system

Abstract

The system according to this embodiment aims to perform early detection of intractable diseases from a large amount of medical data and test results, and to provide personalized treatment plans. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects medical data and test results. The analysis unit analyzes the data collected by the collection unit and grasps pattern linkages and trends. The detection unit performs early detection of intractable diseases based on the pattern linkages and trends grasped by the analysis unit. The provision unit provides personalized treatment plans based on the results detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, early detection of rare diseases from a large amount of medical data and test results has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform early detection of rare diseases from a large amount of medical data and test results and provide an individualized treatment plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a data provision unit. The data collection unit collects medical data and test results. The analysis unit analyzes the data collected by the data collection unit to identify pattern linkages and trends. The detection unit performs early detection of intractable diseases based on the pattern linkages and trends identified by the analysis unit. The data provision unit provides personalized treatment plans based on the results detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform early detection of intractable diseases from a large amount of medical data and test results, and provide personalized treatment plans. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The medical data analysis system according to an embodiment of the present invention is a system that utilizes AI and deep learning technology to grasp pattern linkages and trends from large amounts of medical data and test results, thereby enabling the early detection of intractable diseases. This system makes it possible to provide personalized treatment plans to patients facing risk at an earlier stage, and at the same time, the quality and value of medical care itself will greatly improve. First, medical data and test results are collected. This includes patient medical records, test results, and genetic information. Next, the collected data is analyzed using AI and deep learning technology. The AI ​​grasps pattern linkages and trends from the data and performs early detection of intractable diseases. For example, if a specific combination of genetic information or test results indicates a risk of a specific intractable disease, the AI ​​will detect that pattern. Furthermore, based on the patterns and trends detected by the AI, personalized treatment plans are provided to patients facing risk. For example, a specific treatment method is proposed for patients with specific genetic information. In addition, based on test results, it may be recommended to start treatment early. This system will greatly improve the quality and value of medical care. By analyzing large amounts of data, the AI ​​can grasp patterns and trends that tend to be overlooked by conventional methods. This enables the early detection of intractable diseases and allows for more appropriate treatment to be provided to patients. Furthermore, by providing personalized treatment plans, the effectiveness of treatment can be maximized for patients. For example, by having AI suggest specific treatment methods to patients with specific genetic information, treatment effectiveness can be improved. Also, by starting treatment early based on test results, the progression of the disease can be suppressed. This improves the patient's quality of life (QOL) and significantly advances the quality and value of medical care. Thus, medical data analysis systems can collect data such as patient medical records, test results, and genetic information, and analyze it using AI and deep learning technology to achieve early detection of intractable diseases and provide personalized treatment plans.

[0029] The medical data analysis system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects medical data and test results. The collection unit can collect data such as patient medical records, test results, and genetic information. The collection unit can collect medical records using an electronic medical record system, for example. The collection unit can also acquire data directly from testing equipment. Furthermore, the collection unit can also collect genetic test results. For example, the collection unit can collect patient medical records using an electronic medical record system. The collection unit can acquire data directly from testing equipment. The collection unit can collect genetic test results. The analysis unit analyzes the data collected by the collection unit to understand pattern linkages and trends. The analysis unit can analyze the data using AI and deep learning technologies, for example. The analysis unit can analyze the data using machine learning algorithms, for example. Furthermore, the analysis unit can analyze the data using deep learning technologies. Furthermore, the analysis unit can analyze the data using natural language processing technologies. For example, the analysis unit analyzes the data using machine learning algorithms. The analysis unit can analyze data using deep learning technology. The analysis unit can analyze data using natural language processing technology. The detection unit performs early detection of intractable diseases based on pattern linkages and trends identified by the analysis unit. The detection unit can, for example, detect specific patterns from the analyzed data. The detection unit can, for example, detect patterns when specific combinations of genetic information or test results indicate a risk of a specific intractable disease. The detection unit can, for example, detect specific patterns from the analyzed data. The detection unit can, for example, detect patterns when specific combinations of genetic information or test results indicate a risk of a specific intractable disease. The provision unit provides personalized treatment plans based on the results detected by the detection unit. The provision unit can, for example, propose specific treatments to patients with specific genetic information. The provision unit can, for example, recommend starting treatment early based on test results. The provision unit can, for example, propose specific treatments to patients with specific genetic information.The provisioning unit can recommend initiating treatment early based on the test results. This enables the medical data analysis system according to the embodiment to efficiently collect, analyze, detect, and provide medical data and test results.

[0030] The data collection unit collects medical data and test results. For example, it can collect data such as patient medical records, test results, and genetic information. Specifically, it collects medical records using an electronic medical record system. The electronic medical record system centrally manages patient medical history, prescriptions, test results, and image data, making them easily accessible to healthcare professionals. The data collection unit can also acquire data directly from testing equipment. For example, it acquires data in real time from blood testing equipment and diagnostic imaging devices and stores it in a central database. Furthermore, the data collection unit can collect genetic test results. Genetic testing is performed to analyze a patient's DNA sample and identify specific genetic mutations and risk factors. This allows the data collection unit to efficiently collect diverse data such as patient medical records, test results, and genetic information, enabling centralized management of medical data. The data collection unit manages this data in a secure environment and takes appropriate measures to ensure data privacy and security. For example, it implements data encryption and access control to prevent unauthorized access and data leaks. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific medical situations and patient conditions. This allows the data collection unit to efficiently and effectively collect medical data, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit to identify pattern linkages and trends. The analysis unit can analyze data using, for example, AI and deep learning technologies. Specifically, it analyzes data using machine learning algorithms. Machine learning algorithms can learn from large amounts of medical data and automatically detect specific patterns and trends. For example, by analyzing patient medical records and test results, it can detect patterns where specific symptoms or fluctuations in test values ​​indicate a risk of a specific disease. The analysis unit can also analyze data using deep learning technologies. Deep learning uses multi-layered neural networks to analyze data and achieve more advanced pattern recognition. For example, it can analyze diagnostic imaging data and detect specific lesions or abnormalities with high accuracy. Furthermore, the analysis unit can analyze data using natural language processing technologies. Natural language processing technologies can analyze text data such as medical records and medical notes and extract important information. For example, it can automatically extract patient symptoms and treatment progress from medical records and use them for analysis. This allows the analysis unit to quickly and accurately analyze collected data and support decision-making in the medical field. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in the incidence of specific diseases and treatment effectiveness based on past clinical data, and formulate future medical countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The detection unit performs early detection of intractable diseases based on pattern linkages and trends identified by the analysis unit. For example, the detection unit can detect specific patterns from the analyzed data. Specifically, it detects patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. For instance, if a combination of a specific gene mutation and a specific blood test value indicates a risk of a particular intractable disease, the detection unit can detect this pattern and issue an early warning. The detection unit uses AI to detect these patterns with high accuracy and provides information to healthcare professionals quickly. This allows the detection unit to support the early detection of intractable diseases and improve the effectiveness of patient treatment. Furthermore, the detection unit can continuously perform pattern detection based on real-time data provided by the analysis unit, enabling it to respond to the latest situations. For example, if new test results or medical records are added, the detection unit immediately incorporates the new data and updates the pattern detection. The detection unit can also perform more accurate risk assessments by considering regional characteristics and historical data. This allows the detection unit to always provide highly accurate pattern detection based on the latest information, supporting quick and appropriate responses.

[0033] The service provider provides personalized treatment plans based on the results detected by the detection unit. For example, the service provider can propose specific treatments to patients with specific genetic information. Specifically, based on genetic test results, it selects the most suitable treatment and medication for the patient and proposes it to healthcare professionals. For example, it can recommend specific molecular targeted drugs to patients with specific gene mutations. The service provider can also recommend initiating treatment early based on test results. For example, if certain blood test values ​​are abnormal, initiating treatment early can prevent the progression of the disease. The service provider uses AI to propose these treatment plans with high accuracy and provides information to healthcare professionals quickly. This allows the service provider to support personalized medicine and improve the effectiveness of treatment for patients. Furthermore, the service provider can collect patient feedback and continuously improve the accuracy and effectiveness of treatment plans. For example, it can revise treatment plans or propose new treatments based on feedback from treated patients. The service provider can also reliably transmit information using multiple communication methods. For example, it can not only provide information to healthcare professionals through the electronic medical record system but also notify patients of important information using email and SMS. This enables the information provider to deliver information quickly and reliably to healthcare professionals and patients, thereby realizing personalized medicine.

[0034] The data collection unit can collect data such as patient medical records, test results, and genetic information. For example, the data collection unit can collect patient medical records using an electronic medical record system. The data collection unit can acquire data directly from testing equipment. The data collection unit can collect genetic test results. For example, the data collection unit can collect patient medical records using an electronic medical record system. The data collection unit can acquire data directly from testing equipment. The data collection unit can collect genetic test results. By collecting data such as patient medical records, test results, and genetic information, more detailed medical data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input medical record data acquired from the electronic medical record system into AI and have AI perform data collection.

[0035] The analysis unit can analyze the collected data using AI and deep learning technologies. For example, the analysis unit can analyze the data using machine learning algorithms. The analysis unit can analyze the data using deep learning technologies. The analysis unit can analyze the data using natural language processing technologies. For example, the analysis unit can analyze the data using machine learning algorithms. The analysis unit can analyze the data using deep learning technologies. The analysis unit can analyze the data using natural language processing technologies. This improves the accuracy of data analysis by using AI and deep learning technologies. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0036] The detection unit can grasp pattern linkages and trends from the analyzed data and perform early detection of intractable diseases. For example, the detection unit can detect specific patterns from the analyzed data. The detection unit can detect patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. The detection unit can detect specific patterns from the analyzed data. The detection unit can detect patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. This makes it possible to grasp pattern linkages and trends and perform early detection of intractable diseases. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the detection unit can input the analyzed data into a generative AI and have the generative AI perform the task of grasping pattern linkages and trends.

[0037] The service provider can provide personalized treatment plans to patients at risk based on the detected results. For example, the service provider can suggest specific treatments to patients with specific genetic information. The service provider can recommend initiating treatment early based on the test results. For example, the service provider can suggest specific treatments to patients with specific genetic information. The service provider can recommend initiating treatment early based on the test results. This maximizes the effectiveness of treatment for patients by providing personalized treatment plans. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the detected results into a generative AI and have the generative AI perform the provision of personalized treatment plans.

[0038] The provisioning unit can propose specific treatments to patients with specific genetic information. For example, the provisioning unit proposes specific treatments to patients with specific genetic information. For example, the provisioning unit proposes specific treatments to patients with specific genetic information. This makes it possible to propose more appropriate treatments to patients with specific genetic information. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the provisioning unit can input data of patients with specific genetic information into a generative AI and have the generative AI execute the proposal of specific treatments.

[0039] The service provider can recommend initiating treatment early based on the test results. The service provider can recommend initiating treatment early based on the test results. The service provider can recommend initiating treatment early based on the test results. By initiating treatment early based on the test results, the progression of the disease can be suppressed. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the test results into a generating AI and have the generating AI make a recommendation for early treatment.

[0040] The data collection unit can analyze a patient's past medical records and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the patient's past medical records. For example, the data collection unit can adjust the frequency of data collection based on the patient's past medical records. For example, the data collection unit can analyze a patient's past medical records and collect data based on specific test results. In this way, the optimal data collection method can be selected by analyzing the patient's past medical records. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past medical records into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only the necessary data, taking into account the patient's current health status. For example, the data collection unit can narrow down the target of data collection based on the patient's lifestyle. For example, the data collection unit can adjust the timing of data collection according to the patient's health status and lifestyle. This ensures that only the necessary data is collected by collecting data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient health status data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if a patient lives in a specific region, the data collection unit will prioritize the collection of health data related to that region. For example, if a patient is traveling, the data collection unit will prioritize the collection of data related to the health risks of the travel destination. For example, if a patient is receiving treatment at a specific medical facility, the data collection unit will prioritize the collection of data related to that facility. This allows for the priority collection of highly relevant data by considering the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related information from the patient's social media posts. For example, the data collection unit can analyze the patient's social media activity and collect data related to health risks. For example, the data collection unit can analyze the patient's social media friendships and collect health-related data. In this way, health-related data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a genetic analysis algorithm to genetic information. For example, the analysis unit applies a test data analysis algorithm to test results. For example, the analysis unit applies a medical data analysis algorithm to medical records. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0048] The detection unit can improve detection accuracy by considering the interrelationships of the data during detection. For example, the detection unit analyzes the interrelationships of the data to improve detection accuracy. For example, the detection unit adjusts the detection criteria based on the interrelationships of the data. For example, the detection unit determines the detection priority by considering the interrelationships of the data. This improves detection accuracy by considering the interrelationships of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships of the data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0049] The detection unit can perform detection while considering the patient's attribute information. For example, the detection unit may consider the patient's age and gender when performing detection. For example, the detection unit may consider the patient's genetic information when performing detection. For example, the detection unit may consider the patient's lifestyle when performing detection. By considering the patient's attribute information, more appropriate detection results can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the patient's attribute information into a generating AI and have the generating AI perform the detection.

[0050] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit may prioritize detecting data from the area where the patient lives. For example, if the patient is traveling, the detection unit may prioritize detecting data from the travel destination. For example, the detection unit may prioritize detecting data from the hospitals the patient visits. By considering the geographical distribution of the data, more appropriate detection results can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the patient's geographical distribution data into a generating AI and have the generating AI perform the detection.

[0051] The detection unit can improve the accuracy of detection by referring to related literature during detection. For example, the detection unit improves the accuracy of detection by referring to related literature. For example, the detection unit adjusts the detection criteria based on related literature. For example, the detection unit determines the detection priority by considering related literature. As a result, the accuracy of detection is improved by referring to related literature. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input related literature into a generating AI and have the generating AI perform the detection accuracy improvement.

[0052] The service provider can analyze the patient's past treatment history and select the optimal treatment plan at the time of service provision. For example, the service provider selects the optimal treatment plan based on the patient's past treatment history. For example, the service provider analyzes the patient's past treatment history and determines the priority of treatment plans. For example, the service provider customizes treatment plans considering the patient's past treatment history. This allows the service provider to select the optimal treatment plan by analyzing the patient's past treatment history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's past treatment history into a generating AI and have the generating AI select the optimal treatment plan.

[0053] The service provider can customize the treatment plan based on the patient's current health condition at the time of delivery. For example, the service provider customizes the treatment plan considering the patient's current health condition. For example, the service provider determines the priority of the treatment plan based on the patient's current health condition. For example, the service provider adjusts the treatment plan based on the patient's current health condition. This allows for the provision of more appropriate treatment by customizing the treatment plan based on the patient's current health condition. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's current health condition data into a generating AI and have the generating AI perform the customization of the treatment plan.

[0054] The service provider can select the optimal treatment plan at the time of delivery, taking into account the patient's geographical location. For example, the service provider selects a treatment plan considering the medical resources in the patient's area of ​​residence. For example, if the patient is traveling, the service provider selects a treatment plan considering the medical resources at the travel destination. For example, the service provider selects a treatment plan considering the medical resources at the patient's hospital. In this way, the optimal treatment plan can be selected by taking into account the patient's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's geographical location into a generating AI and have the generating AI perform the selection of the optimal treatment plan.

[0055] The service provider can analyze the patient's social media activity and propose a treatment plan at the time of provision. For example, the service provider can collect health-related information from the patient's social media posts and propose a treatment plan. For example, the service provider can analyze the patient's social media activity and propose a treatment plan related to health risks. For example, the service provider can analyze the patient's social media friendships and propose a treatment plan related to health. In this way, by analyzing the patient's social media activity, a more appropriate treatment plan can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's social media data into a generating AI and have the generating AI execute the treatment plan proposal.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0058] The data collection unit can analyze a patient's past medical records and select the optimal data collection method. For example, it can select the most effective data collection method from a patient's past medical records. It can also adjust the frequency of data collection based on the patient's past medical records. Furthermore, it can analyze a patient's past medical records and collect data based on specific test results. In this way, by analyzing a patient's past medical records, the optimal data collection method can be selected.

[0059] The analysis unit can apply different analysis algorithms depending on the data category. For example, a genetic analysis algorithm can be applied to genetic information. A test data analysis algorithm can be applied to test results. Furthermore, a medical data analysis algorithm can be applied to medical records. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category.

[0060] The detection unit can improve detection accuracy by considering the interrelationships between data during detection. For example, it can analyze the interrelationships between data to improve detection accuracy. It can also adjust the detection criteria based on the interrelationships between data. Furthermore, it can determine the detection priority by considering the interrelationships between data. In this way, the detection accuracy is improved by considering the interrelationships between data.

[0061] The service provider can analyze the patient's past treatment history and select the optimal treatment plan at the time of delivery. For example, it can select the optimal treatment plan based on the patient's past treatment history. It can also analyze the patient's past treatment history and determine the priority of treatment plans. Furthermore, it can customize treatment plans considering the patient's past treatment history. In this way, the optimal treatment plan can be selected by analyzing the patient's past treatment history.

[0062] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if a patient lives in a specific region, health data related to that region can be prioritized. Similarly, if a patient is traveling, data related to health risks in their travel destination can be prioritized. Furthermore, if a patient is receiving treatment at a specific medical facility, data related to that facility can be prioritized. This allows for the priority collection of highly relevant data by considering the patient's geographical location.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects medical data and test results. The collection unit can collect data such as patient medical records, test results, and genetic information. The collection unit can collect medical records using an electronic medical record system, acquire data directly from testing equipment, and collect genetic test results. Step 2: The analysis unit analyzes the data collected by the collection unit to identify pattern linkages and trends. The analysis unit can analyze the data using AI and deep learning technologies, machine learning algorithms, and natural language processing technologies. Step 3: The detection unit performs early detection of intractable diseases based on the pattern linkages and trends identified by the analysis unit. The detection unit can detect specific patterns from the analyzed data, and if a combination of specific genetic information or test results indicates a risk of a specific intractable disease, it can detect that pattern. Step 4: The provider provides personalized treatment plans based on the results detected by the detection unit. The provider may suggest specific treatments for patients with specific genetic information and recommend initiating treatment early based on the test results.

[0065] (Example of form 2) The medical data analysis system according to an embodiment of the present invention is a system that utilizes AI and deep learning technology to grasp pattern linkages and trends from large amounts of medical data and test results, thereby enabling the early detection of intractable diseases. This system makes it possible to provide personalized treatment plans to patients facing risk at an earlier stage, and at the same time, the quality and value of medical care itself will greatly improve. First, medical data and test results are collected. This includes patient medical records, test results, and genetic information. Next, the collected data is analyzed using AI and deep learning technology. The AI ​​grasps pattern linkages and trends from the data and performs early detection of intractable diseases. For example, if a specific combination of genetic information or test results indicates a risk of a specific intractable disease, the AI ​​will detect that pattern. Furthermore, based on the patterns and trends detected by the AI, personalized treatment plans are provided to patients facing risk. For example, a specific treatment method is proposed for patients with specific genetic information. In addition, based on test results, it may be recommended to start treatment early. This system will greatly improve the quality and value of medical care. By analyzing large amounts of data, the AI ​​can grasp patterns and trends that tend to be overlooked by conventional methods. This enables the early detection of intractable diseases and allows for more appropriate treatment to be provided to patients. Furthermore, by providing personalized treatment plans, the effectiveness of treatment can be maximized for patients. For example, by having AI suggest specific treatment methods to patients with specific genetic information, treatment effectiveness can be improved. Also, by starting treatment early based on test results, the progression of the disease can be suppressed. This improves the patient's quality of life (QOL) and significantly advances the quality and value of medical care. Thus, medical data analysis systems can collect data such as patient medical records, test results, and genetic information, and analyze it using AI and deep learning technology to achieve early detection of intractable diseases and provide personalized treatment plans.

[0066] The medical data analysis system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects medical data and test results. The collection unit can collect data such as patient medical records, test results, and genetic information. The collection unit can collect medical records using an electronic medical record system, for example. The collection unit can also acquire data directly from testing equipment. Furthermore, the collection unit can also collect genetic test results. For example, the collection unit can collect patient medical records using an electronic medical record system. The collection unit can acquire data directly from testing equipment. The collection unit can collect genetic test results. The analysis unit analyzes the data collected by the collection unit to understand pattern linkages and trends. The analysis unit can analyze the data using AI and deep learning technologies, for example. The analysis unit can analyze the data using machine learning algorithms, for example. Furthermore, the analysis unit can analyze the data using deep learning technologies. Furthermore, the analysis unit can analyze the data using natural language processing technologies. For example, the analysis unit analyzes the data using machine learning algorithms. The analysis unit can analyze data using deep learning technology. The analysis unit can analyze data using natural language processing technology. The detection unit performs early detection of intractable diseases based on pattern linkages and trends identified by the analysis unit. The detection unit can, for example, detect specific patterns from the analyzed data. The detection unit can, for example, detect patterns when specific combinations of genetic information or test results indicate a risk of a specific intractable disease. The detection unit can, for example, detect specific patterns from the analyzed data. The detection unit can, for example, detect patterns when specific combinations of genetic information or test results indicate a risk of a specific intractable disease. The provision unit provides personalized treatment plans based on the results detected by the detection unit. The provision unit can, for example, propose specific treatments to patients with specific genetic information. The provision unit can, for example, recommend starting treatment early based on test results. The provision unit can, for example, propose specific treatments to patients with specific genetic information.The provisioning unit can recommend initiating treatment early based on the test results. This enables the medical data analysis system according to the embodiment to efficiently collect, analyze, detect, and provide medical data and test results.

[0067] The data collection unit collects medical data and test results. For example, it can collect data such as patient medical records, test results, and genetic information. Specifically, it collects medical records using an electronic medical record system. The electronic medical record system centrally manages patient medical history, prescriptions, test results, and image data, making them easily accessible to healthcare professionals. The data collection unit can also acquire data directly from testing equipment. For example, it acquires data in real time from blood testing equipment and diagnostic imaging devices and stores it in a central database. Furthermore, the data collection unit can collect genetic test results. Genetic testing is performed to analyze a patient's DNA sample and identify specific genetic mutations and risk factors. This allows the data collection unit to efficiently collect diverse data such as patient medical records, test results, and genetic information, enabling centralized management of medical data. The data collection unit manages this data in a secure environment and takes appropriate measures to ensure data privacy and security. For example, it implements data encryption and access control to prevent unauthorized access and data leaks. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses tailored to specific medical situations and patient conditions. This allows the data collection unit to efficiently and effectively collect medical data, improving the overall system performance.

[0068] The analysis unit analyzes the data collected by the collection unit to identify pattern linkages and trends. The analysis unit can analyze data using, for example, AI and deep learning technologies. Specifically, it analyzes data using machine learning algorithms. Machine learning algorithms can learn from large amounts of medical data and automatically detect specific patterns and trends. For example, by analyzing patient medical records and test results, it can detect patterns where specific symptoms or fluctuations in test values ​​indicate a risk of a specific disease. The analysis unit can also analyze data using deep learning technologies. Deep learning uses multi-layered neural networks to analyze data and achieve more advanced pattern recognition. For example, it can analyze diagnostic imaging data and detect specific lesions or abnormalities with high accuracy. Furthermore, the analysis unit can analyze data using natural language processing technologies. Natural language processing technologies can analyze text data such as medical records and medical notes and extract important information. For example, it can automatically extract patient symptoms and treatment progress from medical records and use them for analysis. This allows the analysis unit to quickly and accurately analyze collected data and support decision-making in the medical field. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in the incidence of specific diseases and treatment effectiveness based on past clinical data, and formulate future medical countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The detection unit performs early detection of intractable diseases based on pattern linkages and trends identified by the analysis unit. For example, the detection unit can detect specific patterns from the analyzed data. Specifically, it detects patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. For instance, if a combination of a specific gene mutation and a specific blood test value indicates a risk of a particular intractable disease, the detection unit can detect this pattern and issue an early warning. The detection unit uses AI to detect these patterns with high accuracy and provides information to healthcare professionals quickly. This allows the detection unit to support the early detection of intractable diseases and improve the effectiveness of patient treatment. Furthermore, the detection unit can continuously perform pattern detection based on real-time data provided by the analysis unit, enabling it to respond to the latest situations. For example, if new test results or medical records are added, the detection unit immediately incorporates the new data and updates the pattern detection. The detection unit can also perform more accurate risk assessments by considering regional characteristics and historical data. This allows the detection unit to always provide highly accurate pattern detection based on the latest information, supporting quick and appropriate responses.

[0070] The service provider provides personalized treatment plans based on the results detected by the detection unit. For example, the service provider can propose specific treatments to patients with specific genetic information. Specifically, based on genetic test results, it selects the most suitable treatment and medication for the patient and proposes it to healthcare professionals. For example, it can recommend specific molecular targeted drugs to patients with specific gene mutations. The service provider can also recommend initiating treatment early based on test results. For example, if certain blood test values ​​are abnormal, initiating treatment early can prevent the progression of the disease. The service provider uses AI to propose these treatment plans with high accuracy and provides information to healthcare professionals quickly. This allows the service provider to support personalized medicine and improve the effectiveness of treatment for patients. Furthermore, the service provider can collect patient feedback and continuously improve the accuracy and effectiveness of treatment plans. For example, it can revise treatment plans or propose new treatments based on feedback from treated patients. The service provider can also reliably transmit information using multiple communication methods. For example, it can not only provide information to healthcare professionals through the electronic medical record system but also notify patients of important information using email and SMS. This enables the information provider to deliver information quickly and reliably to healthcare professionals and patients, thereby realizing personalized medicine.

[0071] The data collection unit can collect data such as patient medical records, test results, and genetic information. For example, the data collection unit can collect patient medical records using an electronic medical record system. The data collection unit can acquire data directly from testing equipment. The data collection unit can collect genetic test results. For example, the data collection unit can collect patient medical records using an electronic medical record system. The data collection unit can acquire data directly from testing equipment. The data collection unit can collect genetic test results. By collecting data such as patient medical records, test results, and genetic information, more detailed medical data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input medical record data acquired from the electronic medical record system into AI and have AI perform data collection.

[0072] The analysis unit can analyze the collected data using AI and deep learning technologies. For example, the analysis unit can analyze the data using machine learning algorithms. The analysis unit can analyze the data using deep learning technologies. The analysis unit can analyze the data using natural language processing technologies. For example, the analysis unit can analyze the data using machine learning algorithms. The analysis unit can analyze the data using deep learning technologies. The analysis unit can analyze the data using natural language processing technologies. This improves the accuracy of data analysis by using AI and deep learning technologies. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the data analysis.

[0073] The detection unit can grasp pattern linkages and trends from the analyzed data and perform early detection of intractable diseases. For example, the detection unit can detect specific patterns from the analyzed data. The detection unit can detect patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. The detection unit can detect specific patterns from the analyzed data. The detection unit can detect patterns when specific combinations of genetic information or test results indicate a risk of a particular intractable disease. This makes it possible to grasp pattern linkages and trends and perform early detection of intractable diseases. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the detection unit can input the analyzed data into a generative AI and have the generative AI perform the task of grasping pattern linkages and trends.

[0074] The service provider can provide personalized treatment plans to patients at risk based on the detected results. For example, the service provider can suggest specific treatments to patients with specific genetic information. The service provider can recommend initiating treatment early based on the test results. For example, the service provider can suggest specific treatments to patients with specific genetic information. The service provider can recommend initiating treatment early based on the test results. This maximizes the effectiveness of treatment for patients by providing personalized treatment plans. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the detected results into a generative AI and have the generative AI perform the provision of personalized treatment plans.

[0075] The provisioning unit can propose specific treatments to patients with specific genetic information. For example, the provisioning unit proposes specific treatments to patients with specific genetic information. For example, the provisioning unit proposes specific treatments to patients with specific genetic information. This makes it possible to propose more appropriate treatments to patients with specific genetic information. Some or all of the above processing in the provisioning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the provisioning unit can input data of patients with specific genetic information into a generative AI and have the generative AI execute the proposal of specific treatments.

[0076] The service provider can recommend initiating treatment early based on the test results. The service provider can recommend initiating treatment early based on the test results. The service provider can recommend initiating treatment early based on the test results. By initiating treatment early based on the test results, the progression of the disease can be suppressed. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the test results into a generating AI and have the generating AI make a recommendation for early treatment.

[0077] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit may delay data collection until the patient is relaxed. If the patient is relaxed, the data collection unit may immediately begin data collection. If the patient is anxious, the data collection unit may perform data collection in stages to reduce the patient's burden. This reduces the patient's burden by adjusting the timing of data collection according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit may input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can analyze a patient's past medical records and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method from the patient's past medical records. For example, the data collection unit can adjust the frequency of data collection based on the patient's past medical records. For example, the data collection unit can analyze a patient's past medical records and collect data based on specific test results. In this way, the optimal data collection method can be selected by analyzing the patient's past medical records. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past medical records into a generating AI and have the generating AI select the optimal data collection method.

[0079] The data collection unit can filter data based on the patient's current health status and lifestyle. For example, the data collection unit can collect only the necessary data, taking into account the patient's current health status. For example, the data collection unit can narrow down the target of data collection based on the patient's lifestyle. For example, the data collection unit can adjust the timing of data collection according to the patient's health status and lifestyle. This ensures that only the necessary data is collected by collecting data based on the patient's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient health status data into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the patient is relaxed, the data collection unit will prioritize collecting general health data. For example, if the patient is anxious, the data collection unit will prioritize collecting anxiety-related data. This allows for the priority collection of more important data by determining the priority of data to collect according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if a patient lives in a specific region, the data collection unit will prioritize the collection of health data related to that region. For example, if a patient is traveling, the data collection unit will prioritize the collection of data related to the health risks of the travel destination. For example, if a patient is receiving treatment at a specific medical facility, the data collection unit will prioritize the collection of data related to that facility. This allows for the priority collection of highly relevant data by considering the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0082] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, the data collection unit can collect health-related information from the patient's social media posts. For example, the data collection unit can analyze the patient's social media activity and collect data related to health risks. For example, the data collection unit can analyze the patient's social media friendships and collect health-related data. In this way, health-related data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0083] The analysis unit can estimate the patient's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. For example, if the patient is relaxed, the analysis unit provides a detailed analysis result. For example, if the patient is anxious, the analysis unit provides a reassuring analysis result. In this way, by adjusting the presentation of the analysis according to the patient's emotions, it is possible to provide analysis results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a genetic analysis algorithm to genetic information. For example, the analysis unit applies a test data analysis algorithm to test results. For example, the analysis unit applies a medical data analysis algorithm to medical records. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0086] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the patient is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the patient is relaxed, the analysis unit provides a detailed analysis result. For example, if the patient is anxious, the analysis unit provides a reassuring analysis result. By adjusting the length of the analysis according to the patient's emotions, the analysis unit can provide an appropriate result for the patient. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of older data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This allows for prioritizing the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may adjust the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0089] The detection unit can estimate the patient's emotions and adjust the detection criteria based on the estimated emotions. For example, if the patient is stressed, the detection unit may relax stress-related criteria. For example, if the patient is relaxed, the detection unit may apply normal criteria. For example, if the patient is anxious, the detection unit may relax anxiety-related criteria. By adjusting the detection criteria according to the patient's emotions, more appropriate detection results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not using AI. For example, the detection unit can input the patient's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The detection unit can improve detection accuracy by considering the interrelationships of the data during detection. For example, the detection unit analyzes the interrelationships of the data to improve detection accuracy. For example, the detection unit adjusts the detection criteria based on the interrelationships of the data. For example, the detection unit determines the detection priority by considering the interrelationships of the data. This improves detection accuracy by considering the interrelationships of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships of the data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0091] The detection unit can perform detection while considering the patient's attribute information. For example, the detection unit may consider the patient's age and gender when performing detection. For example, the detection unit may consider the patient's genetic information when performing detection. For example, the detection unit may consider the patient's lifestyle when performing detection. By considering the patient's attribute information, more appropriate detection results can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the patient's attribute information into a generating AI and have the generating AI perform the detection.

[0092] The detection unit can estimate the patient's emotions and adjust the order in which the detection results are displayed based on the estimated emotions. For example, if the patient is stressed, the detection unit will display important results first. If the patient is relaxed, the detection unit will display detailed results sequentially. If the patient is anxious, the detection unit will display reassuring results first. By adjusting the display order of the detection results according to the patient's emotions, the system can provide results that are easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, 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 detection unit may be performed using AI or not using AI. For example, the detection unit can input the patient's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit may prioritize detecting data from the area where the patient lives. For example, if the patient is traveling, the detection unit may prioritize detecting data from the travel destination. For example, the detection unit may prioritize detecting data from the hospitals the patient visits. By considering the geographical distribution of the data, more appropriate detection results can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the patient's geographical distribution data into a generating AI and have the generating AI perform the detection.

[0094] The detection unit can improve the accuracy of detection by referring to related literature during detection. For example, the detection unit improves the accuracy of detection by referring to related literature. For example, the detection unit adjusts the detection criteria based on related literature. For example, the detection unit determines the detection priority by considering related literature. As a result, the accuracy of detection is improved by referring to related literature. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input related literature into a generating AI and have the generating AI perform the detection accuracy improvement.

[0095] The service provider can estimate the patient's emotions and adjust the way the treatment plan is expressed based on the estimated emotions. For example, if the patient is stressed, the service provider will provide a simple and easy-to-understand treatment plan. For example, if the patient is relaxed, the service provider will provide a detailed treatment plan. For example, if the patient is anxious, the service provider will provide a reassuring treatment plan. In this way, by adjusting the way the treatment plan is expressed according to the patient's emotions, it is possible to provide a treatment plan that is easy for the patient to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The service provider can analyze the patient's past treatment history and select the optimal treatment plan at the time of service provision. For example, the service provider selects the optimal treatment plan based on the patient's past treatment history. For example, the service provider analyzes the patient's past treatment history and determines the priority of treatment plans. For example, the service provider customizes treatment plans considering the patient's past treatment history. This allows the service provider to select the optimal treatment plan by analyzing the patient's past treatment history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's past treatment history into a generating AI and have the generating AI select the optimal treatment plan.

[0097] The service provider can customize the treatment plan based on the patient's current health condition at the time of delivery. For example, the service provider customizes the treatment plan considering the patient's current health condition. For example, the service provider determines the priority of the treatment plan based on the patient's current health condition. For example, the service provider adjusts the treatment plan based on the patient's current health condition. This allows for the provision of more appropriate treatment by customizing the treatment plan based on the patient's current health condition. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's current health condition data into a generating AI and have the generating AI perform the customization of the treatment plan.

[0098] The service provider can estimate the patient's emotions and determine the priority of treatment plans based on the estimated emotions. For example, if the patient is stressed, the service provider will provide a treatment plan that prioritizes stress reduction. For example, if the patient is relaxed, the service provider will provide a standard treatment plan. For example, if the patient is anxious, the service provider will provide a treatment plan that prioritizes anxiety reduction. This allows for the provision of more appropriate treatment by determining the priority of treatment plans according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input patient facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The service provider can select the optimal treatment plan at the time of delivery, taking into account the patient's geographical location. For example, the service provider selects a treatment plan considering the medical resources in the patient's area of ​​residence. For example, if the patient is traveling, the service provider selects a treatment plan considering the medical resources at the travel destination. For example, the service provider selects a treatment plan considering the medical resources at the patient's hospital. In this way, the optimal treatment plan can be selected by taking into account the patient's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's geographical location into a generating AI and have the generating AI perform the selection of the optimal treatment plan.

[0100] The service provider can analyze the patient's social media activity and propose a treatment plan at the time of provision. For example, the service provider can collect health-related information from the patient's social media posts and propose a treatment plan. For example, the service provider can analyze the patient's social media activity and propose a treatment plan related to health risks. For example, the service provider can analyze the patient's social media friendships and propose a treatment plan related to health. In this way, by analyzing the patient's social media activity, a more appropriate treatment plan can be proposed. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's social media data into a generating AI and have the generating AI execute the treatment plan proposal.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on those estimates. For example, if the patient is stressed, data collection can be delayed until they are relaxed. If the patient is relaxed, data collection can begin immediately. Furthermore, if the patient is anxious, data collection can be carried out in stages to reduce the patient's burden. In this way, adjusting the timing of data collection according to the patient's emotions can reduce the patient's burden.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0104] The detection unit can estimate the patient's emotions and adjust the detection criteria based on the estimated emotions. For example, if the patient is stressed, stress-related criteria can be relaxed. If the patient is relaxed, the normal criteria can be applied. Furthermore, if the patient is anxious, anxiety-related criteria can be relaxed. By adjusting the detection criteria according to the patient's emotions, more appropriate detection results can be provided.

[0105] The system can estimate the patient's emotions and adjust the way the treatment plan is presented based on those estimates. For example, if the patient is stressed, a simple and easy-to-understand treatment plan can be provided. If the patient is relaxed, a detailed treatment plan can be provided. Furthermore, if the patient is anxious, a reassuring treatment plan can be provided. By adjusting the way the treatment plan is presented according to the patient's emotions, a treatment plan that is easy for the patient to understand can be provided.

[0106] The data collection unit can analyze a patient's past medical records and select the optimal data collection method. For example, it can select the most effective data collection method from a patient's past medical records. It can also adjust the frequency of data collection based on the patient's past medical records. Furthermore, it can analyze a patient's past medical records and collect data based on specific test results. In this way, by analyzing a patient's past medical records, the optimal data collection method can be selected.

[0107] The analysis unit can apply different analysis algorithms depending on the data category. For example, a genetic analysis algorithm can be applied to genetic information. A test data analysis algorithm can be applied to test results. Furthermore, a medical data analysis algorithm can be applied to medical records. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category.

[0108] The detection unit can improve detection accuracy by considering the interrelationships between data during detection. For example, it can analyze the interrelationships between data to improve detection accuracy. It can also adjust the detection criteria based on the interrelationships between data. Furthermore, it can determine the detection priority by considering the interrelationships between data. In this way, the detection accuracy is improved by considering the interrelationships between data.

[0109] The service provider can analyze the patient's past treatment history and select the optimal treatment plan at the time of delivery. For example, it can select the optimal treatment plan based on the patient's past treatment history. It can also analyze the patient's past treatment history and determine the priority of treatment plans. Furthermore, it can customize treatment plans considering the patient's past treatment history. In this way, the optimal treatment plan can be selected by analyzing the patient's past treatment history.

[0110] The system can estimate the patient's emotions and prioritize treatment plans based on those estimates. For example, if a patient is stressed, it can provide a treatment plan that prioritizes stress reduction. If the patient is relaxed, it can provide a standard treatment plan. Furthermore, if the patient is anxious, it can provide a treatment plan that prioritizes anxiety reduction. By prioritizing treatment plans according to the patient's emotions, more appropriate treatment can be provided.

[0111] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if a patient lives in a specific region, health data related to that region can be prioritized. Similarly, if a patient is traveling, data related to health risks in their travel destination can be prioritized. Furthermore, if a patient is receiving treatment at a specific medical facility, data related to that facility can be prioritized. This allows for the priority collection of highly relevant data by considering the patient's geographical location.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects medical data and test results. The collection unit can collect data such as patient medical records, test results, and genetic information. The collection unit can collect medical records using an electronic medical record system, acquire data directly from testing equipment, and collect genetic test results. Step 2: The analysis unit analyzes the data collected by the collection unit to identify pattern linkages and trends. The analysis unit can analyze the data using AI and deep learning technologies, machine learning algorithms, and natural language processing technologies. Step 3: The detection unit performs early detection of intractable diseases based on the pattern linkages and trends identified by the analysis unit. The detection unit can detect specific patterns from the analyzed data, and if a combination of specific genetic information or test results indicates a risk of a specific intractable disease, it can detect that pattern. Step 4: The provider provides personalized treatment plans based on the results detected by the detection unit. The provider may suggest specific treatments for patients with specific genetic information and recommend initiating treatment early based on the test results.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects patient medical records and test results using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI and deep learning technology. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12 and detects the risk of intractable diseases from the analyzed data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the patient with an individualized treatment plan based on the detected risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the patient's medical records and test results using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI and deep learning technology. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects the risk of intractable diseases from the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the patient with an individualized treatment plan based on the detected risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects patient medical records and test results using the camera 42 and microphone 238 of the headset terminal 314 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI and deep learning technology. The detection unit is implemented in the identification processing unit 290 of the data processing unit 12 and detects the risk of intractable diseases from the analyzed data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the patient with an individualized treatment plan based on the detected risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects patient medical records and test results using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI and deep learning technology. The detection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects the risk of intractable diseases from the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the patient with an individualized treatment plan based on the detected risk. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The collection department collects medical data and test results, An analysis unit analyzes the data collected by the aforementioned collection unit to understand pattern linkages and trends, Based on the pattern linkages and trends identified by the analysis unit, a detection unit performs early detection of intractable diseases. The system includes a providing unit that provides an individualized treatment plan based on the results detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as patient medical records, test results, and genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed using AI and deep learning technologies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is By analyzing the data, we can identify pattern linkages and trends, enabling the early detection of intractable diseases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on the detected results, provide individualized treatment plans for patients at risk. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We propose specific treatments to patients with specific genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Based on the test results, we recommend starting treatment early. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the patient's past medical records and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is The system estimates the patient's emotions and adjusts the detection criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is During detection, the accuracy of the detection is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is During detection, the patient's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is The system estimates the patient's emotions and adjusts the order in which detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is During detection, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit is During detection, we refer to relevant literature to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the patient's emotions and adjust the way treatment plans are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, At the time of delivery, the patient's past treatment history is analyzed to select the optimal treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing treatment, the treatment plan is customized based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the patient's emotions and prioritizes treatment plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing treatment, the optimal treatment plan is selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing treatment, we analyze the patient's social media activity and propose a treatment plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects medical data and test results, An analysis unit analyzes the data collected by the aforementioned collection unit to understand pattern linkages and trends, Based on the pattern linkages and trends identified by the analysis unit, a detection unit performs early detection of intractable diseases. The system includes a providing unit that provides an individualized treatment plan based on the results detected by the detection unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as patient medical records, test results, and genetic information. The system according to feature 1.

3. The aforementioned analysis unit, The collected data will be analyzed using AI and deep learning technologies. The system according to feature 1.

4. The detection unit is By analyzing the data, we can identify pattern linkages and trends, enabling the early detection of intractable diseases. The system according to feature 1.

5. The aforementioned supply unit is, Based on the detected results, provide individualized treatment plans for patients at risk. The system according to feature 1.

6. The aforementioned supply unit is, We propose specific treatments to patients with specific genetic information. The system according to feature 1.

7. The aforementioned supply unit is, Based on the test results, we recommend starting treatment early. The system according to feature 1.

8. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

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