to customize the means of support.

[0078] The support unit can estimate the patient's emotions and determine the priority of support based on the estimated patient's emotions. For example, if the patient is feeling stressed, the support unit can prioritize providing support that helps the patient relax. Furthermore, if the patient is feeling relaxed, the support unit can also provide detailed support. Furthermore, if the patient is feeling anxious, the support unit can adjust the priority of support to provide a sense of security. For example, if the patient is feeling stressed, the support unit can prioritize providing support that helps the patient relax. Furthermore, if the patient is feeling relaxed, the support unit can also provide detailed support. Furthermore, if the patient is feeling anxious, the support unit can also adjust the priority of support to provide a sense of security. Thus, by determining the priority of support according to the patient's emotions, important support can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input facial expression data of a patient into the generation AI and have the generation AI estimate emotions.

[0079] When providing support, the support unit can select the optimal support method by taking into account the geographical location information of the medical worker. For example, the support unit can provide a support method for a region-specific disease based on the geographical location information of the medical worker. The support unit can also provide information on optimal medical institutions by taking into account the geographical location information of the medical worker. Furthermore, the support unit can provide a support method related to environmental factors based on the geographical location information of the medical worker. For example, the support unit can provide a support method for a region-specific disease based on the geographical location information of the medical worker. The support unit can also provide information on optimal medical institutions by taking into account the geographical location information of the medical worker. The support unit can also provide a support method related to environmental factors based on the geographical location information of the medical worker. In this way, a support method for a region-specific disease can be provided by taking into account the geographical location information of the medical worker. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the geographical location data of the medical worker to the generation AI and cause the generation AI to select the optimal support method.

[0080] During support, the support unit can analyze the social media activity of the medical professional and suggest support measures. For example, the support unit can analyze the social media activity of the medical professional and suggest support measures related to expertise. The support unit can also suggest support measures related to interests and concerns based on the social media activity of the medical professional. Furthermore, the support unit can analyze the social media activity of the medical professional and suggest support measures based on the latest medical information. For example, the support unit can analyze the social media activity of the medical professional and suggest support measures related to expertise. The support unit can also suggest support measures related to interests and concerns based on the social media activity of the medical professional. The support unit can also analyze the social media activity of the medical professional and suggest support measures based on the latest medical information. In this way, relevant support measures can be suggested by analyzing the social media activity of the medical professional. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the social media data of the medical professional into the generation AI and cause the generation AI to suggest support measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects genetic sequence data and clinical symptoms of a patient using the camera 42 and communication I / F 44 of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes genetic mutations and clinical symptoms using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate treatments and preventive measures based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides the suggestions to medical professionals. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects genetic sequence data and clinical symptoms of a patient using the camera 42 and communication I / F 44 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes genetic mutations and clinical symptoms using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate treatments and preventive measures based on the analysis results. The support unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the suggestions to medical professionals. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above 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 collects genetic sequence data and clinical symptoms of a patient using the camera 42 and communication I / F 44 of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes genetic mutations and clinical symptoms using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate treatments and preventive measures based on the analysis results. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the suggestions to medical professionals. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects genetic sequence data and clinical symptoms of a patient using the camera 42 and communication I / F 44 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes genetic mutations and clinical symptoms using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate treatments and preventive measures based on the analysis results. The support unit is realized, for example, by the control unit 46A of the robot 414, and provides the suggestions to medical professionals.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In addition to a patient's genetic sequence data, the collection unit can also collect data on the patient's diet and exercise habits. For example, the collection unit can obtain data through an application in which the patient records their daily diet. The collection unit can also obtain exercise data from a fitness tracker used by the patient. Furthermore, the collection unit can analyze the patient's diet and exercise data to evaluate how it affects the risk and progression of genetic diseases. This enables more comprehensive genetic disease management based on the patient's lifestyle.

[0083] The collection unit can estimate the patient's emotions and adjust the communication method with the patient based on the estimated patient's emotions. For example, if the patient is feeling stressed, the collection unit can provide information in a gentle tone. If the patient is feeling relaxed, the collection unit can also provide detailed explanations. Furthermore, if the patient is feeling anxious, the collection unit can add words of encouragement to give the patient a sense of security. In this way, communication that is tailored to the patient's emotions can improve the patient's understanding and satisfaction.

[0084] The collection unit can collect not only the patient's past medical history but also the family's medical history. For example, the collection unit can collect the genetic diseases and treatment history that the patient's family has experienced in the past. The collection unit can also more accurately assess the risk of genetic diseases based on the family's medical history. Furthermore, the collection unit can analyze the family's medical history and suggest preventive measures for genetic diseases. This enables comprehensive genetic disease management that takes into account the medical history of the entire family.

[0085] The collection unit can adjust the method of collecting information based on the patient's current living situation and environment as well as the patient's psychological state. For example, if the patient is feeling stressed, the collection unit can collect information in a relaxing environment. The collection unit can also collect detailed information when the patient is relaxed. Furthermore, if the patient is feeling anxious, the collection unit can adjust the method of collecting information to provide a sense of security. This allows for more accurate data to be collected by collecting information according to the patient's psychological state.

[0086] The collection unit can estimate the patient's emotions and adjust the frequency of information collection based on the estimated patient's emotions. For example, the collection unit can reduce the frequency of information collection when the patient is feeling stressed. The collection unit can also increase the frequency of information collection when the patient is relaxed. Furthermore, the collection unit can adjust the frequency of information collection to provide a sense of security when the patient is feeling anxious. In this way, adjusting the frequency of information collection according to the patient's emotions reduces the burden on the patient and makes it possible to collect more accurate data.

[0087] The collection department can utilize local medical resources based on the patient's geographic location information. For example, the collection department can collect information on medical institutions and specialists in the patient's area and provide it to the patient. The collection department can also utilize local medical resources to propose appropriate treatments and preventive measures for the patient. Furthermore, the collection department can customize the patient's treatment plan based on local medical resources. This enables genetic disease management that makes maximum use of local medical resources.

[0088] The collection unit can analyze not only a patient's social media activities but also their activities in online communities. For example, the collection unit can analyze the content posted on online forums and support groups in which the patient participates and collect relevant information. The collection unit can also evaluate the patient's lifestyle and health status based on the health information the patient shares in online communities. Furthermore, the collection unit can infer the patient's emotions and psychological state through their activities in online communities. This makes it possible to collect comprehensive information from a patient's online activities.

[0089] The analysis unit can estimate the patient's emotions and customize the presentation method of the analysis results based on the estimated patient's emotions. For example, if the patient is feeling stressed, the analysis unit can provide simple, visual analysis results. If the patient is feeling relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the patient is feeling anxious, the analysis unit can present the analysis results in stages to provide a sense of security. In this way, customizing the presentation method of the analysis results according to the patient's emotions can improve the patient's understanding.

[0090] During analysis, the analysis unit can take into account the importance of gene mutations as well as the patient's lifestyle and environmental factors. For example, the analysis unit can evaluate the impact of the patient's diet and exercise habits on gene mutations. The analysis unit can also analyze how the patient's living environment and work environment affect the risk of genetic diseases. Furthermore, the analysis unit can suggest preventive measures for genetic diseases based on the patient's lifestyle and environmental factors. This enables comprehensive analysis that takes into account not only gene mutations but also lifestyle and environmental factors.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the patient's age and gender, in addition to the genetic disease category. For example, the analysis unit can perform analysis on young patients taking into account genetic mutations specific to the growth period. The analysis unit can also perform analysis on elderly patients taking into account genetic mutations associated with aging. Furthermore, the analysis unit can evaluate the risk of specific genetic diseases depending on gender. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the patient's age and gender.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection department collects the patient's genetic sequence data, clinical symptoms, treatment history, and other relevant information. For example, it obtains genetic sequence data using whole genome sequencing or exome sequencing technology, records clinical symptoms from electronic medical record systems or wearable devices, and obtains information such as past treatment methods, treatment duration, and treatment results from electronic medical records and medical databases. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates the risk and progression of genetic diseases. For example, it uses AI to analyze gene mutations to evaluate whether specific gene mutations increase the risk of developing the disease, analyzes clinical symptoms to evaluate the progression of the disease, and analyzes treatment history to evaluate how past treatments affect the current disease. Step 3: The proposal unit proposes appropriate treatments and preventive measures for the patient based on the analysis results obtained by the analysis unit. For example, using AI, the unit proposes optimal treatments based on the analysis results, suggests lifestyle improvements, and suggests preventive measures such as vaccinations. Step 4: The Support Department provides the recommendations made by the Proposal Department to the healthcare professionals, for example, by email, through an online platform, in person, or by creating a customized report.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] 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.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0165] [Explanation of symbols]

[0166] 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 department that collects patients' genetic sequence data, clinical symptoms, treatment history and other related information; an analysis unit that analyzes the information collected by the collection unit and evaluates the risk and progression of a genetic disease; a suggestion unit that suggests treatment and preventive measures for a patient based on the analysis results obtained by the analysis unit; a support unit that provides the content proposed by the proposal unit to a medical professional. A system characterized by:

2. The collecting unit Estimate the patient's emotions and adjust the timing of information collection based on the estimated patient emotions 2. The system of claim 1.

3. The collecting unit Analyze the patient's past medical history and select the method of information collection 2. The system of claim 1.

4. The collecting unit When collecting information, filter it based on the patient's current living situation and environment.

2. The system of claim 1.

5. The collecting unit Estimate the patient's feelings and prioritize the information to be collected based on the estimated patient's feelings.

2. The system of claim 1.

6. The collecting unit When collecting information, prioritize the collection of relevant information based on the patient's geographic location 2. The system of claim 1.

7. The collecting unit When collecting information, analyze the patient's social media activity and collect relevant information.

2. The system of claim 1.

8. The analysis unit Estimate the patient's emotions and adjust the presentation of the analysis based on the estimated patient emotions 2. The system of claim 1.

9. The analysis unit During analysis, the level of analysis detail is adjusted based on the importance of genetic variants.

2. The system of claim 1.

10. The analysis unit During analysis, different analysis algorithms are applied depending on the genetic disease category.

2. The system of claim 1.

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